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A Fuzzy Cluster-based Framework for Robot-Environment Collision Reaction
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0334-2554
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0001-8119-0843
2024 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 32, no 1, p. 75-89Article in journal (Refereed) Published
Abstract [en]

Environmental collision is a challenging issue in human-robot collaboration. This article proposes a novel fuzzy cluster-based framework for robots to have reactive responses to various environmental collision scenarios. This framework makes four contributions: First, a fuzzy cluster-based environmental collision detection algorithm is developed to efficiently classify the collision area and non-collision (free) area of the environment. Second, based on the collision detection algorithm, a p-norm approximation-based collision avoidance algorithm is proposed to enable robots to avoid environmental collisions with guaranteed stability. Third, by extending the collision avoidance algorithm, an environmental collision adaptation algorithm is proposed to allow robots to adapt to environmental collisions with intelligently regulated contact force. Fourth, a teleoperation controller is designed to strengthen haptic force rendering and enhance the operator’s perception of collisions. Going beyond existing methods, the proposed framework allows teleoperated robots to have real-time responses to collisions in quasi-static environments without suffering from local optima, where the environments can be unstructured, non-convex, and detected with noisy outliers. In addition, this framework is simple in implementation because the proposed collision avoidance and collision adaptation algorithms work as several linear Quadratic Programming (QP) constraints that can be flexibly used by Inverse Kinematics (IK) solvers. Several experiments using 7-Degree of Freedom (DoF) robots are conducted to test and compare the proposed framework with existing methods, demonstrating the effectiveness of our work.

Place, publisher, year, edition, pages
IEEE, 2024. Vol. 32, no 1, p. 75-89
Keywords [en]
Environmental collision detection and reaction, optimization control, point clouds, telerobotics
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-106703DOI: 10.1109/TFUZZ.2023.3290124ISI: 001136745800016Scopus ID: 2-s2.0-85163475884OAI: oai:DiVA.org:oru-106703DiVA, id: diva2:1777437
Funder
Knowledge Foundation, 20210016Available from: 2023-06-29 Created: 2023-06-29 Last updated: 2024-03-15Bibliographically approved

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Sun, DaLiao, Qianfang

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