Fast approximate clearance evaluation for rovers with articulated suspension systems

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.

Path Planning for Autonomous Mobile Robots: A Review

“…Other terrain shapes can be negotiated by the robot, according to its chassis. For instance, it can overcome rocks using the body clearance, which is the space between the body lower surface and the terrain surface [ 60 , 61 ]. The presence of negative obstacles such as holes or ditches can be problematic as they are difficult to capture by the robot’s sensors [ 62 ].…”

STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation

“…Planning algorithms for such methods take into account the stability of the robot on the terrain [19]. In [30,17], the authors estimate the probability distributions of states based on the kinematic model of the vehicle and the terrain height uncertainty. Furthermore, a method for incorporating sensor and state uncertainty to obtain a probabilistic terrain estimate in the form of a grid-based elevation map was considered in [15].…”

Machine Learning Based Path Planning for Improved Rover Navigation

“…ENav takes as input stereo imagery, maintains a 2.5D heightmap describing the terrain, and chooses the best maneuver to safely move the rover toward the global goal. ENav uses the Approximate Clearance Evaluation (ACE) algorithm [9] to evaluate a sorted list of paths for safe traversal. Running the ACE algorithm on dozens of rover poses along hundreds of candidate rover paths represents a significant computational burden, especially if the list is sorted poorly and many paths fail the ACE check, which is more likely in complex and challenging terrain.…”