Christophe Turpin

Benoît Morin

Eric Bru
email: bru@laplace.univ-tlse.fr

Olivier Rallières
email: rallieres@laplace.univ-tlse.fr

Xavier Roboam
email: roboam@laplace.univ-tlse.fr

Bruno Sareni
email: sareni@laplace.univ-tlse.fr

Marcos Garcia Arregui
email: marcosgarcia.arregui@gmail.com

Nicolas Roux
email: roux@laplace.univ-tlse.fr

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n the context of aviation, this article looks at replacing a ram air turbine (Rat) with a hybrid proton-exchange membrane (PeM) hydrogen/oxygen (h 2 /o 2 ) fuel cell and ultracapacitor system, starting from a theoretical perspective. the structure we propose has two static power converters: one associated with fuel cells and the other with ultracapacitors. the energy management principle presented (frequency sharing) enables two types of control. first, the converter associated with the fuel cell manages the voltage of the dc bus, and then the converter associated with the ultracapacitors controls the high-frequency currents. second, the voltage of the dc bus is oppositely controlled by the ultracapacitor converter.

I I

Digital Object Identifier 10.1109/ MELE.2017.2758879

Power for Aircraft Emergencies

A hybrid proton-exchange membrane H 2 /O 2 fuel cell and ultracapacitor system.

an experimental validation (at a reduced ratio scale of 1:10) allows for an analysis of the proposed principles. the experiment is conducted in two stages. in the initial validation, a fuel cell physical emulator is used as the main source, before introducing an actual fuel cell.

Fuel Cells and Aircraft

in the context of the More electric aircraft (Weimer 1993), one of the concepts supported by the european More open electrical technologies project, the electrical power requirements of an aircraft are set to increase [START_REF] Turpin | Systemic Design Methodologies for Electrical Energy Systems[END_REF]) thanks to recent advances in power electronics (Rosero et al. 2007). an aircraft may be subject to various failures, in particular to the total loss of the engines or electrical power. these failures must remain acceptable (Mehdi and Woods 1989). for most large aircraft, a backup system composed of a Rat is used to generate emergency power (Bolognesi 2009). as new and future flight control actuators are electrical (van Den Bossche 2003), the load profile supplied by this backup system is very intermittent (figure 1). as the increase in electrical power demand is becoming very restrictive for the aircraft structure, which must include an increasingly large Rat, researchers have been looking at replacing this turbine with a fuel cell (Garciaarregui 2007). in addition, a fuel cell system has other advantages compared to the current Rat system's properties (Wörner 2009): it operates independently of the aircraft's speed and altitude; it can be stopped at any time if the main engines are recovered, while the Rat cannot be retracted once it has been extended; and it may reduce maintenance. Motapon et al. have also recently conducted and presented research in canada with experimental tests on replacing the Rat with a hybrid fuel cell.

the fuel cell indirectly [via reduction-oxidation reaction (redox)] makes h 2 react with o 2 ( Pera et al. 2013). there are several possible strategies for supplying the gases (Garcia-arregui 2007). h 2 may be produced on board by reforming kerosene [START_REF] Lenz | optimization of Jet-a fuel reforming for aerospace applications[END_REF]aicher 2005, ibarreta andsung 2006), or embedded in a compressed form. o 2 may be taken from the ambient air using a compressor or carried in a compressed form. all scenarios were examined and assessed by Garcia-arregui, and the most relevant solution for the emergency power application would be carrying both gases in a compressed form, which would eliminate the complex stage of reforming the kerosene and ensuring the quantity and/or quality of the air in the event of an emergency. this h 2 /o 2 solution is studied and experimentally validated in this article.

Research on fuel cell applications in aviation goes beyond the mere use in emergency power units (curtin 2010). a fuel cell could replace the gas turbine that currently performs the auxiliary power unit (aPU) function, which essentially supplies the aircraft with power when it is on the ground. this would reduce aircraft noise and gas emissions on the ground. a hybrid fuel cell/battery system has been proposed to perform this function (eid et al. 2010). Looking even further into the future, the hybridization of a high-temperature fuel cell and a turbine for this aPU has been studied [START_REF] Rajashekara | hybrid fuel cell power in aircraft[END_REF]. in the elektrische Basissysteme in einem cfK Rumpf: architektur und auslegung project (2007)(2008)(2009), funded by the federal Ministry of economics and technology/airbus, eYi-058/07) in Germany, researchers are studying various applications based on fuel cells (Wörner 2009): the emergency power unit, multifunctional fuel cell system (power, water, and inerting), and green taxiing on the ground. incorporating fuel cells in aviation would seem to require multifunctional technology to compete with the other current highly integrated technologies.

Given the intermittent character of the emergency profile to be achieved as displayed in figure 1, it is useful to associate a source with high specific power with the main power source to avoid oversizing (turpin et al. 2012). therefore, the current Rat has already been the subject of optimization studies (to reduce its size and weight) by associating it with ultracapacitors [START_REF] Roboam | hybrid power generation system for aircraft electrical emergency network[END_REF][START_REF] Rafal | hybridization of an aircraft emergency electrical network: experimentation and benefits validation[END_REF] based on the duration of the intermittent loads to be satisfied (high power peaks but with relatively low energy). for a fuel cell emergency power unit, there is also the issue of not oversizing the fuel cell [START_REF] Jiang | a hybrid fuel cell power supply with rapid dynamic response and high peak-power capacity[END_REF]. however, there are other reasons to justify this hybridization, such as in Martínez et al., or Bizon et al. for vehicles, e.g., for h 2 /air operation, it can also be used to compensate for the dynamics of the air compression [START_REF] Zhan | Power converters and controllers for UPs applications with backup PeM fuel cell[END_REF].

the work in this article is in line with previous theoretical work conducted in the context of the european ceLina-fuel cell application in a new configured aircraft-project. several electrical architectures have been studied (Garcia-arregui 2007[START_REF] Morin | Direct hybridization fuel cell-ultracapacitors[END_REF] for associating fuel cells with ultracapacitors. here, the focus is on the architecture that uses two dc-dc converters: one for the fuel cell and the other for the ultracapacitor.

Description of the Hybrid System

various loads used in an emergency may be considered constant. this is because the electronic equipment (computers) and anti-icing items absorb power that is constant over time for the duration of the emergency mission. other equipment, such as fuel pumps, radio equipment, and lights, absorb constant power when used. however, some loads are very variable, in particular the flight control actuators, which require power that fluctuates substantially depending on the flight conditions and practically throughout the whole mission.

