A neurocontroller is described which generates the basic locomotion and controls the sensor-driven behavior of a four-legged and a six-legged walking machine. The controller utilizes discrete-time neurodynamics, and is of modular structure. One module is for processing sensor signals, one is a neural oscillator network serving as a central pattern generator, and the third one is a so-called velocity regulating network. These modules are small and their structures and their functionalities are analyzable. In combination, they enable the machines to autonomously explore an unknown environment, to avoid obstacles, and to escape from corners or deadlock situations. The neurocontroller was developed and tested first using a physical simulation environment, and then it was successfully transferred to the physical walking machines. Locomotion is based on a gait where the diagonal legs are paired and move together, e.g. trot gait for the four-legged walking machine and tripod gait for the six-legged walking machine. The controller developed is universal in the sense that it can easily be adapted to different types of even-legged walking machines without changing the internal structure and its parameters.
Abushama, F.T. (1964). On the behaviour and sensory physiology of the scorpion Leirus quinquestriatus. Animal Behaviour, 12: 140-153.
2.
Albiez, J.C., Luksch, T., Berns, K., and Dillmann, R. (2003). Reactive reflex based control for a four-legged walking machine. Robotics and Autonomous Systems, 443: 181-189.
3.
Anderson, T.L. and Donath, M. (1988). A computational structure for enforcing reactive behavior in a mobile robot. Proceedings of the 1988 SPIE Conference on Mobile Robots, Cambridge, MA, Vol. 1007, pp.198-211.
4.
Arkin, R.C. (1998). Behavior-based Robotics, MIT Press, Cambridge, MA.
5.
Ayers, J. (2002). A conservative biomimetic control architecture for autonomous underwater robots. In Neurotechnology for Biomimetic Robots (eds J. Ayers, J. Davis and A. Rudolph), pp.241-259, MIT Press, Cambridge, MA.
6.
Ayers, J., Witting, J., McGruer, N., Olcott, C., and Massa, D. (2000). Lobster robots. Proceedings of the International Symposium on Aqua Biomechanisms, (eds T. Wu and N. Kato), Tokai University.
7.
Beer, R.D. (1990). Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology, Academic Press, San Diego, CA.
8.
Beer, R.D., Quinn, R.D., Chiel, H.J., and Ritzmann, R.E. (1997). Biologically inspired approaches to robotics: what can we learn from insects?Communications of the ACM, 403: 30-38.
9.
Beer, R.D., Ritzmann, R.E., and McKenna, T. (eds) (1993). Biological Neural Networks in Invertebrate Neuroethology and Robotics, Academic Press, Boston, MA.
10.
Berns, K., Cordes, S., and Ilg, W. (1994). Adaptive, neural control architecture for the walking machine LAURON. Proceedings of the 1994 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, pp.1172-1177.
11.
Berns, K., Ilg, W., Eckert, M., and Dillmann, R. (1998). Mechanical construction and computer architecture of the four-legged walking machine BISAM. Proceedings of the First International Symposium, CLAWAR' 98, VRMech 98, Brussels, Belgium, pp.167-172.
12.
Billard, A. and Ijspeert, A.J. (2000). Biologically inspired neural controllers for motor control in a quadruped robot. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks - IJCNN2000, Vol. 6, pp.637-641.
13.
Breithaupt, R., Dahnke, J., Zahedi, K., Hertzberg, J., and Pasemann, F. (2002). Robo-Salamander: an approach for the benefit of both robotics and biology. Proceedings of the 5th International Conference on Climbing and Walking Robots, CLAWAR' 02 (ed. P Bedaud), Professional Engineering Publishing, pp.55-62.
14.
Brooks, R.A. (1989). A robot that walks: emergent behaviors from a carefully evolved network. Neural Computation, 12: 253-262.
15.
Brooks, R.A. and Stein, L.A. (1994). Building brains for bodies. Autonomous Robots, 11: 7-25.
16.
Clark, J.E., Cham, J.G., Bailey, S.A., Froehlich, E.M., Nahata, P.K., Full, R.J., and Cutkosky, M.R. (2001). Biomimetic design and fabrication of a hexapedal running robot. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Vol.4, pp.3643-3649.
17.
Consi, T.R., Grasso, F., Mountain, D., and Atema, J. (1995). Explorations of turbulent odor plumes with an autonomous underwater robot. Biological Bulletin, 189: 231-232.
18.
Cruse, H. (2002). The functional sense of central oscillations in walking. Biological Cybernatics, 864: 271-280.
19.
Cruse, H., Bläsing, B., Dean, J., Dürr, V., Kindermann, T., Schmitz, J., and Schumm, M. (2004). WalkNet-a decentralized architecture for the control of walking behaviour based on insect studies. In Walking: Biological and Technological Aspects, (eds F. Pfeiffer and T. Zielinska), pp. 81-118, CISM Courses and Lectures No. 467. Interational Centre for Mechanical Sciences, Springer Verlag, Wien, New York.
