Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

1.

Scope and structure of the document

This document is the final deliverable to workpackage 8 -underwater investigation with a networking fleet of underwater vehicles -of the FeedNetBack project.

The aim of this document, which is the final deliverable D8.04 to the is to present the evaluation of the Underwater Fleet Simulator MASIM and results produced with this software tool.

The document is structured in three major parts:

• the case study scenario and the technical approach to its solution are presented in section Ithis section contains as well as the global conclusions from the two sets of simulations below;

• simulation results produced by partner P06 configured for a single TDMA communication network (AUV-to-AUV and AUV-to-ASV links in a unique network scheme) are described and listed in section II;

• simulation results produced by partner P01 configured for a double TDMA communication network (AUV-to-AUV and AUV-to-ASV links in separate networks) are described and listed in section III;

2.

Reference documents

Section I :

Case study summary and general conclusions

3.1.

Case study and problem statement

One of the aims of the FeedNetBack project is to apply the concepts of network control to the field of multiple vehicle underwater investigation.

Descent into the ocean depths has been a dream for mankind for most of the past centuries. Only in the 20th century technology provided means to enlarge the access to the underwater world beyond the depth that a human diver can reach. Pressure and darkness make the ocean a hostile environment -given the impenetrable character of sea water for radio signals, it is easier to communicate with a space shuttle thousands of km away, than with a submersible a few hundred meters below the surface.

Until the 1960ies, the aim was to take human operators to big depth, and then to carry out intervention tasks of scientific or industrial nature in the deep sea environment. In a second time the production of visual images, sediment samples and environmental measurements became possible; and from that point on robots -mainly cable operated -were developed. Industrial, military and scientific applications grew and called for varieties of instrumentation, tools and vehicle typestoday the underwater investigation is largely based on sophisticated multi-sensor platforms allowing systematic coverage of an area with multiple instruments like high resolution bathymetry, sediment profiling, optical images, water probes or even mass-spectrometers.

Autonomous underwater vehicles called AUV are amongst the most advanced robot systems in this field: AUV are non supervised robots, equipped with a control and decision making system, with onboard energy sources, navigation systems, and acoustic communication devices -AUV are capable of carrying out complex dive programs with multiple instrumentation packages without a physical link to the surface vessel.

While today the majority of operational dives are carried out to produce maps in a pre-defined geographical 'box', a growing need is observed for tools and techniques that give an ability to search and localize small scale phenomena in the vast and often monotonous extents of sea-bottom. This last case involves intelligent behavior in the way an underwater vehicle senses and reacts to its observations.

In order to accomplish complex tasks of environmental assessment in an unknown environment, today's research in underwater robotics often emphasizes on cooperating. Several advantages can be identified with robot fleets like spatial distribution of sensors and resources and optimization of ship time and expense.

The coordination of the vehicles in a team is encountering a major difficulty in terms of communication: in fact, data exchange between AUV via acoustic links is subject to considerable delay, a low bandwidth and the risk of packet loss.

The FeedNetBack project addresses the coordination problem by considering the AUV team as a mobile sensor network and by applying techniques from network distributed control theory to a scenario of a team of AUVs programmed to localize a source of sweet water in a coastal area. It is easily understood that a team of vehicles is able to accomplish spatial sampling at an extent that would increase the traveled distance and duration of a single vehicle in a significant way. sampling of a time-varying field of substance concentration can even be considered impossible for a single vehicle.

In ocean sciences, a major scientific impact is given by hydrothermal vents. These sources of hot water, and high concentrations of gas and liquid chemical elements, are key sites to geophysical and biological enquiries. Such vents have been found on the slopes of continental margins, and on intercontinental ridges like the mid-Atlantic ridge. In coastal areas, underwater sweet water sources and rivers retain scientists' interest. All these phenomena can be described by a plume of decreasing concentration of an observable chemical element or physical quantity. Such a plume has a typical extent of several kilometers, it has a complex shape that is exposed to ocean currents and varies with time.

Owners and operators of AUV and possibly of AUV fleets, like Ifremer, clearly identify a number of requirements, in order to making the idea of AUV fleets a realistic approach:

1. given the day rate of AUV operation between 1 000€ and 10 000€ depending on size and equipment, the number of vehicles is a key criterion; the project studies are conducted with a number of two to five AUVs; the AUV fleet is composed of identical vehicles in order to optimize the operator team structure and strength; 2. the AUV team is self-organizing, a failure of one of the systems is compensated automatically; a coordinating surface vehicle ASV can optionally ne envisaged.

the communication network based on underwater acoustic links is based on today's available

-on-the-shelf technology; the fleet control must be robust with respect to the performance of the underwater communication network, which is characterized by a single emission slot at a time.

General concept

The case study focuses on the localization of a source diffusion of substance in the sea water-this can be a hydrothermal vent, a sweet water source, a pollutant spill etc. The concentration of a substance or a physical parameter (e.g; salinity) is measured by an instrument on each of the autonomous underwater vehicles (AUV). The turbulences and the low level of spatial variation of the measurement at increasing distance from the source makes it impossible to estimate a gradient from the measurements taken by a single vehicle. A communicating and cooperating fleet of AUV allows determining the concentration gradient and carrying out a search procedure in order to localize the source.

A particular constraint in this application is the underwater communication based on acoustic signals: small bandwidth, risk of packet loss, delays related essentially to the sound velocity of about 1500m/s.

For this case study we will consider a plume of low salinity water diffusing from an underwater sweet water source. The environment model describing the diffusion plume has to reflect the turbulent character of the diffusion phenomenon. Like clouds of smoke moving upwards from a source location, the plume has irregular shape and drifts laterally due to sea currents. The salinity within the plume is increasing with growing vertical and horizontal distance from the source. In this context, a gradient computed from a series of measurements obtained on a single vehicle do not allow gradient estimation -a wider spatial extent of the measurement points is the key character of the multi-vehicle approach. Furthermore, the gradient descent in the turbulent environment is a non convex search problem. The filtering effect of the multi-vehicle sensor network enhances the performance of the maximum search.

FeedNetBack page 8/52 The system of our control problem is given by a fleet of two to five autonomous underwater vehicles, and optionally a supervising autonomous surface vehicle ASV. The communication network is based on acoustic data transmission under the hypothesis that only one vehicle can emit at a time, and that all vehicles receive the data from all other vehicles in the fleet.

In a configuration where no ASV is present, the fleet is self-organizing and automatically adjusts to the number of active AUVs. Every vehicle transmits its localization and its salinity measurement to the other members of the fleet. The fleet is moving in a circular formation at slow speed; the center of the circle being drawn towards the source location by the gradient descent algorithm. In terms of control theory, the team members reach a consensus of circle center and search direction.

In order to accomplish a solution to the search problem, the control architecture is designed on three levels:

a. vehicle control

The trajectory and dynamics of the individual AUVs are controlled via a classical low level controller; this controller only uses feedback measured on the vehicle itself. The AUV kinematics are described by a hydrodynamic model with six DOF. The control actuators are a single thruster at the tail of the vehicle, and five control fins, two placed in on each side in the front section of the vehicle and three in an inverse Y configuration in the tail. This allows for full control of vehicles when stabilizing the circular formation. The FeedNetBack solution investigates a controller that focuses particularly on the stability and the robustness of the maneuvering AUV.

b. formation control

The geometric formation of the fleet of AUVs is controlled taking into account the position of a vehicle relative to the other team members, and a center position of the formation. This control level is a distributed control loop, with position and formation parameters exchanged through the acoustic network. The way and quantity of data exchange over the acoustic network is a key to the successful control of the formation. Loss and delay of information in the network are factors that may cause divergence in the mutual perception and control of the formation. In this study, the AUV team runs on a circular shape formation.

c. Communication model

Acoustic modems in the state-of-the-art, do not allow sending and receiving simultaneously. This simple rule results in a networking communication scheme where each vehicle has a fixed time slot to send, and for the remaining time slots in a complete cycle listen to the other members of the team. Acoustic data transmission is characterized by very small bandwidth compared to radio or wire transfer. Packet loss caused by vehicle and environment noise as well as multipath and reflection phenomena.

d. gradient climb

the team being brought and hold in a defined formation at the previous level, the next step is to exchange the environmental sensor data and implement the estimation of the concentration gradient. The maximum concentration is reached by following the gradient. Like the formation control, we face here a distributed problem with sensor data from all the team members exchanged through the acoustic network. The computation may differ from one vehicle to another. The turbulent aspect of the concentration plume, and the requirement for a wide spatial base for the gradient computation, translate by the need for a robust search algorithm. e. consensus algorithm The possibility of incomplete and thus different information available on the different vehicles exists at the fleet control level as well as that of gradient climb. A computational mechanism is required whose role is to hold the team together and make the formation move on a unique center trajectory. A consensus algorithm has been designed specifically for the task of ensuring that the formation shape and trajectory on all team members converge.

Interrogations

Source localization with a fleet of underwater vehicles is a novel solution. It faces strong complexity of coordinating a fleet, accomplishing a source seeking strategy, and handling the constraints of communication, sampling and maneuvering of the fleet.

The development and use of fleet simulator does help us reach insight to the following questions:

1. The number of AUVs has strong implication on the investment and running costs, size of the vessel as well as human resources involved: What will be the performance in function of the number of vehicles? Which criteria will aid an operator in choosing the size of an AUV fleet?

2. Which is the implication of shape and geographical extent of a vehicle network? How does the formation depend on the time schedule of the communication network?

3. Is the concept robust with respect to operational considerations like the eventuality of failure of an AUV, communication drop outs, etc. ?