During the ground approach phase, the aircraft's speed is reduced, which means that a significant deflection of the flight control surfaces is required to direct the aircraft. this results in considerable power consumption. in addition, prior to landing, several maneuvers are performed by the pilot to adjust the position of the aircraft with respect to the target. the flight mission we selected (see figure 1) is the low-speed ground approach phase of an aircraft flying in a turbulent environment with maneuvers controlled by the pilot. We defined this as the main mission for sizing our study (Garcia-arregui 2007), i.e., highly variable power consumption that reaches the highest consumption peaks.

this mission profile has a mean power of 40 kW and consumption peaks reaching 70 kW, i.e., a 30-kW difference between the mean power to be supplied and the maximum sizing power for the system. an oversizing rate (ovsR) can be defined as follows: an ovsR close to zero means that power consumption does not vary, while an ovsR close to one means that the sizing power is used only occasionally. in addition, generally speaking, a fuel cell requires fluidic auxiliaries with a slow dynamic response, whereas in an emergency mission, the required power must be available instantly. as a result, hybridization appears to be necessary in this case.

the new avionics standard that seems to be appearing in terms of the voltage level is a dc bus voltage of +270 vdc/0 vdc/-270 vdc (eid et al. 2010(eid et al. , Ravel 2009)). the ultracapacitor is a good candidate for hybridization due to its qualities: high specific power, easy management of the state of charge, long lifetime and life cycles, and enough storable/returnable energy for our mission profile [START_REF] Roboam | hybrid power generation system for aircraft electrical emergency network[END_REF], Langlois 2006, Maxwell technologies inc.).

several solutions are possible for associating a fuel cell with ultracapacitors, as proposed by Garcia-arregui. We take various criteria into account to classify the solutions: weight, gas consumption, stability of the dc bus, ease of use, fuel cell load current, filtering capacity of the ultracapacitors, and system reliability. according to Garcia-arregui, the solution that offers the best compromise for our specifications is the one that uses two static converters: one for the fuel cell and the other for the ultracapacitors (figure 2). this solution has the best bus stability and filtering capacity as well as middling weight and gas consumption, but its use and reliability are yet to be proven as the architecture puts a lot of strain on the power electronics.

We used several criteria to initially size the solution. the issue was to determine the optimal number of cells to be connected in series to create the fuel cell and their surface area. for example, it is possible to oversize the cell surface to reduce gas consumption. Weight is therefore saved for storing gas, but the fuel cell is made heavier so a compromise must be reached. an important criterion in sizing the power electronics is the minimum voltage of the fuel cell. at equivalent power, the lower the cell voltage, the higher the current flowing through the semiconductors.

as for the stored energy, it is important to ascertain its capacity and its minimum reference voltage, allowing for a discharge of less than 50% of its voltage (i.e., 75% of its state of charge). the maximum voltage of the stored energy is used to determine the number of elements to be connected in series. its capacity indicates how many branches of these elements are to be connected in parallel, with the elements being selected from ultracapacitor manufacturers' catalogues. table 1 contains an example of sizing, with an arbitrary choice of criteria to set some general figures. subsequently, the following criteria remain variable to optimize the weight of the overall system and its gas consumption:

Theoretical Energy Management for the Studied Hybrid System

Frequency Sharing Principle hybridization in terms of power is defined as the association of various complementary sources. in our case, this association is achieved with a source of high specific energy (the fuel cell), which poorly withstands rapid variations in power (schindele et al. 2005, thounthong and sethakul 2007), and a source of high specific power (ultracapacitors), which can only supply or absorb the mean power of zero (excluding losses). the idea of this power hybridization is to assign low variations in power to the fuel cell and zero mean power to the ultracapacitors. several methods are available for this, including the distribution of frequency powers band and slope limitation. the method chosen for this study is power frequency sharing (chapoulie [START_REF]interactions between fuel cells and power converters: influence of current harmonics on a fuel cell stack[END_REF]and astier , Garcia-arregui 2007) ) and is shown in figure 3.

the desired filtering frequency will play a significant role in sizing the solution because, for a specific stored energy capacity, a lower frequency will cause the energy level of the ultracapacitors to fluctuate more. in other words, given the ultracapacitors' limitations (the maximum voltage level not to be exceeded and the minimum power reserve to be retained), at a given filtering frequency, a minimum stored energy capacity must be used for the system to be able to operate. in our case, this filtering frequency enabled us to size the stored energy capacity and its reference voltage. this frequency was recommended by a fuel cell manufacturer as the fastest frequency enabling the auxiliaries to correctly control the gas supply (the pressure and flow rate), despite the variations in fuel cell consumption.

an example of power frequency sharing is simulated in figure 4. the presented case is an ideal scenario, taking into account neither the losses in the components nor the auxiliary consumption used to demonstrate frequency sharing. the mission profile is given in figure 1, and the filtering frequency is 50 mhz.

figure 5 shows the changes in voltage for stored energy and fuel cells throughout the mission for the sizing proposed in table 1. Using an ideal model, the state of charge of the stored energy and the limitation voltage of the fuel cell are within their limitations.

Impact of System Losses

now, let us consider some losses in the system, in particular in the stored energy elements (the internal resistance of the ultracapacitors), as shown in figure 6. an observation can be made that the state of charge of the stored energy is no longer within its limitations and that it moves toward a total discharge. in this case, the application quickly becomes no longer able to operate because all of the energy lost in stored energy losses is never compensated. the solution put forward to maintain the state of charge of the stored energy is to add a proportional control loop coupled with a low-pass filter on the ultracapacitor voltage, as shown in figure 7. the difficulty in choosing this corrector lies in choosing the proportional gain value K. With a gain K that is too low, the corrector will not be able to maintain the stored energy at a state of charge stable enough to be within its limitations. With a gain K that is too high, the stored energy voltage undulates at the frequency of the low-pass filter placed at the ultracapacitor voltage measurement point. Without this low-pass filter, high-frequency variations in the ultracapacitor voltage are carried onto the current provided by the fuel cell, which we aim to avoid. in addition, complying with the voltage limitations chosen for the stored energy is strongly linked to its reference voltage. additionally, this loop must be included in the control of the system converter that is current controlled (with ultracapacitors first and fuel cells second) is because the converter that controls the bus voltage no longer has any degrees of freedom (Dof) to perform an additional function, in which case the bus voltage would be degraded.

figure 8 shows various choices for the V ref and K parameters of the control loop for the state of charge of the stored energy and the importance of the choice of these values for system stability. With a stored energy reference voltage that is too low, its mean state of charge cannot fulfill the entire mission. With a reference voltage for the stored energy that is too high, there is a risk of exceeding the maximum ultracapacitor voltage values. the choice of these values can help reduce (or increase, if poorly chosen) 1.

the size of the stored energy system. subsequently, the system sizing algorithm chooses these values. in the prior discussed example (see table 1), resizing the stored energy and its control loop using an iterative process from the optimization algorithm gives rise to the results in table 2. taking into account the stored energy losses and adding a loop to maintain the state of charge leads to a stored energy resizing that is around 23% higher than for the system when considered with no losses. it is also used to determine the criteria (reference voltage, proportional gain, and initial minimum voltage of the stored energy) enabling the mission to be performed, complying with the limitations set for the state of charge of the stored energy and ensuring its optimal operation (see in figure 9).

Final Simulation Results

two versions of the PeM fuel cell were studied by Garciaarregui: an h 2 /o 2 stack and an h 2 /air stack. the weight savings that can be achieved with an h 2 /o 2 stack (higher specific power and no compressor) is consequently canceled out due to the weight of the o 2 to be carried (mainly the weight of the tanks). for the weight minimization criteria, the solution from the theoretical studies is therefore the h 2 /air solution. During the simulations, we considered a rather pessimistic scenario in which we took into account auxiliary consumption (mainly compressor consumption) equal to 20% of the fuel cell production as well as the losses in the converters (losses in switches, inductors, and capacitors). the simulation result for this system sizing is given in figure 10. TABLE 2. The results of iterative resizing with compensation for losses.

