A neural network based monitoring system for safety in shared work-space human-robot collaboration. Issue 4 (18th June 2018)

• Record Type: Journal Article
• Authors: Rajnathsing, Hemant; Li, Chenggang
• Is Part Of: Industrial robot. Volume 45:Issue 4(2018)
• Page Start: 481
• Page End: 491
• Publication Date: 2018-06-18
• Subjects: Safety; Robots, Industrial -- Periodicals; Machinery in the workplace -- Periodicals; 629.892
• DOI: 10.1108/IR-04-2018-0079
• Languages: English
• ISSNs: 0143-991X

Abstract

Abstract : Purpose: Human–robot collaboration (HRC) is on the rise in a bid for improved flexibility in production cells. In the context of overlapping workspace between a human operator and an industrial robot, the major cause for concern rests on the safety of the former. Design/methodology/approach: In light of recent advances and trends, this paper proposes to implement a monitoring system for the shared workspace HRC, which supplements the robot, to locate the human operator and to ensure that at all times a minimum safe distance is respected by the robot with respect to its human partner. The monitoring system consists of four neural networks, namely, an object detector, two neural networks responsible for assessing the detections and a simple, custom speech recognizer. Findings: It was observed that with due consideration of the production cell, it is possible to create excellent data sets which result in promising performances of the neural networks. Each neural network can be further improved by using its mistakes as examples thrown back in the data set. Thus, the whole monitoring system can achieve a reliable performance. Practical implications: Success of the proposed framework may lead to any industrial robot being suitable for use in HRC. Originality/value: This paper proposes a system comprising neural networks in most part, and it looks at a digital representation of the workspace from a different angle. The exclusive use of neural networks is seen as an attempt to propose a system which can be relatively easily deployed in industrial settings as neural networks can be fine-tuned for adjustments.