Fabienne Nouvel
email: fabienne.nouvel@insa-rennes.fr

Embedded Network Combining CAN, ZigBee and DC-PLC for Motorhome

Keywords: Motorhome, Controller Area Networks, CAN, ZigBee, Power Line Communications

Today, the number of motorhomes increases in Europe and North America as they offer greater individual freedom. As motorhome users spend the most of their time in their confined area, it seems essential to develop new solutions that make their life easier. In order to meet the new needs of customers, a new centralized architecture of a control system based on ubiquitous wired and wireless solutions is studied in this paper. The objective of this study is to verify the feasibility of ubiquitous technologies in this original environment. Different measurements have been conducted on a motorhome using Controller Area Networks (CAN), ZigBee and direct current power line communications (DC-PLC). Results have shown that these technologies may be used in a future hybrid control system in a motorhome.

I. INTRODUCTION

Recent results of motorhomes sales confirm the enthusiasm of people for this type of vehicles, especially in Europe and North America [START_REF] Rvbusiness | SSI: Motorhomes sales up 9.1% during October[END_REF]. Motorhomes perfectly meet the needs of freedom, mobility and the wishes of autonomy that users seek. Otherwise, they may be an economic solution with respect to expensive hotel and trains reservation rates in touristic areas.

Motorhomes are similar to homes as they include a kitchen, a bathroom, sleeping facilities and a dining room. One can found several amendies such as refrigerator, gas cylinder, boiler, air-conditioner, folding step, and water (fresh, gray and black) tanks.

Furthermore, similar electric installations can be found. Electrical equipments may be powered by different energy sources available in the motorhome, such as main and auxiliary house batteries, engine battery, solar cells and / or external alternative (AC) 230 V available in the camping areas. Although today's motorhomes have a broad array of choices to be connected to Internet, motorhomes do not use communication network to interconnect the equipments in the home area.

Camper van can be either considered as a vehicle or as a mobile home. If it considered as a vehicle, electronic units are nowadays connected by networks. Many standards are available, the most widespread are control area network (CAN), local interconnect network (LIN) and Flexray [START_REF] Nouvel | Automotive Network Architecture for ECUs Communications[END_REF]. These networks reduce the number of wires while achieving low to medium data rates and better reliability. However, these buses are placed in the motor area and mainly used for driving and automotive controls. Furthermore, they must be separated from the possible camper van "home network", due to security levels.

If we consider it as a "mobile home", many technologies have been developed to make the home smart. The technologies include solution for building automation as well as the domestic control activities [START_REF] Ortiz | Smart Home Automation Using Controller Area Network[END_REF]. Furthermore, devices may allow remote access.

It seems to be interesting to combine networks used for control driving with solutions used for domestic tasks. Actually, all the amenities in camper van are manually controlled by the users or via separated control systems. Some control systems are proposed by manufacturers but these solutions are proprietary solutions and not easy to modified. In this paper, we propose to replace separated non connected control systems in a camping-car by a centralized control system. Another objective is to replace point to point connection using a specific wire by a communication network that will reduce the weight and number of cables. Proposed new architecture may be based on both on wired and wireless communications technologies that connect a central unit to the different amenities in the motorhome. Among the possible solutions, CAN seems to be attractive as it is widely used both in automotive [START_REF] Johansson | Vehicle Applications of Controller Area Network[END_REF] and in a variety of industries including building, automation and manufacturing. Moreover, CAN has been already used to connect home appliances for smart home applications [START_REF] Lee | Network-based fire-detection system via controller area network for smart home automation[END_REF] [START_REF] The | Home Appliances Management System using Controller Area Network (CAN) Communications[END_REF].

We have also to consider the wireless solution in order to offer easy plug and play applications while achieving low cost. Among the technologies, ZigBee, an IEEE 802.15.4 standard-based solution defined by the ZigBee Alliance [START_REF][END_REF], was developed specifically to support sensing, monitoring and control applications. The ZigBee solution offers significant benefits, such as low power, robust communication and a selfhealing mesh network. Common applications supported by ZigBee include: personal monitoring, security, access control and safety monitoring, home, building and industrial automation [START_REF][END_REF].

As many devices in the motorhome are powered by AC or DC power, another solution to consider reducing the number of cables is power line communication (PLC). There is a growing in interest for PLC, including smart grids, home networking and control, as well as automotive uses [START_REF] Lu | Reliable power line communication A vehicle to smart home and smart energy[END_REF].

Hence, the proposed PLC technologies have been studied in cars and indoor environments, but they have not been tested in motorhome environment. Therefore, we will present in this paper the results obtained from different measurements in order to study the feasibility of these three technologies in motorhomes.

The remainder of this paper is organized as follows. Section 2 describes the functionalities and the architecture of the proposed control system. Section 3 deals with the CANbased part of the system, including signals and messages definitions, network dimensioning and simulation and measurement results. Section 4 focuses on ZigBee tests conducted in the motorhome and presents the obtained results. Section 5 presents the results of PLC measurements and results. Finally, a conclusion is drawn in Section 6.

II. SYSTEM FUNCTIONALITIES AND ARCHITECTURE

The considered embedded control system must carry out several functionalities. We can cite according to the priority: We aim to develop a user-friendly control system that can accomplish these functionalities and possible supplementary ones. The system must provide high quality of service in terms of reliability, robustness, delays, and at the same time ensure the convenience and comfort of motorhome users.

To ensure the passengers' convenience and comfort, a unique user interface based on embedded OS (Linux, Android) will then replace the different interfaces used currently for motorhome appliances. Passengers will simply send orders or receive measurements to or from appliances by touching a user-friendly screen.

The second objective is ensuring reliable data communication between the central control unit and equipments. Different communication technologies exist in the market for this kind of applications. We have to select the best adapted solutions to our environment and applications.

