Abstract

This paper proposes an improved sparrow search algorithm for robot path planning. The improved heuristic algorithm introduces Tent map and opposition-based learning scheme into sparrow search algorithm. The Tent map is applied to initialize population for greater diversity, and opposition-based learning enhances the diversity of the population in evolution process, so as to avoid the premature convergence of the algorithm. The proposed algorithm is adopted to mobile robot path planning. Experiment results show that the improved algorithm can guide the mobile robot to find the optimal path quickly, and also prove the superiority and effectiveness of the proposed algorithm over the original algorithm.

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