4. What are the limits for fleet formation in terms of communication characteristics?

3.4.

Technical comment of a simulation

We have designed a configuration with a fleet of a maximum of five autonomous underwater vehicles, equipped with sensors for a given scalar quantity, here the salinity. The aim is to locate a source of fresh water without human intervention.

A first challenge is due to the difficulty of establishing reliable communication in underwater environment. This is a key point for ensuring an effective cooperation. Indeed the data rate is only of a few hundred bits / s, the transmission delay is around a second and about 10% of items are lost.

A second challenge concerns the monitoring of formations. In this first phase of exploration, the vehicles move in a V-shaped formation. Once an agent detects a significant change in salinity, it transmits this information to the others. Then the fleet enters a phase of consolidation. We choose cooperation strategies with the pooling of information from each vehicle to exploit the advantages of using a fleet of vehicles and to reduce the time and cost of exploration.

In this phase, the fleet is regrouping into a circular shape. With such formation, the movement is slower than with the V-formation. However, there is a greater flexibility to move in all directions. Also, the distribution of submarines around the circle allows a more precise localization of the source.

FeedNetBack page 10/52

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

It is also possible to envisage other types of formation: the shape can be "elastic", that is, it deforms to adapt to its environment, to follow a path or to avoid obstacles.

To form and maintain this formation, the submarines must exchange messages according to their relative position to the center of the formation. Although exchanges of the data would allow a faster convergence of the fleet to the formation and its better maintenance, only those of the nearest neighbors are actually needed. Finally, this structure is robust to geographic constraints, limited flow and data loss.

In order to fulfill the objective of sources seeking, we developed a decision algorithm. It is also based on data exchanges between neighbors to ensure the same robustness with respect to communications. It must allow all submarines to agree on a direction for the formation to move towards the source.

3.5.

The FeedNetBack simulator

Simulation objective

The complex formulation of the problem requires that the implemented control solutions are validated by numerical simulation.

Several parameters at system design level allow evaluating the performance of a multi-vehicle team and the implemented control strategies. The parameters of system design and environment constraints are:

• number of underwater vehicles and their capabilities in terms of velocity and heading rate;

• the quality of acoustic data transmission resulting in a ratio of packet loss;

• the geometry of the formation, for example the circle diameter, translating by the temporal dimensions of the communication scheme;

The MASIM simulator

Simulation is becoming in this context a design tool for the definition of a multi vehicle system. It allows evaluating the gain in performance and the justification for a chosen number of vehicles -this is of course a primary criterion for the application scenario, each vehicle having considerable "weight" in terms of investment, operating cost and the operator resources involved.

The MASIM simulator is designed to accomplish the design study simulations in accelerated time, and with a simple configuration of the design parameters. The 3D visualization of the simulation data is a handy tool in a second step to represent the resulting dive features and provide easy understanding of the fleet behavior.

General conclusions

First conclusions can be stated at the end point of the FeedNetBack project:

• Maintaining formation and proceeding on a targeted trajectory is possible with a realistic 'product-based' acoustic communication link. Up to 50% of packet loss is successfully handled in the 'worst case' simulation scenarios.

• With an increasing number of vehicles, the communication cycle duration is also increasing, and the fleet may encounter difficulties maintaining the formation; a number of 3 AUVs seems the ideal fleet size -this is an interesting result with strong potential for practical application.

• The circular shape translates by a low speed of the fleet towards its goal, but it allows adapting easily to changes in direction and amplitude of the fleet movement.

On the level of AUV fleet operation the two following statements are issued and mark the preliminary end point of the project:

1. It has been successfully shown that the control of a fleet formation with a common mission goal is functioning in realistic conditions; a single frame of control theory enables the networking fleet concept and provides a scientifically founded framework for fleet cooperation.

2. The simulations with varying system parameters give insight into the dependencies within the complex system, making the MASIM simulator is a highly valuable tool for the AUV system design in the hands of an AUV operating entity.

Perspectives

In future work, new algorithms for acoustic data transmission will be implemented; techniques already studied in the project, like OFDMA, will increase the effective bandwidth of vehicle-tovehicle communication and eventually allow multiple-input-multiple-output scenarios.

A second fleet architecture will be implemented which does not include a surface vehicle. The AUV organize without a centralized leader.

4.2.

Scenario description We consider a fleet of AUV and a single ASV. The number of AUV can be from 2 to 5 and each one have the dynamic behavior of the Ifremer's AUV AsterX. We fixed the parameter of the fresh water source for all the tests. We consider 2 initial positions of the fleet. We also consider the source seeking into a 2D plan, at a fixed depth. We consider 2 independent underwater acoustic networks: Network 1 is horizontal one between AUV's. Network 2 is vertical one between AUV's and ASV at the surface. We consider that these 2 networks are directive enough in order not to disturb each other. All parameters can be found in the initialization matlab file init_simulation.m in annex 1.

Control architecture of the scenario

4.3.

Control Algorithm description We consider 3 algorithms in cascade.

Local algorithm for AUV stabilization

This algorithm is a robust control of depth, speed and heading. Its description can be found in [R1]. We will call this algorithm "loop0".

Circular formation loop

This algorithm maintains the fleet into a circular uniform formation. Information is exchanged into network 1 using TDMA protocol. Its description can be found in [P1]. We will call this algorithm "loop1".

Gradient search algorithm

This algorithm uses each AUV salinity measurement to find the direction of the formation's displacement. This algorithm is implemented into the ASV and information is exchanged into network 2 using an adapted TDMA protocol. Description of the gradient search algorithm can be find in [P2]. Details on implantation and protocol used can be found in [P3]. We will call this algorithm "loop2".

Definitions of variables and metric

To evaluate performance let define the following variables:

-dc [m]: distance between the center of the formation and the position of the source. -Tf [s]: the fist time dc is lower than 3m. -Tm [s]: Tm is the metric used to compare the performance of different scenario. Tm=Tf -2000. These 2000s correspond in simulation to the time when we activate loop2 and so the formation starts to move. This is the duration loop1 needs to build and stabilize the formation from the initial position. For network 2, we consider that the maximum distance between AUV is 3000m. The probability of packet loss is therefore α2=10%. This assumption is valid for SL>=185 db ref 1µPa at 1m and Nis<=75dbV/sqrt(Hz)

We assume that there is enough energy embedded in ASV to send the acoustic message at a very higth level and ensure no loss of data from ASV to AUV. α2 is therefore only for AUV to ASV communication.

Modified TDMA protocol for network 2

The particularity of network 2 is that all AUV are in the same plane and ASV is at the surface.

One assumption here is that ASV has enough energy and space to have a powerful emitter that make the ASV1AUV communication without any data loss. In the case of a centralized algorithm, all AUV has to send a message to ASV and ASV has to send a message to AUV but there is no need that AUV are sending message to each other. Therefore, we can divide TDMA cycle into 2 phases: This protocol has been used for loop2 over network 2. See annex1 for detailed values.

Improvements from OFDMA protocol

In OFDMA technique, each agent can use its own band of frequency. Several agents can therefore send there message in the same time. As the bandwidth is share between all agents, the bitrate is also reduced. The simplex constraints remain so a TDMA protocol still necessary but could be reduced to only 2 slots. For example: Slot 1 : n-1 first agents send its message and agent n is listening Slot 2 : agent n sends its message and all it received in slot 1 and others are listening In loop2 through network2, all AUV will be able to send in the same time.

If we consider that the bitrate is devide by n, we can calculate the new slot time for all AUV: Tsl=Te1*n witch is (n-1) Tsl shorter than the previous one.

Delay of propagation

For loop1 and loop2, constraint on propagation delay of acoustic waves is included into the principle of TDMA protocol. We don't have to add any extra delay for simulation. Theses algorithm use information from the network only at the end of each cycle of communication.

4.5.

Study on parameters Here is preliminary study to feel what the influence of each parameter is.

Local control parameters

Parameters of loop0 are fixed for all the study. It has no influence on other parameter of the scenario. Its modification can change tracking performance of the depth z, the heading theta, the speed V, and robustness with respect to interference currents or errors of parameters.

Refer to [R1] for more precisions.

6.2. Formation algorithm parameters Kappa and beta

These parameters have to be set to stabilize the formation by taking into account the dynamic of each agent. Theses parameters are fixed for all the study (see annexes for values). However, you'll have to tune it again if you change some parameters of the scenario like R, V0, Vf or the dynamic of agents.

Basically, Kappa defines the speed of convergence of the radius: each agent converges in a rotation around the center at the defined radius. Beta defines the speed of convergence of the uniform distribution. This parameter controls the angle between each agent and so uses the information from the communication network 1. One way to do set these parameters can be: -fix the center position -try different values of Kappa to make all agents converges into the circle: plot the radius of each agent to evaluate the optimum value of kappa.

-try different beta to synchronize uniform formation convergence with the radius convergence: plot the angle between each agent to evaluate the optimum value of beta.

Refer to [P1] for more precisions. This results the following constraints on Vf, V0 and R: V0+Vf<VmaxAUV V0/R< ThetaptmaxAUV(V0-Vf)

To fulfill theses constraints, we set the parameter V0 for all the study (see annex1 for value).

Speed of the formation Vf

During the entire mission, the magnitude of the speed of the formation's center is set to Vf. This is a parameter of loop2. This parameter strongly influences the mission time Tm. It has to be set as big as possible but with respect to constraints that has to fulfill V0. Therefore, we increase Vf until the simulation show too big deformations on the formation during the mission.

