Current state-of-the-art motion planners rely on sampling-based planning to
rapidly cover and explore the problem space for a solution. However, sampling valid configurations in narrow or cluttered workspaces remains a challenge because the free space is relatively restricted. If a valid path for the robot is highly correlated with a path in the workspace, then the planning process would benefit from a representation of the workspace that captured its salient topological features. Prior approaches have investigated exploiting geometric decompositions of the workspace to bias sampling; while beneficial in some environments, complex narrow passages remain challenging to navigate.
In this work, we present Dynamic Region-biased RRT, a novel sampling-based
planner that guides the exploration of a Rapidly-exploring Random Tree (RRT) by dynamically moving sampling regions along an embedded graph that captures the workspace topology. These sampling regions are dynamically created,
manipulated, and destroyed to greedily bias sampling through unexplored passages that lead to the goal. We compare our approach with related methods on a set of maze-like problems.