V ref (V) Gain C (F) V 0 (V) 68 3 12.1 47.5
fuel cell production was noted to be higher than the mean load power. this would have also been valid in nonhybrid cases, taking into account the converter losses and the auxiliary consumption. however, the fuel cell supplies few high-frequency components, which should enable its compressor to correctly control the air supply flow rate and not speed up its aging. compared to the ideal case presented in figure 4, apart from the mean value for the power produced by the fuel cell, which differs for the reasons given above, slightly larger fluctuations can be seen for the supplied power. these fluctuations are due to the stored energy charging loop, which itself is dependent on its capacitance.

Implementation of the Selected Energy Management

DC Bus Voltage Control Strategy

as the architecture has two static converters, there is a Dof for controlling the bus voltage because there are three components connected to the dc bus: the loads, the stored energy and its converter, and the fuel cell and its converter. only one of these elements can manage the bus voltage. clearly, the loads cannot do this. it is therefore possible to control the bus voltage via the fuel cell converter or the stored energy converter.

Both of these solutions are shown in figure 11. in some cases, it is of particular interest to assign the bus voltage management to the stored energy. this is true, e.g., when the fuel cell is replaced by a Rat, as maximum power point tracking can be associated with it [START_REF] Rafal | hybridization of an aircraft emergency electrical network: experimentation and benefits validation[END_REF][START_REF] Langlois | the influence of power electronic dynamics on PeM fuel cell system[END_REF]. however, there currently seems to be minimal industry interest in a fuel-cellbased solution.

Regardless of the choice of dc bus control, frequency sharing must be performed as shown in figure 3, i.e., the high-frequency power components required by the application must be supplied by the ultracapacitors and the low-frequency components by the fuel cell. this is achieved by a filter applied to the measured load current (figure 11). the difference between both possible bus control strategies is that, when it is the fuel cell that commands the dc bus voltage [i.e., strategy one, figure 11(a)], the converter associated with the stored energy must be more dynamically effective than the converter associated with the fuel cell. if it is less effective, the fuel cell will also inevitably supply the highfrequency components to control the bus. for strategy two [figure 11(b)], the fuel cell will supply the low-frequency components only in the event its converter directly receives this instruction.

Dynamic Compensation of Losses

as explained in the "impact of system Losses" section, losses in the converters and stored energy have a slow effect (low frequencies) on the state of charge of the stored energy and, therefore, on the system sizing. Ultimately, the fuel cell will have to supply power for these losses to maintain the state of charge for the stored energy.

however, for the strategy shown in figure 11(a), these losses by the stored energy will consequently have only a 1).

The high-frequency power components required by the application must be supplied by the ultracapacitors and the low-frequency components by the fuel cell.

partial supply of the high-frequency part of the load current, and this noncompensated part will therefore be found in the fuel cell current. Given the nature of the stored energy converter [dc-dc, bidirectional in current, working as a booster in the stored energy to bus direction [START_REF] Rafal | hybridization of an aircraft emergency electrical network: experimentation and benefits validation[END_REF]], it enables the current in the stored energy or the bus voltage to be controlled. in this case, we would like to control the converter output current, i.e., on the bus side, as the converter's instruction is to supply the high frequencies of the load current so that the fuel cell does not have to supply this part. as this current management method is not permitted because of the nature of the converter and we were not able to control this in our experimental system, a way of managing the current on the bus side would be to apply the voltage ratio to the reference current of the stored energy. this transposition requires a yield per unit from the converter. in practice, with no specific action, the final current at the bus will be lower than the current required. to be able to control the bus current via this converter, the chosen method is to add a converter loss compensation loop. the current was therefore managed as where IConverter HVdc is the output current of the converter and Istor is the input current to be managed. the equation includes converter losses. however, it is difficult to ascertain them in real time. We chose to compensate for them via a theoretical estimation. this is because the converter losses are mainly composed of losses in the switches (3) and ( 4)-here, the insulated-gate bipolar transistor (iGBt) and diode-in addition to losses in the passive elements (5):

( , ) ( ( ( , ) ( , )) ) , ) )

P V I P P V I R I E V I E V I f CE r r 2 dec IGBT cond comm eff O N O FF o r IGBT IGBT $ $ $ = + = + + + r (3) ( , ) ( ) ( (
P V I P P V I R I E V I f D d d 2 Diode c ond c omm eff R EC dec o d Diode D iode $ $ $ = + = + + r (4) ( ) . P I R I L L L 2 $ = (5)
therefore, in requiring the stored energy to supply the high-frequency parts of the load power and the losses calculated for its converter, full compensation for the load power high frequencies is close to being achieved. the mean value for these losses does not affect the control loop for the state of charge of the stored energy, as the losses are considered to be an additional load and go through the high-pass filter for the frequency sharing in figure 11(a). the disadvantage of this method is that, if the estimation of these losses is incorrect or if they change over time (through component wear and tear), compensation no longer functions. the ideal solution would be a bidirectional dc-dc current converter for which we manage the current on the high-voltage side. there is a similar situation for the strategy shown in [figure 11(b)] because the converter associated with the fuel cell is a dc-dc unidirectional current boost converter (in the fuel cell to bus direction). it enables the management of the fuel cell current or the bus voltage. here, we want to manage the current sent to the bus. as for the previous case, the losses are compensated by a theoretical estimation and considered to be an additional load. they therefore go through the low-pass filter for frequency sharing and are compensated using a mean value. the compensation for these losses has less of an effect on the hybridization performance as they have a low-frequency effect, which would be compensated for by the control loop for the state of charge of the stored energy.

Problem Starting the Frequency Sharing

effective filtering for the fuel cell requires a low cutoff frequency for the frequency-sharing filter (here, 10 mhz). this means a long starting time for the filter. Used as is, the filter rise corresponds to the rise in the fuel cell current, i.e., during that whole time, the stored energy provides the power required for the loads. as the stored energy cannot fulfill this mission for long, it becomes fully discharged when the fuel cell reaches the mean current required by the frequencysharing filter. Meanwhile, the bus voltage is no longer controlled as the stored energy is discharged and the mission is not fulfilled. at startup, there is an issue initializing this filter. Let us suppose that we are not aware of the steady state; we, therefore, cannot initialize the filter at its value in the steady state or at the value of the current at the startup time (in which case, we would risk starting on a peak). to compensate for these difficulties, we suggest successively switching to slower and slower filters to arrive at the final desired filter. therefore, the chosen solution (shown in figure 12) proposes starting with a fast filter (for which the cutoff frequency depends on the dynamics acceptable by the fuel cell and its fluidic regulations), while other slower filters start at the same time. Given the order of two and the damping factor ( / 2 2) of the filters, they naturally have a slight exceedance, which means that their values will inevitably coincide for a limited profile. this will enable switching to the lower-frequency filter with no value jumping. to prevent a return from a low-frequency filter to a higher-frequency filter, switching should be irreversible.