The above mentioned functionalities may be categorized into three main categories: high level security, low level security and infotainment functionalities. The first two categories do not need high data rates but they must be secure, the frames are mainly orders or sensor measurements. However, infotainment functionalities, multimedia data, video surveillance or internet, necessitate high data rates. Therefore, we propose to combine different communication technologies with respect to considered functionalities. High level security functionalities will be accomplished using sensor and actuators connected to corresponding equipments and exchanging via a CAN bus to a central control unit. Low level security functionalities such as measuring weather data and controlling lights will be accomplished by ZigBee nodes. High data rates functionalities will be finally exchanged using PLCs without using additional wires. Figure 1 shows the general architecture of proposed control system and communication technologies used with respect to functionalities.

In order to verify the feasibility of these technologies, we have conducted several measurements in a motorhome provided by the motorhome manufacturer Autostar. The motorhome comprises two main parts. The sleeping compartment contains a fixed bed, a shower cubicle and a wardrobe. The living compartment includes a kitchenette area with a refrigerator, grill and sink. Below the floor of the camper, we can find other amenities such as water tanks and pump, gas cylinders, batteries and boiler. Following sections will describe the performed experiments and present the obtained results. We will begin with CAN measurements.

Figure 1. Architecture of the proposed control system based on CAN bus, ZigBee and PLC networks III. CAN MEASUREMENTS Most of system functionalities will be performed via the CAN bus, like monitoring water tanks, switching between energy sources, controlling air-conditioner and ventilator, etc. Several CAN nodes (each one connected to one or more equipment) will communicate to the central node in the camping car. The user interface will help the user to send orders and analyze measurements sent by sensors given by the other CAN nodes.

A. CAN Overview

CAN 2.0A/B is a network protocol developed for connecting the sensors, actuators, and controllers in a vehicle. CAN supports data rates from 5 kbps up to 1 Mbps, which allow the CAN network to be used to share status information and is available for real-time control. The medium access control (MAC) level uses the carrier sense multiple access/collision detection (CSMA/CD) protocol to access the network, using the field "Identifier" of the frame.

CAN allows multiple devices (referred to as "CAN nodes") to connect to each other on a single bus. CAN nodes do not have strict master/slave roles. Instead, each CAN node may operate as a transmitter or receiver at any time. In reception, each node decides if the data is relevant by looking at the message frame's "Identifier", which describes the content of the message.

B. System Design

The CAN bus functionalities are accomplished by sensors or actuators connected to corresponding equipments. A sensor (actuator) will send (receive) a digital signal to (from) the central CAN node via the CAN bus.

The first step in the network design was the definition of signals that will be exchanged through CAN messages and the identifiers between the central node and the appliances. A database of about 100 signals has been defined for the system.

The second step was the definition of CAN messages and the assignment of digital signals. One or several signals have been assigned to each CAN message. A total of 35 CAN messages/identifiers have been finally defined for the whole network. These messages can be categorized into four different categories:

 Order messages are generated by the central node and contain an order sent to one or several actuators, such as opening or closing a gas cylinder. This type of message may be generated manually by the user when touching the screen, or automatically by the central node for security reasons, such as ordering the closure gas cylinders and folding the step when the motorhome starts moving.

 Alert messages are automatically generated by CAN nodes to warn the central node about a special event, such as intrusion detections.

 Sensing messages are periodically generated by CAN nodes and contain sensor measurements such as water tank levels, batteries voltages, and others.

 Life sign messages are periodically generated by CAN nodes to ensure their connectivity to the bus. When a CAN node sends a life sign message, it waits for an acknowledgment from the central node. In absence of this acknowledgment, it repeats the transmission of the life sign message for a user-predefined maximum number of trials. If no acknowledgment is received, a CAN node will be considered disconnected from the bus and must stop sending its alert and sensing messages. However, receiving any type of message (acknowledgment or order message) from the central node will reactivate the CAN node.

As the identifier of a CAN message determines its priority on the bus, we have assigned to each message an identifier based on its priority in the application. Lowest identifiers (highest priority) have been assigned to order messages. The second, third, and fourth levels of priority have been assigned to alert messages, sensing messages and life sign messages respectively.

The third step is the network dimensioning, i.e., the estimation of number of CAN nodes in the network. In order to minimize the number of CAN nodes in the network, we have connected equipments that are nearly placed in the motorhome, to the same CAN node. For example, Gas cylinders and water tanks have been connected to one CAN node.

C. CAN Bus Simulation

To study the performance of our CAN architecture, we have used the development and testing software tool CANoe from Vector GmbH. CANoe is a versatile tool for the development, testing and analysis of entire Electronic Control Unit (ECU) networks as well as individual ECUs. It supports network designers, development and test engineers at equipment manufacturers and suppliers over the entire development process -from planning to the start-up of entire distributed systems or individual ECUs. CANoe supports CAN, LIN, FlexRay and other networks.

At the beginning, CANoe has been used to create simulation models which simulate the behavior of the CAN nodes. Over the further steps of nodes development, these models serve as the basis for analysis, testing and the integration of bus systems and nodes. This makes it possible to detect problems early and correct them. Graphic and text based evaluation windows are provided for evaluating the results [START_REF][END_REF].

The simulation procedure consists of developing all CAN nodes, exchanged signals and messages, and finally a GUI to simulate the central control node. CAN nodes have been firstly developed through Vector CAPL (CAN Access Programming Language) based on the C programming language. Using CANoe in combination with CAPL makes it possible to create custom tool applications with user defined behavior. Secondly, the database of CAN messages and signals is recreated in Vector CANdb++ (which is a data administration program with which communication databases can be created and modified in the form of CAN databases) and added to the simulated bus. Signal generators have been added to generate random or predefined sensing values to the CAN nodes. Figure 2 explains the architecture of our CAN bus simulator using CANoe. Several simulations scenarios have been carried out to verify the correct behavior of individual CAN nodes (success and periodicity of CAN messages transmission) and the whole network (bus load and end-to-end delays). As stated before, this step helps us to detect problems early to prevent them in the implementation phase. Different data rates between 5 up to 1000 kbps and periods of data transmission, between 0.2 and 10 seconds, have been tested. In all simulation scenarios, all nodes send their CAN messages at the same time to simulate worst case scenarios.

