Löffler A, Klahold J, Rückert U. The Dynamical Nightwatch's Problem Solved by the Autonomous Micro-Robot Khepera. In: Hao J-K, Lutton E, Ronald E, Schoenauer M, Snyers D, eds. Selected Papers of the 3rd European Conference on Artificial Evolution (AE97). Vol 1363. Nimes, France: Springer-Verlag;  1998: 303-313.In this paper, we present the implementation, both in a simulator
and in a real-robot version, of an efficient solution to the so-called
dynamical nightwatch’s problem on the micro-robot Khepera. The problem
consists mainly in exploring a previously unknown environment while
detecting, registering and recognizing light sources which may dynamically
be turned on and off. At the end of each round a report is requested
from the robot. Therein we made use of an agent-based approach and
applied a self-organizing feature map in order to refine some of the behaviour
generating control-modules

Hao, Jin-Kao

Klahold, Jürgen

Lutton, Evelyne

Löffler, Axel

Ronald, Edmund

Rückert, Ulrich

Schoenauer, Marc

Snyers, Dominique

English

Publications at Bielefeld University

The Dynamical Nightwatch’s Problem Solved bythe Autonomous Micro-Robot KheperaA. Lo¨ffler?, J. Klahold, and U. Ru¨ckertPaderborn University, Heinz Nixdorf Institute, System and Circuit Technology,Fu¨rstenallee 11, D-33102 Paderborn, GermanyE-mail: loeffler,klahold,rueckert@hni.uni-paderborn.dehttp://hni.uni-paderborn.de/fachgruppen/rueckertIn Jin-Kao Hao, Evelyne Lutton, Edmund Ronald, Marc Schoenauer, andDominique Snyers, editors, selected papers of the 3rd European Conference onArtificial Evolution (AE97), volume 1363 of Lecture Notes in Computer Science,pages 303-313, Nıˆmes, France, 22. - 24. October 1997. Springer-Verlag 1998.This work was awarded with the first prize at the international Khepera contest.Abstract. In this paper, we present the implementation, both in a sim-ulator and in a real-robot version, of an efficient solution to the so-calleddynamical nightwatch’s problem on the micro-robot Khepera. The prob-lem consists mainly in exploring a previously unknown environment whiledetecting, registering and recognizing light sources which may dynami-cally be turned on and off. At the end of each round a report is requestedfrom the robot. Therein we made use of an agent-based approach andapplied a self-organizing feature map in order to refine some of the be-haviour generating control-modules.1 Introduction1.1 MotivationOur basic motivation to consider the implementation of behaviour generatingcontrol structures in mobile autonomous systems is represented by the followingquestion: “What is the most complex task a robot is able to carry out, therebycoping with its limited resources, e.g. the available sensor sources, finite energysupply and limited processor power? The encountered consequences may be arestricted perception of its environment, runtime constraints and the problem ofsevere real-time demands.” To be able to successfully envisage this kind of prob-lem, we strongly believe in adopting the following two-step program: firstly, toimplement software solutions making recourse to concepts of neural informationprocessing; secondly, to replace single software modules by resource-efficient mi-croelectronic components. In this framework, the contents of the present papercorresponds to the first point, i.e. the implementation of a software solution toa hard robotics’ problem. Hereby, the micro-robot Khepera (see Fig.1) serves asan exemplary model for a mobile autonomous system.Processing the sensor data of a mobile robot appropriately seems still tobe a difficult and largely unsolved problem [1]. Hence, it remains an in generalchallenging task to consistently implement a set of behaviour generating control? Supported by DFG-Graduiertenkolleg “Parallele Rechnernetzwerke in der Produk-tionstechnik”, GRK 124/2-96.modules based on the different sensor sources of the system in question. Consid-ering the micro-robot Khepera in its basic configuration, there are three mainsources of sensor data which may be used to implement such modules:source of sensor data task of the respective control-module(1) the infra-red proximity sensors −→ exploration of the environment(2) the ambient light sensors −→ detection and registration of lightsources(3) the wheel-based step counters −→ basic positioning system.Fig. 1. Khepera is a micro-robot which has a diameter of 55mm, a height of 30mmand weighs 70g. The processor system contains a Motorola 68331 with 256Kbyte RAMand 256Kbyte ROM. Khepera perceives its environment using 8 infrared distance- andlight-sensors; moreover incremental encoders placed on each motor axis are availablefor step counting.In the next paragraph, we define an appropriate problem, i.e. one whichincludes all three