Small-Scale Experimental Validation

Description of the Experiment Performed

the experimental validation shown in figure 13 takes place in two stages. the first is an experiment with the fuel cell replaced by an emulator, and the second is with an actual fuel cell. these trials will be used, among other things, to assess the relevance of an experiment employing an emulator and will guide the choice for full-scale validation. in addition, the use of the emulator is less restrictive than using a fuel cell in terms of the cost of gas and the risk of damage. it is used to adjust and validate all the control loops and push the strategies to their limits, with minimal risks for the equipment.

for the fuel cell, we have a stack of 50 cells measuring 130 cm 2 , with h 2 /o 2 operation, provided by aReva energy storage inc. [figure 14(a)]. for the stored energy, we are equipped with 16-v, 58-f ultracapacitors (Maxwell technologies' BMoD0058 e016). they will be connected in series, in a sufficient number to adapt to the level of the reference voltage for the stored energy [figure 14(b)].

the load profile will be to scale based on the bench elements, i.e., 4 kW mean power and 8 kW peak power. Given the unique elements of the bench, the experiment could not take place in the exact sizing conditions resulting from the optimization. The use of the emulator is less restrictive than using a fuel cell in terms of the cost of gas and the risk of damage.

Emulation of a Fuel Cell the fuel cell emulator consists of a programmable voltage source, with its output voltage adjusted to suit the current output so as to correspond to the almost static curve of a fuel cell. its transfer function ( where N is the number of cells in the stack, E 0 the standard nominal voltage of the h 2 /o 2 redox pair, R the perfect gas constant, T the fuel cell temperature, n the number of electrons involved in the reaction, f faraday's constant, and PH2 and PO2 the respective partial pressures of h 2 and o 2 . in addition, a (the transfer coefficient), j n (the crossover equivalent current density), j 0 (the exchange current density), and R eq (the membrane and diffusion equivalent surface resistance) are parameters taken per fitting on the actual almost static curve of an aReva energy storage elementary cell, where j = I/S is the current density going through our emulated fuel cell.

the experiment was conducted with a 1:10 ratio for the power with respect to the simulation results (Garciaarregui 2007). in addition, it was performed on a highvoltage dc half bus (i.e., +270 vdc/0 vdc instead of +270 vdc/0 vdc/-270 vdc), which, in the end, is a 1:5 ratio for the power involved for this half bus.

figure 15 shows power sharing via frequency filtering operated with the fuel cell emulator described previously.

We can see actual filtering of the load power by the stored energy. for both of these cases, we can see a certain thick area for the power supplied by the fuel cell. this thick area is due to our fuel cell emulator because the voltage source used for the emulator takes its power from the french network (230 vac/400 vac, 50 hz) to supply its power. it is composed of a three-phase rectifier, then a dc-dc converter, which unfortunately allows some of the fundamental harmonic of the rectified network through (six times the network frequency, i.e., 300 hz). as acquisition is performed with a step of 1 ms, these undulations appear.

however, for strategy one [figure 15(a)], the high-frequency undulations are even more present. as expressed in the "dc Bus voltage control strategy" section, this is due to a fuel cell converter bandwidth that is higher than the bandwidth of the stored energy converter, as well as an estimation of the dynamic losses for this converter that is not accurate, as it is based on theoretical calculations. the frequency band for these phenomena is found at the fuel cell current, as the frequencies are too high to be managed by the stored energy converter. this frequency band is in addition to the 300-hz undulation supplied by the fuel cell emulator.

System Stability

Using the emulator tests the system to its limits and notably tests the stored energy stability. to test this power management stability, we considered two situations that are theoretically the worst cases that could cause the system difficulties: x

x case 1: the load disconnects (step of nominal one to zero); all of the current supplied by the fuel cell is therefore absorbed by the ultracapacitors; they charge but must above all not exceed their maximum voltage.

x

x case 2: the load reconnects (step of zero to nominal one); the ultracapacitors empty to supply the power required for the load, but they must not discharge too substantially so the system can continue to operate. this is shown in figure 16, where we guarantee that the state of charge for the stored energy does not endanger the system when faced with these two situations. the high and low limits for the stored energy are 150 v and 0 v.

figure 17 shows the purpose of the stored energy charging loop described in the "impact of system Losses" section. indeed, when this loop is not present, the stored energy starts to completely discharge, and the more its state of charge decreases, the higher the requested currents, which makes the voltage decrease even further at its limitations.

the issue of starting for hybridization mentioned in the "Problem starting the frequency sharing" section must be experimentally validated to check the stability of the filter switching with the stored energy charging loop. this is because hybridization, when starting, is launched with all of the power consumed by the loads supplied by the stored energy; its state of charge decreases very quickly, and its control loop will tend to increase the power supplied by the fuel cell to recharge it. figure 18 shows this startup for a test with an emulator. the fuel cell quickly reaches its nominal power, and we can easily distinguish between the moments where the frequency-sharing filter switches thanks to the harmonics present on the power supplied by the fuel cell. the experiment on the fuel cell power increase is in line with the theoretical switching of the filters shown in figure 12. Tests with an Actual Fuel Cell the tests in figure 15 were repeated with the real fuel cell stack, the characteristics of which are given in the "Description of the experiment Performed" section. figure 19 shows the hybridization tests with the fuel cell stack for both strategies put forward in this article. the same phenomena as in figure 15 can be seen, with one less high-frequency undulation because a significant part of the high-frequency part present at the power supplied by the fuel cell emulator was due to the three-phase electrical network. as the fuel cell is a dc source, these undulations no longer appear. however, even more harmonics provided by the fuel cell are found in strategy one for the same reasons as in the "emulation of a fuel cell" section, i.e., a stored energy converter that is less dynamically effective than the fuel cell converter.

Conclusions

Going from simulation to experiment confirmed the theoretical results but also revealed phenomena that were not apparent during the simulations (e.g., interference between the converters, voltage rise of the elements, connections between them, immunity to noise, resonance, and so forth). in addition, while figures 10 and 19 are relatively similar, we can see that the mean current provided by the fuel cell during the simulations (figure 5) is higher than that observed in the experiment. this is due to the ultracapacitor technology, which had developed in the meantime (the internal resistance is now lower than during the theoretical study), but is also due to the scale factor, which considered the losses to be greater in the more powerful converters. however, the emulation showed very satisfactory results with respect to the experiment with an actual fuel cell, confirming all of the potential offered by fuel cell emulation (e.g., to go toward full scale in terms of power). this work was continued by replacing the ultracapacitors with a battery for different aviation specifications ( Jaafar et al. 2014).

Figure 1 .

 1 Figure1. A typical load profile for the sizing of an emergency power system (approach phase).

Figure 2 .

 2 Figure 2. A solution using two dc-dc converters for the hybridization of a fuel cell and ultracapacitors.

xx

  fuel cell minimal voltage (v) = 100 x x ultracapacitor maximal voltage (v) = 80 x x ultracapacitor maximal discharge (%) = 50 x x cutoff frequency (hz) = 0.05 x x fuel cell maximal current density (a/cm 2 ) = 0.5 x x switching frequency (hz) = 20,000 x x number of parallel converter branches = four.

Figure 3 .

 3 Figure 3. The principle of hybridization by frequency filtering, illustrat- ed in a power-frequency drawing.

Figure 4 .