By studying the simulation database and filtering messages by CAN node, we have found that all nodes can send successfully their CAN messages to the central unit for data rates higher than 50 kbps regardless the period of transmission. However, the bus load may reach values from 20 to 100% for rates less than 100 kbps. The maximum end-toend delay, defined as the time between the generation of a CAN message by a CAN node and its reception by the central node, is less than 100 ms for all data rates higher than 30 kbps.

Using these results, a data rate of 500 kbps and a period of 1 second have been adopted in our application. For this scenario, a bus load of only 0.77% and a maximum end-to-end delay of 3.79 ms have been obtained. These values show that data traffic exchanged in our application is largely lower than the capacity of the CAN network. That proves also that possible adding of future supplementary functionalities, will not affect the performance of existing network. Simulation results motivate us to start the implementation step.

D. Implementation and Tests of CAN Nodes

Each CAN node has been developed using an ARM Cortex-M3 LPC1758 board from NXP Semiconductors which supports two CAN peripherals. A generic embedded C code has been developed for all CAN nodes. A 4 bits local address represented by a 4 bits input to the microcontroller, has been assigned to each CAN node. It determines the part of the generic code to be executed to perform the node functionalities. Based on the CAN node address, different General Purpose Input Output (GPIO), serial ports and/or analog-to-digital converter pins are configured to exchange digital signals with connected sensors and actuators.

CANoe has been also used to test the behavior of the developed CAN nodes. Using a particular interface, CANoe provides the possibility of replacing one, several or all simulated nodes by real ones. The conducted test includes 3 real CAN nodes ( based on the LPC1758-microcontroller) and 4 simulated CAN nodes as well as the simulated central node. The interface CANcardXL allows mixing hardware and software running CANoe (on a laptop). In order to test the behavior of all CAN nodes, the test has been repeated several times. In each test, identifiers of simulated and real CAN nodes have been exchanged.

The CANcardXL interface is a hardware interface between the real bus and the simulated one. This hybrid network has been tested each time for 3.5 hours (12600 seconds) and each CAN node was sending its sensing message every one second and its life sign message every 15 minutes. The data rate was set to 500 kbps for all nodes.

By studying the database of exchanged CAN messages provided by the Trace window of CANoe, we have found that each CAN has sent 12600 sensing messages to the central node as well as 16 life sign messages, which means that all CAN messages have been successfully sent. Moreover, the period between two successive CAN messages was around 1 second (with several tens or hundreds of microseconds corresponding to the arbitration phase between CAN nodes trying to send at the same time).

A last experiment has been carried out without simulation using different hardware CAN nodes and a central node emulated thanks to an USB-MUX-4C2L board. Similarly, the results of this test have shown a frame transmission ratio equal to 100% for the CAN nodes and the periodicity of transmission (one second between two successive messages) has been also respected.

IV. ZIGBEE MEASUREMENTS

Several low rate and low security level functionalities such as turning on and off lights, measuring external temperature, wind speed, TNT signal levels may be communicated to central node by ZigBee communications. In order to verify the possibility of wireless communications between central and peripheral actuator/sensor nodes inside and outside the motorhome, point-to-point measurements have been performed using two ZigBee nodes in a motorhome to evaluate the radio link quality.

A. Measurement Procedure

Two ZigBee development boards from Silicon Laboratories have been used in these measurements (one transmitter (Tx) and one receiver (Rx)). Each board features a a C8051F121 microcontroller and a Chipcon CC2420 2.4 GHz 802.15.4 transceiver. The used frequency was 2.45 GHz and the transmit power was 0 dBm for both nodes. The sensitivity of both ZigBee nodes is -95 dBm. Tx was connected to a laptop via USB connection to configure the measurement parameters and to save results. Tx, which represents the central node, was fixed above the entrance of the motorhome. Rx has been moved between 9 different locations inside and outside the motorhome. For each location, 180 packets have been transmitted by Tx and the Packet Delivery Ratio (PDR) (percentage of packets received successfully) as well as the Received Signal Strength Indication (RSSI) have been measured. A packet is considered as successfully received only when Tx receives an acknowledgment from Rx. A,B,C,D,F,G,H and I are all inside the motorhome. However, location E is on the roof of the camper.

B. Results

Table 1 presents obtained measurement results. For each Rx location, we give the minimal, maximal, and mean values of RSSI, as well as the PDR. A, B, C, D, F, G, H andI show an excellent connectivity inside the motorhome with a PDR of 100 % and an average of RSSI above -70 dBm. The wireless node of the central control unit may have a reliable communication with all the ZigBee sensor/actuator nodes placed inside the motorhome. Closing the curtain (represented by a green dashed line in Figure 3) which separates the two compartments and blocking the line-of-sight between Tx and Rx at locations (A, B, C, D and F) has not affected the link quality. Moreover, the communication between Tx and Rx (location D) has not been blocked when closing the door of wardrobe, in spite of the attenuation of 10 dB with respect to other locations in the sleeping compartment. This remarkable link quality may be explained by the short distances separating Tx and Rx (the length of the motorhome is only 7 m) and the absence of severe obstacles that can totally block wireless communication (metallic walls for example).

Measurement results of locations

However, location E (on the roof of the camper) does not show the same link quality. PDR decreased to 93% and the average of RSSI to -77 dBm. There is no direct visibility between Tx and Rx for this location. Radio signals are received by Rx due to reflections on walls and ceiling. It is worth mentioning here that all these measurements were performed when the motorhome was parked in a huge hangar. Therefore, reflections on the walls of the hangar guided radio signals between Tx and Rx. Although the mentioned results are not very bad, they may be worst if the motorhome was moving in an open area (outside reflections). In order to have an efficient connection between the central node and the external node, it will be recommended to place the node inside the motorhome and connect it by short cables to external sensors (temperature, humidity, etc.). In this case, successful data transmission will be more guaranteed and independent of the motorhome location. However, in this system, the security aspect has not been considered. It may be taken into account in future work. V. PLC MEASUREMENTS Several functionalities like video monitoring, multimedia exchange or Internet sharing may be added to the system. These functionalities do not need high level of security, but need a relatively high throughput which cannot be provided by CAN or ZigBee networks. We propose to study the feasibility of PLC inside the motorhome, on the DC power lines. This technology does not need new wires and is already used in indoor networks. Two types of measurements have been carried out in the same motorhome. The first one studies the transfer function on the electrical harness and the second one uses indoor PLC modems to determine the possible throughput in a motorhome.

