Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines
This paper describes reactive neural control used to
generate phototaxis and obstacle avoidance behavior of walking
machines. It utilizes discrete-time neurodynamics and consists of
two main neural modules: neural preprocessing and modular neural
control. The neural preprocessing network acts as a sensory fusion
unit. It filters sensory noise and shapes sensory data to drive the
corresponding reactive behavior. On the other hand, modular neural
control based on a central pattern generator is applied for locomotion
of walking machines. It coordinates leg movements and can generate
omnidirectional walking. As a result, through a sensorimotor loop this
reactive neural controller enables the machines to explore a dynamic
environment by avoiding obstacles, turn toward a light source, and
then stop near to it.
[1] S. Fujii and T. Nakamura, "Development of an amphibious hexapod robot
based on a water strider," in Proc. 10th International Conference on
Climbing and Walking Robots, pp. 135-143, 2007.
[2] A. J. Ijspeert, A. Crespi, D. Ryczko, and J. M. Cabelguen, "From
swimming to walking with a salamander robot driven by a spinal cord
model," Science, vol. 315, pp. 1416-1420, 2007.
[3] R. A. Brooks, "A robot that walks: emergent behaviors from a carefully
evolved network," Neural Computation, vol. 12, pp. 253-262, 1989.
[4] H. Cruse, T. Kindermann, M. Schumm, J. Dean, and J. Schmitz,
"Walknet-A biologically inspired network to control six-legged walking,"
Neural Networks, vol. 11, pp. 1435-1447, 1998.
[5] H. Kimura, Y. Fukuoka, and A. H. Cohen, "Adaptive dynamic walking
of a quadruped robot on natural ground based on biological concepts,"
International Journal of Robotics Research, vol. 26, pp. 475-490, 2007.
[6] R. C. Arkin, K. Ali, A. Weitzenfeld, and F. Cervantes-Perez, "Behavior
models of the praying matis as a basis for robotic behavior," Robotics
and Autonomous Systems, vol. 32, pp. 39-60, 2000.
[7] W. G. Walter, The Living Brain, New York: Norton, 1953.
[8] P. Manoonpong, Neural Preprocessing and Control of Reactive Walking
Machines: Towards Versatile Artificial Perception-Action Systems, Cognitive
Technologies, Springer, 2007.
[9] P. Manoonpong, F. Pasemann, and F. Woergoetter, "Sensor-driven neural
control for omnidirectional locomotion and versatile reactive behaviors
of walking machines," Robotics and Autonomous Systems,
doi:10.1016/j.robot.2007.07.004, 2007, in press.
[10] F. Pasemann, M. Huelse, and K. Zahedi, "Evolved neurodynamics
for robot control," in Proc. European Symposium on Artificial Neural
Networks, vol. 2, pp. 439-444, 2003.
[11] F. Pasemann, "Discrete dynamics of two neuron networks," Open
Systems and Information Dynamics, vol. 2, pp. 49-66, 1993.
[12] M. Huelse, S. Wischmann, and F. Pasemann, "The role of non-linearity
for evolved multifunctional robot behavior," in Proc. 6th International
Conference on Evolvable Systems-ICES 2005, LNCS vol. 3637, pp. 108-
118, 2005.
[13] S. L. Hooper, "Central pattern generators," Current Biology, vol. 10, pp.
R176-R177, 2000.
[14] P. Manoonpong, F. Pasemann, J. Fischer, and H. Roth, "Neural processing
of auditory signals and modular neural control for sound tropism of
walking machines," International Journal of Advanced Robotic Systems
(ARS), vol. 2, no. 3, pp. 223-234.
[1] S. Fujii and T. Nakamura, "Development of an amphibious hexapod robot
based on a water strider," in Proc. 10th International Conference on
Climbing and Walking Robots, pp. 135-143, 2007.
[2] A. J. Ijspeert, A. Crespi, D. Ryczko, and J. M. Cabelguen, "From
swimming to walking with a salamander robot driven by a spinal cord
model," Science, vol. 315, pp. 1416-1420, 2007.
[3] R. A. Brooks, "A robot that walks: emergent behaviors from a carefully
evolved network," Neural Computation, vol. 12, pp. 253-262, 1989.
[4] H. Cruse, T. Kindermann, M. Schumm, J. Dean, and J. Schmitz,
"Walknet-A biologically inspired network to control six-legged walking,"
Neural Networks, vol. 11, pp. 1435-1447, 1998.
[5] H. Kimura, Y. Fukuoka, and A. H. Cohen, "Adaptive dynamic walking
of a quadruped robot on natural ground based on biological concepts,"
International Journal of Robotics Research, vol. 26, pp. 475-490, 2007.
[6] R. C. Arkin, K. Ali, A. Weitzenfeld, and F. Cervantes-Perez, "Behavior
models of the praying matis as a basis for robotic behavior," Robotics
and Autonomous Systems, vol. 32, pp. 39-60, 2000.
[7] W. G. Walter, The Living Brain, New York: Norton, 1953.
[8] P. Manoonpong, Neural Preprocessing and Control of Reactive Walking
Machines: Towards Versatile Artificial Perception-Action Systems, Cognitive
Technologies, Springer, 2007.
[9] P. Manoonpong, F. Pasemann, and F. Woergoetter, "Sensor-driven neural
control for omnidirectional locomotion and versatile reactive behaviors
of walking machines," Robotics and Autonomous Systems,
doi:10.1016/j.robot.2007.07.004, 2007, in press.
[10] F. Pasemann, M. Huelse, and K. Zahedi, "Evolved neurodynamics
for robot control," in Proc. European Symposium on Artificial Neural
Networks, vol. 2, pp. 439-444, 2003.
[11] F. Pasemann, "Discrete dynamics of two neuron networks," Open
Systems and Information Dynamics, vol. 2, pp. 49-66, 1993.
[12] M. Huelse, S. Wischmann, and F. Pasemann, "The role of non-linearity
for evolved multifunctional robot behavior," in Proc. 6th International
Conference on Evolvable Systems-ICES 2005, LNCS vol. 3637, pp. 108-
118, 2005.
[13] S. L. Hooper, "Central pattern generators," Current Biology, vol. 10, pp.
R176-R177, 2000.
[14] P. Manoonpong, F. Pasemann, J. Fischer, and H. Roth, "Neural processing
of auditory signals and modular neural control for sound tropism of
walking machines," International Journal of Advanced Robotic Systems
(ARS), vol. 2, no. 3, pp. 223-234.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:60286", author = "Poramate Manoonpong and Frank Pasemann and Florentin Wörgötter", title = "Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines", abstract = "This paper describes reactive neural control used to
generate phototaxis and obstacle avoidance behavior of walking
machines. It utilizes discrete-time neurodynamics and consists of
two main neural modules: neural preprocessing and modular neural
control. The neural preprocessing network acts as a sensory fusion
unit. It filters sensory noise and shapes sensory data to drive the
corresponding reactive behavior. On the other hand, modular neural
control based on a central pattern generator is applied for locomotion
of walking machines. It coordinates leg movements and can generate
omnidirectional walking. As a result, through a sensorimotor loop this
reactive neural controller enables the machines to explore a dynamic
environment by avoiding obstacles, turn toward a light source, and
then stop near to it.", keywords = "Recurrent neural networks, Walking robots, Modular
neural control, Phototaxis, Obstacle avoidance behavior.", volume = "1", number = "11", pages = "665-6", }