Greedy Heuristics for Sampling-based Motion Planning in High-Dimensional State Spaces

Sampling-based motion planning algorithms are very effective at finding solutions in high-dimensional continuous state spaces as they do not require prior approximations of the problem domain compared to traditional discrete graph-based searches. The anytime version of the Rapidly-exploring Random Trees (RRT) algorithm, denoted as RRT*, often finds high-quality solutions by incrementally approximating and searching the problem domain through random sampling. However, due to its low sampling efficiency and slow convergence rate, research has proposed many variants of RRT*, incorporating different heuristics and sampling strategies to overcome the constraints in complex planning problems. Yet, these approaches address specific convergence aspects of RRT* limitations, leaving a need for a sampling-based algorithm that can quickly find better solutions in complex high-dimensional state spaces with a faster convergence rate for practical motion planning applications. This article unifies and leverages the greedy search and heuristic techniques used in various RRT* variants to develop a greedy version of the anytime Rapidly-exploring Random Trees algorithm, denoted as Greedy RRT* (G-RRT*). It improves the initial solution-finding time of RRT* by maintaining two trees rooted at both the start and goal ends, advancing toward each other using greedy connection heuristics. It also accelerates the convergence rate of RRT* by introducing a greedy version of direct informed sampling procedure, which guides the sampling towards the promising region of the problem domain based on heuristics. We validate our approach on simulated planning problems, manipulation problems on Barrett WAM Arms, and on a self-reconfigurable robot, Panthera. Results show that G-RRT* produces asymptotically optimal solution paths and outperforms state-of-the-art RRT* variants, especially in high-dimensional planning problems.

Path tracking control of self-reconfigurable robot hTetro with four differential drive units

The research interest in robots with more than one steerable wheel has been increasing over recent years due to their high mobility while having a better payload capacity than systems using omnidirectional wheels. However, with more controllable degrees of freedom, almost all of the platforms include redundancy which leads to a modeling method based on the instantaneous center of rotation. The self-reconfigurable the robotic platform, hTetro, is designed for floor cleaning tasks. It also has four differential-drive units which can steer individually. Differing from most other steerable wheeled mobile robots, the wheel arrangement of this robot changes because of its reconfigurability. In this paper, we proposed a robust path tracking controller that can handle discontinuous trajectories and sudden orientation changes. Singularity problems are resolved on both the mechanical aspect and control aspect. The controller is tested experimentally with the self-reconfigurable robotic platform hTetro, and results are discussed.