To address the limitations of the traditional rapidly exploring random tree (RRT) algorithm, including redundant exploration, limited adaptability in dense environments, insufficient obstacle clearance, and poor path smoothness, this paper proposes an integrated multi-strategy improved RRT framework for robot path planning. The proposed method combines KD-Tree accelerated nearest-neighbor search, adaptive step-size adjustment, safety-boundary-based collision checking, direct goal-connection, safety-validated shortcut pruning, and spline-based path smoothing. Comparative experiments were conducted in four representative scenarios, including simple sparse, complex dense, narrow-passage, and highly cluttered environments. Traditional RRT, RRT*, RRT-Connect, and the proposed method were evaluated through 30 independent runs in each scenario. The results show that all algorithms achieved a 100% success rate in the tested scenarios. RRT* generated near-optimal paths but required substantially longer planning time and a much larger search tree, while RRT-Connect achieved the shortest planning time but provided relatively small obstacle clearance. In contrast, the proposed method achieved a better balance among path length, tree compactness, obstacle clearance, geometric smoothness, and computational efficiency. Ablation experiments further verified the contribution of each module, and parameter sensitivity analysis demonstrated the influence of the balancing factor, safety margin, and spline sampling number. The proposed framework provides a practical and lightweight path-planning solution for static two-dimensional environments.
Liu et al. (Thu,) studied this question.