20.
Dürr, V., Schmitz, J., and Cruse, H. (2004). Behaviour-based modelling of hexapod locomotion: Linking biology and technical application. Arthropod Structure and Development, 333: 237-250.
21.
Endo, G., Nakanishi, J., Morimoto, J., and Cheng, G. (2005). Experimental studies of a neural oscillator for biped locomotion with QRIO. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Barcelona, Spain, April, pp.596-602.
22.
Fend, M., Yokoi, H., and Pfeifer, R. (2003). Optimal morphology of a biologically-inspired whisker array on an obstacle-avoiding robot. Proceedings of the 7th European Conference on Artificial Life (ECAL), Dortmund, September, pp.771-780.
23.
Filliat, D., Kodjabachian, J., and Meyer, J.A. (1999). Incremental evolution of neural controllers for navigation in a 6-legged robot. Proceedings of the Fourth Interational Symposium on Artificial Life and Robotics (eds Sugisaka and Tanaka), Oita University Press, pp.745-750.
24.
Fischer, J. (2004). A Modulatory Learning Rule for Neural Learning and Metalearning in Real World Robots with Many Degrees of Freedom. PhD thesis, University of Muenster, Shaker Verlag.
25.
Fischer, J., Pasemann, F., and Manoonpong, P. (2004). Neurocontrollers for walking machines - an evolutionary approach to robust behavior. Proceedings of the 7th International Conference on Climbing and Walking Robots, CLAWAR'04(eds M. Armada and P. Gonzalez de Santos), pp.97-102.
26.
Floreano, D. and Mondada, F. (1994). Active perception, navigation, homing, and grasping: an autonomous perspective. From Perception to Action Conference, PERAC'1994 (eds P.H. Gaussier and J.-D. Nicoud), IEEE Computer Society Press, Los Alamnitos, CA.
27.
Frik, M., Guddat, M., Karatas, M., and Losch, D.C. (1999). A novel approach to autonomous control of walking machines. Proceedings of the 2nd International Conference on Climbing and Walking Robots, CLAWAR'99, Professional Engineering Publishing, Bury St. Edmunds, pp.333-342.
28.
Gaβmann, B., Scholl, K.-U., and Bems, K. (2001). Locomotion of LAURON III in rough terrain. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Vol. 2, pp.959-964.
29.
Geng, T., Porr, B. and Wörgötter, F. (2006). A reflexive neural network for dynamic biped walking control. Neural Computation, 185: 1156-1196.
30.
Goerke, N., Müller, J., and Brüggemann, B. (2005). SAM: a sensory-actuatory map for mobile robots. Proceedings of the Third International Symposium on Adaptive Motion in Animals and Machines, AMAM 2005, ISLE Verlag, Ilmenau.
31.
Hafner, V.V. (2004). Agent-environment interaction in visual homing. Embodied Artificial Intelligence: International Seminar, LNCS, Vol. 3139, Springer Verlag, pp.180-185.
32.
Hagras, H., Callaghan, V., and Colley, M.J. (2000). Online learning of fuzzy behaviour co-ordination for autonomous agents using genetic algorithms and real-time interaction with the environment. Proceedings of The 9th IEEE International Conference on Fuzzy Systems, Vol. 2, pp.853-858.
33.
Hilljegerdes, J., Spenneberg, D., and Kirchner, F. (2005). The Construction of the Four Legged Prototype Robot ARAMIES. Proceedings of the 8th International Conference on Climbing and Walking Robots, CLAWAR'05, London, pp.335-342.
34.
Hooper, S.L. (2000). Central pattern generators. Current Biology, 105: R176-R179.
35.
Huber, M. (2000). A Hybrid Architecture for Adaptive Robot Control. PhD thesis, University of Massachusetts at Amherst.
36.
Hülse, M., and Pasemann, F. (2002). Dynamical neural Schmitt trigger for robot control. Proceedings of the International Conference on Artificial Neural Networks, ICANN 2002 (ed. J.R. Dorronsoro), LNCS Vol. 2415, Springer Verlag, pp.783-788.
37.
Hülse, M., Wischmann, S., and Pasemann, F. (2004). Structure and function of evolved neuro-controllers for autonomous robots. Connection Science: Special Issue on Evolutionary Robotics, 164: 249-266.
38.
Hülse, M., Zahedi, K., and Pasemann, F. (2003). Representing robot-environment interactions by dynamical features of neuro-controllers. In Anticipatory Behavior in Adaptive Learning Systems (eds M. Butz et al.), LNAI Vol. 2684, Springer Verlag, pp.222-242.
