Planning and Navigation of Climbing Robots in Low-Gravity Environments
Steven Morad, Himangshu Kalita and Jekan Thangavelautham, University of Arizona
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. Apart from climbing, path planning, and navigation is another critical challenge that needs to be solved to make the whole approach feasible. For climbing navigation, a multirobot system needs to have up to date info of its location, together with a macroscopic map of the climbing surface and a detailed map ahead. This system with limited sensor range needs to discern and identify feasible pathways to make the next climbing step much like a human mountaineer. These climbing pathways consist of a series of anchor points for the robot to grip onto next. Identifying one or more feasible pathways is a critical challenge as the terrain ahead needs to be acquired, followed by identification and ranking of anchor points to grip. The climbing task resembles a maze with wrong pathways leading to dead-end. The multirobot systems need to autonomously explore climbing pathways and know when to give up. 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.