Robot motion planning for sensor-based control with uncertainties

Abstract

This paper describes a new representation for sensing and control uncertainties in sensor-based robot control, and presents motion planning algorithms that use this representation. The planning algorithms employ a robot's sensing capabilities as needed to accomplish a task, by activating sensors at points or intervals during motion. A sensor is represented by three quantities: a domain, which is the set of robot configurations at which a valid measurement can be taken; an absolute sensing uncertainty held, which describes the sensor's absolute (global) accuracy; and an incremental motion uncertainty, which describes the sensor's relative (pertaining to displacements) accuracy. Control uncertainty represents the ability of a controller to drive the measured error near zero. These descriptions of sensing and control capability determine the evolution of uncertainty in a sensor-based motion plan. Two complementary algorithms for motion planning with the new representation are presented. One is a backprojection algorithm which searches globally (in configuration space and the available sensors) for ways to achieve a particular subgoal. The other searches locally (in path space) to satisfy all the constraints in a planning problem. Examples of uncertainty evaluation and motion planning in two degrees of freedom are presented.