 4 Figure 4. The use of frequency sharing for a frequency of 50 mHz.

RFigure 7 .

 7 Figure 6. The ultracapacitor (UC) voltage evolution, taking into account losses due to internal resistances.

Figure 5 .

 5 Figure 5. The voltages in (a) a storage device and (b) a fuel cell throughout the mission for the sizing displayed in Table1.

Figure 8 .

 8 Figure 8. Some choices of parameters for the state-of-charge control loop: (a) gain = 5, V ref = 60; (b) gain = 1.5, V ref = 60; (c) gain = 5, V ref = 72; (d) gain = 1.5, V ref = 72.

Figure 10 .

 10 Figure10. The power sharing obtained via simulation for the weight- and consumption-optimized system.

Figure 9 .

 9 Figure 9. The change in stored energy voltage in resizing for the case in question (Table1).

Figure 11 .

 11 Figure 11. The DoF offered by the chosen architecture: (a) the bus managed by the fuel cell (strategy one); (b) the bus managed by the stored energy (strategy two). HP: high pass; LP: low pass.

  Figure 12. An example of filter switching. p.u.: per unit.

Figure 13 .

 13 Figure13. A diagram and structure of the test bench for the experiment.

Figure 14 .Figure 15 .

 1415 Figure 14. Photos of the test bench elements: (a) the fuel cell test bench; (b) the ultracapacitors; (c) the electrical prototypes; (d) and the moni- toring room.

Figure 16 .

 16 Figure 16. A stored energy charging loop enters into difficulty.

Figure 18 .

 18 Figure 18. Hybridization startup with an emulator.

TABLE 1 .

 1 An example of ideal hybrid system sizing (with no losses).

	Fuel Cell Surface (cm 2 )	Fuel Cell Number	C (F)	V 0 (V)
	627.2	154	9.83	61.16

C: equivalent capacitance of ultracapacitors; V 0 : Initial ultracapacitor voltage.

Acknowledgments

the authors would like to thank france's General Directorate for civil aviation for funding this article as part of the iss Power and control project led by airbus. this study was conducted in partnership with airbus and the aKKa technologies Group.

For Further Reading J. a. Weimer, "electrical power technology for the more electric aircraft," in Proc. 12th AIAA/IEEE Digital Avionics Syst. Conf., oct. 1993, pp. 445-450. x. Roboam, B. sareni, and a. D. andrade, "More electricity in the air: toward optimized electrical networks embedded in more-electrical aircraft," IEEE Ind. Electron. Mag., vol. 6, no. 4, pp. 6-17, 2012. J. a. Rosero, J. a. ortega, e. aldabas, and L. Romeral, "Moving toward a more electric aircraft," IEEE Aerosp. Electron. Syst. Mag., vol. 22, no. 3, pp. 3-9, Mar. 2007. i. s. Mehdi and e. J. Woods, "electrical power systems for high mach vehicles," in Proc. IEEE Energy Conversion Engineering Conf., Washington, Dc, 1989, pp. 625-630. P. Bolognesi, f. Papini, and L. taponecco, "hybrid-excitation dc machines as highly reliable generators for ram air turbines," in Proc. IEEE Industrial Electronics Society Conf., Porto, Portugal, 2009, pp. 2569-2574. D. van Den Bossche, "More electric control surface actuation: a standard for the next generation of transport aircraft," in Proc. 10th European Power Electron. andApplicat. Conf., toulouse, france, 2003, pp. 1-6. M. Garcia-arregui. (2007). theoretical study of a power generation unit based on the hybridization of a fuel cell stack and ultracapacitors. Ph.D. dissertation, institut. nat. Polytech.,


Darío R Quiñones

Gonçalo Lopes
email: g.lopes@ucl.ac.uk

Danbee Kim
email: danbee.kim@ucl.ac.uk

Cédric Honnet

David Moratal
email: dmoratal@eln.upv.es

Adam Kampff
email: adam.kampff@ucl.ac.uk

HIVE Tracker: a tiny, low-cost, and scalable device for sub-millimetric 3D positioning

Keywords: centered computing → Virtual reality, • Computing methodologies → Motion capture, • Hardware → Wireless devices, • Software and its engineering → Open source model, • Information systems → Global positioning systems, Wireless-Sensor, Open Source, Virtual Reality, Motion Capture, Low cost, Indoor, Tracker, Neuroscience

Figure 1: Hive Tracker prototype, with a US 25 cent coin for size comparison.

INTRODUCTION

Humans and other animals use bodies as our primary interface with the outer world, and as a powerful tool for expressing our inner worlds [START_REF] Michael | Embodied Cognition: A field guide[END_REF][START_REF] Wilson | Six views of embodied cognition[END_REF][START_REF] Wilson | The Case for Motor Involvement in Perceiving Conspecifics[END_REF][START_REF] Daniel | Perspectives and problems in motor learning[END_REF]. Movement is therefore a phenomenon of interest to many, such as neuroscientists, surgeons, engineers, makers and artists. Though we have long appreciated movement on a macro scale, it has become increasingly clear that much could be gained from studying movement in greater detail and with higher precision.

Applications in performing arts and athletics

Two areas with long-standing interests in the precise study of human movement are the performing arts and athletics. In the 1920s, Rudolf Laban, a dance artist and theorist, developed with his colleagues a written notation system to precisely and accurately describe movement in terms of body parts, actions, floor plans, temporal patterns, and a three-dimensional use of space [START_REF] Monthland | Schrifttanz: a view of German dance in the Weimar Republic[END_REF]. This has evolved into the modern-day Labanotation, or Kinetography Laban. However, many movement artists find Labanotation too complex and cumbersome for easy daily use. These days it is much more common to use videos to record, analyse, and teach the specific movements of performing artists and athletes. The increased prevalence of cheap video recording devices, especially phone cameras, has increased the use of this technique. But even high quality, state-of-the-art video recording technology struggles to capture the movements of aerial artists, acrobats, and other circus performers; the finer details of object manipulations performed by jugglers and athletes (e.g. finger placement and movements for optimal archery technique); and the tiny, fast movements of smaller body parts often found in hip hop and modern dance.

Applications in neuroscience and medicine

The fields of neuroscience and medicine are also interested in precisely recording and analysing movement, both for research and for clinical applications. A fundamental question in neuroscience is how nervous systems generate and execute movement goals. Historically, neuroscientists have prioritized the collection of cellular signals when answering research questions, so experiments are designed around the need to keep an electrode, or other data collecting device, stuck in the animal's head. This means that most neuroscience experiments study animals that are either held in place ("head-fixed") or tethered to a wire. However, a growing body of evidence supports the idea that cellular activity in the brain is significantly different when an animal is actively and freely moving in three dimensions through complex physical spaces, as opposed to when it is head-fixed or tethered to a wire. [START_REF] Mcnaughton | Cortical Representation of Motion during Unrestrained Spatial Navigation in the Rat[END_REF][START_REF] Edvard | Place Cells, Grid Cells, and the Brain's Spatial Representation System[END_REF][START_REF] Nishijo | The relationship between monkey hippocampus place-related neural activity and action in space[END_REF][START_REF] O'keefe | A review of the hippocampal place cells[END_REF][START_REF] O'keefe | The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat[END_REF][START_REF] Soechting | Moving in three-dimensional space: frames of reference, vectors, and coordinate systems[END_REF]. Studies of the development and degeneration of nervous systems in humans also show that nervous systems are profoundly affected by the movements and physical contexts of their bodies [START_REF] Nicholai | Dexterity and its development[END_REF][START_REF] Röhricht | Body oriented psychotherapy. The state of the art in empirical research and evidence-based practice: A clinical perspective[END_REF][START_REF] Vaynman | License to Run: Exercise Impacts Functional Plasticity in the Intact and Injured Central Nervous System by Using Neurotrophins[END_REF]. Better systems for precise positional tracking in humans and animals would significantly impact the scientific questions that neuroscientists and clinical researchers could ask.