A. Transfer Functions

In order to determine the best frequency band for PLCs as well as the effect of the battery and amenities in a motorhome, we started by measuring the transfer function of the electrical harness of a Autostar has provided an electrical harness before its installation in a motorhome as well as a motorhome with installed harness. Using a Vector Network Analyzer (VNA), we have measured the S21 parameter for two different locations (AB and AC) on the harness in three different scenarios: a free harness (without equipment and battery connected), in the motorhome with only connected equipments (without battery) and finally with connected battery and equipments. The 12 V DC battery of the camper is totally independent from the engine one. The measurement point B is located in the living room and corresponds to the television location. Position C is located in the bed room. The performed measurements may simulate multimedia sharing between the two compartments of the motorhome. The measurement parameters are presented in Table 2.

Figure 4 shows the measurements positions in the motorhome. Figure 5 and Figure 6 present the obtained results in the three scenarios for paths AB and AC, respectively. One can observe a remarkable attenuation for frequencies less than 3 MHz may be noticed for the two scenarios. Secondly, the effect of connecting equipments start at frequencies higher than 50 MHz, however the effect of connecting a battery is clear for all frequencies (an average of 15 dB of attenuation). Finally, we can clearly conclude from these measurements that frequency band [START_REF] Ortiz | Smart Home Automation Using Controller Area Network[END_REF]30 MHz] is the less affected by the battery and equipments and may be convenient for PLCs in a motorhome. More particularly, the bandwidth between 8 and 14 MHz has a minimum attenuation in this band.

B. Throughput Measurements

The study of PLC throughputs is realized using PLC Devolo 200Av modems [START_REF]Website of PLC modem Devolo[END_REF] based on the HomePlug AV standard [START_REF]HomePlug Powerline Alliance[END_REF]. These modems are commonly used in indoor networks and cars [START_REF] Tanguy | Power Line Communication standards for in-vehicule networks[END_REF] and high throughputs have been demonstrated. It is interesting to test them in a motorhome. As the power line network in-motorhome is different from that in a house, the modems have been modified to be used and plug on the DC power lines. The modifications affect mainly the coupling and the power supply.

The HomePlug AV standard is the second generation of PLC systems developed by the HomePlug Powerline Alliance. Now it is suitable for multimedia applications like HDTV or VOIP. The PHY layer uses a windowed-OFDM modulation in the [2 -28 MHz] frequency band over 972 subcarriers. The HPAV can use different modulation order from binary phaseshift keying (BPSK) up to 1024 quadrature amplitude modulation (QAM) for each sub-carrier according to the channel characteristics. To counteract the channel multipath effects and the inter-symbol interferences, the HPAV uses a guard interval (GI). Moreover, several GI (5.56 μs, to 47.12 μs) can be used depending on the channel and so the throughput can be improved. A frequency mask is used to avoid interferences mainly with amateur radio bands. This is the reason why the pulse-shaped OFDM symbols is different than the classic rectangular window. Thanks to this specific window, the out-of-band noise is reduced and the notches are deeper. The central coordinator uses the channel estimation in order to establish a specific QAM modulation for each OFDM sub-carrier. HPAV uses a two-level MAC framing scheme. Indeed, the MAC frames are divided into 512 bytes segments called PHY Block (PB). An uncorrectable Forward Error Correction (FEC) code is used and a header is added with the numbers of the PB.

In the measurement setup, we use a test bed with two PLC modems and two laptops running jperf tool, which is used for network testing by transmitting transmission control protocol (TCP) and user datagram protocol (UDP) streams. These modems have been plugged firstly to positions A and B, and then to positions A and C respectively to measure the throughput of the same scenarios studied in the previous section.

Figure 7 shows the obtained throughput for the two paths with disconnected and connected battery respectively. We can remark from these scenarios that obtained throughputs are quasi-similar for the two paths and are about 20 Mbps, which is higher than throughputs provided by other bus technologies (maximum 10 Mbps for FlexRay). However, we can notice a small decrease of about 1 Mbps for the two paths when the battery is connected. Similar results have been achieved with UDP and so not represented. These results show that HPAV modems can be used for PLCs in a motorhome for multimedia applications.

VI. CONCLUSION

We have presented in this paper centralized hybrid architecture for motorhome control mainly based on CAN bus, in addition to PLC and ZigBee technologies. Different simulations and measurements have been carried out to study the performance of these technologies in the motorhome.

Firstly, CAN bus simulations have been carried out using CANoe to evaluate the performance of the future network. Then, CAN nodes board and an embedded C code have been developed and tested successfully on the network in the motorhome.

Otherwise, ZigBee measurements have shown an excellent link quality between the central node and all locations inside the motorhome. Motorhome environment is not a difficult from a "radio wave propagation" view, as distances between nodes will be small and there is no severe obstacle that can totally block wireless communications. The security may be considered in future work.

Finally, PLC experimentations have shown the possibility of using PLC modems used in indoor applications in a motorhome. Possible data rates of about 20 Mbps in realistic scenarios can be provided by PLCs.

The obtained results confirm the possibility of using ubiquitous communication technologies to achieve a smart, convenient and comfortable motorhome. In future work, electromagnetic measurements will be performed in order to test the robustness of the network in real environment with the camper van in motion.

Figure 2 .

 2 Figure 2. Architecture of the CAN bus simulator using CANoe.

Figure 3

 3 Figure 3 represents the mapping of the motorhome and the locations of Tx and Rx nodes. Locations A, B, C, D, F, G, H and I are all inside the motorhome. However, location E is on the roof of the camper.

Figure 3 .

 3 Figure 3. Locations of ZigBee transmitter and receiver in the motorhome.

Figure 4 .

 4 Figure 4. Locations of DC-PLC transmitter and receiver in the motorhome.

Figure 5 .

 5 Figure 5. Transfer function (S21) for path AB.

Figure 6 .

