Lixia Deng, Xin Ma, Jason Gu, and Yibin Li
Multi-robot dynamic formation, path planning, polyclonal algo-rithm, leader–follower, control graph theory
The paper presents a novel multi-robot dynamic formation path planning algorithm. A combination of leader–follower method and improved polyclonal artificial immune algorithm is used to derive the formation architecture. Multi-robot formation is maintained through controlling the distance and angle between the leader and followers. Formation-change and leader-change are used to avoid obstacles. Control graph theory is used to the smooth exchange between two different isomorphic formation shapes. When follower detects obstacles, leader temporarily changes till it successfully avoids obstacles. Robots reach the desired positions and avoid obstacles with improved polyclonal artificial immune algorithm. Artificial immune network has been widely used in obstacles avoidance with the strong searching ability and learning ability. Improved polyclonal artificial immune algorithm increases the diversity of antibodies. Extensive experiments show that the proposed algorithm effectively maintains the geometrical shape of formation, smoothly changes the geometrical shape from a triangle formation to a line formation and successfully avoids obstacles.
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