Filter on the search direction 1f

When the number of agent is low, the search direction 1f is oscillating around the right value and so add a parasite movement into the formation's trajectory. Therefore, we implement a filter on the direction search 1f. This is a low pass filter of the 1 st order and its cutting frequency is fc=V0/(4/3.pi².R). This corresponds to a time response of the filter equal to one turn for an AUV. This method is reduction oscillations of the 1f but not avoid loops describes in part 6.6. because frequency of theses loops are lower.

Radius of the formation R

Radius can be seen as a spatial sampling for gradient computation. A big R is makes the gradient search robust to noise measurement. The probability to lose a packet through network is α2M=α2^M2.

To reduce the number of parameters, we can set M2 to an optimum value analytically calculated. Loop 2 is computing the direction if it receive all the packet from AUVs so the probability to compute the command is (1-α2^M2)^n0. We can compute the typical number of cycle we need to calculate the command N=1/(1-α2^M2)^n0 Therefore, as M2 is decreasing this value but increases the cycle time, we can plot the typical time to wait between 2 calculated commands. For example, we can see in the following figure that M2*=3 for 2 AUVs and M2*=4 for 4 AUVs. Typical Cycle time for loop 2 for 4 AUVs, α2=10%, nTsAUV=6, nTslastAUV=nTsASV=13

Study on Number of agents

As the radius, the number of agent into the circular formation can be seen as a spatial sampling for gradient computation: more agents on the circle there is, the better is the evaluation of the gradient. In this section, we expose different consequences of this parameter.

Analytical proof of formation's convergence

In [P2] we can find that "Asymptotic convergence of the formation's rotational center to the location of the source was proven analytically for signal distributions that have circular level sets for two or more agents, and for those that have elliptical level sets for groups with an even number of agents."

In this study, the source, which is a sum of 3 ellipses, does not fulfil the condition of convexity. In the same way as in figures 8 and 9 of this paper, simulations confirms that dc is not always decreasing but formation still always find the source location. Moreover, some situations in simulation show that respectively 2 or 4 agents gives better performances than 3 or 5.

[Example A, conditions: nTstransloop1=2, nTsAUV=6, nTslastAUV=nTsASV=13, Vf=0.05m/s, alpha1=alpha2=0, no filter on 1f]

Cost of an agent

In a mission, an extra agent costs a lot of money. For example, a single day mission causes the following costs: Coast boat: 5000€/day (for crew + maintenance + depreciation) First AUV: 5000 €/day (for crew + maintenance + depreciation) Additional AUV: 1500€/day In case of a more than one day mission, this is more expansive because of watch organization for the crew.

7.3. Number of agent and available bandwidth

In a TDMA protocol, an additional agent leads to increase cycle time. In a OFDMA protocol, an extra agent leads to a decrease of the available bandwidth shared between all agents. This decrease of bandwidth increase the emission time and so can also lead in certain case to increase the cycle time. This increasing of the cycle time can compromise the stability of the fleet and can oblige to set a slower speed of formation Vf. The optimum number of agent is therefore linked to acoustic communication conditions and AUV technology (sample rate of AUV, modem performance, bitrate, ambient noise…).

[

4.7.

Key results

We present here some results under certain conditions. We represent a large number of possible situations in terms of packet loss because it reflects both different conditions of communication (configuration subject to multipath, ambient noise level, performance modems ...) and different choices in terms of operational data exchanged. For example, in the case of a larger volume to be exchanged for operational reasons (security, supervision ...), cycle times of communication will increase and the probability of packet loss as well. In addition, through the repeater mechanism presented in section 5.4 that will compensate packet lost, we can also make the equivalence between packet loss and cycle times of communication.

Quite favorable communication conditions

Cycle time are calculated with number of bits exchanged, modem bit rates and maximum distance between agents. For example, cycle time used in example A, B and C corresponds to the following quite good conditions: Parameter influencing cycle time of protocol : Maximum distance between AUV = 200m Maximum distance between ASV and AUV = 1500m Bitrate between ASV and AUV = 100 bit/s FeedNetBack page 24/52 Trajectory of the fleet, 2 AUV, R=50m, Vf=0.15m/s, α1=α2=50%, M1=2, M2=3, Tt=7487s, filter on 1f

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

IMN/SM

Conclusion for the scenario

These results show that in this scenario, using more than 2 AUV does not increase the performance of the task significantly, both in good and bad conditions of communication. In addition, thank to the repetition technique and the robustness of the algorithms with respect to loss of communication, the task's performance is not significantly affected by bad communication condition.

4.8.

Other algorithms for practical implementation Here is some proposition of other algorithms that would be useful to make the scenario implementable in real condition.

Anti collision algorithm

Anti collision algorithm has not been included in loop2 because that kind of security algorithm has to be implemented independently at a lower level (like loop0). A way to decrease the risk of collision is to set a relatively low but not null difference of depth for each AUV.

Survey algorithm for first phase of the mission

The first phase of prospection into V-shape formation is not considered in theses test. An algorithm to manage the fleet from the launch of each AUV (probably one by one) to the V formation has also to be done.

5.

Section III Results of single network simulations

In this section we present simulation results obtained with the single network architecture.

Overview

The communication between underwater vehicles and that between vehicles and surface system use the same acoustic channel, and since all transmissions must be sequentially organized in a time division media access (TDMA) scheme, the communication cycle duration is significantly longer compared to the double network architecture used in section II. The mission duration Tm is calculated as explained in section 4.3.4

The set of simulations has been realized with the aim to establish insight on the impact and choice of the main simulation parameters, which are:

• the number of underwater vehicles;

• the radius of the fleet formation circle;

• the acoustic reliability of communication in shape of the rate of packet loss;

• the scale of the plume which center (source) is to be localized.

In this second section of AUV fleet simulations, the underlying communications network concept is that of a single SIMPLEX channel used for both vehicle-to-vehicle communication and vehicle-tosurface communication.

The overall cycle duration is of course longer than in section2, and it increases with growing depth of the fleet.

Catalogue of simulated configurations and results

The following paragraphs summarize the sets of simulations which aim is to show the influence of different system parameters. Conclusion:

At least one repetition of each acoustic packet significantly increases the convergence of the consensus algorithm, and by doing this improves the performance of formation holding and gradient climbing of the fleet. This means that the increase in the communication cycle duration due to message repetition has fewer drawbacks than it is beneficial to the information exchange and gradient search.

The best performance is achieved with a fleet of four AUVs.

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 52 Evaluation of the Underwater Fleet Simulator MASIM and description of simulation resultsIMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

4 .on communication 4 . 4 . 1 .

 4441 -R [m]: radius of the formation -V0 [m/s]: magnitude of the nominal speed of an AUV -Vf [m/s]: magnitude's speed of the formation -1f [rd]: direction of formation's displacement computed by loop2 -n number of agent 4.Constraints Data loss calculation Acoustic modem performance is calculated from equation given in [D8.01] and literature. See annex 2 for details of equations used. We get α the probability packet loss function of the following parameters: SL = source level Nis = ambient noise level f = nominal frequency BW= receiver bandwidth Kb=number of bits / packet Gamma1 and Gamma2 = modem algorithm performance R = distance of communication adb=0.11*(f^2/(1+f^2))+44*(f^2/(4100+f^2))+2.75*10^(-4)*f^2+0.003 TL=k*10*log10(R)+adb*R/1000 SNRdb=SL-TL-Nis-10*log10(BW)+DI Ps=erfc(sqrt(gamma1.*10.^(SNRdb/10)))/2 Probability of packet loss for a packet of Kb bits : FeedNetBack page 14/52

  ]=f(R[m]), range(Pertes<11%)=217 m Distance [m] Probability of packet loss [%] FeedNetBack page 15/52Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011 Network2 : Probability of packet loss for DI=2, SL=185 db ref 1µPa at 1m, Nis=75 dbV/sqrt(Hz) at f, BW=239.8833Hz, f=10kHz, gamma1=0.246, Kb=60 bits,k=24.4.2. Data loss calculation and problem of multipathIn some case, the problem of multipath can be neglected and included into the 10% of data loss we add (see part 5.1). In [D8.01], we can find the following condition: « 6.2.3. […] The relative horizontal range between the vehicles are supposed to be greater than five time the relative altitude. » We can consider that it does in network 2 but not in network 1. To evaluate communication constraints coming from multipath of acoustic waves, we consider 5 path: Path 1 : direct Path 2 : 1 reflection on the surface Path 3 : 1 reflection on the sea bed Path 4 : 1 reflection on the sea bed then 1 at the surface Path 5 : 1 reflection on the surface then one at the seabed ]=f(R[m]), range(Pertes<11%)=3232 m Distance [m] Probability of packet loss [%]

1 -

 1 Each AUV sends his message to ASV one by one 2-ASV sends is message to all AUV Let define the following variables: D: bit rate in bit/s Lmax: maximum distance between AUV and ASV [m] dL1: maximum difference between all distances between AUV and ASV [m] Nbits1: the number of bit for each AUV message Nbits2: the number of bit for each ASV message Vsound=1500[m/s] : sound speed n: number of AUV Te1=Nbits1/D: emission time for AUV [s] Te2= Nbits1/D: emission time for ASV [s] Tt=Lmax/Vsound : maximum travel time of a message between AUV and ASV [s] Ts1=dL1/Vsound : security time to avoid collision between 2 AUV messages. Tsl1=Te1+Ts1 : slot time of (n-1) first AUV [s] Tsl1e=Te1+Tt : slot time of the last AUV [s] Tsl2=Te2+Tt : slot time of ASV [s] Tc=(n-1).Tsl1+Tsl1e+Tsl2 : total cycle time of the protocol [s] Because Ts1<Tt, Tc is (n-1).(Tt-Ts1) shorter than standard TDMA.As network2 is also subject to data loss α2 (in AUV1ASV communication), we uses the same method describes in 5.3. to compensate this problem. The total cycle time is now: Tc=M2.(n-1).Tsl1+M2.Tsl1e+Tsl2[s] See details of this protocol in [P3].

