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Planning Three-Dimensional Collision-Free Optimized Climbing Path for Biped Wall-Climbing Robots

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Biped wall-climbing robots well adapt for carrying out tasks in large-scale 3-D environments such as skeleton-facade structures, due to their good abilities to transit between walls and surmount obstacles. However,… Click to show full abstract

Biped wall-climbing robots well adapt for carrying out tasks in large-scale 3-D environments such as skeleton-facade structures, due to their good abilities to transit between walls and surmount obstacles. However, to plan 3-D collision-free climbing paths in such environments is challenging and significant for biped wall-climbing robots. In this article, we propose a novel hierarchical framework for planning 3-D biped climbing navigation, including three levels, namely the global route, the foothold, and the single-step motion. We also present three effective planners incorporated in the framework to solve this challenging planning problem efficiently. A global route planner is designed on top, for rapidly analyzing the possibility for the robot to transit between walls, and searching walls that must be passed through and key footholds for performing transition, in order to optimize the route length globally. A local foothold planner is implemented in the middle, to search and optimize footholds on each via wall, considering the robot's ability to step over, step on, and bypass obstacles. A single-step motion planner is deployed at the bottom, for planning collision-free movement swinging the robot suction module from a foothold to another. Simulations are conducted to fully verify the effectiveness and performance of the presented planners. Experiments with W-Climbot are carried out to demonstrate that the proposed framework and planners succeed in planning 3-D collision-free optimized climbing paths for biped wall-climbing robots.

Keywords: collision free; wall climbing; biped wall; wall; climbing robots

Journal Title: IEEE/ASME Transactions on Mechatronics
Year Published: 2021

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