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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.

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SUMMARYIn this paper an optimal path planning method based on a new evolutionary algorithm is presented for higher order robotic systems. It is a combination of immune system and wavelet mutation. By increasing the system's dimensions, the complexity of algorithm grows linearly. The obtained results have been compared with other optimal path producing algorithms, and its excellence in terms of optimality has been proved. Strengths of this method are simplicity in large-scale path planning, being free of most of the common deadlocks in usual method, and ability to obtain more optimized results than other similar methods. The effectiveness of this approach on simulation case studies for a three-link planar robot and 5 degrees of freedom mobile manipulators as well as an experiment for a mobile robot called K-joniour is shown.

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