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 52 Evaluation of the Underwater Fleet Simulator MASIM and description of simulation resultsIMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

4. 5 .

 5 3. 6.3. Nominal speed of AUV V0 AUV AsterX torpedo shape has some dynamics constraints: VmaxAUV=2.67m/s ThetaptmaxAUV=f(VAUV) (see following figure) FeedNetBack page 19/52 Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

V

  AUV speed [m/s] θ' maximum rotational speed [rd/s] FeedNetBack page 20/52 Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011 A small R makes the gradient search more precise, especially when le variation of salinity is abrupt. When the plume of concentration is thin, a too big R slows down the movement by describing loops. Trajectory of the fleet, 4 AUV, R=50m, Vf=0.15m/s, α1=α2=10% Tt=10706s, filter on 1f Trajectory of the fleet,4 AUV, R=100m, Vf=0.15m/s, α1=α2=10% Tt=4827s, filter on 1f Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011 4.5.7. Optimum number of repetitions M2* for loop 2 M2 is the number of repetitions into network 2 to reduce effect of packet loss.

  Evaluation of the Underwater Fleet Simulator MASIM and description of simulation resultsIMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

  Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

	FeedNetBack	page 9/52
	IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011

Section II Test report of control algorithms 4.1. Context of this document

  This document exposes the first tests of control algorithms developed in Feednetback project and applied to underwater exploration by multi-agent systems. Theses algorithms have been implemented into the MASim simulator witch is coded into Matlab-Simulink software. A description of the study case's constraints can be found in [D8.01]. A description of the simulator can be found in [D8.03].

	FeedNetBack	page 12/52

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

IMN

/SM/PRAO/11.290 indice : A Date : 8 December 2011 4.

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

  For network 1, we consider that the maximum distance between AUV is 200m. The probability of packet loss is therefore α1=10%. This assumption is valid for SL>=173 db ref 1µPa at 1m and Nis<=90 dbV/sqrt(Hz) at f

	P=min(1-(1-Psdb).^Kb+0.1,1)
	Network1 : Probability of packet loss for DI=2, SL=173 db ref 1µPa at 1m, Nis=90 dbV/sqrt(Hz) at
	f, BW=239.8833Hz, f=10kHz, gamma1=0.246, Kb=60 bits,k=2
	IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

  =2 but results are perturbed by a resonance (see annex 3). As M2=1 and M2=2 are very clothe in term of typical cycle time (see section 6.2), we present here results for M2=1. Maximum distance and number of bits exchanged are also influencing packet lossIn network 1, for 200 to 300m of range, α1 rapidly increases from 10% to 100%. We consider here to represent a difficult case that α1=50% for all communications. Again, in network 2, for 2000 to 3000m of range, α2 rapidly increase from 10% to 100%. We consider here to represent a difficult case that α2=50% for all communications.

				FeedNetBack FeedNetBack								page 25/52 page 26/52
	Bitrate between AUV = 480 bit/s Maximum distance between AUV = 300m
	Number of bit exchanged between AUV and ASV = 60 Maximum distance between ASV and AUV = 3000m
	Number of bit exchanged between AUV = 30 Bitrate between ASV and AUV = 100 bit/s
	Bitrate between AUV = 480 bit/s
	Parameter influencing cycle packet loss: Number of bit exchanged between AUV and ASV = 200
	Emission level for network 1 SL1=173 db ref 1µPa at 1m Number of bit exchanged between AUV = 200
	Acoustic noise for network1 Nis1=90 dbV/sqrt(Hz) at f
	Emission level for network 2 SL2=185 db ref 1µPa at 1m Parameter influencing cycle packet loss:
	Acoustic noise for network2 Nis2=75dbV/sqrt(Hz) Emission level for network 1 SL1=173 db ref 1µPa at 1m
	Maximum distance and number of bits exchanged are also influencing packet loss Acoustic noise for network1 Nis1=90 dbV/sqrt(Hz) at f
	Emission level for network 2 SL2=185 db ref 1µPa at 1m
	[Example C, conditions: nTstransloop1=2, nTsAUV=6, nTslastAUV=nTsASV=13 M1=M2=1, filter Acoustic noise for network2 Nis2=80dbV/sqrt(Hz)
	on 1f, alpha1=alpha2=0% ] -Most favorable case for reference
	Nb AUV					2		3				4		5
	Cycle time loop 1	0.5		0.75			1		1.25
	Tc [s]
	Cycle time loop 2		4		4.75			5.5		6.25
	Tc [s]
	Vf max m/s			0.15		0.15			0.15		0.15
	Mission Tm [s], filter on 1f duration ,α1=α2=0% 100	4877.6 P[%]=f(R[m]), range(Pertes<11%)=201 m 5587.1 (>4)* 4282.5		9993 (>4)*
	[Example D conditions: nTstransloop1=2, nTsAUV=6, nTslastAUV=nTsASV=13 , filter on 1f, 90
	alpha1=alpha2=10% ]
	80 Table for M1, Vf=0.15m/s Mission duration Tm [s], M2*=1 n0=2 M1=1 5020.1 M1=2 12714 M1=3 M1=4 2 20 30 For n0=4, M2*Nb AUV 40 50 60 70 Probability of packet loss [%]	n0=3 M2*=1 6712.5 4883(>4)* 5431.3 3	n0=4 M2=1 4958.3 4252.4 4286.4	4	n0=5 M2*=2 4375 4205.1 8783 5
	M1 M2	10	0	50	1 1 100	150	200	2 1 250	300	2 1 350	400	2 2 450	500
	Cycle time loop 1	0.5		1.5 Distance [m]		2		2.5
	Tc [s]
	Cycle time loop 2 Network1 : Probability of packet loss for DI=2, SL=173 db ref 1µPa at 1m, Nis=90 4 4.75 5.5 9.375 Tc [s] Vf max m/s 0.15 0.15 0.15 0.15 dbV/sqrt(Hz) at f, BW=239.8833Hz, f=10kHz, gamma1=0.246, Kb=200 bits,k=2
	Mission duration	5020.1	4883(>4)*		4252.4	4205.1
	Tm [s], filter on
	1f ,α1=α2=10%
			4.7.2. Quite difficult communication conditions
	Parameter influencing cycle time of protocol:
									/PRAO/11.290 IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011 indice : A Date : 8 December 2011

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

			FeedNetBack					page 27/52
				P[%]=f(R[m]), range(P<11%)=1986 m
	100
		90
		80
	Probability of packet loss [%]	40 50 60 70
		30
		20
		10	0	1000	2000	3000	4000	5000	6000
						Distance [m]
	Network2 : Probability of packet loss for DI=2, SL=185 db ref 1µPa at 1m, Nis=80
	dbV/sqrt(Hz) at f, BW=239.8833Hz, f=10kHz, gamma1=0.246, Kb=200 bits,k=2
	[Example E conditions: nTstransloop1=5, for M2=1, nTsAUV=17, nTslastAUV=nTsASV=33 ,
	filter on 1f, ,α1=α2=50%]
	Table for M1, Vf=0.15m/s
	Mission duration	n0=2		n0=3	n0=4	n0=5
	Tm [s],			M2*=3		M2*=3	M2*=4	M2*=4
	M1=1			10903			6783.3	6829.6
	M1=2			5487.1		7565	4961.4	5234.1
	M1=3			14739		6328.1	6524.1	5812.5
	M1=4					10855		6320.3
	For n=2 to 5:
	Nb AUV			2		3		4	5
	M1			2		3		2	2
	M2*			3		3		4	4
	Cycle time loop 1	2.5		5.625	5	6.25
	Tc [s]
	Cycle time loop 2	18.375	24.5		38.625	46.75
	Tc [s]
	Vf max m/s		0.15	0.15		0.15	0.15
	Mission		duration	5487.1	6328.1	4961.4	5234.1
	Tm [s], filter on 1f
	,α1=α2=50%
						IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

	IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

  Sebastien Varrier, Daniel Simon, Olivier Sename, Emilie Roche and John-Jairo Martinez-Molina, "Robust control of autonomous underwater vehicles" , public report of ANR project Connect http://www.gipsa-lab.inpg.fr/projet/connect/reports.php

