A novel global optimal path planning and trajectory method based on adaptive dijkstra-immune approach for mobile robot

In this paper a new method to find global optimal path is obtained. Utilization of standard graph searching methods leads to eliminate uncertainness of heuristic algorithms. By using graph searching method a suboptimal solution is obtained, it causes to increase speed, precision and performance of heuristic algorithms. Firstly, the environment is defined with using a useful graph theory. Then by adaptive Dijkstra algorithm a suboptimal path is obtained. Finally, Continuous Clonal Selection Algorithm (CCSA) that is combined with negative selection algorithm, improves this suboptimal path and derives global optimal path. The simulation results show that this suggested method in compression with ant colony and elistic genetic algorithms, has more accuracy and precision, with competitive speed. Also, our suggested algorithm can be used for solving more complicated dynamic problems. Moreover, this proposed approach can be used as standard method in optimization problems especially in path planning and trajectory.