GEAR-RRT: A Path Planning Algorithm for Complex Environments with Adaptive Informed-Ellipse Sampling and Layered Expansion*

In complex ground environments, conventional RRT* often suffers from poor path quality and slow expansion during robot path planning. To address these issues, this paper proposes GEAR-RRT* (Goal-guided, adaptive informed-Ellipse sampling, layered obstacle-Avoidance expansion, and cost-driven Rewiring), which constructs a collaborative optimization mechanism across the three stages of sampling, expansion, and rewiring. First, the proposed method employs an adaptive informed ellipse to concentrate sampling within feasible regions while dynamically adjusting the informed-ellipse sampling domain, and further integrates Halton-directional hybrid sampling to generate high-quality candidate samples within that domain. Meanwhile, a layered expansion strategy is adopted: the planner first performs direct goal connection for rapid progress toward the goal; when this expansion is blocked by obstacles, it switches to local multi-directional offset to search for feasible expansion directions; if this still fails, an adaptive Artificial Potential Field is introduced to guide subsequent expansions until a feasible path is found. Next, a multi-factor rewiring parent selection strategy is used to optimize path length, safety clearance, and turning angle, while cubic B-spline smoothing is applied to improve path continuity. Finally, GEAR-RRT* is evaluated in five simulation environments as well as in joint ROS and physical-robot validation and is compared with five improved RRT* variants. The results demonstrate that the proposed method achieves superior overall performance in planning time, path length, and safety clearance.