Modifying ABIT* for Tethered Rappelling Robot Motion Planning
Austen Goddu, Travis L. Brown, Michael Paton, James Motes, Tan Chen · 2025
In this paper, we improve a path planner for a tethered rappelling robot to find initial solutions efficiently. Implemented on NASA-JPL’s Axel rover, the new planner offers an increase in success rate as high as 20% while using fewer resources compared to the original. This higher performance is achieved by modifying the underlying random geometric graph configuration to use a k-nearest neighbor approach, and biasing the sampling portion of the algorithm to add more consideration to sloped regions. These improvements are tested on a number of sloped maps constructed to have specific features or model the real world, using a pipeline involving generated terrain models and a simulated depth camera. In addition to the comparison to the original planner, various configurations are found to improve the success and efficiency of the motion planner on large and noisy maps.