Yu Kataoka

Design and implementation of fuzzy cooperative catching controller for multiple mobile robots

In this paper, the use of cooperative knowledge for a catching problem with multiple mobile robots is considered. To achieve successful capture of an escaping target, the robots have to move toward the escaping target after the robots drive the target into the catching area formed by themselves. Then, the cooperative knowledge is represented as the fuzzy formation rules, and used to surround the escaping target. The fuzzy formation rules are tuned by genetic algorithm in advance of experiments. The same controllers with the approach rules and the tuned fuzzy formation rules are mounted on three robots. The catching motions of three robots and the target are measured. Experimental results demonstrate that the proposed cooperation control is effective to catch the escaping target.

Autonomous mobile robot based on behavior decision skill and control skill of the operator

This paper introduces a human skill base control algorithm using artificial neural networks and fuzzy reasoning for an autonomous mobile robot. Neural networks are used to select a suitable motion control pattern in actual environments. The back propagation algorithm adjusts the weights of the neural networks so that the selected motion control pattern corresponds to the action, which is obtained by the operators behavior decision skill. To realize the selected motion control pattern, the orientation angle and the speed of the mobile robot are determined by fuzzy reasoning in which fuzzy rules are also automatically tuned so as to simulate the operators control skill. We have implemented and tested the proposed control algorithm on an autonomous mobile robot and some experimental results demonstrate the effectiveness of the proposed control algorithm for the autonomous mobile robot.

Generation of jumping motion pattern for a hopping robot using genetic algorithm

This paper describes an application of genetic algorithm to generate a jumping motion pattern for a hopping robot. A central pattern generator is used to generate the motion pattern. The tuning parameters of the central pattern generator are regarded as genes and adjusted by the genetic algorithm, so that the hopping robot can jump continuously to the reference height with the minimum force. To realize online tuning of the parameters, new genetic operations such as few individuals, quick estimation, instant selection, and intentional mutation are introduced. The experimental results demonstrate the effectiveness of the proposed scheme.