Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems

TL;DR

This paper introduces ACE, a fast, conservative clearance evaluation algorithm for Mars rover path planning that avoids complex iterative computations, ensuring safety with minimal computational resources.

Contribution

The paper presents a novel, lightweight algorithm for rover clearance evaluation that provides conservative safety bounds without iterative optimization, suitable for onboard planetary rover navigation.

Findings

ACE runs significantly faster than traditional methods.

ACE provides guaranteed conservative safety bounds.

Experimental validation confirms ACE's effectiveness and efficiency.

Abstract

We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst-case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments.