Exploiting Hierarchical Probabilistic Motion Planning for Robot Reachable Workspace Estimation

Given an environment and a robot, how much of the environment is reachable or accessible to the robot? This fundamental problem in robotics is known as reachable workspace estimation and is closely related to the problem of determining possible kinematic motions of a robot. For mobile kinematic structures with high degrees of freedom (DOFs) in cluttered environments, the motion planning problem is known to be NP-hard. Given the intractability of the problem, we present an efficient probabilistic method for workspace estimation based on the use of a hierarchical strategy and a probabilistic motion planner. The probabilistic motion planner is used to identify reachable portions of the workspace but rather than treating each DOF equally, a hierarchical representation is used to maximize the volume of the robot’s workspace that is identified as reachable for each probe of the environment. Experiments with a simulated mobile manipulator demonstrate that the hierarchical approach is an effective alternative to the use of an estimation process based on the use of a traditional probabilistic planner.