A BioInspired Neural Controller For a Mobile Robot

Michele Folgheraiter, Giuseppina Gini, Alessandro Nava, Nicola MottolaDepartment of Electronic and Information

Politecnico di MilanoPiazza L. da Vinci, MILANO , I-20133, Italy32�

folghera, gini � @elet.polimi.it

Abstract— This paper focuses on the study of a bio-inspiredneural controller used to govern a mobile robot. The network’sarchitecture is based on the understanding that neurophysi-ologists have obtained on the nervous system of some simpleanimals, like arthropods or invertebrates. The neuronal modelmimics the behavior of the natural cells present in the animal,and elaborates the continuous signals coming from the robot’ssensors. The output generated by the controller, after scaling,commands the wheel rotation and therefore the robot’s linearand angular velocity. The mobile robot, thanks to the controller,presents different behaviors, like reaching a sonorous source,avoiding obstacles and finding the recharge stations. In thenetwork architecture different modules, charged of differentfunctionality, are regulated and coordinated using an inhibitionmechanism. In order to test the control strategy and the neuralarchitecture, we implemented the system in Matlab and finally inhardware using a dedicated dual processor board equipped withan ARM7TDMI micro-controller. Results show that the neuralcontroller can govern the robot efficiently with performancescomparable with those described about the animal.

Keywords: Biorobotics, Neural controller, Robot naviga-tion.

I. INTRODUCTION

Service robotics today requires synthesizing robust auto-matic systems able to cope with a complex and dynamicenvironment. Even for simple behaviors, like autonomousnavigation and obstacle avoidance, the most advanced systemssometime fail, especially in presence of noisy information.However, if we look at nature, we can see that in very”simple” animal, insects or invertebrates, the deambulationbehavior is always accomplished [1],[2].

Biorobotics, in this context, tries to give an answer tothese issues mimicking [3], in the machine, the behaviorsand the structure of living creatures. Studying the anatomyand the physiology of the animal it is possible to understandhow nature has attempted to solve crucial functional issues.Many scientists are focusing their attention on the part of theanimal’s nervous system that is involved in the sensorimotorcoordination. This part, considering the phylogenetic evolu-tion of the living organism, is the simplest and oldest one [4].From the functional point of view, it covers a primary rolebecause it permits the animal to perceive, explore and changethe environment where it lives. Because it is relatively simpleand accessible, we have a deeper understanding on how itworks in comparison with the higher nervous centers.

II. THE NEURAL CONTROLLER ARCHITECTURE

Many researchers have considered a bio-inspired controlsystem in order to control a robot [5], [6], [7], [8]. Sometimesthe animal not only inspires the control strategy for therobot, but also its kinematics and functionalities. In ourpoint of view there are two possible goals for bio-robotics:the first is to use the robotic system to test and validate themodels we have for the animals, the second is to use theproposed models to design new kinds of robots. Reachingboth these goals at the same time is very difficult and attimes dangerous because a compromise is required. In thiswork we are more focused on the second goal, with themain idea to use the knowledge we have from the biologicalstudies of the animal to synthesize a ”better” robotic system.Better, from the functionalities point of view, than a similarsystem not based on biological knowledge.

The neural controller we implemented is based on theearly studies conducted by Braitenberg [9] twenty years agoon very simple automata vehicles, and on the more recentlystudies that Barbara Webb et al. [10],[8] carried out on arobot cricket, whose principal behavior is to follow sonoroussources.

Inspired by these studies we tried to implement newparadigms that do not have any evidence in the biologicalstudies of the animal. Sometimes it is near impossible toperform a complete comparison between our model and thebiological model, since we are more interested in the roboticfunctionalities than in mimicking the animal. Nevertheless weare convinced that studying the living organism gives us a bigopportunity to synthesize new kinds of ”intelligent” machines.In the neural architecture we propose (Figure1) it is possibleto individuate two neuron layers: a sensory layer and a motorlayer. The sensory layer is composed by 7 neurons connectedwith different sensors: contact sensors, sound sensors, energystations sensors, and an energy level sensor. The motor layeris composed by two neurons whose outputs, opportunelyscaled, control the velocity of the two robot’s wheels. Thesynapses of each neuron can be excitatory or inhibitory, so toregulate the activation level and therefore the neuron output.In the network we can also distinguish four principal parts thatare assigned to four different behaviors: collision avoidance,reaching the sound emitter, reaching the recharge platforms,

energy level monitoring. In the next four paragraphs we willenter in detail in each of these single parts.