Current state-of-the-art in positional tracking

All of these areas would benefit hugely from having greater access to precise movement tracking. Motion capture systems, or mo-cap for short, are the current state-of-the-art for recording the movements of people and objects. However, current motion capture technologies require multiple specialized cameras, in addition to a whole slew of accessories, which unfortunately make these systems inaccessibly expensive and bulky. Industry standards for mo-cap, such as VICON and OptiTrack [START_REF] Chang | Towards pervasive physical rehabilitation using Microsoft Kinect[END_REF][START_REF] Windolf | Systematic accuracy and precision analysis of video motion capturing systems -exemplified on the Vicon-460 system[END_REF], require a minimum investment of 10-15 thousand USD in order to assemble a viable system. Another option out on the market are inertial measurement units, or IMUs, which combine accelerometers, gyroscopes and magnetometers to track small and fast movements [START_REF] Madgwick | An efficient orientation filter for inertial and inertial/magnetic sensor arrays[END_REF][START_REF] Madgwick | Estimation of IMU and MARG orientation using a gradient descent algorithm[END_REF][START_REF] Winer | 9 DoF Motion Sensor Bakeoff[END_REF][START_REF] Winer | Affordable 9 DoF Sensor Fusion[END_REF]. Despite their sensitivity and speed, such IMUs do not measure absolute position, only relative motion. This means that to recover absolute position one must integrate the measured motion over time. Even small measurement errors will accumulate during this integration process, a phenomenon referred to as "drift". This makes IMUs inadequate for precise, continuous tracking of natural movement sequences. Another currently available alternative (at the time of writing) for motion tracking is Microsoft's Kinect, a consumer level 3D motion-sensing camera device for video game consoles. Using depth information and machine learning, the Kinect can infer in real-time the pose (position and orientation) of a human body located in front of the camera. However, a single Kinect cannot track motion in 360 degrees, as it was originally designed to track the movements of gamers facing a video game display. Some motion tracking systems combine Kinects and IMUs in an attempt to supplement one technology's weaknesses with the strengths of the other [START_REF] Tian | Upper limb motion tracking with the integration of IMU and Kinect[END_REF], but IMUs will always drift, and multiple Kinects will always be required to gain 360 degree tracking. While all these systems have found a niche, and are of great use to the military and entertainment industry, they are neither affordable to most artists, athletes, researchers, or clinicians, nor accurate enough for use in research or clinical settings [START_REF] Pfister | Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis[END_REF].

Affordable, smaller, and more scalable

Simpler solutions are already coming out of artistic research, wearable applications and even implant experimentations [START_REF] Freire | Second Skin: An Exploration of eTextile Stretch Circuits on the Body Video Figure[END_REF][START_REF] Schlegel | From Ordinary to Expressive Objects Using Tiny Wireless IMUs[END_REF][START_REF] Strohmeier | Developing an Ecosystem for Interactive Electronic Implants[END_REF]. Building upon this line of work, we present here an affordable, compact, and scalable positional tracking device called "Hive Tracker" (Figure 1), which can measure movement with sub-millimetric precision along six degrees of freedom; allows for untethered movement within a 5x5x5 m 3 space; connects easily and simply to virtual reality; and can scale up to as many devices as desired. This approach would allow the niches described above to take advantage of precise positional tracking technology, and opens the door to a plethora of new human augmentation applications.

MATERIALS AND METHODS

We present here the details of our multi-component system, composed of commercial products as well as custom devices and software. We first developed and benchmarked a small proof-of-concept of the system using an off-the-shelf microcontroller board described in Section 2.2.2. We present below some of this benchmarking data, which we used to constrain the subsequent design of the first Hive tracker prototype, described in Section 4.

Figure 2: Overall system overview

Figure 2 shows an overview of the complete signal processing pipeline, which we describe in the following section.

Valve tracking system

The Hive Tracker is a data-collection device that piggybacks on a commercial virtual reality system developed by Valve (HTC VIVE). The commercial system consists of a headset, two hand controllers, and two light-emitting devices ("lighthouses" or "base stations"). Each lighthouse contains an LED matrix and two mirrors mounted on high precision rotors. In order to achieve sub-millimetric precision, this system needs to be set up in a space no larger than a 5x5x5 meter cube. We reverse-engineered the communication protocol between the lighthouses and the commercial devices in order to replace the commercial devices with custom devices optimized to fit our needs.

The signal from the lighthouses is composed of four components [Table 1], which enable the system to synchronize the two lighthouses and to track devices within the VR space (Figure 3). When the system initializes, the lighthouses are automatically assigned as lighthouse A and B. To synchronize the two lighthouses, lighthouse A first emits a flash of light with known pulse length. Soon after, lighthouse B emits a similar flash of light, as described in Table 1. The length of the flash determines which lighthouse will start the laser plane sweep, and whether that sweep will be horizontal or vertical. (Figure 4) of the Chiclet uses the TS3633 integrated circuit (IC). The IC converts a weak and noisy analog signal (obtained with the photodiode) to a digital signal which is simpler to use with a microcontroller. It provides both high-gain noise filtering and envelope detection of pulsed IR light that is incident on the photodiode.

Signal processing

Acquisition

Hardware: Teensy. The Hive Tracker proof-ofconcept was developed on a Teensy 3.2 (PJRC.COM, LLC., Sherwood, Oregon, USA). This 35x17mm microcontroller uses an ARM 1 cortex M4 processor overclocked at 120MHz to reduce interrupt handling latency. The Teensy timestamps the digital signals coming from the TS3633 and sends them to a computer to be converted into angles (Section 2.2.3).

2.2.3

Acquisition Software: BONSAI. For data collection, integration, and online visualization, we used the Bonsai visual programming language [START_REF] Lopes | Bonsai: an event-based framework for processing and controlling data streams[END_REF]. Photodiode activation timestamps were collected and serialized into User Datagram Protocol (UDP) packets via the Open Sound Control (OSC) protocol [START_REF] Wessel | Intimate Musical Control of Computers with a Variety of Controllers and Gesture Mapping Metaphors[END_REF]. These packets were streamed wirelessly into the host computer using WiFi. To reconstruct the position and orientation of each HiveTracker, we used the VR package of the Bonsai programming language to access the estimated 6 DOF (degrees of freedom) location of each lighthouse in parallel with the OSC messages.

Triangulation algorithm.

