Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control

TL;DR

This paper presents a framework for nonlinear robot motion planning that explicitly incorporates perception and prediction uncertainties to mitigate risks of constraint violations, using distributionally robust risk constraints and a layered autonomy stack.

Contribution

It introduces a novel integration of perception, motion planning, and control that accounts for uncertainties explicitly, improving robustness in nonlinear robot operations.

Findings

Effective uncertainty propagation with Unscented Kalman Filter

Risk mitigation through distributionally robust constraints

Successful validation in urban driving simulations

Abstract

Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity. Robots with nonlinear dynamics and complex sensing modalities operating in an uncertain environment demand more careful consideration of how uncertainties propagate across stack layers. We propose a framework to integrate perception, motion planning, and control by explicitly incorporating perception and prediction uncertainties into planning so that risks of constraint violation can be mitigated. Specifically, we use a nonlinear model predictive control based steering law coupled with a decorrelation scheme based Unscented Kalman Filter for state and environment estimation to propagate the robot state and environment uncertainties. Subsequently, we use distributionally robust risk constraints to limit the risk in the presence of these uncertainties. Finally, we present a layered autonomy stack consisting of a nonlinear steering-based distributionally robust motion planning module and a reference trajectory tracking module. Our numerical experiments with nonlinear robot models and an urban driving simulator show the effectiveness of our proposed approaches.