 6 Figure 6. Transfer function (S21) for path AC.

Figure 7 .

 7 Figure 7. Obtained throughputs for paths AB and AC.

TABLE I

 I

	.	OBTAINED RESULTS FROM ZIGBEE MEASUREMENTS
	Rx Location	Minimal	RSSI (dBm) Maximal	Mean	PDR (%)
	A	-67	-54	-58.95	100
	B	-62	-54	-57.43	100
	C	-65	-49	-54.82	100
	D	-76	-61	-69.93	100
	E	-85	-60	-77.12	93
	F	-62	-54	-59.15	100
	G	-58	-51	-55.91	100
	H	-47	-55	-49.44	100
	I	-60	-50	-56.95	100

TABLE II

 II

	.	S21 MEASUREMENT PARAMETERS
	VNA Model	Agilent FieldFox Handheld Analyzer N9918A
	Frrequency band	[30 kHz -100 MHz]
	Resolution	4001 points (24,986 kHz)
	IF bandwidth	30 kHz
	Coupling	Capacitive


Pierre--Yves Oudeyer
email: pierre--yves.oudeyer@inria.fr

Open challenges in understanding development and evolution of speech forms: the roles of embodied self--organization, motivation and active exploration 1

Keywords: Origins of speech forms, self--organization, curiosity, social reinforcement, active exploration, development, evolution, evo--devo 1. Comparing

This article discusses open scientific challenges for understanding development and evolution of speech forms, as a commentary to Moulin--Frier et al. (Moulin--Frier et al., in press). Based on the analysis of mathematical models of the origins of speech forms, with a focus on their assumptions, we study the fundamental question of how speech can be formed out of non--speech, at both developmental and evolutionary scales. In particular, we emphasize the importance of embodied self--organization, as well as the role of mechanisms of motivation and active curiosity--driven exploration in speech formation. Finally, we discuss an evolutionary--developmental perspective of the origins of speech.

1994; [START_REF] Warlaumont | Prespeech motor learning in a neural network using reinforcement[END_REF][START_REF] Howard | Learning to pronounce first words in three languages: an investigation of caregiver and infant behavior using a computational model of an infant[END_REF][START_REF] Moulin-Frier | Self-organization of early vocal development in infants and machines: the role of intrinsic motivation Frontiers in Psychology[END_REF]. In this perspective, the COSMO Bayesian framework (Moulin--Frier et al., in press) is a significant step forward as it leverages Bayesian modeling to develop a formal framework that allows us to formulate in a unified manner many key theories of speech [START_REF] Moulin-Frier | Adverse conditions improve distinguishability of auditory, motor and perceptuo-motor theories of speech perception: an exploratory Bayesian modeling study[END_REF], as well as theories of how speech forms can arise in populations of individuals. A particularly useful feature of such a Bayesian framework is that it constrains model designers to be as explicit as possible on the assumptions of their models. Furthermore, Bayesian modeling allows compact expression of relationships between subparts of a model, abstracting details of implementation to highlight the global functional dynamics. In their article, Moulin--Frier et al. show in detail how various theories of speech perception and production compare in the context of communication loops among individuals. They also show how such a framework can encode the dynamics of so--called "language games" to account for the formation of shared speech codes in a population of individuals, as well as to explain why certain vowel and consonant structures are more frequent than others in world languages.

Speech from non--speech: open questions

As such formalism provides a compact and general view on a large family of models of the formation and learning of speech structures, it also affords identifying open scientific questions. This can be done in particular through analyzing the assumptions of the COSMO framework. As Moulin--Frier et al. very clearly state, COSMO attempts to explain some properties of phonological systems out of speech communication principle, i.e. out of (Bayesian) optimization processes that lead individuals to learn and negotiate a communication system that is efficient under physiological constraints. Furthermore, thanks to the method of Bayesian modeling, Moulin--Frier et al. also make it explicit that their model assumes mechanisms that solve requirements of "adequacy" (availability of a system of forms easy to produce and perceive), "parity" (capability to play symmetric roles in speech interaction) and "reference" (capability to use a device like pointing to ensure shared attention on a referent). Thus, the model relies on a pre--existing set of linguistic abilities, as well as abstracts away from many non--linguistic processes, such as sensorimotor development outside speech, non--linguistic activities such as sensorimotor coordination in joint tasks, or properties of the body outside the speech system. This in itself is not a weakness of the framework, especially because this is made explicit. But it points to a very important question, formulated already long ago by Lindblom [START_REF] Lindblom | Can the models of evolutionary biology be applied to phonetic problems[END_REF]: "[Which are the processes that allow to] derive language from non language"?. This question applies at several scales: development in individuals, cultural evolution and phylogenetic evolution in populations. At the developmental scale, one needs to explain how young infants come to discover and master speech sounds, and how they understand that these sounds can be used to produce effects on their social peers and coordinate with them. Infants indeed are not born with a refined understanding of what "speech communication" is, and optimization processes driving the formation of (speech) codes efficient for communication may hardly be at work at the beginning. Indeed, notions of "code" and "communication" shall themselves be formed though cognitive and social development, leveraging in particular the capability to assign new functions to behaviors previously mastered (which has been called "functional flexibility", [START_REF] Oller | Functional flexibility of infant vocalization and the emergence of language[END_REF]. At the evolutionary scales, cultural and phylogenetic, an analogous mystery is still far to be solved: how did the capacity to linguistically communicate through speech or gestures appear out of non--language? Language games models such those presented in Moulin--Frier et al. have mostly focused on the question of how a shared linguistic convention can form and change at the population level, but assuming the capacity to handle the syntax and meaning of these language games, i.e. assuming a capacity for language. But how did language form in communities of individuals who did not already have such tools to build and negotiate a linguistic system? Speech, and language in general, are embedded in a network of diverse non--linguistic activities, as well as influenced by constraints due to biological implementation of the body and the brain: what consequences can this have on the formation of speech forms at developmental and evolutionary scales? Such non--speech mechanisms from which speech communication shall emerge, or be influenced by, are bound to have consequences both at the individual/developmental level and at the population/evolutionary level, acting as structure providers and filters constraining and guiding the formation of speech forms. We will now discuss three families of non--speech mechanisms that may be useful starting points to further understand how speech can be formed out of non--speech: self-organization and spontaneous pattern formation in physical systems, the role of intrinsic and extrinsic motivational systems, and finally some commonalities between speech development and the development of other sensorimotor skills through the prism of active exploration.