Fig. 1. The Neural Controller Architecture

A. Collisions Avoidance

This behavior involves the action of neurons SN1, SN6,MN1, and MN2 (Figure 1 ). In particular SN1 and SN2 haveonly an excitatory input that receives the signal directly fromthe sensors. The output of SN1 excites the motoneuron MN1and inhibits the motoneuron MN2, making the robot to turnleft when the right contact sensor (Contact R) is activated bythe collision with an obstacle. The output of SN6 excites themotoneuron MN1, and permits the robot to turn right whenan object is revealed by the left contact sensor. As in theschema, there is an asymmetry in the cross inhibition; thisis necessary in order to force a left turning when an objectis encountered exactly in front of the robot. Depending onthe synapses value, the robot turn with less or more strengthwhen it encounters the obstacle.

B. Reaching the Sound Emitter

The principal goal of our robot is to reach a sound source,mimicking the behavior of the cricket female in trackingthe male position. This behavior is possible thanks to theneurons SN3, SN4, MN1, MN2. As we see from the schema(Figure1), SN3 realizes an inhibitory synapse with MN1 andan excitatory synapse with MN2, so the robot turns right ifit receives n the right ear (EAR R) a signal stronger thanthe one received by the left ear (EAR L). The other twoconnections (SN4-MN1 and SN4-MN2) of this sub-networkare completely symmetric, and permit the robot to turn leftif the sound signal perceived by the left ear is stronger thanthat of the right ear.In this network the symmetry in the direct inhibitions works

because we want to reach the source, not to avoid it.In reality it is possible to use this kind of architecture todevelop other kinds of behaviors if we use also other kindsof sensors.

C. Recharge Platforms Reaching

The Recharge Platforms Reaching behavior, with the En-ergy Level Monitoring, is critical for the robot ”life”, to guar-antee energy for some activity. The corresponding behaviorin the animal behaviors is searching for food, that the animalcan perceive using olfactory or chemical receptors.The sub-network involved in this task is that one constitutedby neurons: SN2, SN5, MN1, MN2. The architecture issimilar to that one which permits the Sound Emitter Reach-ing behavior, but now only the energy-stations sensors areinvolved.

D. Energy Level Monitoring

This sub-network, located in the bottom part of figure 1,has a key role in the control system. It permits to regulate thepriority of the concurrent behaviors: Sound Emitter Reachingand Recharge Platforms Reaching. They are concurrent be-cause it is not possible to follow two different targets at thesame time .The neural circuit contains two different parts: one constitutedby neurons SN7, MN1 and MN2, and the other by IN1 andIN2. Both these circuits receive as input the signal comingfrom the sensor that measures the available energy. When theenergy level goes below a fixed threshold, a signal reachesboth the excitatory synapse of neuron IN1 and the inhibitorysynapse of the neuron IN2. Because of this, the neuronIN1 increases its membrane activity and IN2 decreases it.Their outputs go directly to influence the synapses values ofneurons SN2, SN3, SN4 and SN5. When IN1 is activated, andtherefore IN2 results deactivated, the Sound Emitter Reachingbehavior is suppressed and the Recharge Platforms Reachingbehavior takes control of the motoneurons. Note that thismechanism doesn’t control the Obstacles Avoiding behavior,because it needs to be active also during the energy stationstracking.When the robot needs energy it is attracted by the energystations, the more the energy level is low the more theRecharge Platforms Reaching behavior takes control of therobot. When the robot reaches a recharge station, the changinglevel of energy is perceived by the neuron SN7 that becomesactive and rises its output. This causes the motoneuronsinhibition and therefore the robot remains motionless untilthe recharge is complete.

III. THE NEURONS MODEL

Each neuron in the neural controller is modelled usingequations 1, where P is the membrane potential and Ythe neuron’s output. The potential changes depend on theexcitatory inputs ��� and on the inhibitory inputs ��� , weightedby ��� and ��� � respectively. The term ��� performs aforgetting mechanism, regulated by the forgetting constant � .

This permits the neuron to avoid the saturation, and thereforeto adapt to different stimulation patterns [11].���������� ��� ������������ �!��� ��� �"���#�$���% �'&)(+* �-, (1)

In this neuron model the activation function is a piecewiselinear function (Equation 2), that bounds the output in therange 0 - 1, and at the same time keeps the system linear.Usually, in many neural networks architectures[12], a nonlinear activation function is introduced to improve the perfor-mance of the network in approximating non linear functions.But here what is important is to avoid the neuron saturationand therefore the network instability.

&)(+* �-,.�0/12134 �65 4� 487 �65�99:�6;<9 (2)

In figure 2 we can see the potential and the output ofthe neuron when stimulated with one excitatory and twoinhibitory signals.

