Planning and Navigation of Climbing Robots in Low-Gravity Environments

TL;DR

This paper introduces a cooperative multirobot climbing system for low-gravity environments, utilizing autonomous mapping, planning, and navigation algorithms to traverse rugged, obstacle-filled terrains in simulated conditions.

Contribution

It presents a novel multirobot climbing approach with a new bounded-leg A* algorithm for autonomous navigation in challenging terrains.

Findings

Successful simulation of autonomous climbing in complex environments

Effective mapping and navigation using laser and vision sensors

Promising results for future real-world demonstrations

Abstract

Advances in planetary robotics have led to wheeled robots that have beamed back invaluable science data from the surface of the Moon and Mars. However, these large wheeled robots are unable to access rugged environments such as cliffs, canyons and crater walls that contain exposed rock-faces and are geological time-capsules into the early Moon and Mars. We have proposed the SphereX robot with a mass of 3 kg, 30 cm diameter that can hop, roll and fly short distances. A single robot may slip and fall, however, a multirobot system can work cooperatively by being interlinked using spring-tethers and work much like a team of mountaineers to systematically climb a slope. We consider a team of four or more robots that are interlinked with tethers in an 'x' configuration. Each robot secures itself to a slope using spiny gripping actuators, and one by one each robot moves upwards by crawling, rolling or hopping up the slope. In this paper, we present a human devised autonomous climbing algorithm and evaluate it using a high-fidelity dynamics simulator. The climbing surfaces contain impassable obstacles and some loosely held rocks that can dislodge. Under these conditions, the robots need to autonomously map, plan and navigate up or down these steep environments. Autonomous mapping and navigation capability is evaluated using simulated lasers, vision sensors. The human devised planning algorithm uses a new algorithm called bounded-leg A*. Our early simulation results show much promise in these techniques and our future plans include demonstration on real robots in a controlled laboratory environment and outdoors in the canyons of Arizona.