Path planning with uncertainty: A set membership approach

Abstract

The paper addresses the path planning problem in a set theoretic framework. The considered scenario is that of a mobile robot exploiting range and bearing measurements with respect to known landmarks to localize itself. By assuming unknown-but-bounded measurement noise, set membership localization techniques are used to estimate the uncertainty of each robot pose within the considered environment. The path planning problem is formulated and solved, with the aim of minimizing the total uncertainty associated with the travelled path. Practical issues such as limited sensory range and obstacle avoidance are taken into account. The proposed technique is validated via numerical simulations.