A neural network based monitoring system for safety in shared work-space human-robot collaboration

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.

Technology jump in the industry: human–robot cooperation in production

“…A neural network-based monitoring system was implemented and studied, which detected the operator position in the workspace. Two cameras identified the objects with 99% spatial accuracy (Rajnathsing and Li, 2018). In another study, human position tracking used Kinect sensor, where a camera provides the information.…”

Visual quality and safety monitoring system for human-robot cooperation

“…Literature review regarding the use of vision systems in HRC safety [8] shows that they can be classified into four categories: a) systems that compute the distance between several points and moving obstacles, for example between robotic joints and a human utilizing a depth camera, b) collision avoidance systems, c) human intent detection systems and d) systems that use visualization and monitoring of safety zones created by projecting virtual optical barriers with lasers or projectors. H. Rajnathsing et al [14] proposed a monitoring system for the shared HRC workspace that complements the robot to locate the human operator and always ensures that a minimum safe distance is maintained from the robot to its human partner. H. Liu et al [18] present a context awarenessbased collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time.…”

Symbiotic human-robot collaborative assembly

“…Similar to an assembly environment, the potential map using diffusion equation is employed to control the behaviour of the manipulator to avoid collision between the manipulator and an obstacle or a user. In addition, other researchers focused on combining different sensing techniques to track humans and robots on shop floors [46,207,135,212,157,259] which used both ultrasonic and infrared proximity sensors to establish a collision-free robotic environment. Among commercial systems of safety protection solutions, SafetyEYE  [200] of Pilz is a popular choice.…”