A 3D UAV Path Planning Algorithm Based on Bidirectional RRT* with Adaptive Directional Sampling and Cooperative Dual-Tree Expansion

UAV path planning in complex three-dimensional obstacle environments requires a balance between search efficiency and flight feasibility. However, existing RRT*-based methods often fail to satisfy this requirement, as their random sampling lacks directional guidance and makes limited use of environmental information. To this end, this paper proposes an environment-aware cooperative bidirectional RRT* algorithm (EAC-Bi-RRT*). In the sampling stage, the sampling probability of each direction is adaptively adjusted according to the obstacle distribution across 26 directional sectors and the relative goal orientation, so that the search receives stronger directional guidance. During bidirectional expansion, the two trees are assigned leader and follower roles according to the local expandability on the start and goal sides, and their cooperative search is combined with an environment-adaptive step size and a climbing-angle constraint to balance search efficiency and flight reachability. When an expanding node approaches an obstacle, a repulsive-only local directional correction suppresses oscillation, and the initial path is then smoothed by a curvature-constrained B-spline to form a continuous flight trajectory. Across all test scenarios, EAC-Bi-RRT* achieves a 100% planning success rate. Compared with the baseline algorithms, it reduces planning time by approximately 54–90% and path length by approximately 5–18% while maintaining low average turning angles, which demonstrates competitive overall performance.