Paper Info
Title
Artificial Immune Network-Based Multi-Robot Formation Path Planning With 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 self-recognition 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.
Year
DOI
Venue
2016
10.2316/Journal.206.2016.3.206-4746
INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION
Keywords
Field
DocType
Artificial immune network algorithm, position tracking control method, multi-robot formation, obstacle avoidance
Obstacle avoidance,Motion planning,Immune network,Control engineering,Engineering,Robot
Journal
Volume
Issue
ISSN
31
3
0826-8185
Citations 
PageRank 
References 
1
0.36
1
Authors
6
Name
Order
Citations
PageRank
Li-xia Deng162.66
Xin Ma28917.25
Jason Gu342174.77
Yibin Li422659.56
Zhigang Xu510.36
Yafang Wang613413.56