of the above mentioned tasks.1.2 The Dynamical Nightwatch’s ProblemImagine the town’s nightwatch strolling over central square at late evening, look-ing for light sources, e.g. street lamps, lighted windows, etc., thereby avoidingobstacles, following walls (because he is a bit short-sighted) and turning roundat dead ends. Having found the first light source, he fixes his report-board atthe wall next to it to write down everything he will have encountered when hewill arrive again at this location. In fact, he notes whether he has found a newlantern or recognized an old one and its current state (on/off) since the laststop at the board. Probably he would try to guess his position on the square bycounting his steps and using already registered light sources as local landmarksto verify his counting.The micro-robot Khepera should be able to perform an analogous task whentransferred to a kind of suitable toy-world.1.3 Design PrinciplesAnimals may be considered as biological analogies to mobile autonomous robots.Other than the latter, they constantly had (and still have) to prove their fitnessby surviving a natural selection process. Thus, it seems profitable to exploittheir evolutionary optimized strategies for robot design, in this context especiallythose which deal with information processing. In order to do this, we adopted thefollowing design principles inspired by concepts of neural information processingthereby making recourse to the so called agent paradigm [2–4] and some earlierworks in cybernetics [5] :– simple modules cope with simple tasks (e.g. obstacle avoidance, edge follow-ing, etc.)– the control-modules are organized in parallel, i.e. no explicit hierarchicalstructure is implemented, and their results are simply superpositioned. Incase of this not being possible, a data/event-driven decision is made (e.g. indead ends, the turning-round-module takes control).– no global goals are explicitly given to the robot– arising complex behaviour is thus a result of the interaction of the basicmodules– adaptive algorithms, e.g. neural networks, are used to incorporate acquiredinformations about the environment.2 ExplorationEnabling Khepera to explore its environment, we had to ensure free motion,i.e. avoiding obstacles, leaving substructures of the environment and not gettingstuck in dead ends or similar structures. This was attained by implementing thefollowing three basic control-modules:2.1 Obstacle AvoidanceThe obstacle avoidance was implemented as in Braitenberg’s vehicle IIIb [5]using the very simple neural concept of cross-inhibition. This means that if oneof Khepera’s infra-red sensors detects an obstacle, the opposite wheel will beslowed down proportionally to the sensor input. By this way the obstacle isavoided (see Fig.2).2.2 Edge FollowingKhepera tries to keep the distance to the wall, i.e. the value of the outmost sen-sor, constant (see Fig.3). Note that no explicit distance to the encountered wallSensorDistanceFig. 2. A sketch of the robot avoiding a cylindrical obstacle (left) and an infra-redsensor vs. distance-to-light source characteristic are shown (right).is be kept which allows a more flexible behaviour of the robot than otherwise bepossible, especially in narrow corridors and similar structures. When a wall isdetected for the first time (and avoided by means of the obstacle avoidance mod-ule), the outmost sensorvalue is registered. Afterwards, the robot will turn awayfrom the wall, if this sensorvalue increases, and respectively will turn towardsit, if it decreases. This algorithm is particularly simple, easy to implement androbust. Edge following allows to leave substructures of the environment quickerthan it would be possible otherwise.Fig. 3. Visualisation of the simple edge-following algorithm where three different sit-uations might occur: a wall is detected (register sensorvalue memory), turn awayfrom wall (if sensorvalue > sensorvalue memory), turn towards wall (if sensorvalue< sensorvalue memory).2.3 TurningIf a situation occurs in which Khepera is not able to leave a small area (symbol-ized by the circle) after some tries, it simply turns round to where it came from(see Fig.4). This module ensures that the robot does not get stuck in dead endlike structures.Fig. 4. The turning procedure.Obstacle avoidanceEdge followingTurningchange ofpositionFig. 