		FeedNetBack FeedNetBack FeedNetBack FeedNetBack				page 35/52 page 36/52 page 37/52 page 38/52
	[Example E conditions: nTstransloop1=5, for M2=1, nTsAUV=17, nTslastAUV=nTsASV=33
	4.10. Annex 2 : Acoustic communication calculation , filter on 1f, ,α1=α2=50%] 4.12. Bibliography
	From [D8.01], we get the following equation and parameters: « 6.2.3. […] Acoustic modem Transmit Input values: Source Level SL = from 173 dB up to 185 dB ref 11Pa at 1m by step 3 Baud rate = 100 bps up to 500bps by step 100 4.11. Annex 3: Other table of results [Example C', conditions: nTstransloop1=2, nTsAUV=6, nTslastAUV=nTsASV=13 M1=M2=1, filter on 1f, alpha1=alpha2=0% ] -Most favorable case for reference Nb AUV 2 3 4 5 For n=2 table for M1 and M2, Vf=0.15m/s Mission duration Tm [s], M2=1 M2=2 M2*=3 M2=4 M2=5 M1=1 7729 10206 10903 [R1] : [P1] : Lara Briñon-Arranz, Alexandre Seuret, and Carlos Canudas-de-Wit, "Elastic formation
	Transmitter = omni directional Acoustic modem Receive parameters: Probability of Detection (PD) 2 1 -erf(3MSNR/2) = 10-2 / 10-3 / 10-4 With respectively "MSNR" = 9 dB/ 12,5dB/ 16dB "Minimum Signal to Noise Ratio" to Cycle time loop 1 Tc [s] 0.5 0.75 1 1.25 Cycle time loop 2 Tc [s] 4 4.75 5.5 M1=2 7723 11574 5487.1 6111.5 control based on affine transformations" , American Control Conference 2011, 10736 M1=3 6437.5 18889 SanFrancisco, USA 14739 6.25 M1=4 9529 26191 [P2] : Brandon Moore, Carlos Canudas-de-Wit, "Source Seeking via Collaborative
	obtain the wished maximum bit error rate Receiver = omni directional Acoustic Channel parameters: Transmission Loss TL = 20*log(R) + ao * R/1000 With Relative Slant Range in meters (100m up to 2000m by step 100m) Vf max m/s 0.2 0.2 0.15 Mission duration Tm [s], filter on 1f 3470.1 (>12)* 4562.8 4282.5 Measurements by a Circular Formation of Agents" IEEE American Control Conference, Jun 0.1 For n=3, table for M1 and M2,Vf=0.15.m/s 2010, Baltimore, Maryland, United States. pp. s/n 6264.8 Mission duration M2=1 M2=2 M2*=3 M2=4 M2=5 M2=6 (>7)* Tm [s], [P3]: Alireza Farhadi, Jonathan Dumon, Carlos Canudas-de-Wit "Data Fusion And Tracking ,α1=α2=0% M1=1 Of An Underwater Source Flow By A Fleet Of AUVs". Submitted to ACC2012
	ao in dB/km can be computing with the François Garrison formula (Annex I) or deducted from Tables (Annex I) Environment: M1=2 7800.4 7565 7175.6 13536 [Example D conditions: nTstransloop1=2, nTsAUV=6, nTslastAUV=nTsASV=13 , filter on M1=3 7260.1 6328.1 6361.8 5311.9 20771 [D8.01] : Jan Opderbecke ,"Description of the scientific mission scenario(s) to be 1f, alpha1=alpha2=10% ] M1=4 10855 8471 9029 investigated for the marine application", Feednetback public report For n=4, table for M1 and M2, Vf=0.15m/s Nis = Ambient Noise Level in dBV/2Hz for the receiver nominal frequency . Nis = 70-16.6*Log(f nominal) + x dB and x = 0 to 30 dB step 5 The geometrical configuration: The relative horizontal range between the vehicles are supposed to be greater than five time the relative altitude. The results are obtained from the sonar equation and correspond to maximum range in SL -TL -Nis -10*log( BW) + DI + GT >= MSNR With Receive Level RL = SL -TL Mission M2=1 M2*=2 M2=3 M1=4 7741.9 For n=5, table for M1 and M2, Vf=0.15m/s M1=3 11320 6315.5 7550.3 6524.1 function of Nis. Mission M2=1 M2*=2 M2=3 For n=4, table for M1 and M2, Vf=0.15m/s [D8.02] : Jan Opderbecke, Alain Kibangou ," Tools for Underwater Fleet Communication", duration Tm Mission M2=1 M2=2 M2=3 M2*=4 M2=5 Feednetback public report [s], M1=1 4958.3 14282 duration Tm 4715.6 M1=2 4252.4 15698 [s], [D8.03] : Jan Opderbecke, Michel Perrier, Jonathan Dumon, "Description of the multi 4319.9 M1=3 4286.4 M1=2 6409.4 9617*(>6) 4961.4 4996.3 11379(>7)* M1=1 6783.3 vehicle simulator", Feednetback internal report
	Noise Level NL = Nis + 10 log(BW) duration Tm
	S/B computed = RL -NL + DI [s], For n=5, table for M1 and M2, Vf=0.15m/s
	For DI an example is given Annex I M1=1 11755(>4)* 4375 Mission duration M2=1 M2=2		M2=3	M2*=4	M2=5
	[…] M1=2 Tm [s],	7032.6	4205.1	4205.3
	6.2.4 […] M1=3 M1=1 Additional effects like local turbulence, local shading, multipath etc. is represented by a 8783 9314 6829.6 M1=2 7842.6 4629.8 5234.1 8536 10% constant packet failure rate. » From these equations, to calculate the probability of packet loss, we use the following method: Probability of bad detection Ps=erfc(sqrt(gamma1*10^(SNRdb/10)))/2; [s] We define Gamma1 the detection algorithm performance implemented into the modem: Cycle time loop 1 Tc 0.5 0.75 1 1.25 SNRdb received = SL-TL-Nis-10log(BW)+DI M1=3 13617 5148.5 4847.3 5812.5 [Example D' conditions: nTstransloop1=2, nTsAUV=6, nTslastAUV=nTsASV=13 M1=M2=1, M1=4 13725 4716 8193 6320.3 filter on 1f, alpha1=alpha2=10% ] Nb AUV 2 3 4 5 M1=5 5841.9
	Probability of packet loss for a packet of Kb bits: Pp=min(1-(1-Ps)^Kb+0.1,1) Cycle time loop 2 Tc 4 [Example E' conditions: nTstransloop1=5, for M2=1, nTsAUV=17, nTslastAUV=nTsASV=33 4.75 5.5 6.25 [s] , filter on 1f, ,α1=α2=50%]
	Vf max m/s For n=3, table for M1 and M2,Vf=0.2.m/s 0.15		0.2	0.2	0.1
	Mission duration Tm Tm [s], [s], filter on 1f Mission duration	M2=1	5020.1 M2=2	M2=3	5001.9 M2=4	3401 M2=5	8101
	,α1=α2=10% M1=1
	M1=2			11955	10044
	M1=3			5440.6 4721.4 5441.8
	M1=4			9923	12284
	Evaluation of the Underwater Fleet Simulator Evaluation of the Underwater Fleet Simulator Evaluation of the Underwater Fleet Simulator	IMN/SM/PRAO/11.290 IMN/SM/PRAO/11.290 IMN/SM/PRAO/11.290 IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011 indice : A Date : 8 December 2011 indice : A Date : 8 December 2011 indice : A Date : 8 December 2011
	MASIM and description of simulation results MASIM and description of simulation results MASIM and description of simulation results

Data loss and repetitions

  Effect of repetitions in a bad acoustic environment.

			FeedNetBack FeedNetBack		page 39/52 page 40/52
	Configuration: • AUV's messages: • ASV's messages: • bitrate • max. distance: • radius of the fleet : • speed of formation: • vehicle speed:	30 bit 60 bit 480 byte/s 1500 m 50 m 0.15 m/s 1 m/s
	Results:
		7000
	Missioh duration (sec)	6200 6400 6600 6800				0% 10% 10% -2 rep. 20% -3 rep.
		6000
		5800
			2 AUV	3 AUV	4 AUV	5 AUV
	0%		6651	6643	6288	6342
	10%		6874	6632	6370	6566
	10% -2 rep.	6651	6597	6343	6203
	20% -3 rep.	6646	6598	6327	6541
				number of AUV
			1 repetition			2 repetitions

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

IMN/SM

/PRAO/11.290 indice : A Date : 8 December 2011 5.2.1. Behaviour: (For 4 AUVs with 10% of loss.) Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011 3 repetitions Note for comparison with section II : n repetitions corresponds to parameter M1=n

2. Radius and number of agents

  Only a two-vehicle fleet can hold a circle formation with radius below 50m. We can explain this by the dynamic constraints to fulfill described in section 4.5.3: there is a strong dependency between vehicle velocity V0, fleet velocity Vf and circle radius R.Larger values of the circle radius give good convergence in the first part of gradient climb, when approaching the source, meaning a distance from the source in the same order than the radius; smaller circles provide more precise convergence. Adaptive adjustment of the radius is a promising perspective to optimize convergence in all phases of the search.

		FeedNetBack FeedNetBack FeedNetBack		page 41/52 page 42/52 page 43/52
	Configuration: • AUV's messages: • ASV's messages: • bitrate • max. distance: • radius of the fleet : • speed of formation: • data loss • vehicle speed:	200 bit 200 bit 480 byte/s 3000 m 50 m 0.1 m/s 0% 1 m/s
	Results:
		19000
	Mission duration	11000 13000 15000 17000
		9000
		7000
			40	50	60	70
		2 AUV	8799	8749	8709	8657
		3 AUV	18857	9168	8789	8620
		4 AUV	18076	9166	8191	8422
		5 AUV	30000	9440	30000	30000
				Radius
	Behaviour: (case 2 AUV)
		R = 40m		R = 60m

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

IMN/SM

/PRAO/11.290 indice : A Date : 8 December 2011 5.2.Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011 R = 80 m Conclusion: Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011 5.2.1.

Radius and data loss

  Simple or double packet repetition allows maintaining similar performance up to 20% packet loss.

