Online collision avoidance for human-robot collaborative interaction concerning safety and efficiency

Liu, Guoliang, He, Haoyang, Tian, Guohui, Zhang, Jianhua and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2020. Online collision avoidance for human-robot collaborative interaction concerning safety and efficiency. Presented at: 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2020), Online, 6-10 July 2020. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, pp. 1167-1672. 10.1109/AIM43001.2020.9158647 Item availability restricted.

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Abstract

With the development of robot technology and the arrival of industry 4.0 era, society pays more attention to collaboration and interaction between human and robots. However, safety is still main concern in the development of human-robot collaboration. In this paper, a novel real-time collision avoidance approach for manipulator is proposed by considering the motion status of the human, which includes the relative minimum distance and velocity (both magnitude and direction) between the robot and the human. The distance and velocity of the human hand are first estimated online using a vision sensor, and then defined as danger factors in the potential function of the potential field. The novel potential function proposed in this paper considers not only the safety problem, but also the efficient problem, i.e., the manipulator canmakesmartcontroldecisiontoavoidthecollisionaccording to the relative velocity in case of the cross over. To overcome the local minimum problem and choose a best motion direction, we propose a motion sampling mechanism for motion planning. For each sample, the robot calculates the potential function to evaluate the safety and efficiency, and chooses a direction which is best for avoidance. We finally demonstrate our idea on a real manipulator platform in a human co-existance environment.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9781728167954
ISSN:	2159-6247
Date of First Compliant Deposit:	1 June 2020
Date of Acceptance:	27 May 2020
Last Modified:	07 Dec 2022 16:11
URI:	https://orca.cardiff.ac.uk/id/eprint/132090

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