Control of distributed autonomous robotic systems using principles of pattern formation in nature and pedestrian behavior

Self-organized and error-resistant control of distributed autonomous robotic units in a manufacturing environment with obstacles where the robotic units have to be assigned to manufacturing targets in a cost effective way, is achieved by using two fundamental principles of nature. First, the selection behavior of modes is used which appears in pattern formation of physical, chemical and biological systems. Coupled selection equations based on these pattern formation principles can be used as dynamical system approach to assignment problems. These differential equations guarantee feasibility of the obtained solutions which is of great importance in industrial applications. Second, a model of behavioral forces is used, which has been successfully applied to describe self-organized crowd behavior of pedestrians. This novel approach includes collision avoidance as well as error resistivity. In particular, in systems where failures are of concern, the suggested approach outperforms conventional methods in covering up for sudden external changes like breakdowns of some robotic units. The capability of this system is demonstrated in computer simulations.

[1]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[2]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Toshio Fukuda,et al.  Dynamic Robot-Target Assignment - Dependence of Recovering from Breakdowns on the Speed of the Selection Process , 2000, DARS.

[4]  J. Starke,et al.  Dynamic control of distributed autonomous robotic systems with underlying three-index assignments , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[5]  Khadija Iqbal,et al.  An introduction , 1996, Neurobiology of Aging.

[6]  Michael Schanz,et al.  Dynamical System Approaches to Combinatorial Optimization , 1998 .

[7]  Michael Schanz,et al.  Self-Organized Behaviour of Distributed Autonomous Mobile Robotic Systems by Pattern Formation Principles , 1998, DARS.

[8]  Jens Starke,et al.  Communication Fault Tolerance in Distributed Robotic Systems , 2000, DARS.