Risk-Averse Traversal of Graphs with Stochastic and Correlated Edge Costs for Safe Global Planetary Mobility

TL;DR

This paper introduces a risk-averse planning method for planetary surface exploration, optimizing routes under uncertainty using a novel CVaR-based algorithm that accounts for correlated terrain risks.

Contribution

It formalizes a new risk-averse variant of the Canadian Traveller Problem for planetary mobility and proposes an exact CVaR-optimal search algorithm extending existing AND-OR search techniques.

Findings

The algorithm finds exact CVaR-optimal policies for planetary traversal.

Simulations on Martian maps demonstrate adaptive decision-making based on risk levels.

Accounting for terrain correlations enables risk mitigation through information-seeking detours.

Abstract

In robotic planetary surface exploration, strategic mobility planning is an important task that involves finding candidate long-distance routes on orbital maps and identifying segments with uncertain traversability. Then, expert human operators establish safe, adaptive traverse plans based on the actual navigation difficulties encountered in these uncertain areas. In this paper, we formalize this challenge as a new, risk-averse variant of the Canadian Traveller Problem (CTP) tailored to global planetary mobility. The objective is to find a traverse policy minimizing a conditional value-at-risk (CVaR) criterion, which is a risk measure with an intuitive interpretation. We propose a novel search algorithm that finds exact CVaR-optimal policies. Our approach leverages well-established optimal AND-OR search techniques intended for (risk-agnostic) expectation minimization and extends these methods to the risk-averse domain. We validate our approach through simulated long-distance planetary surface traverses; we employ real orbital maps of the Martian surface to construct problem instances and use terrain maps to express traversal probabilities in uncertain regions. Our results illustrate different adaptive decision-making schemes depending on the level of risk aversion. Additionally, our problem setup allows accounting for traversability correlations between similar areas of the environment. In such a case, we empirically demonstrate how information-seeking detours can mitigate risk.