Embodied self--organization of speech forms

Nature is full of complex organized patterns, in particular in the inorganic world: spiral galaxies, sand dunes, deltas of rivers, polyhedrons in water foam, ice crystals are all macro--patterns that spontaneously form out of the physical interaction of their micro-components. Such self--organization of structures, due to the laws of physics in complex systems, is also at play in the living world. For example, it has been identified in the formation of spots and stripes on the skin of animals, of hexagonal honeycombs, or for organizing group behavior in insects or birds (Ball, 2001). At the developmental scale, these spontaneous patterns can be at play to generate organized behavior without any process of explicit optimization. At the evolutionary scale, such self--organized developmental processes can act as constraints, or might have been recruited and shaped to serve optimally a functional purpose. Let us take the example of biped locomotion. Walking implies the real--time coordination of many body parts. Each of our bones and each of our muscles are like the musicians of a symphonic orchestra: they must produce a movement impulse (or silence) at the right moment; and it is the juxtaposition and integration of all these impulses and silences which builds the symphony of the whole body walking forward with elegance and robustness. But is there a musical score which plans these coordination details? Is there a mechanism in the brain that, every few milliseconds, observes the current state of the body and environment and computes the optimal muscular activations to maintain balance and move forward with minimal energy consumption? This is the hypothesis pursued in several strands of research, for example the theory of optimal motor control in humans and robots [START_REF] Todorov | Optimal feedback control as a theory of motor coordination[END_REF]. However, experiments on passive dynamic walking have shown that explicit optimization of balance and energy consumption may not be the full story to account for the structure of biped gaits. For example, Tad McGeer built a pair of mechanical legs, without a motor and without a computer (thus without the possibility to make calculations), and reproduced the geometry of human legs [START_REF] Mcgeer | Passive dynamic walking[END_REF]. Then, he threw the robot on a little slope, and the robot walked: automatically, through the physical interaction between the various mechanical parts and gravity, the two legs generated a gait that looked surprisingly similar to a human gait, and was robust to disturbances. Other laboratories replicated the experiment many times [START_REF] Collins | Efficient bipedal robots based on passivedynamic walkers[END_REF]. The vocal tract and its motor system constitute also a complex physical system with coupled dynamics. Are there structures of speech which, like passive dynamic gaits, form spontaneously out of the physics of the vocal tract, already providing a highly constrained space of forms in which speech communication principles can carve signals? A theoretical exploration in this direction has been the dynamical systems approach to speech motor control elaborated by Kelso et al. [START_REF] Kelso | The dynamical perspective on speech production: Data and theory[END_REF]. Such a perspective partially questions the scope of modeling approaches that aim to predict precisely the forms of speech without relying on a detailed model of the physics of the vocal production system. Coupled mechanical systems are not the only potential source of pattern formation which stands out of an optimal control perspective. The neural system may also have intrinsic properties, not necessarily specific to a modality like speech, guiding the formation of structures outside optimization. For example, recent work on human motor control of muscle synergies in the arm have shown that the brain may prefer to reuse good--enough synergies to solve tasks rather than find optimal solutions (De [START_REF] Rugy | Muscle coordination is habitual rather than optimal[END_REF][START_REF] Loeb | Optimal isn't good enough[END_REF]. To what extent could this apply to speech communication systems? An example comes from a model of the formation of speech sounds in populations of individuals presented in [START_REF] Oudeyer | Self-Organization in the Evolution of Speech[END_REF]. In this model, individuals were equipped with perceptuo--motor neural maps connecting a vocal tract model and an auditory model. These neural maps were composed of neurons which have (initially random) spatiotemporal receptive fields and mechanisms of cellular death under low activation. Random spontaneous activations of these maps lead each individual to produce vocal babbling movements, to learn the association with their auditory consequences, and to stimulate the auditory system of the neighbouring individuals. Experiments showed that these maps spontaneously self--organized combinatorial speech forms shared across individuals of the same group. If an individual was alone with no auditory stimulation from others, self--babbling also led to combinatorial vocalizations. Furthermore, these speech forms were characterized by a phonotactic organization encoding systematic and constrained possibilities of sound combinations that matched coarse tendencies of human languages. Yet, no mechanisms optimizing for speech communication was assumed: the resulting organized speech forms were rather the collateral effect of the natural dynamics of the coupled neural maps in interaction with the morphological properties of the vocal tract (Oudeyer, 2005;2006;). A significant challenge thus relies in how we can reconcile (or not) models of the formation of organized vocal structures that do not rely on the optimization of speech communication principles, and models that target to explain properties of speech as an optimal system for transmitting signals efficiently over a physical system. An open question is also whether the Bayesian approaches, which aim to abstract the physical implementation of biological processes, provide the adequate language to account for pattern formation arising because of details of the physical and biological substrate which may a priori be unrelated to the functional structures to be explained.