Excitatory Input

Inhibitory Input1

Inhibitory Input2

Membrane Potential

Neuron Output

Time (s)

Fig. 2. The neuron signals.

A. Variable Synapse

Describing the network architecture, in paragraph II-D, weindicated the capability of IN1 and IN2 to change the inputsynapse value of the neurons SN2, SN3, SN4 and SN5. Thisis possible modelling the synapse with a first order differentialequation (Eq.3 ). �=��?>@�<�?AB�$�DC�E�'&)(+*F�?>G, (3)

where �?> is the synapse internal state, �HA is the tuningsignal coming from the neuron IDJK� , LMC a term that allows thedepolarizing mechanism, necessary to decrease the synapsevalue when the tuning signal is low, and finally &)( is thefunction described in equation 2. In figure 3 we see that, whenthe signal �?A decreases to zero also the weight decreases,and therefore the excitatory input of the neuron doesn’t haveinfluence on its potential.

This inhibition mechanism is very important to regulateand coordinate the robot behaviors. What is interesting here,

Excitatory Input

Weight Control (Wc)

Weight (W)

Membrane Potential

Neuron Output

Time (s)

Fig. 3. The weight tuning mechanism.

is the possibility to modulate the behaviors in a continuousway, this means that it is possible to switch from a behaviorto another with a smooth trend.

IV. THE ROBOT MODEL

In order to test our controller we developed a virtual worldwhere the robot can move and interact with objects.The arena (10x10 meters, see figure 4) contains obstaclesrepresented by circles of different diameters, a sound source(the target position for the robot) and two recharge platforms.The mobile robot (0.6x0.4 meters) has two wheels in adifferential drive configuration; controlling independently thevelocity of the left and right wheels the robot can moveforward, backward, turn left or turn right. The robot directkinematic can be solved using the system of equations 4 :

/1213��*�NO,P� �QSR *�TVUO*�NO,+W$TYXZ*�NO,O,�[]\D^_*�`a*�NO,O,cbZNd *�NO,B� �Q R *�TVUO*�NO,+W$TYXZ*�NO,O,�^Oe f�*�`a*�NO,O,cbZN`a*�NO,P� �QSR *�TVUO*�NO,g�?TYXZ*�NO,O,cbZN (4)

where *���*�NO,Gh d *�NO,O, is the robot position, `a*�NO, its orientationand TVU , TYX the linear velocities of the left and right wheelrespectively, obtained directly from the wheel angular ve-locities. All of these quantities are respective of an inertialreference system. In this model we neglected the dynamicsof the robot, therefore we do not considered mass and inertia.This simplification is plausible, especially if it is possibleassume that the robot is very light, nevertheless future modelsmay also include this aspect.The robot is equipped with two sound sensors located at theright and left side in front of the robot, two energy stationsensors located in the same positions, and two circular contactsensors (see figure 4).

The intensity of the sound signals received by the soundsensors is modelled by equation 5:

I]i�jcAkjk� l]jcCm��I"npoOqXrAkj 9Ls�.WtL Q buW�Lwv"b Q (5)

The intensity of the sound received ( I"i�jcAkjk� l]jcC ) by thesensors is directly proportional to the intensity of the sound

Contact Sensor

Obstacle

Recharge Stations Sensor

Sound Sensor

Fig. 4. The robot equipped with sensors inside the Arena.

source ( I"npoOqXrAkj ) and inversely proportional to a quadraticpolynomial of the source distance b . A similar equation canbe used also to represent the signal level received by therecharge-station sensors.The level of the signal generated by the contact sensor canbe model by equations 6.

� I]i�jcAkjk� l]jcC-��Ls�Gxuyz=W�L Q xuyz Qxuyz{��|m}]~"Nc�p�]� ����c~"Nc�D���]�@�$�.�_�+~_y�V�u�pbZ����~ (6)

Here xuyz is the compression of the circular sensor whenit encounters the obstacle.

V. RESULTS IN SIMULATION

All the simulations were done using Matlab; for theintegration method of the differential equations we used theRunge-Kutta algorithm with an integration step of 0.001s.The first simulation we performed, was done to test the SoundEmitter Reaching and the Obstacles Avoiding behaviors. Asmentioned before this two behaviors work together to governthe robot movements. The robot moves from a Start(in thefigure is the star symbol) position to the position of thesound source.

Fig. 5. Simulations of the Sound Emitter Reaching and the ObstaclesAvoiding behaviors with different values for the inhibitory synapses.

