Rapidly-Exploring Random Vines (RRV) for Motion Planning in Configuration Spaces with Narrow Passages
Classical RRT algorithm is blind to efficiently explore configuration space for expanding the tree through a narrow passage when solving a motion planning (MP) problem. Although there have been several attempts to deal with narrow passages which are based on a wide spectrum of assumptions and configuration setups, we solve this problem in rather general way. We use dominant eigenvectors of the configuration sets formed by properly sampling the space around the nearest node, to efficiently expand the tree around the obstacles and through narrow passages. Unlike classical RRT, our algorithm is aware of having the tree nodes in front of a narrow passage and in a narrow passage, which enables a proper tree expansion in a vine-like manner. A thorough comparison with RRT, RRT-connect, and DDRRT algorithm is provided by solving three different difficult MP problems. The results suggest a significant superiority the proposed Rapidly-exploring Random Vines (RRV) algorithm might have in configuration spaces with narrow passages.