Robot Path Planning based on Improved Sparrow Swarm Algorithm

Thanks to its swarm-based nature, the sparrow swarm algorithm (SSA) has recently drawn increasing interest in field of robot path planning. However, the standard SSA has been proven to be easily falling into the local optimum. To enhance the performance of SSA on robot path planning via remedying the aforementioned flaw, this paper proposes an improved SSA (ISSA) by leveraging the standard SSA, the tent chaos mapping method and the T-differential variant algorithm. Firstly, the TCMP is implemented to diversify the sparrows at the initial stage of the swarm. Subsequently, the T-differential variant is applied to enhance the global search ability of SSA. Lastly, An improved cubic spline interpolation method is used for trajectory smoothing. The performance of the proposed method over robot path planning has been compared with those of four state-of-the-art algorithms. The simulation results confirm that the proposed method dominates its contenders in terms of the path optimality and robustness.