qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems
This work addresses the problem of optimal motion planning for non-holonomic systems when cost functions are unknown, which is relevant for robotics and autonomous systems.
This paper introduces qRRT, a sampling-based method for optimal motion planning in non-holonomic systems without known cost functions. It learns cost-to-go information through experience to bias an incremental graph search, producing asymptotically optimal solution trajectories.
This paper presents a sampling-based method for optimal motion planning in non-holonomic systems in the absence of known cost functions. It uses the principle of learning through experience to deduce the cost-to-go of regions within the workspace. This cost information is used to bias an incremental graph-based search algorithm that produces solution trajectories. Iterative improvement of cost information and search biasing produces solutions that are proven to be asymptotically optimal. The proposed framework builds on incremental Rapidly-exploring Random Trees (RRT) for random sampling-based search and Reinforcement Learning (RL) to learn workspace costs. A series of experiments were performed to evaluate and demonstrate the performance of the proposed method.