5. The interplay of the three control modules ensuring constant exploration.The interplay of the three modules is shown in Fig.5. A module, recognis-ing the change of the position of Khepera, decides whether obstacle avoidanceand edge following or the turning procedure get active. This means that if therobot is not able to change its position sufficiently during some 50 steps, it willturn around thereby suppressing the two other exploration modules; otherwisea superposition of the normal obstacle avoidance/edge following algorithms isused.3 PositioningAn at least locally reliable positioning system had to be necessarily at basis ofthe light-source mapping process. For this purpose, we used the step countingfunctionality of Khepera. The position vector of Khepera consists of an x-value,y-value and the direction-indicating angel α. The new position (x, y, α) is cal-culated from the old one (x0, y0, α0) using the values nr and nl given by theincremental encoders placed on each motor axis. The table below shows how thenew position is calculated:Table 1. Calculation of the change of positionnl nr|nl| = |nr||nl| < |nr||nl| > |nr|nrnrS nr - nlnr + nl-2 * nl- ( nr + nl )nlnr - nlnr - nl2 * nrSCSCTTTBCnr - nlnr + nl-2 * nl- ( nr + nl )2 * nrTTTCCnrnrSnlnr - nlnr - nlSCSCCurve:Strait: Turn:α α0n2 r⋅--------- l∆⋅+= x x0 r αcos α0cos–( )⋅+=y y0 r αsin α0sin–( )⋅+=α α0n2 r⋅--------- l∆⋅+=x x0 n l∆⋅ αcos⋅+=y y0 n l∆ αsin⋅ ⋅+=nl nrnl nrnl nr4 Sensor CalibrationAdapting an one-dimensional self-organizing feature map [7, 8] by training witha real robot data set, we managed to calibrate the ambient light-sensors (seeFig.6), i.e. to transform the coarse grained information of Khepera’s angle-to-light-source into a fine grained one; this also helped to overcome sensor-responsevariations due to fabrication tolerances. Calibration of the ambient light sensorsmeans to classify the sensor vectors by an one-dimensional Kohonen featuremap. Each neuron is active for a certain class of sensor vectors giving a directinformation about Khepera’s angle-to-light source.The charts (Fig.7) show the sensor data, respectively used to learn and totest the neural network. Before usage, the data were standardized by a scalingprocess, which increased the accuracy considerably.12 3 4 56ϕ1 2 3 4 5 6Fig. 6. The picture on the left shows that every neuron belongs to a specified angel.Each neuron is trained to react on a special characteristic of sensor values (e.g. theneuron represented by the filled circle reacts on the sensor vector given in the bar charton the right).0100200300400500-100 -50 0 50 100SensorinputAnglesen0sen1sen2sen3sen4sen50100200300400500-100 -50 0 50 100SensorinputAnglesen0sen1sen2sen3sen4sen50100200300400500-100 -50 0 50 100SensorinputAnglesen0sen1sen2sen3sen4sen50100200300400500-100 -50 0 50 100SensorinputAnglesen0sen1sen2sen3sen4sen5Fig. 7. The sensor data sets used to train and test the neural net; upper left: learningdata, upper right: test data, lower left: scaled learning data, lower right: scaled testdata.The network consists of 60 neurons. It was trained in 20 cycles with anincremental change of the neighbourhood-width from 10 to 1 and a learning ratefrom 0.6 to 0.1. The result was an average error of 1.52 and a maximum one of6.66 degrees at the recall process. The test at double distance caused an averageerror of 6.52 and a maximum one of 21.89 degrees. The results are graphicallydepicted in Fig.8.-100-80-60-40-20020406080100-100 -80 -60 -40 -20 0 20 40 60 80 100Angel from activ NeuronAngle to lightsourceTestedLearnedOptimalFig. 8. The result of the adaptation process is shown in this chart. The dotted lineserves as a referential, the bold dots correspond to the data being learned and the straitline belongs to data registered at the double distance with respect to the learning set.5 Mapping ProcedureThe procedure of the position-determination of detected light-sources was im-plemented as follows (see Fig.9). If the robot detects a light-source (1), Kheperaapproaches it (2) until one sensor value exceeds a certain threshold (optimalrange for the neural network is reached) (3). Then Khepera registers its positionvector (x1, y1, α1), turns by 90 degrees and moves in a strait line (4) to a secondturning point (5). Afterwards, the robot reapproaches the light-source (6) untilthe optimal range is reached again (7). With the new position vector (x2, y2, α2)and the old one, the location of the target can be calculated.In this section, the conditions for changing the state of a light source areexplained (see Fig.10). If Khepera penetrates the inner circle (in the Manhattandistance) of a previously discovered light source without detecting it, the state ofthe light source will be changed from on to off. If a “new” light source is registeredwithin the outer circle of a previously discovered one, they are recognized asbeing identical. Eventually, the internal position of the robot is readjusted by acomparison of the previously and the currently registered location of the lightsource.1234567(x2,y2)(x1,y1)α2α1xyFig. 