A Study on Rapid and Smooth Path Planning for UAVs in Multi-Obstacle and Narrow Environments
Hou Jiabing, Jiaying Lu · IEEE Access · 2026
Path planning in complex three-dimensional environments with multiple obstacles and narrow passages remains a critical challenge for unmanned aerial vehicles (UAVs). To address this issue, this paper proposes an improved Rapidly-exploring Dense Tree (RDT) algorithm for rapid and smooth UAV path generation. Unlike conventional RRT-based planners that rely on uniform or weakly biased sampling, the proposed method explicitly enhances local exploration efficiency while preserving the global connectivity exploration capability of the sampling tree.Specifically, the concept of density in RDT is clarified as a localized and relative sampling property, rather than a global increase in sampling nodes. Based on this definition, candidate channels are identified through three-dimensional geometric gap analysis to represent potential narrow passages, which are then used to trigger adaptive local high-density sampling. In addition, a cost-based tree expansion strategy combined with heuristic guidance is introduced to reduce redundant searches and accelerate convergence toward high-quality paths. To ensure trajectory smoothness and dynamic feasibility, a post-processing stage using cubic B-spline smoothing with curvature constraints is employed, followed by multi-constraint re-optimization considering path length, curvature, climb rate, and collision risk.Simulation experiments are conducted in two representative environments, including multi-obstacle and narrow-channel scenarios, to validate the proposed approach. Comparative studies against FS-RRT and GVP-RRT demonstrate that the proposed method achieves a 17.6% reduction in average path length and a 62.4% reduction in planning time, while generating smoother trajectories and maintaining strong obstacle avoidance capability. Additional statistical evaluations under repeated trials further confirm the improved stability and robustness of the proposed RDT framework in narrow and highly constrained environments.