A mobile robot path planning algorithm combining kinematic constraints and variable-density sampling

This paper proposes a novel path planning algorithm—K-BIT*, which incorporates kinematic constraints and a variable-density sampling strategy into the Batch Informed Trees (BIT*) framework. Unlike traditional uniform sampling methods, the proposed approach dynamically adjusts the sampling distribution according to environmental features and kinematic feasibility, thereby improving search efficiency and generating executable paths. The algorithm adopts a dual-queue mechanism consisting of a vertex queue and an edge queue, and integrates pruning and escape strategies during the path generation process to effectively avoid local optima. Experimental results demonstrate that, compared with traditional BIT*, K-BIT* achieves faster convergence, higher path quality, and satisfies the kinematic constraints of the robot, making it suitable for autonomous navigation in complex environments.