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arXiv · 1808.00593

Perception-driven sparse graphs for optimal motion planning

Thomas Sayre-McCord·Sertac Karaman
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Abstract

Most existing motion planning algorithms assume that a map (of some quality) is fully determined prior to generating a motion plan. In many emerging applications of robotics, e.g., fast-moving agile aerial robots with constrained embedded computational platforms and visual sensors, dense maps of the world are not immediately available, and they are computationally expensive to construct. We propose a new algorithm for generating plan graphs which couples the perception and motion planning processes for computational efficiency. In a nutshell, the proposed algorithm iteratively switches between the planning sub-problem and the mapping sub-problem, each updating based on the other until a valid trajectory is found. The resulting trajectory retains a provable property of providing an optimal trajectory with respect to the full (unmapped) environment, while utilizing only a fraction of the sensing data in computational experiments.

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Thomas Sayre-McCord, Sertac Karaman. 2018-08-01. Perception-driven sparse graphs for optimal motion planning. https://arxiv.org/abs/1808.00593

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