Cooperative Behavior of Parent-Children Type Mobile Robots

This paper proposes a parent-children type robot system that moves in unstructured environments. The parent robot works as a leader of the system. The children robots work as sensors to sense their environments while touching them. The parent collects the sensory information of its environment and generates a map. In order to express the map effectively, this paper applies a structured neural network to memory. The neural network learns the sensory information incrementally. While using the network, the parent robot determines their behavior. On the other hand, the children are disposable. When some of the children malfunction because of their dangerous environment, the remaining children compensate for them and continue to work. Simulations are performed to show the effectiveness of the proposed system.

[1]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[2]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[3]  Ren C. Luo,et al.  Multisensor integration and fusion in intelligent systems , 1989, IEEE Trans. Syst. Man Cybern..

[4]  Toshio Fukuda,et al.  Hierarchical intelligent control for robotic motion , 1994, IEEE Trans. Neural Networks.

[5]  Takanori Shibata,et al.  Sensor-based behavior using a neural network for incremental learning in family mobile robot system , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[6]  Nils J. Nilsson,et al.  A mobius automation: an application of artificial intelligence techniques , 1969, IJCAI 1969.

[7]  V. Lumelsky,et al.  Dynamic path planning for a mobile automaton with limited information on the environment , 1986 .

[8]  Toshio Fukuda,et al.  Coordinative behavior by genetic algorithm and fuzzy in evolutionary multi-agent system , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[9]  Toshio Fukuda,et al.  A neural network architecture for incremental learning , 1995, Neurocomputing.

[10]  柴田 崇徳 Hierarchical intelligent control of robotic motion , 1992 .

[11]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[12]  Tracy L. Anderson,et al.  Animal behavior as a paradigm for developing robot autonomy , 1990, Robotics Auton. Syst..

[13]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[14]  Fumihito Arai,et al.  A new neuron model for additional learning , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[15]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[16]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[17]  Hajime Asama,et al.  Efficient method to generate collision free paths for an autonomous mobile robot based on new free space structuring approach , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[18]  Ronald C. Arkin,et al.  Behavior-Based Robot Navigation for Extended Domains , 1992, Adapt. Behav..