HIERARCHICAL PROBABILISTIC ESTIMATION OF ROBOT REACHABLE WORKSPACE
Jing Yang, Patrick W. Dymond, Michael R. M. Jenkin · 2009
Abstract: Estimating a robot’s reachable workspace is a fundamental problem in robotics. For simple kinematic chains within an empty environment this computation can be relatively straightforward. For mobile kinematic struc-tures and cluttered environments, the problem becomes more challenging. An efficient probabilistic method for workspace estimation is developed by applying a hierarchical strategy and developing extensions to a prob-abilistic motion planner. Rather than treating each of the degrees of freedom (DOFs) ‘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 hierarchi-cal approach is an effective alternative to the use of an estimation process based on the use of a traditional probabilistic planner. 1