Intrinsic and extrinsic motivations in speech development

In models of language games [START_REF] Loreto | Emergence of Language[END_REF][START_REF] Steels | Experiments in Cultural Language Evolution[END_REF], for example in the deictic games of the COSMO framework (Moulin--Frier et al., under review), interactions among individuals happen by pairs: in each interaction episode, two individuals "decide" to choose a topic and exchange information about which signals they use to name it. Individuals are pre--programmed to "want" to communicate with each other. In other models of speech formation at the population level [START_REF] Berrah | From form to formation of phonetic structures: An evolutionary computing perspective[END_REF][START_REF] De Boer | Self organization in vowel systems[END_REF][START_REF] Oudeyer | Self-Organization in the Evolution of Speech[END_REF], or in some models of the acquisition of an existing speech system [START_REF] Guenther | A neural network model of speech acquisition and motor equivalent speech production[END_REF][START_REF] Howard | Learning to pronounce first words in three languages: an investigation of caregiver and infant behavior using a computational model of an infant[END_REF], one assumes mechanisms which push individuals to systematically produce vocalizations through babbling or be responsive to social feedback within a linguistic perspective. Individuals are here pre--programmed to "want" to practice their speech skills. But what is the origins of such motivational mechanisms? Are they specific and ad hoc to speech and language, or are they the result of a more fundamental developmental process? Interestingly, observations of infant vocal behavior show that infants explore sound production even in the absence of peers (e.g. in bed babbling) and before they have been flexibly linked to the function of speech communication. Is this a form of play that was specifically selected by evolution to prepare the individual to later language development? Or is this an instance of a more general form of play and exploration? Recent work on modeling intrinsic motivational systems has suggested hypotheses regarding this question. Research in psychology and neuroscience has identified that our brains have an intrinsic motivation to explore novel activities for the sake of learning and practicing [START_REF] Lowenstein | The psychology of curiosity: a review and reinterpretation[END_REF][START_REF] Gottlieb | Information Seeking[END_REF]. Neuroscience is beginning to identify brain circuits involved in spontaneous exploratory behaviors and curiosity-driven learning [START_REF] Gottlieb | Information Seeking[END_REF]. A fruitful line of computational models has been considering intrinsically motivated exploration as being driven by the search of learning progress niches [START_REF] Schmidhuber | Curious model-building control systems[END_REF][START_REF] Oudeyer | What is intrinsic motivation? a typology of computational approaches[END_REF]Oudeyer and Smith, in press): the learner is viewed as a little scientist trying to understand its own body and its relations with the environment through actively selecting experiments which improve the quality of its predictive model, i.e. which provide maximal information gain. These active learning mechanisms have been applied to understanding the exploration of various kinds of sensorimotor spaces, ranging from arm reaching, locomotion and object manipulation (Baldassarre and Mirolli, 2013;[START_REF] Baranes | Active learning of inverse models with intrinsically motivated goal exploration in robots[END_REF][START_REF] Nguyen | Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner, Paladyn[END_REF][START_REF] Ivaldi | Object learning through active exploration[END_REF]. Focusing on vocal development, Moulin--Frier et al. conducted experiments where a robot explores the control of a physical model of the vocal tract in interaction with vocal peers, driven by an intrinsic motivation to improve its predictions and mastery of its own body [START_REF] Moulin-Frier | Self-organization of early vocal development in infants and machines: the role of intrinsic motivation Frontiers in Psychology[END_REF]. The robot explores the relation between vocal tract movements and the corresponding auditory effect driven actively by an intrinsic motivation to improve its model of the world. Experiments showed how such a mechanism can explain the adaptive transition from vocal self--exploration with little influence from the speech environment, to a later stage where vocal exploration becomes influenced by vocalizations of peers, as observed in human infants [START_REF] Oller | The Emergence of the Speech Capacity[END_REF]. Within the initial self--exploration phase, a sequence of vocal production stages self--organizes, and shares properties with infant data: the vocal learner first discovers how to control phonation, then focuses on vocal variations of unarticulated sounds, and finally automatically discovers and focuses on babbling with articulated proto--syllables. As the vocal learner becomes more proficient at producing complex sounds, imitating vocalizations of peers starts to provide high learning progress explaining an automatic shift from self--exploration to vocal imitation. Thus, in such a model speech structures (up to proto--syllables) develop as the result of a form of curiosity--driven exploration that is not yet connected to the function of speech communication. This is an optimization process (improvement of a predictive model is maximized), but such optimization is not driven by a speech communication purpose. Another line of work, studying the role of emotional social reinforcement, has explored how other pre--speech mechanisms may help build the ground for speech communication forms. Oller et al. [START_REF] Oller | Functional flexibility of infant vocalization and the emergence of language[END_REF] has for example discussed in depth the functional flexibility of early speech sounds, and in particular how they can initially be bootstrapped through an extrinsic motivation to share and express emotions with social peers. In a computational model, Warlaumont (Warlaumont et al., 2013) showed how non--linguistic social reinforcement could progressively drive a vocal learner to produce syllable--like vocalizations. [START_REF] Howard | Learning to pronounce first words in three languages: an investigation of caregiver and infant behavior using a computational model of an infant[END_REF] complementarily explored how such social reinforcement could lead a vocal learner to acquire and match adult speech forms. These lines of work suggest that intrinsic and extrinsic forms of motivations that are not specifically geared towards speech communication may play early on an important role in carving forms of vocalizations that transform later on into speech (Oudeyer and Smith, in press). A major open challenge appears to understand and model a full account of the transition from these non--speech systems to speech communication systems. For example, an open question is to understand how an intrinsically motivated learner that discovers speech sounds through curiosity--driven exploration and/or through a social process for sharing emotions, could discover that these sounds can also be used as tools to manipulate others and coordinate with them, and how they can be shaped and negotiated through a cultural evolution process alike the COSMO model.

