Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty

TL;DR

This paper introduces a risk-averse planning framework for nonlinear robotic systems that combines a novel RRT* variant with robust tracking controllers, ensuring safe navigation under uncertainty.

Contribution

It proposes RANS-RRT*, a risk-aware RRT* extension that incorporates nonlinear dynamics and distributionally robust collision checking for safer planning.

Findings

Effective planning under heavy-tailed noise demonstrated

Robust controllers improve tracking accuracy

Safe navigation in cluttered environments achieved

Abstract

We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT* (RANS-RRT*) as an RRT* variant that incorporates nonlinear dynamics by solving a nonlinear program (NLP) and accounts for risk by approximating the state distribution and performing a distributionally robust (DR) collision check to promote safe planning. The generated plan is used as a reference for a low-level tracking controller. We demonstrate three controllers: finite horizon linear quadratic regulator (LQR) with linearized dynamics around the reference trajectory, LQR with robustness-promoting multiplicative noise terms, and a nonlinear model predictive control law (NMPC). We demonstrate the effectiveness of our algorithm using unicycle dynamics under heavy-tailed Laplace process noise in a cluttered environment.