Abstract
Sampling-based methods, such as Probabilistic Roadmap Method(PRM)[l], Rapidly-Exploring Random Tree(RRT)[2], have been proposed as promising solutions for kinodynamic problems. Nevertheless, it's still a challenge for practical application especially for complex systems. In particular, most of the forward propagation is fruitless, which lead to heavy computation and be time-consuming. This paper presents a greedy kinodynamic motion planner: Guided RRT. The main characteristics are that the environments are explored by Geometric trees previously and nodes near the geometric feasible path enjoy more preference. Instead of exploring the environments uniformly, the new approach expands towards the goal greedily along a series of waypoints, with probabilistically completeness. And to guarantee the effective of the greed, a new distance metric based on Euclidean metric are proposed by considering both the current position and the following position with zero-input. We compare our technique with standard RRT and show that it achieves favorable performance when planning under kinodynamic constraints.
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