Active exploration: links with the development of other sensorimotor systems

From a sensorimotor learning point of view, developing the skills to produce controlled sounds with a vocal tract shares very similar challenges with learning other skills like arm reaching, legged locomotion or object manipulation. Indeed, learning in all these sensorimotor spaces is difficult because 1) these spaces are high--dimensional and non-linear; 2) learning happens incrementally through physical experiments (e.g. trying a vocal tract movement or an arm movement, observing the produced sounds or hand positions); 3) these physical experiments are costly in time and energy. As a consequence, random exploration of these sensorimotor spaces, i.e. random babbling, is bound to fail, leading to the collection of very sparse sensorimotor observations which cannot be used to infer the regularities of the underlying manifolds [START_REF] Baranes | Active learning of inverse models with intrinsically motivated goal exploration in robots[END_REF]. Thus, exploration needs to be guided. One could wonder whether there exists guiding mechanisms that are specific to vocal exploration, arm exploration, locomotion exploration, or object manipulation exploration. Maybe there are, beginning with the specific physiological properties of muscle synergies in each modality. But the commonalities between these learning problems strongly suggest a commonality of learning mechanisms, and this is emphasized by computational models of sensorimotor development [START_REF] Oudeyer | Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints[END_REF]. In the previous section, we discussed in particular how intrinsic motivation and social reinforcement mechanisms could guide vocal development. These guiding mechanisms are in fact orthogonal to speech, and have been shown to be highly efficient in guiding the development and acquisition of other sensorimotor skills. For example, the architecture of curiosity--driven learning used to model speech development in [START_REF] Moulin-Frier | Self-organization of early vocal development in infants and machines: the role of intrinsic motivation Frontiers in Psychology[END_REF] is also a highly efficient active learning method allowing a robot to acquire legged locomotion with a high--dimensional body [START_REF] Baranes | Active learning of inverse models with intrinsically motivated goal exploration in robots[END_REF], and to learn object manipulation and actively choose who and when to ask help from human teachers [START_REF] Nguyen | Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner, Paladyn[END_REF]. As Moulin--Frier et al. (Moulin--Frier et al., 2013) showed that such active exploration mechanisms could self--organize a developmental path where certain speech forms appear with a particular order and timing, similarly the formation of sensorimotor forms in a certain order has been shown in other modalities. In [START_REF] Baranes | Active learning of inverse models with intrinsically motivated goal exploration in robots[END_REF], specific coordination structures leading the robot to walk backwards self--organize before other coordination structure appear for walking forward (a structure also known to appear in human infants). In Oudeyer et al. [START_REF] Oudeyer | What is intrinsic motivation? a typology of computational approaches[END_REF]Oudeyer and Smith, in press), the same architecture for active exploration leads a robot to first discover basic affordances between its mouth and objects it can bite, then affordances between its legs and objects it can push or grasp, and finally discover that sounds produced towards another robot provoke predictable reaction in its social peer. The social guidance mechanisms used by Warlaumont [START_REF] Warlaumont | Prespeech motor learning in a neural network using reinforcement[END_REF] or [START_REF] Howard | Learning to pronounce first words in three languages: an investigation of caregiver and infant behavior using a computational model of an infant[END_REF] to drive the vocal exploration of a learner are also in fact general guiding mechanisms across modalities of sensorimotor learning. Closely related mechanisms have for example shown how a human could use reinforcement signals to progressively shape the exploration and learning of balancing an object [START_REF] Knox | Interactively Shaping Agents via Human Reinforcement: The TAMER Framework[END_REF], controlling an arm [START_REF] Pilarski | Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning[END_REF], or combining objects to reach a goal (Thomaz and Brezeal, 2008).

Towards an evo--devo theory of the origins of speech forms

In the previous sections, we have seen that non--speech mechanisms may play a fundamental role in progressively guiding a young learner into discovering how to use his/her vocal tract to produce speech forms of increasing complexity. Hence, modality-general mechanisms such as active intrinsically motivated exploration and social reinforcement can lead an organism to acquire basic speech forms before he can participate to and understand speech communication per se. A difficult scientific challenge remains to explain how the child flexibly discovers the means and goals of speech communication, i.e. how the child comes to understand and master how certain behaviors he produces (like producing particular speech waves, or gestures if he/she has speech impairments) can be used flexibly and adaptively to get its social peers respond. Non--speech mechanisms that impact speech development may also be key in understanding how language and languages form at the cultural and evolutionary scale. As we have discussed above, models of speech and language formation in groups of individuals have shown how spontaneous pattern formation in embodied coupled systems could foster the establishment of speech and linguistic conventions. In addition, the mechanisms of intrinsically motivated active learning we discussed also open windows over the evolutionary dynamics of language. Within a context of cultural evolution, an open question is how adult speech forms came to have the structure they have. Language and speech evolve not only through linguistic negotiation between adult peers, but also through cultural transmission to children. And as children acquire the language system of their parents, their learning biases act as filters and modulators over the input from adults. Deacon [START_REF] Deacon | The Symbolic Species: The Co-evolution of Language and the Brain[END_REF], and a subsequent series of computational models [START_REF] Zuidema | How the poverty of the stimulus solves the poverty of the stimulus[END_REF]Oudeyer, 2005;[START_REF] Kirby | Iterated learning and the evolution of language In[END_REF], have shown that cultural processes of language evolution make that linguistic forms adapt not only to become useful tools of communication for adults, but also to be learnable by and "interesting" for infants. Hence, mechanisms of intrinsic motivation which assess "interestingness" in terms of learning progress/learnability [START_REF] Oudeyer | What is intrinsic motivation? a typology of computational approaches[END_REF][START_REF] Gottlieb | Information Seeking[END_REF] should directly impact what infant will learn and not learn easily, and thus will be key in the cultural evolution of language forms. In addition, mechanisms of active curiosity--driven learning used within the process of language games may significantly improve the speed of convergence towards shared linguistic conventions, as suggested in [START_REF] Schueller | Active Learning Strategies and Active Control of Complexity Growth in Naming Games[END_REF]. Beyond understanding the structure of speech forms in human languages, a fundamental question at the cultural and phylogenetic evolutionary scales has been to understand the origins of language itself: how can populations of individual invent and shape the means and goals of language? As argued in Oudeyer and Smith (Oudeyer and Smith, in press), it is important to consider that the non--speech developmental mechanisms that help the child discover speech may also be instrumental in helping populations of individuals to invent language. For example, behavioural innovations resulting from curiosity--driven sensorimotor exploration of what one's body can do to objects and others may provide repertoires of skills, such as organized vocalizations [START_REF] Moulin-Frier | Self-organization of early vocal development in infants and machines: the role of intrinsic motivation Frontiers in Psychology[END_REF], that could form important elements of a starting kit for language. In this perspective, understanding the interaction between developmental and evolutionary dynamics appear to be a key challenge, within an evo--devo approach [START_REF] Müller | Evo-devo: extending the evolutionary synthesis[END_REF]. However, mathematical and computational models of the formation of speech and language systems at the population level have so far largely abstracted the developmental dimensions. Reversely, models of speech acquisition and development have considered single individuals acquiring an existing speech/language system. Establishing the foundations of an evo--devo computational theory is now a new target horizon in the scientific exploration of speech origins.