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Perception-Driven Sparse Graphs for Optimal Motion Planning
Name
1808.00593.pdf
Description
Submitted version
Size
466.54 KB
Format
Adobe PDF
Checksum (MD5)
3daa26eaa6108dd53394cdf31c35721d
Date Issued
October 2018
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
2018. "Perception-Driven Sparse Graphs for Optimal Motion Planning."
Version
Original manuscript
Abstract
© 2018 IEEE. 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.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/IROS.2018.8594209