		FeedNetBack		page 44/52
	Configuration: • AUV's messages: • ASV's messages: • bitrate • max. distance: • radius of the fleet : • speed of formation: • number of agents • vehicle speed:	200 bit 200 bit 480 byte/s 3000 m 50 m 0.1 m/s 2 1 m/s
	Results:
		8900
		8850
	Missuib duration	8700 8750 8800				0% 10% -1 rep 10% -2 rep 20% -1 rep
		8650
		8600
			40	50	60	70
	0%		8799	8749	8709	8657
	10% -1 rep	8862	8772	8749	8648
	10% -2 rep	8817	8764	8700	8650
	20% -1 rep	8862	8826	8664	8716
				Radius
	Conclusion:

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

			5.2.2. Speed of formation
	Configuration: • AUV's messages: • ASV's messages: • bitrate • max. distance: • radius of the fleet : • data loss • vehicle speed:	200 bit 200 bit 480 byte/s 3000 m 50 m 0% 1 m/s
	Results:
		17000
	Mission duration (sec)	9000 11000 13000 15000
		7000
		5000
			0,05	0,1	0,2	0,3	0,35
		2 AUV	15304	8749	6496	6007	5860
		3 AUV	16308	9168	5869	5334	30000
		4 AUV	14749	9166	6800	30000	30000
		5 AUV	15888	9440	8163	30000	30000
					speed of formation
	Behaviour: (case with 2 AUV)
			Vf = 0.1 m/s				Vf = 0.2 m/s
					IMN/SM/PRAO/11.290	indice : A Date : 8 December 2011

Evaluation of the Underwater Fleet Simulator MASIM and description of simulation results

IMN/SM/PRAO/11.290 indice : A Date : 8 December 2011

We consider that there is attenuation only for reflection at the seabed.

At this stage of the study, it is considered that multipath effects have a strong influence and significantly increase the rate of packet loss. The effective cycle time is also modified by the sample rate of AUV: each slot has to be a multiple of the embedded computer's sample time.

Modified TDMA protocol for channel subject to data loss

In our study case, we consider the acoustic channel as a packet erasure channel with erasure probability α. See part 5.1 for details. Performances of control algorithm that uses this type of channel decrease with α. We choose to use the repetition technique proposed in [P3] to adapte the standard TDMA protocol. The idea is that each agent will send his message M times instead of one. The probability of erasure will be αM=α^M and total cycle time Tc=n.M.Tsl. Simulations we made of this phase were basically by defining the same speed and direction for each AUV (in an open loop way). When one of the agents detects a concentration bigger than a threshold, the algorithm switches to the circular formation mode.

Depth management

Loop0 includes depth control but the depth has not been treated into loop1.

To perform the task of localization of a source, we consider the sub-task of finding the maximum of concentration in a plane. Another algorithm has to be implemented to increase the depth when the maximum has been localized. The way to detect that the minimum has been localized seems not to be trivial.

Radius management

To improve performances, we can imagine an algorithm that would change the radius in some cases, for example: -increase R when the computed gradient is too small -decrease R when the gradient is high -decrease R when the plume is too thin by detecting a loop shape trajectory.

Speed of formation Vf management

To improve performances, an especially the robustness of the task, a management of Vf seems to be necessary. Indeed, in case of large disturbance (current, communication ...), the circular formation can be broken. In this case, the calculated gradient and thus the direction of advance of the formation become false. It is then necessary to stop moving and wait for the formation is properly reformed and then permit to move again. This algorithm would have to compute criteria that reflect the uniform distribution of agents around the circle and set Vf to 0 when the criteria is under a defined threshold.

Displacement of the ASV

Even if ASV is not used in Loop2 for gradient search (centralized version), it should be used to perform the task of security and supervision of AUV. In all cases, an algorithm has to be implemented to make it follow the fleet by tracking the [x,y] position of the center's formation.

Gradient search for one single AUV

This could be interesting to develop and test a single AUV gradient search algorithm in order to compare with fleet performance and to use in some situation to perform the mission even when there is problem on all but one AUV.

Use limited embedded energy consideration

To evaluate different scenario, we can uses energy consumption of AUV to evaluate different strategy.

Use mean and variance for metric

In addition of mission time, we can use mean and variance of the position of the center from the mission time and during a fixed time. ---------communication------------------------------------------------ ));%from pdpprd.mat using always the same random vector %%loop 2:grad search------------------------------------------------nbit20=60;%200 or 60 emission angle and salinity nbit21=60;%200 or 60 emission cx,cy,cpx,cpy,cppx,cppy header2=0; D2=100;%bits/s DeltaDmax2=88.31;%maximum difference of distance between 2 sources and receiver : AUV can be very close to each other %88.31 is for zmin=500m and dd1max=100m l2=1500;%1500maximum or 3000m distance Te20=(header2+nbit20)/D2;%emission time ASV Te21=(header2+nbit21)/D2;%emission time AUV Tt2=l2/Vsound;%travel time Ts2=DeltaDmax2/Vsound;%security travel tim M2=1;%repetition alpha2=0.1^M2; Ttauv=M2*Te20+Ts2;%4 first auv Ttlastauv=M2*Te20+Tt2;%last auv Ttasv=Te21+Tt2;%asv feedback nTsTtauv=floor(Ttauv/Ts)+1; nTsTtlastauv=floor(Ttlastauv/Ts)+1; nTsTtasv=floor(Ttasv/Ts)+1; Tmod2=(nTsTtauv*(n0-1)+nTsTtlastauv+nTsTtasv)*Ts; Tasv=(nTsTtauv*(n0-1)+nTsTtlastauv)*Ts; nTsDelay2=0;%dely included into tdma protocol Tdelay2=Ts*nTsDelay2; pdprd20=pdprd2(1:floor(30000/Tmod2*5));%from pdpprd.mat using always the same random vector %modèle dynamique AUV- --------------------------------------------------- %-----------------------paramètres algo formation--------------------------------- ---------------paramètres algo source seeking---------------------------% SPEED LIMIT FOR SOURCE SEEKING speed_limit=0.15;%0.4;%5;%5%1.5;%0. 2%6 %---------------affichage situation initale------------------------------ Increasing the velocity of the formation centre relative to the single vehicle velocity is leading to a degraded formation control; up to a certain degree (here 0.2m/s) the degradation is outweighed by a faster approach of the source. The larger the fleet, the larger the risk that the formation collapses, it is then unable to reach the source. The smaller the fleet, especially with two AUVs, the better the formation resists to a higher formation velocity.

Conclusion:

The capability of the fleet to converge towards the source is impacted by the size/scale of the plume.

This result shows that one has to be careful when interpreting the various simulations in this document -it is easy to understand that the spatial extent of an AUV fleet must be adapted to the nature and shape of the diffusion problem.

Conclusions

The geometric extension of the fleet formation proves to be sensitive to the shape and amplitude of the diffusion plume. The reason for this is the robustness of the gradient estimation. Since it is easy to see that the model for the diffusion plume used in the presented simulation is an arbitrary choice, the impact of the formation size is difficult to analyze.

We emphasize in the analysis on the capability of the fleet to hold the formation, to follow the consensus elaborated trajectory, and to perform a globally successful gradient search.

In this context, the principle observations are:

1. the maintaining of the formation is realistically achieved with the network concept of this section, even if the exchange of information is longer;

2. the loss of data within the acoustic transmission is to a significant extent compensated by a systematic repeat strategy (1 or 2 repeat cycles);

3. the fleet velocity cannot easily be increased without disturbing the formation process;

4. increasing the vehicle velocity permits increasing the fleet velocity to some extent;

5. the performance of the gradient climb algorithm is largely depending on a complete set of parameters -not all of the configurations lead to a stable trajectory towards the target, some configurations prove to be unstable; the complexity of the system induces a chaotic behavior of the results based on some parameters which explains the variability of results;

6. the number of vehicles has a low impact on the performance; the network cycle duration is increasing with a growing number of vehicles, and maintaining the formation is taking more effort; the quality of gradient estimation does not seem to gain significantly from a number of four vehicles onward; an advantage in increasing the number of agents can be seen in higher robustness to single AUV failure. 1

The gradient climb is depending on two main points: 1) the geometric quality of the gradient estimation which is linked to the shape of the plume and the spatial extent of the fleet, 2). Adaptive handling of the size/radius of the fleet could be an answer to this problem;

Working at a larger scale of the concentration field translates by a less efficient gradient climb; this is explained by the small amplitude change of the gradient between different vehicles, and the higher sensitivity of the gradient estimation with respect to the fleet geometry.

The global conclusions obtained from the two sets of simulations according to sections 1 and 2 are compared and commented in the corresponding paragraph of section 1.


Serge Ormaux
email: serge.ormaux@univ-fcomte.fr

Laurent Couderchet

FROM REAL-LIFE EXPERIENCE TO MAP Using landscape as a tool for decentralized urban planning

Keywords: Map, Landscape, Photography, Decentralisation, Participatory approaches, Urban planning

The large diffusion of GIS is greatly improving the capacities of territorial analysis; yet these tools are increasing the weight of cartographic representations and the gap between the everyday's space and the space of town planning projects. GIS are certainly essential tools, but they require a reflection about their territorial effects. The maps of "intervisibility" which are produced this way are difficult to understand and address the issue of mediation. We propose to contribute to the reflection through experiments conducted in France. We successively used maps and photos within the framework of town planning projects; we are now able to propose a protocol which formalizes the interactions between the project space and the real-life space. This process is improving the dialogue and the decentralization; the landscape becomes a tool for urban development.

Introduction

The European Landscape Convention states that "Landscape" means an area, as perceived by people, whose character results from the action of natural and/or human factors and their interrelationships.

The landscape concept is indeed based on a two-way interaction: firstly, it has the dual status of physical object and mental construction; in other respects, it refers to representations of two distinct universes, that of tangential vision, which is the territory as perceived by people from the ground, and the vision of it from above, which is the map projection.

The first interaction is fairly widely accepted, the landscape being often viewed as a relationship and this relationship implies a subject and an object, presupposing the encounter between an environmental materiality and an operation of perception accompanied by the production of meaning.

