Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor
This addresses the problem of safe robot navigation for autonomous systems in unknown environments, but it appears incremental as it builds on existing control and safety methods.
The paper tackles safe autonomous navigation in unknown cluttered environments by developing a control method using invariant ellipsoids and a reference governor to adaptively track paths while ensuring safety, with theoretical guarantees provided for nonlinear system path-following control.
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing. We consider a control-affine nonlinear robot system subject to bounded input noise and rely on feedback linearization to determine ellipsoid output bounds on the closed-loop robot trajectory under stabilizing control. A virtual governor system is developed to adaptively track a desired navigation path, while relying on the robot trajectory bounds to slow down if safety is endangered and speed up otherwise. The main contribution is the derivation of theoretical guarantees for safe nonlinear system path-following control and its application to autonomous robot navigation in unknown environments.