Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling

TL;DR

This paper introduces a risk-sensitive sampling-based planning algorithm for stochastic shortest-path problems that minimizes Conditional Value-at-Risk (CVaR), resulting in more robust paths under uncertainty compared to traditional methods.

Contribution

It proposes a novel risk-aware RRT* algorithm that optimizes CVaR at each step, improving robustness in stochastic path planning.

Findings

Paths are less sensitive to environmental noise.

Algorithm maintains similar complexity to RRT*.

Reduced planner failure rates in experiments.

Abstract

With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes. Mainstream chance-constrained incremental sampling techniques for solving SSP problems tend to be overly conservative and typically do not consider the likelihood of undesirable tail events. We propose an alternative risk-aware approach inspired by the asymptotically-optimal Rapidly-Exploring Random Trees (RRT*) planning algorithm, which selects nodes along path segments with minimal Conditional Value-at-Risk (CVaR). Our motivation rests on the step-wise coherence of the CVaR risk measure and the optimal substructure of the SSP problem. Thus, optimizing with respect to the CVaR at each sampling iteration necessarily leads to an optimal path in the limit of the sample size. We validate our approach via numerical path planning experiments in a two-dimensional grid world with obstacles and stochastic path-segment lengths. Our simulation results show that incorporating risk into the tree growth process yields paths with lengths that are significantly less sensitive to variations in the noise parameter, or equivalently, paths that are more robust to environmental uncertainty. Algorithmic analyses reveal similar query time and memory space complexity to the baseline RRT* procedure, with only a marginal increase in processing time. This increase is offset by significantly lower noise sensitivity and reduced planner failure rates.