Planning multi-robot formation with improved poly-clonal artificial immune algorithm

In this paper, a novel algorithm to solve multi-robot formation path planning problem is proposed. A combination of the leader-follower and improved poly-clonal artificial immune algorithm is used to derive the formation architecture. The formation of multi-robot is maintained through controlling the distance and angle between leader and followers. Robots reach the desired positions and avoid obstacles with improved poly-clonal artificial immune algorithm. Artificial immune network has been widely used in obstacles avoidance with the strong searching ability and learning ability. Improved poly-clonal artificial immune algorithm increases the diversity of antibodies. Concentration of every antibody is computed based on the algorithm. Only the antibody with the highest concentration is selected to act on robot. Meanwhile, formation control system changes the leader temporarily when the original followers encounter with obstacles. Extensive experiments show that the proposed algorithm effectively maintains the formation and successfully avoids obstacles. Simulations validate the effectiveness and stability of the proposed algorithm.