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Avoiding obstacles is challenging for autonomous robotic systems. In this work, we examine obstacle avoidance for legged hexapods, as it relates to climbing over randomly placed wooden joists. We formulate the task as a 3D joist detection problem, and propose a detect-plan-act pipeline using a SLAM algorithm to generate a pointcloud and a grid map to expose high obstacles such as joists. A line detector is applied on the grid map to extract parametric information of the joist, such as height, width, orientation, and distance; based on this information the hexapod plans a sequence of leg movements to either climb over the joist or move sideways. We show that our perception and path planning module works well on the real-world joists with different heights and orientations.

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