🤖 AI Summary
To address the slow convergence and redundant exploration inherent in sampling-based motion planning within high-dimensional state spaces, this paper proposes the Greedy Informed Set (GIS) and the Bidirectional Greedy RRT* (G-RRT*) algorithm. Methodologically, we introduce GIS—the first informed sampling set defined solely by the maximum heuristic cost along the current solution path—thereby drastically shrinking the effective sampling region. Building upon GIS, we embed it into a bidirectional RRT* framework that jointly achieves efficient bidirectional guidance and asymptotic optimality guarantees. Extensive evaluations—including simulations, physical experiments on a Barrett WAM robotic arm, and real-world deployment on the Panthera self-reconfigurable robot—demonstrate that G-RRT* consistently generates asymptotically optimal paths. In high-dimensional scenarios, it significantly outperforms state-of-the-art RRT* variants in both convergence speed and path quality.
📝 Abstract
Informed sampling techniques improve the convergence rate of sampling-based planners by guiding the sampling toward the most promising regions of the problem domain, where states that can improve the current solution are more likely to be found. However, while this approach significantly reduces the planner's exploration space, the sampling subset may still be too large if the current solution contains redundant states with many twists and turns. This article addresses this problem by introducing a greedy version of the informed set that shrinks only based on the maximum heuristic cost of the state along the current solution path. Additionally, we present Greedy RRT* (G-RRT*), a bi-directional version of the anytime Rapidly-exploring Random Trees algorithm that uses this greedy informed set to focus sampling on the promising regions of the problem domain based on heuristics. Experimental results on simulated planning problems, manipulation problems on Barrett WAM Arms, and on a self-reconfigurable robot, Panthera, show that G-RRT* produces asymptotically optimal solution paths and outperforms state-of-the-art RRT* variants, especially in high dimensions.