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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...
The most challenge of dynamic path planning lies in that the high unpredictability of environmental information. With the strong space search ability and learning ability, artificial immune network (AIN) has been used for path planning. Polyclonal artificial immune network (PCAIN) solves the problems of immature convergence and local minima with the increasing diversity of antibodies. In this paper,...
This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments. As a computational approach to learning through interaction with the environment, reinforcement learning algorithms have been widely used for intelligent robot control, especially in the field of autonomous mobile robots. However, the learning process...
Multi-robot system can improve the efficiency of mapping and exploration. One of the key problems is when and how to merge the partial maps acquired by robots independently to share environmental information between robots. This paper studies the problem of fusing two partial maps without common reference frames and relative position information of robots. On the basis of the similarity metric, the...
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