An Improved Three-Dimensional RRT Path Planning Method Incorporating Path-Aware Whale Optimization
Zhaoyang Wang, Da Xu, Yuze Ma · Algorithms · 2026
Complex three-dimensional path planning requires a planner to generate collision-free, short, and smooth paths within limited computation time, but traditional RRT-based methods often suffer from unguided sampling, repeated expansion failures in dense obstacle regions, redundant initial paths, and collision-prone post-processing. To address this problem, this study defines the planning task as efficient path generation in a bounded three-dimensional obstacle space and proposes an environment feedback hybrid sampling bidirectional RRT method integrated with a path-aware improved whale optimization algorithm. In the initial search stage, the algorithm uses the collision rate of each random tree to switch among open-space exploration, heuristic convergence, and blocked region escape sampling. Local obstacle density estimation is further introduced to fuse the sampling direction, goal direction, opposite tree attraction, and obstacle repulsion, while adaptive dual step sizes, backtracking safe step size adjustment, and local rewiring reduce invalid expansions and improve the quality of the first feasible path. In the post-processing stage, the whale optimization algorithm is used to optimize key path nodes rather than all nodes, with path corridor constraints, dynamic fitness weighting, collision repair, elastic band refinement, and B-spline smoothing to shorten the path and improve smoothness while maintaining feasibility. Tested independently 100 times in each of four MATLAB three-dimensional obstacle environments and compared with the best-performing comparison algorithm in each environment, the proposed method reduced planning time by 64.4%, 83.4%, 80.1%, and 39.5%, respectively, and shortened path length by 4.9%, 7.1%, 13.4%, and 10.1%. The success rate reached 100% in the first three environments and 97% in the most complex dense obstacle environment. These results show that the proposed framework improves search efficiency, path quality, and robustness for three-dimensional collision-free path planning under complex obstacle constraints.