9. A schematic view of the successive steps of the mapping procedure (right) anda sketch of the triangulation process using the registered data (left), hence determiningthe detected light source’s location.Fig. 10. Proportional view of Khepera and the two circles (in the Manhattan distance)relevant for changing the state of a previously discovered light source.6 ReportWe developed a simple code to enable the robot to report its findings during thelast round using the two LEDs of Khepera.The signals have the following meaning (legend: x on, h off):x x New light source foundx h Previously discovered light source recognizedh x Previously discovered light source turned offx x Previously discovered light source turned on again7 ResultsThis paragraph is dedicated to the obtained results for both the simulator solu-tion to the dynamical nightwatch’s problem and the real-robot implementation(see Fig.11, Fig.12) using the obtained paths of the robot in the respective en-vironments as an evaluation benchmark. We state a very satisfactory behaviourof the simulator solution whereas the real-robot version unveils to be feasibleonly locally. Note that the compiled C-code of the whole program has 87Kbyte,hence we are using only about one third of the RAM capacities of Khepera’sprocessor.Fig. 11. In the visualisation window of the Khepera simulator [6], the robot’s currentangle-to-light-source is depicted.020040060080010000 200 400 600 800 1000YXPath of Khepera Robot’path.dat’’light.dat’-400-300-200-10001002003004000 200 400 600 800YXPath of Khepera Robot’path.dat’’light.dat’’testpath.dat’Fig. 12. The left gnuplot shows the path of the simulated Khepera. Note that the smalllocation errors are solely caused by the remaining inaccuracies of the neural network.The right gnuplot shows the path of a real Khepera wherein the bold line representsthe path with and the dotted line one without readjustment of the position using thealready discovered light source as a landmark.8 Conclusion and Future WorkInspired by concepts of neural information processing and the agent paradigm,we implemented a solution to the dynamical nightwatch’s problem, both on thesimulator and on the real Khepera. In particular, we used an one-dimensionalself-organizing feature map for sensor calibration attaining maximum accuracyin average. The simulator solution proofed to be globally reliable, whereas thereal-robot implementation had some short-comings due to step-counting errorsinducing inaccuracies into the position determination procedure. This was partlycured by taking into account the already registered light sources as local land-marks in order to readjust the current position. The results showed to be sat-isfactory locally, i.e. when either the robot’s environment was restricted to acertain area or the runtime was limited.Nevertheless, some further research on the topic of navigation is to be done.We hope to enhance the robot’s performance considerably by incorporating as-sociative memory and neural classifiers into the control structure. Moreover, itis also envisaged to replace some of the software modules by resource-efficientmicroelectronic devices as described for example in [9, 10].References1. S. Geva, J. Sitte and H. Sira-Ramirez, When are tasks “difficult” for learning con-trollers?, 1994 World Congress on Computational Intelligence WCCI, July 1994,Orlando, Florida, USA. Proceedings of the IEEE International Conference on Neu-ral Networks, Volume IV, pp.2419–24232. P. Maes, Modeling Adaptive Autonomous Agents, Artificial Life Journal, editedby C. Langton, Vol 1, No. 1 & 2, MIT-Press, 19943. R. A. Brooks, Intelligence without Representation, Artificial Intelligence, 47, pp.139-159,19874. R. A. Brooks, Intelligence without Reason, Computers and Thought lecture, Pro-ceedings of IJCAI 91, Sydney, Australia, 19915. V. Braitenberg, Vehicles: Experiments in Synthetic Psychology, MIT Press/Brad-ford Books, 19846. O. Michel, Khepera Simulator, version 2.0, 1996,http://diwww.epfl.ch/lami/team/michel/khep-sim/7. K. Malmstrom, L. Munday, J. Sitte, A Simple Robust Robotic Vision Systemusing Kohonen Feature Mapping, Proceedings of the 2nd IEEE Australia and NewZealand Conference on Intelligent Information Systems, pp. 135-139, 19948. T. Kohonen, Self-Organization and Associative Memory, Springer-Verlag, 2nd edi-ton, 19889. A. Heittmann, J. Malin, C. Pintaske, U. Ru¨ckert, Digital VLSI Implementationof a Neural Associative Memory, 6th International Conference on Microelectronicsfor Neural Networks, Evolutionary & Fuzzy Systems, 24.-26. September, Dresden,Germany, 199710. S. Ru¨ping, M. Porrmann, U. Ru¨ckert, SOM Hardware-Accelerator, WSOM’97:Workshop on Self-Organizing Maps, June 4-6, pp. 136-141, Espoo, Finland, 1997

The Dynamical Nightwatch's Problem Solved by the Autonomous Micro-Robot Khepera

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