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Enhancing human-robot interaction using mixed reality
University of Skövde, School of Engineering Science.
2023 (English)Independent thesis Advanced level (degree of Master (One Year)), 12 credits / 18 HE creditsStudent thesis
Abstract [en]

Industry 4.0 is a new phase of industrial growth that has been ushered in by the quick development of digital technologies like the Internet of Things (IoT), artificial intelligence (AI), and robots. Collaborative robotic products have appeared in this changing environment, enabling robots to collaborate with people in open workspaces. The paradigm changes away from autonomous robotics and toward collaborative human-robot interaction (HRI) has made it necessary to look at novel ways to improve output, effectiveness, and security. Many benefits, including more autonomy and flexibility, have been made possible by the introduction of Autonomous Mobile Robots (AMRs) and later Automated Guided Vehicles (AGVs) for material handling. However, this incorporation of robots into communal workspaces also brings up safety issues that must be taken into account.

This thesis aims to address potential threats arising from the increasing automation in shopfloors and shared workplaces between AMRs and human operators by exploring the capabilities of Mixed Reality (MR) technologies. By harnessing MR's capabilities, the aim is to mitigate safety concerns and optimize the effectiveness of collaborative environments. To achieve this the research is structured around the following sub-objectives: the development of a communication network enabling interaction among all devices in the shared workspace and the creation of a MR user interface promoting accessibility for human operators.

A comprehensive literature review was conducted to analyse existing proposals aimed at improving HRI through various techniques and approaches. The objective was to leverage MR technologies to enhance collaboration and address safety concerns, thereby ensuring the smooth integration of AMRs into shared workspaces. While the literature review revealed limited research utilizing MR for data visualization in this specific domain, the goal of this thesis was to go beyond existing solutions by developing a comprehensive approach that prioritizes safety and facilitates operator adaptation.

The research findings highlight the superiority of MR in displaying critical information regarding robot intentions and identifying safe zones with reduced AMR activity. The utilization of HoloLens 2 devices, known for their ergonomic design, ensures operator comfort during extended use while enhancing the accuracy of tracking positions and intentions in highly automated environments. The presented information is designed to be concise, customizable, and easily comprehensible, preventing information overload for operators. 

The implementation of MR technologies within shared workspaces necessitates ethical considerations, including transparent data collection and user consent. Building trust is essential to establish MR as a reliable tool that enhances operator working conditions and safety. Importantly, the integration of MR technologies does not pose a threat to job displacement but rather facilitates the smooth adaptation of new operators to collaborative environments. The implemented features augment existing safety protocols without compromising efficacy, resulting in an overall improvement in safety within the collaborative workspace.

In conclusion, this research showcases the effectiveness of MR technologies in bolstering HRI, addressing safety concerns, and enhancing operator working conditions within collaborative shopfloor environments. Despite encountering limitations in terms of time, complexity, and available information, the developed solution showcases the potential for further improvements. The chosen methodology and philosophical paradigm have successfully attained the research objectives, and crucial ethical considerations have been addressed. Ultimately, this thesis proposes and provides a comprehensive explanation for potential future implementations, aiming to expand the actual capabilities of the solution.

Place, publisher, year, edition, pages
2023. , p. viii, 51
Keywords [en]
Human-robot interaction, mixed reality, autonomous mobile robots, safety
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:his:diva-22918OAI: oai:DiVA.org:his-22918DiVA, id: diva2:1778397
Subject / course
Virtual Product Realization
Educational program
Intelligent Automation - Master's Programme, 60 ECTS
Supervisors
Examiners
Available from: 2023-07-01 Created: 2023-07-01 Last updated: 2023-07-01Bibliographically approved

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CiteExportLink to record
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