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Online multilayered motion planning with dynamic constraints for autonomous underwater vehicles

Vidal, Eduard, Moll, Mark, Palomeras, Narcis, Hernández, Juan David, Carreras, Marc and Kavraki, Lydia E. 2019. Online multilayered motion planning with dynamic constraints for autonomous underwater vehicles. Presented at: IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20-24 May 2019. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 8936-8942. 10.1109/ICRA.2019.8794009

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Underwater robots are subject to complex hydrodynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot’s onboard computer is challenging due to the limited computational resources available. In this paper an efficient motion planning framework for autonomous underwater vehicles (AUVs) is presented. By introducing a loosely coupled multilayered planning design, our framework is able to generate dynamically feasible trajectories while keeping the planning time low enough for online planning. First, a fast path planner operating in a lower-dimensional projected space computes a lead path from the start to the goal configuration. Then, the lead path is used to bias the sampling of a second motion planner, which takes into account all the dynamic constraints. Furthermore, we propose a strategy for online planning that saves computational resources by generating the final trajectory only up to a finite horizon. By using the finite horizon strategy together with the multilayered approach, the sampling of the second planner focuses on regions where good quality solutions are more likely to be found, significantly reducing the planning time. To provide strong safety guarantees our framework also incorporates the conservative approximations of inevitable collision states (ICSs). Finally, we present simulations and experiments using a real underwater robot to demonstrate the capabilities of our framework.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Additional Information: "© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."
Publisher: IEEE
ISBN: 9781538660270
ISSN: 2577-087X
Date of First Compliant Deposit: 2 February 2021
Date of Acceptance: 29 January 2019
Last Modified: 12 Feb 2021 13:51

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