Abstract
Underwater autonomous manipulation is the capability of a mobile robot to perform intervention tasks that require physical contact with unstructured environments without continuous human supervision. Being difficult to assess the behaviour of existing motion planner algorithms, this research proposes a new planner evaluation metric to identify well-behaved planners for specialized tasks of inspection and monitoring of man-made underwater structures. This metric is named NEMU and combines three different performance indicators: effectiveness, safety and adaptability. NEMU deals with the randomization of sampling-based motion planners. Moreover, this article presents a benchmark of multiple planners applied to a 6 DoF manipulator operating underwater. Results conducted in real scenarios show that different planners are better suited for different tasks. Experiments demonstrate that the NEMU metric can be used to distinguish the performance of planners for particular movement conditions. Moreover, it identifies the most promising planner for collision-free motion planning, being a valuable contribution for the inspection of maritime structures, as well as for the manipulation procedures of autonomous underwater vehicles during close range operations.
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Ioan A. Sucan and Sachin Chitta,“MoveIt”, [Online] Available at moveit.ros.org.
\(\beta \) values for MPP 1, MPP 2 and MPP 3 are 10000, 900 and 600, respectively.
“Qualisys | MotionCaptureSystems.”[Online]. Available:https://www.qualisys.com/
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This research has received funding from the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014–2020, under grant agreement No. 871571. This work is co-financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project NESSIE: POCI-01-0247-FEDER-039817. The work of Renato Silva was supported in part by the Portuguese Government through the Fundação para a Ciência e a Tecnologia (FCT) by the Ph.D. Grant under Grant 2020.08349.BD.
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Silva, R., Matos, A. & Pinto, A.M. Multi-criteria metric to evaluate motion planners for underwater intervention. Auton Robot 46, 971–983 (2022). https://doi.org/10.1007/s10514-022-10060-x
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DOI: https://doi.org/10.1007/s10514-022-10060-x