A path planning algorithm of deterministic mobile robot based on immune

Aiming at the problems of low efficiency of path planning, a new node storage structure is introduced and the search method is optimized to improve the deterministic iterative path planning algorithm. First, the number of antibodies is determined based on the connectable starting path point, when generating the initial antibody with the inspiration of the optimal angle vaccine. Then, the connectable path point of the starting point is treated as the root node to rebuild the new path with path filtering by means of the optimal path fit value. The initial optimal antibody is used as the screening criterion to avoid invalid path point mutation with 100% confidence conditions. Finally, a path point is a basic unit that forms the connection network and stores the parameter information that reaches it. After the algorithm iteration, the optimal path is output. In different maps, the algorithm in this paper is compared with others. The results show that the algorithm effectively reduces the computational cost and has better adaptability to different maps.