In figure 5 we see the paths followed by the robot withthree different values for the cross inhibition synapses (a 0.1,b 0.5, c 0.6), located in the sub-network that performs the

Sound Emitter Reaching behavior. Increasing the values forthese two synapses makes the robot to narrow the curves. Thisis useful to more precisely reach the target, however near thesound source a strong inhibition (quite similar in both themotoneurons) slows down the robot velocity.

Another experiment was for testing all the behaviors. Nowthe robot has a limited amount of energy that doesn’t permit itto directly reach the target (sound source). In this experimentwe located two recharge platforms at the two side of theupper part of the arena. As we see in figure 6 the robot,at the beginning, performs a trajectory quite similar to thatone obtained without considering the Recharge PlatformsReaching behavior; however, because now the robot has afinite energy storage, it needs to refill.

Fig. 6. Simulation with all the behavior active (prospect view).

When the energy level is under a certain value the RechargePlatforms Reaching behavior takes the control of the robot.Now the robot is more attracted by the energy stations thanby the sound source. In the graphs of figure 7 we can see theprogress of the energy level and the distance travelled by therobot.

After 137 seconds the energy reaches the bottom thresholdand the robot changes the direction of movement. At the 158thsecond the recharge platform is attained and the robot staysfor 8 seconds in recharging; after it moves around the platformfor 7 seconds. This action is quite strange, it seems that thecontroller enters in a condition of instability. The phenomenonwas interpreted considering that the station can supply a finitelevel of energy. When the energy is terminated, the robot isnot anymore attracted by it and can go to the final target.

VI. HARDWARE IMPLEMENTATION OF THE NEURALCONTROLLER

In this section we illustrate a specific hardware implemen-tation of the robot model and its benefits with respect to

Time (s)

S (m)

ChargeLevel (%)

Fig. 7. The distance covered by the Robot and its energy level.

other possible implementations. There are different method-ologies and strategies to develop the architecture presentedin the previous paragraphs, for instance by using FPGA(Field Programmable Gate Array) based boards, reconfig-urable devices, etc., but all these solutions have been shownin the robotic context to have disadvantages that make thehardware implementations in some cases to be worse thanthe software simulation. If we consider the FPGA-basedboards, for instance, we often have to face with severearea and speed constraints, and the expressivity of hardwaredescription languages like VHDL is, in some cases, too muchlimited for custom robotic applications, while we need moreflexibility and better performances. Furthermore, becausewe are considering a mobile robot, we have to deal withenergetic issues, that should be taken into account in order tominimize the number of recharges needed by the robot andmaximize the space covered; that is, in most cases, a trade-offbetween board performances and board power consumption.By observing the architecture of the neural controller and thekind of computations involved (floating point multiplications,mantissa shifting, sums and threshold comparisons due to thelogical activation functions) we focused on the possibility todevelop a very fast hardware implementation of the system byexploiting a novel solution. We chosen a dedicated dual pro-cessor board equipped with an ARM7TDMI micro-controllerfor general purpose computation, and a floating-point VLIWdigital signal processor core for hard computations like FFT(Fast Fourier Transform) and frequency domain phase-shiftalgorithms. The Diopsis D740 board by Atmel satisfies ourneeds, delivering 1 billion floating-point operations per sec-ond (1 GFLOPS). The board is equipped with two serial ports,two USARTS, an USB connection, a timer counter, watchdog,parallel I/O port (PIO), peripheral data controller, 8 ADCand 8 DAC (high quality, 24 bit precision) interfaces, clock

generator and interrupt controller. Only some features of thisboard have been used to build the neural circuit (in a mobilesetting, we would have the lightest robot). After the controlarchitecture is encoded into a program, the system is able tooperate in a completely standalone mode, this in order to givethe robot a full autonomy.

Fig. 8. a) Some of the signals exploited in the first hardware implementationb) Wave Forms of the signals.

We partitioned the tasks on the two board’s processors. Thejob performed by the ARM processor, that constitutes thevegetative system of the cricket, is very simple: it definesthe shared (between the two processors through a commoninterchange bus) memory space, parameters and constants andconfigures interrupts, timers and everything’s needed to takeadvantage of the ADDA’s (ADCs+DACs) interfaces and of the7-segment display. It is intended to pass the stimuli receiptfrom the environment (for instance by the sensors located inthe moustaches and in the ears) and then acquire the stimulielaborated by the neural network in order to actuate themovement for autonomous navigation. The neural controllerhas been implemented through the DSP Processor: this meansthat all the heavy floating point computations required by theneural network and needed to obtain the new actuation signalsare performed by this parallel-dedicated processor. Morespecifically, the DSP is really engaged managing all the audiosamples and converting them in a numerical form, the sampletime it operates is NS��9V��Z�a9 4Z4 ~ . In addition to the networkcomputations (computational time is in average ��� �Vz�~ ), thewhole audio elaboration (that represent the sensorial stimulifrom the surrounding environment) takes place inside thisprocessor.