For each light signal sequence (Table 1), each lighthouse will first flash, then scan a beam of light either horizontally or vertically [START_REF] Shtuchkin | DIY Position Tracking using HTC Vive's Lighthouse[END_REF]. Every photodiode gets hit by both the flash and the scans, but the light hits each photodiode at different times. Each lighthouse sweeps at 120Hz. The "incident plane" is the plane defined by the angle between a photodiode and a lighthouse (Figure 3). The cross product of the normals of the horizontal and vertical incident planes defines a vector ("incident line") between the tracking device and the lighthouse. The absolute position and orientation of each lighthouse is given by the 1 Acorn RISC Machine -RISC: Reduced Instruction Set Computer commercial system, which allows us to project the incident lines from each lighthouse into the global coordinate system. The closest pair of points between these two incident lines defines the absolute location of a tracking device [START_REF] David | 3D game engine design: a practical approach to real-time computer graphics[END_REF], which can be determined at 30 Hz (Table 1).

RESULTS AND DISCUSSION 3.1 Tracking inside an ideal room

We first compared the Hive Tracker proof-of-concept against the hand-held controllers of the commercial Valve tracking system in an ideal room. We taped a non-regular hexagonal shape on the floor of the testing room, then traced this shape by hand with both devices, recording the devices' positions using Bonsai. The acquired trajectories were overlapped to compare the accuracy of the commercial controller (32 photodiodes) against that of the first Hive Tracker proof-of-concept (1 photodiode). In this comparison, we used the commercial device as our baseline "ground truth". Since the tracing movements were parallel to the floor plane, we used only the sensors' X and Y axes for this benchmark (Figure 5).

To quantify the comparison, we fit a polygon shape to the tracking data from the commercial device. We then compared this trajectory to the average traces from the Hive Tracker by calculating the average distance of each point in the tracker trajectory to the fitted hexagon. The results of this comparison are shown in Figure 5. The Hive Tracker proof-of-concept, which uses only one photodiode, had an average error on the order of 10 mm more than the average error of a commercial tracker.

Tracking in a non ideal room

We then tested the Hive tracker proof-of-concept in the worst possible scenario: in a 1.2x1x1.5m 3 space with a glass floor. In this situation the proof-of-concept was not able to achieve good accuracy, due to the short distances between the lighthouses and the reflection of the laser light on the glass floor (Figure 6).

Light reflections

Light reflections are a potential source of errors in this setup. As the laser plane (see Table 1) sweeps across the VR space, it is possible for light to bounce off a wall (or any shiny surface) and hit a photodiode sensor. Figure 7 shows false detections in the photodiode signal. We have addressed this issue in our first PCB prototype by adding extra photodiode sensors for more redundancy in the system. One can further shield the photodiodes from non-direct hits by embedding them in a shallow depression.

Tracking refresh rate

Another limitation of the proof-of-concept was the refresh rate of positional measurements. In order to find the closest pair of points between the two incident lines from the lighthouses using only one photodiode, we needed to collect light data for at least four full cycles from each lighthouse. This means that the Hive Tracker proof-of-concept updates positional tracking measures at 30Hz (See Fig. 4). This limitation can be addressed by using multiple photodiodes placed in a known geometric configuration relative to each other. In this way, the sequence of incident lines hitting each of the photodiodes in a single sweep can be used to constrain estimations of the absolute position and orientation of the Hive Tracker. This situation can be framed as a "Perspective-n-Point" problem, or the more general problem of estimating the position and orientation of a calibrated camera based on the projection of 3D points in the world onto that 2D camera image. Given this framing, we can consider each lighthouse as an ideal pinhole camera where the 2D-to-3D point correspondences are known exactly. Efficient algorithms to solve this problem for 3 or more points have been introduced by the computer vision community. [START_REF] Lepetit | EPnP: An Accurate O(n) Solution to the PnP Problem[END_REF] We can further constrain the reconstruction by using inertial motion measurements to estimate motion over time. This makes it possible to reconstruct the position and orientation of an entire Hive Tracker device from single sweeps at 120Hz. These insights motivated the development of the first Hive Tracker PCB prototype described in the next section.

PCB PROTOTYPE AND NEXT STEPS

Our tests with the Hive Tracker proof-of-concept confirmed our initial suspicions, namely that we would need to use more photodiode sensors. However, this also increases the computational load on the microcontroller processing system, as multiple photodiode sensors may be hit simultaneously. To address this, our first PCB prototype includes a dedicated system for processing in parallel the photodiode signals. The hardware, firmware and software designs are open source and available online: http://HiveTracker.github.io

Hardware

Increasing the number of photosensors increases the computational load on the system's processing units. The most common way to deal with this is to use FPGA (Field Programmable Gate Array) processors, which enable true hardware parallel processing. However, we found a simpler approach to achieving the necessary parallelization while maintaining an extra-compact board, such that the device does not hamper free movement. We chose to use the nRF52 by Nordic Semiconductors (Oslo, Norway), a "System on Chip" (SoC) that replaces the functionality of the FPGA with a Programmable Peripheral Interconnect (PPI). The nRF52 also includes a BLE (Bluetooth Low Energy) and an ARM cortex M4 core.

Figure 8 shows the design of the first custom board, with 5 Chiclet connectors. On the top side of the board, shown on the right of the figure, the largest component (labeled "MCU" for Micro Controller Unit) is the ISP1507 by Insight SIP (Sophia Antipolis, France). This 8 x 8 mm 2 system-on-package (SoP) includes the nRF52, its necessary passives, a high accuracy crystal resonator to define the radio communication speed, and a low power oscillator, which enables the microcontroller to save power while in deep sleep. The rectangular chip above the MCU is the Bosch BNO055 (Reutlingen, Germany) , an IMU SoC with a 3D accelerometer, a 3D gyroscope, a 3D magnetometer, and an ARM cortex M0 to perform sensor fusion. The other parts are the battery connector to connect LiPo batteries, a regulator, an RGB LED, a button, and 5 analog inputs that can behave as any GPIO (General Purpose Input/Output -to connect other MCU, sensors, etc). This first custom miniaturization prototype is far from cost-optimal, as the Chiclets are quite expensive. We have kept them in our design because their reliability is proven, and the cables connecting them to our PCB give us greater flexibility when placing the photodiodes. The next iteration of the Hive Tracker will not use Chiclets; instead, it will use the TS4231, a new IC by Triad Semi, and through-hole photodiodes that can accommodate custom orientations.

Firmware

The embedded software (firmware) configures the Triad Semi IC, processes the signal, merges it with the IMU data, and then sends it to a computer or a smartphone over BLE. This firmware fulfills the same function as the Teensy on the first proof-of-concept. The Teensy measures timing differences using interrupts, but this method can degrade the positioning accuracy. In the time it takes to handle an interruption, other light signals may have occurred. As mentioned earlier, an FPGA would solve this problem, but would make the Hive Tracker bulkier. While trying to keep the PCB small, we were able to validate that a rare feature of the MCU, the Programmable Peripheral Interconnect (PPI), could connect the edge detector and the capture register. This connection would normally need to happen via a CPU or FPGA, but using a PPI allows peripherals such as GPIOs and timers to interact autonomously with each other using tasks and events. The lack of interruptions makes it possible to simultaneously process up to 5 signals, which would improve robustness to potential occlusion. For a trackable object to detect IR signals in any 3D orientation, the geometric placement of at least 4 photodiodes must be on the vertices of a tetrahedra. Figure 9 shows how these peripherals are connected.

