Multi-Strategy Improved Petal Kingfisher Algorithm for Robot Path Planning

Abstract

Aiming at the problems of slow convergence speed and easy to fall into local optimal solutions of the original spotted kingfisher algorithm, this paper proposes an improved spotted kingfisher algorithm, which firstly initializes the population through Logistic chaotic mapping to improve the uniformity of the population position, secondly introduces the learning decay rate to dynamically adjust the search step to accelerate the convergence of the algorithm; and adopts Gaussian random variation strategy to avoid the algorithm from falling into the local optimum. The algorithm is Experiments are conducted on six kinds of benchmark test functions, and the results show that the improved algorithm significantly improves the original PKO algorithm in terms of solution accuracy and convergence speed, which effectively improves the performance of the algorithm. Finally, it is applied to robot path planning in Matlab and ROS, which shows that the improved algorithm has shorter path planning length, shorter convergence time and better robustness than other optimization algorithms.