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

TL;DR

This paper introduces a novel output-feedback path planning method that enhances robustness against systematic, state-dependent measurement errors, particularly in vision-based sensing, with theoretical guarantees and simulation validation.

Contribution

It extends previous work by enabling the use of depth measurements affected by systematic errors, improving robustness in sensor-impaired environments.

Findings

The new method provides quantitative robustness guarantees.

Simulation results validate the theoretical performance limits.

The approach outperforms previous methods in error-prone sensing scenarios.

Abstract

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.