Research on 3D Path Planning Method for UAV Based on TSDF-IPSO Fusion

Abstract

Addressing the challenges of low environmental modeling accuracy and inadequate obstacle avoidance precision in complex obstacle scenarios in unmanned aerial vehicle (UAV) 3D path planning, this study proposes a UAV 3D path planning method that integrates the truncated signed distance field (TSDF) with an improved particle swarm optimization algorithm (IPSO). A unified planning space integrating a voxel occupancy grid with a truncated signed distance field is constructed offline: the Euclidean distance to obstacle surfaces is truncated and confined within an effective band, whose extent is coordinated with the UAV safety distance threshold determined by physical dimensions and task requirements, thereby preserving the continuous geometric information needed for safety assessment. On this basis, the continuous distance and gradient information provided by the truncated distance field are utilized to formulate a piecewise continuous, distance-based threat cost function, replacing traditional binary collision detection; the distance and gradient are further embedded into the initialization, fitness evaluation, and velocity update procedures of the particle swarm. Moreover, an adaptive inertia weight and a Lévy escape mechanism are introduced to improve search efficiency and global exploration capability. Experimental results demonstrate that under dense discrete safety verification, the proposed method achieves a 100% success rate in complex unstructured environments and that the safety distance threshold can be flexibly adjusted according to task requirements while consistently satisfying the specified safety requirement. The resulting paths achieve a favorable balance among length, smoothness, and controllable safety margin, validating the effectiveness of the proposed method.