Output-Feedback Path Planning with Robustness to State-Dependent Errors

We consider the problem of sample-based feedback motion planning from measurements affected by systematic errors. Our previous work presented output feedback controllers that use measurements from landmarks in the environment to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear Programming. In this paper, we build on this previous work with a novel strategy that allows the use of measurements affected by systematic errors in perceived depth (similarly to what might be generated by vision-based sensors), as opposed to accurate displacement measurements. As a result, our new method has the advantage of providing more robust performance (with quantitative guarantees) when inaccurate sensors are used. We test the proposed algorithm in the simulation to evaluate the performance limits of our approach predicted by our theoretical derivations.