Decentralized Control for Intelligent Robot System to Avoid Moving Obstacles

We propose a novel control law to deploy a decentralized multiagent mobile intelligent robot system in a dynamic environment containing moving obstacles. Since a collision to an obstacle causes failure of the robot and may break the whole system, obstacle avoidance is one of the fundamental task for the mobile robot system. Potential based approaches have been widely applied to deploying a mobile robot system for various goals, including obstacle avoidance, coverage maximization and target tracking. However, previous methods consider only a snapshot at each time to decide the dynamics of a robot. Therefore they have limitations in dynamic environments, possibly leading to frequent collisions to obstacles. In contrast, we show that by considering the velocities of the obstacles, the number of collision can be significantly reduced. In our method each robot records a few past histories of sensed obstacles, and based on the record the robot predicts a future trace of the obstacle. We introduce a new dynamics for robots by combining the current state information with the prediction. By extensive experiments, we compare our method with others and show that by our method robots rarely collide with moving obstacles while keeping the potential low.