Path planning algorithm based on the improved Informed-RRT* using the sea-horse optimizer
YAN Guiseng
YANG Jie
Abstract：[Objective]In order to solve the problems of random sampling,inefficient search,and difficulty in providing op-timal paths in complex environments faced by traditional Informed-RRT* algorithms,an improved Informed-RRT* path plan-ning algorithm based on the sea-horse optimizer(SHO)was proposed.[Methods]This algorithm combined the strengths of In-formed-RRT* and SHO.An adaptability function was introduced to evaluate the suitability of sampled nodes,thereby enhanc-ing guidance towards sampling objectives.Additionally,adaptive step sizes and random perturbations were employed to adapt to obstacles in the environment,and the best individuals were chosen to guide the expansion direction of the random tree.[Results]Through multiple sets of simulation and prototype tests,it is demonstrated that the improved Informed-RRT* algorithm exhibits faster convergence speed,higher search efficiency,and superior path planning performance,providing an efficient solution for path planning in complex environments.
Keywords：SHO algorithmInformed-RRT* algorithmPath planningSampling guidanceAutonomous obstacle avoid-ance
Publication Date：2025-02-14
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 93-100 )