Abstract
Remote Assistance is a research area focusing on how to support remote collaborative problem solving among workers in a given domain often supported by video and audio communication and non-verbal guidance techniques. Remote Assistance will potentially save industrial companies the time and travel expenses of sending experts to a remote location to solve problems, and can support inexperienced workers in solving problems that they would not be able to solve on their own.
The majority of research on remote assistance has been conducted in the lab and focuses on improving non-verbal guidance techniques for remote assistance and increasing the visual information richness of communication. This research is important, because it focuses on closing the gap between remote and co-located collaboration experiences. However, our field studies with companies from the manufacturing industry show that more attention needs to be paid to industry specific requirements and challenges for remote assistance, otherwise there will be a new gap between research agendas and prototypes and the needs of lead users of remote assistance in industry. Such a gap is problematic, because workers in the manufacturing industry such as machine operators and service technicians, who are faced with complex physical tasks, stand to benefit greatly from advancements in remote assistance technology that addresses their needs.
Therefore, in this dissertation, manufacturing workers’ requirements for remote assistance were investigated through interviews and observations in industry. The investigations revealed a rich variety of needs and requirements depending on the context of work, and it became clear that in order to support remote assistance in manufacturing, tailorability of remote assistance systems is crucial. Thus, the needs and requirements were synthesized into a tailorable toolkit for remote assistance called Remote Assist Kit (RAK), which supports tailoring aspects of interface mobility, guidance format, guidance location, and task space capturing with one or multiple cameras. A user evaluation of RAK was conducted in manufacturing industry settings to investigate how workers use remote assistance in various realistic scenarios.
In turn, this user evaluation led to a series of new findings on requirements and challenges for remote assistance in the manufacturing industry, including the challenge of maintaining awareness between collaborators in large task spaces. Hence, the exploration of two additional tailorable remote assistance systems, SceneCam and CueCam, contributes new techniques for improving awareness during remote assistance with multiple cameras in large task space. SceneCam contributes focus-in-context views and context-aware camera selection techniques, and CueCam contributes three augmented reality (AR) awareness cues. The AR awareness cues were evaluated in a lab study that revealed the superiority of one of the awareness cues. The work on multi-camera remote assistance and awareness techniques lead to a reflection on how these concepts can be applied in a manufacturing context.
The field studies in industry, the development of RAK and AR multi-camera systems, and the user evaluations have led to new concepts for remote assistance that fit the rich and challenging context of the manufacturing industry.
The majority of research on remote assistance has been conducted in the lab and focuses on improving non-verbal guidance techniques for remote assistance and increasing the visual information richness of communication. This research is important, because it focuses on closing the gap between remote and co-located collaboration experiences. However, our field studies with companies from the manufacturing industry show that more attention needs to be paid to industry specific requirements and challenges for remote assistance, otherwise there will be a new gap between research agendas and prototypes and the needs of lead users of remote assistance in industry. Such a gap is problematic, because workers in the manufacturing industry such as machine operators and service technicians, who are faced with complex physical tasks, stand to benefit greatly from advancements in remote assistance technology that addresses their needs.
Therefore, in this dissertation, manufacturing workers’ requirements for remote assistance were investigated through interviews and observations in industry. The investigations revealed a rich variety of needs and requirements depending on the context of work, and it became clear that in order to support remote assistance in manufacturing, tailorability of remote assistance systems is crucial. Thus, the needs and requirements were synthesized into a tailorable toolkit for remote assistance called Remote Assist Kit (RAK), which supports tailoring aspects of interface mobility, guidance format, guidance location, and task space capturing with one or multiple cameras. A user evaluation of RAK was conducted in manufacturing industry settings to investigate how workers use remote assistance in various realistic scenarios.
In turn, this user evaluation led to a series of new findings on requirements and challenges for remote assistance in the manufacturing industry, including the challenge of maintaining awareness between collaborators in large task spaces. Hence, the exploration of two additional tailorable remote assistance systems, SceneCam and CueCam, contributes new techniques for improving awareness during remote assistance with multiple cameras in large task space. SceneCam contributes focus-in-context views and context-aware camera selection techniques, and CueCam contributes three augmented reality (AR) awareness cues. The AR awareness cues were evaluated in a lab study that revealed the superiority of one of the awareness cues. The work on multi-camera remote assistance and awareness techniques lead to a reflection on how these concepts can be applied in a manufacturing context.
The field studies in industry, the development of RAK and AR multi-camera systems, and the user evaluations have led to new concepts for remote assistance that fit the rich and challenging context of the manufacturing industry.
| Original language | English |
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| Publisher | Århus Universitet |
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| Number of pages | 132 |
| Publication status | Published - Aug 2021 |