The second interaction is much more rarely considered, in both research and development practices, yet it introduces a gap in the participatory approaches in spatial planning. Urban planning is indeed faced with two topologies of reference, one of which is the zoning decision and one that is lived by actors, usually the first one winning over the second one. This double referencing can lead to a dialogue of the deaf between the technician sphere and the public opinion during consultations.

In what follows, we will examine a relationship between the weight of vertical images (top view) and descending approaches in urban development projects; the local decision is indeed still very much regulated, despite the decentralization policy and the emergence of grassroots democracy. The formalization of the relationship between space and landscape will then inform the operating difficulties of the public debate. The last section of the communication proposes a re-articulation of the spatial representations in the steering of projects in real-world cases.

I. From a narrow conceptual framework on landscape to a broader approach

The status of studies in landscape planning

In France, the Act of 1976 requires the consideration of the landscape, among other environmental parameters in urban planning. For years, it has not exceeded the evocation of the environmental problems. The transformation of landscape by development was addressed only through the ambiguous notion of integration into the site. These studies routinely included a diagnosis, an estimate of the project's impacts on the landscape in a very conservative manner, and the proposed project accompanying measures, intended to reduce its impacts on landscapes.

The Act of 1993 changes the status of the landscape. Somehow, it leads to convert the landscape impact study into a complete file by itself. Throughout the following years, the interpretations of the law led to push the landscape diagnosis upstream of spatial studies and to make it a foundation of the urban development project. It is by the abundance of the questioning that the "landscape law" bore its fruits more than the procedural decrees of application of the text. Elected officials were stakeholders in this collective reflection which attended the confrontation between the outside landscape experts and the lived landscape of the representatives of the population.

Landscape maps were used in the process. Complex in substance, they placed the expert in a dominant position over elected officials and did not offer high efficiency in the exchange. In contrast, the landscape photographs helped building the bases for a common language between stakeholders and formulating, in landscape terms, the objectives of development.

In 2000, the law "Solidarity and urban renewal" brought into the process a new procedure referred to as the "Land-use and sustainable development project". It was offering the opportunity to bring into regulations the landscape basis of planning documents.

The burden of descending (top-down) instructions

The local development operations are decentralized but certainly the state determines the framework by recalling the main principles of the Urban Code: section L110 prescribes that "the French territory is the common heritage of the nation and every public authority is manager and guarantor of it"; the new section L121.1 emphasizes on the concept of urban renewal in respect of sustainable development objectives, without telling of the nature of these objectives. In the paragraph devoted to environment issues, the section emphases on the efficient use of natural spaces, urban, suburban and rural areas, and the desire to limit urban sprawl.

The law lays down the basic principles but does not specify the means of achieving them; during the presentation of the "Bring to knowledge" prescription, the prefectural authority therefore stresses the need for urban intensification within the built perimeter, the latter maintaining its dimensions, whenever possible, so as to preserve natural areas for future generations. The speech systematically ignores the size and functions of the agglomeration; it applies equally to shy communal maps and ambitious local development plans.

Beyond the recalls of the law and its interpretations, the central authorities present the upper-tier issues to be respected, in the form of maps taken from various geographic databases established at the levels of the department or the region. Thus many perimeter lines are imposed to lower-tier governments' planning documents: noise zones along major roads, highways and railways, flood-risk areas, constructability limitations on both sides of main roads and city entrances, etc. 1

Fig. 1. Perimeter lines imposed to planning documents in the community Montbéliard

Ultimately, regulatory constraints and upper-tier issues have such an importance as that the local zoning by-law can be developed almost from the central administrative services. This possibility is reinforced by the technical capabilities that are now with these services since the spatial databases and GIS offer systematic zoning areas between urbanization and potentially non-development areas. In this context, communities, especially the smallest of them, could no longer have the prerogative other than those of the second level: the type of urbanization within the perimeter and the opposition agricultural/natural outside.

In urbanised communities, we find the same kind of constraints, projections of the master plan or plan of territorial coherence leaving little latitude to elected officials in the registered municipalities 2 1 Section L111.1.4 code sets planning conditions restricting the arrangements of city entrances and outskirts of major roads within a band ranging from 75 to 100 meters thick on both sides of the road by its importance.

2

We base our observations on the study of the urban communities of Besançon and Montbéliard in the Doubs department.

, somehow fixing quotas of activities or residential surfaces by municipality.

Constructability limitations Noise zones Flood-risk areas High level Low level

Intermediary level

Doubs river

Doubs river

Doubs river

In the suburb of the city of Besançon, for example, the interpretation of the concept of urban renewal in the direction of the intensification of the land occupation of urban space has led the upper-tier institution to provide a supra-communal map of "hollow teeth" to be filled before considering any extension of the perimeter frame of the communal territory. The pejorative term used to describe the open spaces of the town is indicative of the reducing nature of the doctrine; once again, the GIS capabilities have been involved in identifying all the corresponding plots and to attribute them the vocation of urban renewal areas, irrespectively of their functional and landscape intrinsic characteristics.

This table somewhat oppressive does not impede digressions, including the use of the landscape lever.

Other passages in the "Bring to knowledge" presciption, referring in fact to the landscape law and the importance it gives to some remarkable elements that can precisely occupy the open spaces of the city: urban green spaces, orchards and family gardens notably. In the Community of Montbéliard, the "Bring to knowledge" of planning operations thus insists on the heritage character of fruit trees. In the years 1999-2000, a program for the preservation of orchards was launched from the regional capital through territorial exploitation contracts (CTE become DAC in 2003).

The protection of these iconic elements of the landscape of North-eastern Franche-Comté, located mainly in the periphery of urban areas and in vacant spaces, was recalled to elected officials as a goal of the regional coherence policy. The contradiction between the intentions of heritage protection and those of densification of the city then offered the opportunity to operate a decentralized hierarchy of territorial issues. The landscape, used as a raw material for the development project, allowed elected officials to recover the authority on the development of their communities.

II. From space to landscape

The inertia of representations and ambiguity of the map a. Tangent view and projection view

In scientific terms, relations that the photograph has with the landscape are not univocal. In our view, the main caesura between different ways of representing the landscape is not between photography and other media, but between documents which apprehend the surface of the earth as seen from the surface, of which the photograph is usual, and those which represent the area from above, including aerial photography, satellite imagery, maps, planning documents. Both documents refer in fact to two distinct functional universes. The first one is the field of experience, of everyday life and perception; the second one refers to the vision of the Prince, that of the power and of zoning, that also of scientific abstraction.

On one side is the "egoreferenced" space of the inhabitant, and on another side the "georeferenced" space of the decision-maker. Those are two very different universes of reference and often this duality undermines mutual understanding. Many conflicts between policymakers and citizens stem from a confusion between these two topologies and are often wrongly interpreted as a conflict of scale.

The opposition between the tangential and projectional reference systems described above thus appears to light in built areas such as social housing. In projectional view, the ratio between empty and full is largely in favor of empty, according to the principles of the Charter of Athens. However, to locals or those who walk in the neighbourhood, the tangential vision gives to the facades an overwhelming presence in the landscape scene and produces a real effect of confinement. It is not surprising that the ground plan used to build the district is unable to provide support for mediating or retraining; it even shatters retrospectively its own technical legitimacy.

b. The image in the public debate

The land-use planning procedures in France set the public consultation at the public inquiry. Then it is on a draft plan that citizens are consulted. Although the emergence of participatory democracy is universally viewed as a progress, it is not without difficulties. Models of democracy can indeed hardly be projected onto a society that is not prepared for it. Moreover, concertation without education may have more negative consequences than benefits on the involvement of people in public life. The hierarchy of consultation is not necessarily understood by those who are familiar with the public investigation, thus the confusion is frequent between meetings for presentation and collection of information on the one hand and meetings for validation of a project on the other hand. When the confusion is overcome, the land implications of land planning schemes are such that the interest of a participatory sequence may be compromised.

However, the use of landscape photographs at meetings between land-planners and residents may be a way to establish a dialogue between them. For residents, the landscape picture refers to the experience that everyone has of the land and has a suggestive power that does not have a map or a plan. It brings out representations, it provokes reactions, it leads to talk of land and guidance for its stewardship.

The landscape picture is less scaring than the maps because it is unsuitable for delineation of boundaries or zoning, it focuses on customs, permanence and change, rather than assignments and constraints. But in a second time, it can be the basis of the outline plan, allowing a return to the map or GIS, and thus to complete the process with georefereced space. In this manner, it is an intermediary object, an instrument of mediation.

Systematics of intervisibility

In recent years, a certain number of research teams have focused on scientific analysis of the landscape through photographic sampling on ground3

This work has fed the landscape impact studies which have proliferated in the 90's and 2000's (L. [START_REF] Couderchet | Propositions pour un protocole d'étude des paysages, définition dans le cadre des lignes de transport d'électricité[END_REF][START_REF] Couderchet | Paysages de synthèse et réduction d'impact des lignes de transport d'électricité", Les choix énergétiques, de l'évaluation des impacts à l'évaluation environnementale stratégique, actes du sixième colloque international des spécialistes francophones en évaluation d'impacts[END_REF]. Some research questions: what is seen, from where? With what intensity is this portion of space viewed? From what point can an object be seen, foreground or background? So many questions to which this analysis could provide answers. But more than ad hoc . This involves mobilizing the flow of information circulating within the usual vision, to collect this information through systematic sampling procedures and to process the data with standardised grids in the logic of a spatial information array. These steps helped giving substance to the notion of visible landscape and bringing out the concepts of intervisibility and visual proposal. They have shown that there is a landscape materiality, defined as a set of signals pre-existing to the landscape observer's perception procedures, and raw material thereof. In reference to the information theory, it is the information released, whereas what we see is information received (sorted, processed, decoded, connoted by our cultural and personal filters).