39.
Ijspeert, A.J. (2001). A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander. Biological Cybernetics, 845: 331-348.
40.
Ilg, W., Albiez, J., Jedele, H., Berns, K., and Dillmann, R. (1999). Adaptive periodic movement control for the four-legged walking machine BISAM. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Vol. 3, pp.2354-2359.
41.
Ingvast, J., Ridderström, C., Hardarson, F. and Wikander, J. (2003). Warpl: Towards walking in rough terrain - control of walking. Proceedings of the 6th International Conference on Climbing and Walking Robots, CLAWAR'03 (eds G. Muscato and D. Longo), Professional Engineering Publishing.
42.
Jacobi, N. (1998). Running across the reality gap: octopod locomotion evolved in a minimal simulation. Proceedings of the First European Workshop on Evolutionary Robotics: EvoRobot'98 (eds P. Husbands and J.-A. Meyer), LNCS Vol. 1468, Springer Verlag, pp.39-58.
43.
Kato, K., and Hirose, S. (2001). Development of quadruped walking robot, TITAN-IX -mechanical design concept and application for the humanitarian de-mining robot. Advanced Robotics, 152: 191-204.
44.
Kerscher, T., Albiez, J., and Berns, K. (2002). Joint control of the six-legged robot AirBug driven by fluidic muscles. Proceedings of the Third International Workshop on Robot Motion and Control, RoMoCo'02, Bukowy Dorek, Poland, November, pp. 27-32.
45.
Kimura, H., and Fukuoka, Y. (2004). Adaptive walking of a quadruped robot in outdoor environment based on biological concepts. Proceedings of the 9th International Symposium on Experimental Robotics, ISER2004, Singapore, ID-132.
46.
Kirchner, F., Spenneberg, D., and Linnemann, R. (2002). A biologically inspired approach toward robust real-world locomotion in legged robots. In Neurotechnology for Biomimetic Robots (eds J. Ayers, J. Davis and A. Rudolph), pp. 419-447, MIT Press, MA.
47.
Kurazume, R., Yoneda, K., and Hirose, S. (2002). Feed-forward and feedback dynamic trot gait control for quadruped walking vehicle. Autonomous Robots, 122: 157-172.
48.
Lewis, M.A., Tenore, F., and Etienne-Cummings, R. (2005). CPG design using inhibitory networks. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Barcelona, Spain.
49.
Manoonpong, P., Pasemann, F., and Fischer, J. (2004). Neural processing of auditory-tactile sensor data to perform reactive behavior of walking machines. Proceedings of the IEEE International Conference on Mechatronics and Robotics, Vol. 1, pp. 189-194.
50.
Manoonpong, P., Pasemann, F., Fischer, J., and Roth, H. (2005). Neural processing of auditory signals and modular neural control for sound tropism of walking machines. International Journal of Advanced Robotic Systems, 23: 223-234.
51.
Marder, E., and Calabrese, R.L. (1996). Principles of rhythmic motor pattern production. Physiological Reviews, 76: 687-717.
52.
Markelic, I. (2005). Evolving a neurocontroller for a fast quadrupedal walking behavior. Master thesis, Institut für Computervisualistik Arbeitsgruppe Aktives Sehen, Universität Koblenz-Landau.
53.
Matsuoka, K. (1985). Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics, 526: 367-376.
54.
Mondada, F., Franzi, E., and lenne, P. (1993). Mobile robot miniaturisation: A tool for investigation in control algorithms. Proceedings of the Third International Symposium on Experimental Robotics, Kyoto, Japan, October, pp.501-513.
55.
Moravec, H.P. (1977). Towards automatic visual obstacle avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, pp.584-590.
56.
Nakada, K., Asai, T., and Amemiya, Y. (2003). An analog neural oscillator circuit for locomotion control in quadruped walking robot. Proceedings of the International Joint Conference on Neural Networks, Vol. 2, pp.983-988.
57.
Nehmzow, U., and Walker, K. (2003). Quantitative description of robot-environment interaction using chaos theory. Proceedings of European Conference on Mobile Robotics, ECMR, Warsaw.
58.
Nilsson, N.J. (1969). A mobile automaton: An application of artificial intelligence techniques. Proceedings of the 1st International Joint Conference on Artificial Intelligence, Washington DC, pp. 509-520.
59.
Nolfi, S. and Floreano, D. (1998). Coevolving predator and prey robots: do `Arms Races' arise in artificial evolution?Artificial Life, 44: 311-335.
60.
Okada, M., Nakamura, D., and Nakamura, Y. (2003). Hierarchical design of dynamics based information processing system for humanoid motion generation. Proceedings of the 2nd International Symposium on Adaptive Motion of Animals and Machines, AMAM2003, Kyoto, SaP-III-1.
