A geometric approach to error detection and recovery for robot motion planning with uncertainty

摘要

Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometric models of the environment and of the robot. The last, which we will call model uncertainty, has received little previous attention. In this paper we present a formal framework for computing motion strategies which are guaranteed to succeed in the presence of all three kinds of uncertainty. We show that it is effectively computable for some simple cases. The motion strategies we consider include sensor-based gross motions, compliant motions, and simple pushing motions.