Here the cliché has the very specific status of data carrier; it is not illustrative, it does not transmit the codes or the inclinations of the observer, thus it is an anti-cliché. Neither does the cliché itself makes sense, but the whole photographic file, through its logical construction and spatial resolution. Nothing to do with a holiday photo album.

responses, such approaches have permanently installed the field of "visible landscape" within the general problem of the landscape geography.

Gradually but never completely, the relay was taken by the methods of simulation of landscape scenes from digital elevation models and remote sensing. In the context of development projects. It is then possible to feed the public debate with various maps of overall visual submission, submission from builtup areas, from remarkable points and routes, etc. (L. [START_REF] Couderchet | L'illusion du visible -Paysage et aménagement d'infrastructures de transport d'électricité", dans les actes du colloque L'évaluation du paysage : une utopie nécessaire ? Montpellier[END_REF]). Yet these documents, closer a priori to the vision of ordinary people than projectional planning instruments, are often inaccessible in their make-up and their design, and can be an object of the mediation process only at the expense of a patient pedagogy. The mistrust of these "invented" landscapes, as well as the adhesion to their magical aspect without question, leave the researcher-expert in a situation of dissatisfaction and in the difficulty to refocus these documents to the lived landscape of residents and elected officials.

Thus the inference of this discussion brings a reconsideration of the order of things. Would it not be better to start the process by a phase of emergence of representations from iconic photographs discussed with local residents or elected officials, staking on the results of these exchanges to inform in a second time a more systematic phase, based on geomatics, more focused on the problems and opportunities raised ?

The fundamental interest of ground landscape photographs relies in the fact that they can function both as a basis for objectification (in respect of the space continuum), and as a medium for dialogue between stakeholders and experts. This is their strength, provided that the discussion is not confined to a discourse of exclusion, which would prioritize the significant in detriment of the systematic or, conversely, which would consider only materiality and ignore the phenomenological dimension.

III. The project: between space and landscape

In this section, we present the case of two municipalities in the agglomeration community of Pays de Montbéliard, whose opposite developmental trajectories have resulted in contrasting landscape signatures.

In this area, the intercommunal services possess structured information on the dynamics of land use, providing valuable insights on the development of the commons over the last fifty years. The excerpts of the map database, however, have the abstract characteristics of the projectional representation and are quite difficult to use in the process of shaping the urban planning project. The photograph is then a means of dialogue between stakeholders (elected officials) and experts/researchers, positioning the landscape in the heart of the planning project. These images of the everyday life raise numerous and interactive reactions from the stakeholders of the urban development project: they instigate the discussion on the landscape and on the urban policy options that produced the city.

Protocol

Spatial data and maps have also helped making a choice of sites to be observed based on the socio-spatial dynamics of each common.

Under these operational exercises, we have proposed a typology of landscape elements and landscape forms on the whole municipal territory and produced a photography collection, representative of each of these landscape types. The preliminary analysis of the pictures has focused on the uses and functions of corresponding areas and their self-replication capacity. It was then presented in the Planning Commission, discussed and amended.

Urban policies and landscape signatures in Exincourt and Voujeaucourt

The Community of the Montbéliard Agglomeration has a population of a little above a hundred thousand people organized in small communities. The population of the city of Montbeliard is less than 30000 and Audincourt half of it. The remaining 27 municipalities of the community of agglomeration present population figures between 1000 and 5000. Under the impetus of major industries such as Japy and Peugeot, the community of Montbéliard has known a tremendous increase in population in the nineteenth century and early twentieth. In the second half of the twentieth, industrial restructuring caused a dramatic reduction in jobs related to the automobile business. In this context, the population of the commons has undergone a steady decline since the 70's to stabilize at the very end of the century. The demographic profile is of course variable from one municipality to another, according to local contexts.

The two commons that we chose to analyze both have a population of about 3000 inhabitants. However, Exincourt has lost more than 1000 people since 1968 while the population of Voujeaucourt increased by more than 500 residents over the same period, being not affected by the social and demographic crisis in the community of Montbéliard.

Exincourt

While the business was still flourishing, Exincourt gained over 1500 inhabitants between 1950 and 1968. The town had committed early in a systematic suburban housing policy for the middle classes of Peugeot employees (supervisors and technicians). The territory was thus built on a rational scheme similar to that of housing estates started 50 years before by the Japy family.

Fig 2. Map and landscape as planning arguments in Exincourt

The map at the time of completion of constructions, extracted from the GIS of the Agglomeration Community, gives an idea of how space is consumed by successive and regular waves until the early 60's.

Yet it is much less this image than the photography of the school, built in the early 60's and now oversize, which has impressed the politicians and prompted the search for the development solutions.

The collection of photographs also allows to establish a relationship between a type of landscape inherited of this optimized occupation process of space by the city and the current moroseness.

In that location, the houses are all alike. The architectural forms provide a key to articulate the spaces of the landscape and those of the map: the two sided-roofs correspond to the years 50 buildings, represented in red on the map; the turn of the 50-60's is marked by a change in architecture, these objects being well identified landscape objects represented in orange on the map. The owners had 30 years in the sixties, they are now 70-80. The built park is released gradually and the urban renewal raises the question of restructuring of these neighbourhoods. Clearly, the urban landscapes of the common may no longer be attractive to the privileged categories of the local population, as was previously the case.

Voujeaucourt

The development of Voujeaucourt has been done much more spontaneously. Accompanying the growth of the Community of Montbéliard, buildings were erected along existing roads, without the concern for rationalization that was observed in the nearby common of Exincourt. The pace of development has not been as spectacular and utilities could be put in place more progressively. The map shows that the envelope of the town has emerged very early, combining empty and built lots into a complex mosaic. Originally, the modest populations of workers were reserving spaces for small-scale farming to supplement their incomes and improve their lives. While this socio-spatial organization is not very visible on the map extracts, it appears prominently in the photographs. Thus the orchards, considered as part of the heritage of the Community of Montbéliard, still mark the landscape.

The gradual land intensification of neighbourhoods now mixes architectural forms of the 50's to the 2000's. The heart of the city has kept a village atmosphere with its two churches, characteristics of the region, laid in the green, 10 minutes from downtown Montbeliard and Peugeot factories. This set of elements is perceived as very positive and the newcomers are from more affluent socio-professional categories, so there is a movement opposite to that found in Exincourt.

In both cases, presentation of photographs has led the Planning Commission to decide on its landscapes. The debate has emerged of a series of analyses on the landscape production process and on the function of corresponding space units. In Voujeaucourt, the orchards, which were regarded only as food for their purposes and were condemned to disappear with the last peasant-workers, took another status. They now determine the image and attractiveness of the territory. Their durability is not garanteed and will depend on the community to adopt tools for their preservation in its urban renewal policy.

Interactions between development and landscape

After the war, Exincourt had resources that were lacking in Voujeaucourt. Japy and Peugeot occupying the territory of the first common, while the industry shunned the second. The revenue generated by the activity helped the rigorous application of a planning doctrine that radically transformed the landscape of Exincourt.

In Voujeaucourt, the economic context, along with the presentation of results from the mapping database and the state recommendations for urban renewal are likely to encourage the development of large slices of urbanization instead of the "hollow tooth" which amount in superficy up to several tens of hectares in one piece. Before the implementation of a comprehensive reflection on landscape, the town had already started to succumb to the lure of the easy development, and opened subdivision programs. Finally, the intervention of the landscape in the planning project has led the community to rethink its model of development based on its own characteristics and merits, and not on stereotypical proposals from central bodies.

Year of finishing habitations

Fruit trees and meadow inside the town -Hollow tooth?

Conclusion

Hence, speaking about the landscape and observing from the inside have provided the opportunity to bring it upstream of the procedure, and to get around the interpretations of regulations: the objectives of the urban renewal policy give appropriate foundation for the two urban projects, but they nevertheless produce diametrically opposed results on the two territories.

The images used are mainly informative. The landscape photography was preferred for practical reasons, but other vectors, such as drawing or game, may have contributed to trigger the process of mediation. The city-production processes emerged from the landscape debate, leaving a mark in the geographic information system of the Community of agglomeration. The initiation of the planning process has proceeded to a selection of landscape dynamics recognized as positive and took the form of draft informative plans in the map database. At that point, the loop was looped: indeed, it is essential to be able to bring the landscape patterns into the georeferenced space in order to have a reproducible information, the planning document being based fundamentally on zoning.

Highlighting the issue of landscaping offers an alternative to often stereotypical proposals of prefectural bodies and methodological guides of the Ministry of Equipment. The proposed approach is still experimental but it points in the direction of a more authentic decentralization, based on the local landscape material that produces the planning project. The examples of Exincourt and Voujeaucourt show the ambiguity of the State "Bring to knowledge" prescription, which stresses systematically on the need to limit urban sprawl and on construction intensification. The first common identified in this article had anticipated these orientations, whereas the second one had followed a more unbridled way of development. Today the effects of these policies, opposite as far as the socio-spatial system and the landscape material mobilization is concerned, offer potential for other developments... which are not favourable to the orthodox model.

  Fig. 3. Map and landscape as planning arguments in Voujeaucourt

These include the School of Besancon, of course: (J.P.[START_REF] Massonie | Approche théorique de la lecture du paysage : la soumission à la vue[END_REF], (J.C.[START_REF] Wieber | Le paysage, trois modes de définition, un mode d'analyse et de cartographie[END_REF], (D.[START_REF] Joly | Usage des données satellites et altitudinales pour l'étude du paysage et l'aménagement de l'espace[END_REF], But research also Swiss and German.