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Online mapping and motion planning under uncertainty for safe navigation in unknown environments

Pairet, Èric, Hernández, Juan David, Carreras, Marc, Petillot, Yvan and Lahijanian, Morteza 2021. Online mapping and motion planning under uncertainty for safe navigation in unknown environments. IEEE Transactions on Automation Science and Engineering 10.1109/TASE.2021.3118737

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Abstract

Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localisation information, but also their manoeuvrability is constrained by their dynamics and often suffer from uncertainty. In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment, and (ii) iteratively (re)planning trajectories to goal that are kinodynamically feasible and probabilistically safe through a multi-layered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely, (a) the multi-layered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees, and (b) the proposed routine for probabilistic collision checking results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature. Furthermore, real-world in-water experimental evaluation on a non-holonomic torpedo-shaped autonomous underwater vehicle and simulated trials in an urban environment on an unmanned aerial vehicle demonstrate the efficacy of the method as well as its suitability for systems with limited on-board computational power.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Engineering
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1545-5955
Date of First Compliant Deposit: 26 October 2021
Date of Acceptance: 13 September 2021
Last Modified: 11 Nov 2021 13:27
URI: https://orca.cardiff.ac.uk/id/eprint/144697

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