Stable Heteroclinic Channel Networks for Physical Human–Humanoid Robot Collaboration

Author:

Brecelj Tilen1ORCID,Petrič Tadej1ORCID

Affiliation:

1. Department of Automatics, Biocybernetics and Robotics, Jožef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia

Abstract

Human–robot collaboration is one of the most challenging fields in robotics, as robots must understand human intentions and suitably cooperate with them in the given circumstances. But although this is one of the most investigated research areas in robotics, it is still in its infancy. In this paper, human–robot collaboration is addressed by applying a phase state system, guided by stable heteroclinic channel networks, to a humanoid robot. The base mathematical model is first defined and illustrated on a simple three-state system. Further on, an eight-state system is applied to a humanoid robot to guide it and make it perform different movements according to the forces exerted on its grippers. The movements presented in this paper are squatting, standing up, and walking forwards and backward, while the motion velocity depends on the magnitude of the applied forces. The method presented in this paper proves to be a suitable way of controlling robots by means of physical human-robot interaction. As the phase state system and the robot movements can both be further extended to make the robot execute many other tasks, the proposed method seems to provide a promising way for further investigation and realization of physical human–robot interaction.

Funder

Slovenian Research Agency

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Utilizing a Phase State System for Reliable Physical Assistance in Human-Humanoid Robot Collaboration;2023 21st International Conference on Advanced Robotics (ICAR);2023-12-05

2. On relaxation times of heteroclinic dynamics;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-10-01

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