ARTIFICIAL IMMUNE NETWORK-BASED MULTI-ROBOT FORMATION PATH PLANNING WITH OBSTACLE AVOIDANCE

Lixia Deng, Xin Ma, Jason Gu, Yibin Li, Zhigang Xu and Yafang Wang

Keywords

Artificial immune network algorithm, position tracking control method, multirobot formation, obstacle avoidance

Abstract

Artificial immune network algorithm (AINA) combined with position tracking control method is used for multi-robot formation path planning. The proposed algorithm avoids obstacles and recovers formation for follower robot after passing around obstacles. Different methods are adopted to calculate the steering direction and the linear velocity of the follower robot. Steering direction of the follower robot is computed with AINA. AINA has abilities of selfrecognition and diversity, and solves the problems of local minima and immature convergence. The optimal steering direction selected with AINA quickly tends towards the steering direction of leader robot, and successfully avoids obstacles. The linear velocity of follower robot is computed with position tracking control method. It is computed based on the state of leader robot, current position of follower robot, and position of virtual robot. It guarantees that the position errors of follower robot quickly converge to zeros. The asymptotic stability of the entire formation system is proven with Lyapunov theory. Numerous experiments validate that the proposed algorithm successfully avoids obstacles and quickly tracks the leader robot for follower robot.

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