Abstract
With the development of the global economy, the demand for manufacturing is increasing. Accordingly, human–robot collaborative assembly has become a research hotspot. This paper aims to solve the efficiency problems inherent in traditional human-machine collaboration. Based on eye–hand and finite state machines, a collaborative assembly method is proposed. The method determines the human’s intention by collecting posture and eye data, which can control a robot to grasp an object, move it, and perform co-assembly. The robot’s automatic path planning is based on a probabilistic roadmap planner. Virtual reality tests show that the proposed method is more efficient than traditional methods.
Highlights
Global competition in manufacturing is becoming increasingly fierce, with greater consumer demand for high-quality but less expensive products [1]
This research aimed to solve the problems of human–robot interactive assembly
An interaction system based on eye–hand coordination with finite state machine (FSM) in virtual reality (VR) to control robots was proposed
Summary
Global competition in manufacturing is becoming increasingly fierce, with greater consumer demand for high-quality but less expensive products [1]. Combining the advantages of automation (reliability and stability) with those of people (flexibility and adaptability) can make assembly processes more flexible, cheap, and productive [5]. Human–robot collaboration (HRC) is where humans and machines work together to accomplish tasks. The humans are responsible for controlling and monitoring production, while the robots do hard physical work [6]. Petruck [7] proposed an alternative configuration of HRC workplaces called “CoWorkAs,” which combines human cognitive and sensorimotor skills with the precision, speed, and fatigue-free operation of robots to achieve effective collaboration. HRC can solve various industry problems and so has become an area worthy of study [12] Research in this area studies the responses of the human body to the adaptive level of robots to determine the impacts on team fluency, human satisfaction, safety, and comfort. Magrini [13] studied the safety of human–robot interactions and used gestures to change the operating modes of robots
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