Hierarchical control architecture for Cellular Robotic System-simulations and experiments

Abstract

Describes the hierarchical control architecture of real mobile robots for Cellular Robotic System Mark-V (CEBOT Mark-V). A parallel processing control system has been adopted, and by adjusting the role of parallel processes standing for the states of independent primitive behaviors, the change of system organization is realized to adapt the redefinition of plural tasks and the variation of environments. The authors propose a method for decision making of a mobile robot's behavior through integrating multiple behavioral processes. The authors define two relation matrices denoting the relationship among the processes: the priority matrix and the interest relation matrix. The matrices are used to adjust the outputs of behavioral processes and optimize the behavior of mobile robots. To obtain the most suitable priority matrix, the authors introduce a learning-adapting algorithm. The results of simulation and experiment with a real CEBOT Mark-V showed the effectiveness of the proposed matrices and learning-adapting algorithm. On the other hand, instead of simply selecting processes for decision making of the robot's behavior, the integration of multiple processes based on the proposed matrices enhanced the control robustness of robot system.