Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound

Existing belief space motion planning methods are not efficient for underwater robots that are subject to spatially varying motion and sensing uncertainties arising from the non-uniform current disturbances and landmark populations, respectively. Based on a closed-loop stochastic control framework,...

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Bibliographic Details
Main Authors: Huan Yu, Wenjie Lu, Dikai Liu, Yongqiang Han, Qinghe Wu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Underwater robots
motion planning
belief space
localization uncertainty
stochastic control
Online Access:https://ieeexplore.ieee.org/document/8788534/

Internet

https://ieeexplore.ieee.org/document/8788534/

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