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Reid, William

Publications and source records attributed to Reid, William.

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Autonomous Navigation over Europa Analogue Terrain for an Actively Articulated Wheel-on-Limb Rover

The ocean world Europa is a prime target forexploration given its potential habitability [1]. We proposea mobile robotic system that is capable of autonomouslytraversing hundreds of meters to visit multiple sites of intereston a Europan analogue surface. Due to the topology of Europanterrain being largely unknown, it is desired that this mobilitysystem traverse a large variety of terrain types. The mobilitysystem should also be capable of crossing unstructured terrainin an autonomous manner given the communications limitationsbetween Earth and Europa.A wheel-on-limb robotic rover is presented that may activelyconform to terrain features up to 1.5 wheel diameters tall whiledriving. The robot uses a sampling-based motion planner togenerate paths that leverage its unique locomotive capabilities.The planner assesses terrain hazards and wheel workspacelimits as obstacles. It may also select a mobility mode basedon predicted energy usage and the need for limb articulationon the terrain being traversed. This autonomous mobility wasevaluated on the chaotic salt-evaporite terrain found in DeathValley, CA, an analogue to the Europan surface. Over the courseof 38 trials, the rover autonomously traversed 435m of extremeterrain while maintaining a rate of 0.64 traverse ending failuresfor every 10m driven.

Meirion-Griffith, Gareth↗
NASA NTRS2020

A Reinforcement Learning Framework for Space Missions in Unknown Environments

A land-and-traverse mission to icy worlds such as Europa and Enceladus is challenging due to lack of prior knowledge regarding the terrain conditions. Previous work [1] showed that rovers with high degrees of freedom (DoF) can achieve robust traversal by leveraging redundant modes for mobility to counter terrain uncertainty (e.g. walking, driving, or inch-worming). This paper presents a generic and scalable reinforcement learning scheme for enabling on-board decision making on rovers to automatically switch between modes of traversal based on online performance feedback. The objective is to maximize energy efficiency, minimize operator input and successfully negotiate unstructured terrain conditions without relying on exhaustive prior knowledge. The proposed methodology is well grounded in the literature on reinforcement learning and has been adapted to address conformance to validation and verification requirements and JPL flight operations history of using per-sol prescribed sequences for a space mission.

Tavallali, Peyman↗
NASA NTRS2020

Mobility Mode Evaluation of a Wheel-on-Limb Rover on Glacial Ice Analogous to Europa Terrain

In this paper, we discuss the development of a multi-modal locomotion system and the results of field trials performed on fractured, glacial ice. Work was performed using the RoboSimian rover: a 32 degree-of-freedom, actively articulated mobility system. Three modes of mobility are compared: wheelrolling, inchworming (push-rolling) and wheel-walking. Each mobility mode is designed to operate with articulated suspension whereby the normal load per wheel, body orientation, and available limb workspace are actively controlled. Each mode is presented individually alongside a discussion of its performance on terrain of varied slope and topographic roughness. Further, the utility of a multi-modal approach is presented, whereby rover immobilization was avoided during field trials through the selection of appropriate mobility modes as a function of terrain properties. Lastly, the results of trials performed using a bodymounted sampling system and its ability to collect and process samples taken 10 cm beneath the surface are discussed.

Reid, William↗
NASA NTRS2019

Towards Articulated Mobility and Efficient Docking for the DuAxel Tethered Robot System

Sites of increasing interest for planetary science,such as craters, cold traps, and vents lie in terrains that are inaccessible to state-of-the-art rovers. The Jet Propulsion Laboratory, in collaboration with Caltech, is actively developing a tethered mobile robot, Axel, for traversing and exploring extremely steep terrain, such as Recurring Slope Lineae on Mars and vertical pits on the Moon. However, on Mars, where landing-site uncertainty is high due to the presence of an atmosphere, Axel may need to traverse several kilometers from its lander untethered due to a finite tether carrying capacity (⇠300m). This paper proposes a novel design for a hybrid mobility system that allows a pair of Axel rovers to dock, lock, and drive long distances as a four-wheeled, articulated steering vehicle. The design improves upon prior efforts to achieve DuAxel mobility by leveraging two actuated docking mechanisms attached on opposite ends of a central module to enable ‘sit/stand’ functionality; the prior DuAxel system was limited to skid steering, which was inefficient due to Axel’s grouser-style, high-friction wheels. In the proposed system, the ‘sit’ configuration is achieved by aligning each dock parallel to the surface, allowing one Axel to detach and explore while the other remains docked and serves as a backup. While ‘sitting’, the central module rests on the ground and is outfitted with shovel-style wedges for passive anchoring to sandy terrain (an optional drill can be integrated for anchoring to rock). In order to ‘stand’, the exploring Axel reattaches, locks, and both docks are rotated until Axel’s tether caster arm is upright and the central module is lifted off the ground. Once upright, each Axel rotates about a pivot point for articulated, all-wheel steering, which is accomplished by applying differential wheel torques. The main contributions of this paper are i) a detailed systems design of the docking mechanism and central module, ii) kinematic modeling of articulated mobility and ‘sit/stand’ docking functionality, and iii) initial testing in a relevant environment to characterize the mobility of the proposed system.

Nesnas, Issa↗