RRT+ : Fast Planning for High-Dimensional Configuration Spaces
This work addresses motion planning challenges for complex hyper-redundant systems, offering incremental improvements in speed for domain-specific applications.
The paper tackles the problem of slow planning in high-dimensional configuration spaces by proposing RRT+, a family of algorithms that find paths in lower-dimensional subspaces, resulting in faster solutions compared to existing RRT variants, with experiments showing superior performance in high-dimensional motion planning.
In this paper we propose a new family of RRT based algorithms, named RRT+ , that are able to find faster solutions in high-dimensional configuration spaces compared to other existing RRT variants by finding paths in lower dimensional subspaces of the configuration space. The method can be easily applied to complex hyper-redundant systems and can be adapted by other RRT based planners. We introduce RRT+ and develop some variants, called PrioritizedRRT+ , PrioritizedRRT+-Connect, and PrioritizedBidirectionalT-RRT+ , that use the new sampling technique and we show that our method provides faster results than the corresponding original algorithms. Experiments using the state-of-the-art planners available in OMPL show superior performance of RRT+ for high-dimensional motion planning problems.