Presents a method for control of autonomous mobile robots to acquire fine actions based on real-time search. In the proposed method, the information criterion for the environment is defined based on the Kullback-Leibler divergence, which measures the quality of the environmental information used for the action search. The robot searches suitable actions based on the environmental information which is improved step by step in the sense of this criterion. According to this, we can harmonize the trade-off between the calculation amount and information quality, and make the search process faster. The proposed method is applied to the moving obstacles avoidance problem, and its usefulness is shown through some simulation results.

We propose a method based on a real-time search for the proper action of an autonomous mobile robot using the framework of the Anytime Algorithm. This is available for a real-time search in a dynamic environment. In order to apply the Anytime Algorithm to the multi-objective real-time search in the dynamic situation around the autonomous mobile robot, we adopt prediction, a switching evaluation function technique, the action watch dog and an evolution strategy. These enable the robots to realize the real time search. The paper explains the proposed method and shows the feasibility through experimental results using real robots.

We propose a method based on a real-time search for the optimal action of an autonomous mobile robot using an evolution strategy. By searching the optimal action for the facing situation in real time the robots need not suffer from the problem of categorization of the situations and problem of pre-definition of rules. The basic idea of the proposed real time search technique is to harmonize computational amount with a property of solution. In order to realize this requirement we adopt a two-stage evaluation technique. The proposed method also adopts the prediction and action watch dog systems. These function enable the robots to realize the real time search. The paper explains the proposed method and shows the feasibility through the experimental results using the real robot.