The DSP effectively builds the neural network and itsrelative interconnections, this is replicating the effective jobof each basic block with smart loops. The software runningon the DSP when invoked by the ARM processor is muchmore complicated than ARM’s: it represents the real kernelof the application. The exploitation of this kind of processorpermits us to gain true real time rendering. As we can seein figure 8, thanks to a Win32 tool it is possible to supervisethe signals of the two processors, this is critical especiallyfor synchronization procedures between different tasks. We

can observe here an interesting similarity with the biologicalactivation and spikes signals in the biological neurons. This ishowever not easy to see at first sight, also because most of thesignals are opportunely encoded to permit to the processors toexchange all the data with a single transfer per time, reducingthe time required for data exchange. To implement the audiomanagement part of the bio-cricket, we used the analogicalto digital and viceversa interfaces (ADDA). It’s possible touse only a single-input and single-output (exploiting a stereo-channel solution) or alternatively a more complex (but closerto the biologic configuration of a real cricket) solution usingseparated channels for the two ears (using two mono-auralmicrophones instead of one stereo), two outputs as actuationchannels for the engines (tension control) and other two ad-ditional inputs as proximity sensors (the moustaches). Aboutthe performances, there are techniques that permit to evaluateat a glance where the application spends the major numberof cycles (e.g. profiling information), and so it is possible todecide which routines must be optimized in order to speedupthe overall system. Furthermore, it’s possible to change theassembler-linker code produced by the DSP code compilerand implement optimizations by attempting to parallelize asmuch of the overall architecture as possible. The great partof optimizations can be done exploiting the 2-way parallelarchitecture of DSP processor, because in our application alloperations are 2- way parallel due to the intrinsic charac-teristic of the bio-cricket brain, sensor system and actuationsystem (left and right for each sensor, neuron, actuation).Making a comparison with the software implementation, weobtained a speed-up of 100x with respect to the Matlabsoftware. By inserting the assembler-like optimizations andsimulating the same number of neural cycles (1000), theresults have shown a second speed-up of 2.5x. This factshows once again how dedicated hardware and optimizedsoftware can perform better than traditional software-onlyarchitectures. During this development of neural networks-based applications, the DSP dedicated boards performed verywell, also for robotic applications. The application takesadvantages from the features that give the Atmel board ahigher place than other traditional architectures. Its single-chip high-performance ARM RISC and dedicated S.O.C. 1Gflops DSP VLIW processor with dual ported shared memoryarchitecture significantly decrease the computation time. The24 bit ADDA interface brings highest precision in audioelaboration and with 8 total in/out connectors offers manypossible sensorial choices for robotic applications.

VII. CONCLUSION AND FUTURE WORK

In this paper we presented a bio-inspired neural controllerfor a mobile robot. The network architecture is organizedin two neurons layers: the sensory layer receives the outputsignals coming from the robot sensors and feeds with excita-tory and/or inhibitory connections the motoneurons, the motorlayer combine its input signals to govern the robot wheels.Inside the architecture it is present also a sub-network, thatusing information about the energy level, regulates the robot

behaviors. The regulation is based on an inhibition mechanismthat acts directly on the synapses of the sensory-motor layer.

From the first results obtained in a simulated environmentwe have shown that the controller is able to govern the robotin its primary task, that is following a sound source. Wechanged the values for the inhibitory synapses that connectthe Ear sensory neuron to the motoneuron and evaluated therobot performance.

Compared with other neural controllers [8] [10], we intro-duced a more complex architecture able to perform differentkind of behaviors concurrently. This is possible thanks to aninhibition mechanism that modulates the synaptic strengthof different sensory-neurons. Related to the subsumptionarchitecture [13], we developed a control system that ismore biomimetic, in the sense that the control layers hereare represented by different dynamical neural networks thatresemble parts of the neural circuits of the insects.

Experiments suggest us to consider and develop a mech-anism to adjust the synapses in order to improve the robotperformances. The synapse optimization may be done forexample on the time needed by the robot to reach the targetand on the level of energy consumed to perform this task. Orit is possible to think to use a learning paradigm [14], [15].

After the hardware implementation of the controller usinga DSP processor, we can conclude that the time needed toactuate the robot using the neural architecture is absolutelyacceptable.

A more realistic scenario to test the robot may be developedto contribute to the wide area of service robots.

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