Dynamic path planning of mobile robots with improved genetic algorithm

In this study, a new mutation operator is proposed for the genetic algorithm (GA) and applied to the path planning problem of mobile robots in dynamic environments. Path planning for a mobile robot finds a feasible path from a starting node to a target node in an environment with obstacles. GA has been widely used to generate an optimal path by taking advantage of its strong optimization ability. While conventional random mutation operator in simple GA or some other improved mutation operators can cause infeasible paths, the proposed mutation operator does not and avoids premature convergence. In order to demonstrate the success of the proposed method, it is applied to two different dynamic environments and compared with previous improved GA studies in the literature. A GA with the proposed mutation operator finds the optimal path far too many times and converges more rapidly than the other methods do.

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► We propose a new mutation operator for the genetic algorithm.
► The proposed mutation operator is used for the path planning of mobile robots.
► We compared the proposed method with previous improved GA studies.
► Our mutation operator finds the optimal path many times than the other methods do.
► Our mutation operator converges more rapid than the other methods do.