Research on three-dimensional path planning with spherical obstacles combining RIME and APF

Purpose Safe and efficient three-dimensional path planning is important for unmanned aerial vehicles (UAVs) operating in obstacle-constrained environments. This study aims to propose an improved RIME-based algorithm, named ARIME, for UAV path planning in static spherical-obstacle environments. Design/methodology/approach A multi-objective fitness function was constructed by integrating UAV constraints, obstacle threats, path length, smoothness and runtime. The original RIME algorithm was enhanced by embedding artificial potential field (APF)-based obstacle guidance into the soft-rime search stage and introducing a spherical-coordinate search strategy. Six static benchmark scenarios based on a digital elevation model were established to evaluate the proposed algorithm. Findings Simulation results show that ARIME achieved lower fitness values, fewer convergence iterations and shorter execution time than the original RIME in the tested scenarios. Compared with Slime Mould Algorithm, Harris Hawks Optimization and Hunger Games Search, ARIME also showed competitive convergence behavior and path-planning performance under the same simulation settings. Research limitations/implications This study focuses on static benchmark scenarios with spherical obstacles. Dynamic environments with moving obstacles, uncertain perception, real-time sensing and online replanning remain important directions for future research. Originality/value This study integrates RIME-based global optimization, APF-guided local obstacle-avoidance guidance and spherical-coordinate path representation into a unified UAV path-planning framework. The proposed method improves search efficiency and path feasibility in static three-dimensional spherical-obstacle environments.