This embedded software is developed using the SDK provided by Nordic Semiconductors, the MCU manufacturer, to allow optimal performances. It is also Arduino compatible, to allow the research community, makers, or artists to experiment openly and freely.

Size/accuracy trade-off

Our custom PCB prototype can fit into a bounding box volume about 100 times smaller than the one necessary to enclose a commercial tracking device, and our device weighs about 10 times less than the commercial tracker (Figure 10). These improvements in size and weight did cost some tracking precision (see section 3), but since our error calculations were made with the proof-of-concept, which used only one photodiode, we can treat those error measurements as a "worst-case scenario". The increased number of photodiodes in our PCB prototype can only improve the precision of the proofof-concept.

These reductions in size and weight make the Hive Tracker much more convenient to use in a wider variety of human applications. Because it is not only small but also quite flat, the Hive Tracker can be integrated into clothing, gloves, or shoes worn by circus performers, theater artists, and dancers to capture their movements in real-time without hindering them. The Hive Tracker can also be easily integrated into objects manipulated by jugglers and athletes, in order to track and capture the movements of their props in addition to their bodies.

For medical and neuroscience research, which primarily use rodent subjects, the size and weight reductions are crucial. In neuroscience research, ethical review boards generally find it acceptable to use implants that do not exceed 10% of the body weight of the animal to be implanted. The average adult laboratory rat weighs between 250 to 500 grams [START_REF] Paxinos | The Rat Brain in Stereotaxic Coordinates[END_REF], and the average adult rat head measures about 5 cm in length [START_REF] Paxinos | The Rat Brain in Stereotaxic Coordinates[END_REF]. Given that the Hive Tracker measures 2.4 cm x 1.4 cm and weighs 8 grams, the Hive Tracker is already both small and light enough to be approved for use with rats, who are often implanted with devices that weigh 15 to 25 grams.

The accuracy of positional tracking that the Hive Tracker needs to achieve in order to be useful to medical and neuroscience research varies depending on the research question. Most research questions require a system that can precisely track the 3-dimensional movements of the whole body and the direction and orientation of the head. Current setups in neuroscience and medical research usually use video cameras to create a record of animal behavior, from which body trajectory and head movements are later extracted offline. This is both computationally and financially costly, and so many researchers simplify or ignore completely the behavioral validations required to thoroughly investigate their hypotheses. Even with the proof-of-concept's "worst-case scenario" precision, the Hive Tracker would already greatly increase researchers' ability to perform behavioral validations with a similar level of rigor as other controls currently used in neuroscience and medical research.

Cost

To produce the first 10 prototypes, the cost of the current version was about 75 USD, as opposed to 99 USD for the commercial tracker. This cost will be improved in our next version as the Chiclets won't be necessary anymore, so we can easily anticipate the cost of each Hive Tracker to be under 60 USD, especially if we produce them in larger quantities.

Autonomy

Various batteries can be used, but the one shown in figure 10 has a capacity of about 100mA. Given that our maximum power consumption is estimated to be about 40mA, this version of the Hive Tracker can run autonomously for at least 2 hours, depending on the usage. Embedded devices are in sleep mode most of the time, so the autonomy would greatly depend on the desired refresh rate. In addition, we do not need to process nor transmit data during periods of inactivity. For the applications mentioned in this paper, the inactivity rate might range from 10% to 90%, so the Hive Tracker could potentially run autonomously for 20 hours.

CONCLUSION

This paper showed an affordable and scalable custom positional tracking device that can integrate easily with a commercial consumerlevel VR system. The Hive tracker is totally wireless and battery powered, which allows us to attach this small tracker to humans, animals, or even objects without interfering with natural movements and complex interactions. Furthermore, there is no limit to the number of trackers that can be used simultaneously. Based on the proof-of-concept presented here, we plan to use the Hive Tracker in a variety of applications, including neuroscience experiments of natural behavior; tracking and capturing object manipulations; 3D haptics in VR; and detailed and precise documentation of movements in artistic and clinical settings. These applications of the Hive Tracker can directly enhance our understanding of interactive movement and behaviour in humans and other animals. Thus, devices like the Hive Tracker are crucial to the kinds of research necessary for developing the next generation of human augmentation tools.

2. 2 . 1

 21 Photodiode circuit. In both our proof-of-concept and our first PCB prototype, we used the Chiclet, a sensor processing development board made by Triad Semiconductors. The first-generation Pulse start, µs Pulse length µs Source station Meaning

Figure 3 :

 3 Figure 3: Representation of the computed lines in the 3D space. Picture represents how two intersected planes represent a 3D line pointing to each base station.

Figure 4 :

 4 Figure 4: Light signal sequences (4 are shown): wide pulses are base station flashes and short pulses are laser plane scans.

Figure 5 :

 5 Figure 5: Accuracy comparison between a commercial controller and Hive Tracker proof-of-concept in an ideal room. Traces from both devices are superimposed over a photo of a polygon taped to the floor. Blue = commercial controller, Orange = Hive Tracker, Green = tape on the floor.

Figure 6 :

 6 Figure 6: Accuracy comparison between commercial controller and Hive Tracker proof-of-concept in a non-ideal room. Traces from both devices are superimposed over a photo of a polygon taped to the floor. Blue = commercial controller, Orange = Hive Tracker, Green = tape on the floor.

Figure 7 :

 7 Figure 7: Reflections off the walls cause undesired pulses (asterisk) in the photodiode signal (compare to Figure 4).

Figure 8 :

 8 Figure 8: PCB design (left: bottom layer, with the photodiode connectors -right: top layer includes MCU, BLE, IMU, battery connector, button, LEDs, etc.)

Figure 9 :

 9 Figure 9: Programmable peripheral interconnect (PPI), ©Nordic Semiconductors[START_REF]Programmable Peripheral Interface documentation[END_REF]

Figure 10 :

 10 Figure 10: Size/weight comparison. Commercial tracker: 10 x 10 x 4.2 = 420cm 3 ; 85g. Hive Tracker: 2.5 x 1.5 x 1.1 = 4.13cm 3 ; 8g.

Table 1 :

 1 Activation Timings

ACKNOWLEDGMENTS

The authors would like to thank Yvonne Jansen and Alexis Polti for their support and expertise in the making of the latest Hive Tracker prototype. The authors would also like to thank NeuroGEARS Ltd for the financial support and all the help that they provide. Darío R. Quiñones is supported by grant "Ayudas para la formación de personal investigador (FPI)" from Universitat Politècnica de València. Darío also acknowledges financial support from the Universitat Politècnica de València, through the "Ayudas para movilidad dentro del Programa para la Formación de Personal investigador (FPI) de la UPV". David Moratal acknowledges financial support by the Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER funds under grant BFU2015-64380-C2-2-R. This work was also partially performed within the Labex SMART (ANR-11-LABX-65), supported by French state funds managed by the ANR under reference ANR-11-IDEX-0004-02.