61.
Orlovsky, G.N., Deliagina, T.G., and Grillner, S. (1999). Neural Control of Locomotion. From Mollusk to Man, Oxford University Press.
62.
Parker, G.B., and Lee, Z. (2003). Evolving neural networks for hexapod leg controllers. Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, pp.1376-1381.
63.
Pasemann, F. (2002). Complex dynamics and the structure of small neural networks. Network: Computation in Neural Systems, 132: 195-216.
64.
Pasemann, F. (1993). Discrete dynamics of two neuron networks. Open Systems and Information Dynamics, 2: 49-66.
65.
Pasemann, F., Hild, M., and Zahedi, K. (2003). SO(2)-networks as neural oscillators. Computational Methods in Neural Modeling: Proceedings of the 7th International Work-Conference on Artificial and Natural Networks, IWANN 2003 (eds J. Mira J.R. and Alvarez), LNCS Vol. 2686, Springer, Berlin, pp. 144-151.
66.
Payton, D.W. (1986). An architecture for reflexive autonomous vehicle control. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, San Francisco, CA, pp.1838-1845.
67.
Pfeifer, R. and Scheier, C. (1999). Understanding Intelligence, MIT Press, Cambridge, MA.
68.
Quinn, R.D., Nelson, G.M., Bachmann, R.J., Kingsley, D.A., Offi, J., and Ritzmann, R.E. (2001). Insect designs for improved robot mobility. Proceedings of the 4th International Conference on Climbing and Walking Robots, CLAWAR'01 (eds K. Berns and R. Dillmann), Professional Engineering Publishing, pp. 69-76.
69.
Reeve, R. (1999). Generating walking behaviours in legged robots. PhD thesis, University of Edinburgh, Scotland, UK.
70.
Ridgel, A.L. and Ritzmann, R.E. (2005). Effects of neck and circumoesophaegeal connective lesions on posture and locomotion in the cockroach. Journal of Comparative Physiology. A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 1916: 559-573.
71.
Righetti, L., and Ijspeert, A.J. (2006). Programmable central pattern generators: an application to biped locomotion control. Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, Orlando, FL, pp.1585-1590.
72.
Ritzmann, R.E., Quinn, R.D., and Fischer, M.S. (2004). Convergent evolution and locomotion through complex terrain by insects, vertebrates and robots. Arthropod Structure and Development, 333: 361-379.
73.
Schmucker, U., Schneider, A., Rusin, V., and Zavgorodniy, Y. (2005). Force sensing for walking robots. Proceedings of the Third International Symposium on Adaptive Motion in Animals and Machines, AMAM 2005, ISLE Verlag, Ilmenau.
74.
Smith, R. (2004). Open Dynamics Engine v0.5 User Guide, http://ode.org/ode-0.5-userguide.html.
75.
Spenneberg, D. and Kirchner, F. (2002). SCORPION: a biomimetic walking robot. Robotik 2002, VDI-Bericht, Vol. 1679, pp. 677-682.
76.
Stein, P.S.G, Grillner, S., Selverston, A.I., and Stuart, D.G. (1997). Neurons, Networks, and Behavior, MIT Press, Cambridge, MA.
77.
Taga, G., Yamaguchi, Y., and Shimizu, H. (1991). Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics, 653: 147-159.
78.
Téllez, R., Angulo, C., and Pardo, D. (2006). Evolving the walking behaviour of a 12 DOF quadruped using a distributed neural architecture. Proceedings of the 2nd International Workshop on Biologically Inspired Approaches to Advanced Information Technology, Bio-ADIT'2006, LNCS Vol. 3853, Springer, pp.5-19.
79.
Webb, B. (2000). What does robotics offer animal behaviour?Animal Behaviour, 605: 545-558.
80.
Wei, T.E., Quinn, R.D., and Ritzmann, R.E. (2004). CLAWAR that benefits from abstracted cockroach locomotion principles. Proceedings of the 7th International Conference on Climbing and Walking Robots, CLAWAR'04) (eds M. Armada and P. Gonzalez de Santos), Madrid, Spain.
81.
Wischmann, S., Hülse, M., Knabe, J., and Pasemann, F. (2006). Synchronization of internal neural rhythms in multi-robotic systems. Adaptive Behavior, 142: 117-127.
82.
Yamaguchi, T., Watanabe, K., and Izumi, K. (2005). Neural network approach to acquiring free-gait motion of quadruped robots in obstacle avoidance. Artificial Life and Robotics, 94: 188-193.
83.
Yoneda, K., Ota, Y., Ito, F., and Hirose, S. (2001). Quadruped walking robot with reduced degrees of freedom. Journal of Robotics and Mechatoronics, 132190-197.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
