Dynamic Two-way Communication Using Gestures for Human-machine Teaming: DaRT-4 Project Report

Research output: Book/ReportOther output

Abstract

Despite radical advancements in the capabilities of robots and automated
technologies, there remains a critical need to optimise the
control of, and interaction with these machines. Current human-tomachine
communication methods impose significant mental workload
costs onto the human operator, and require a dedicated controller role
resulting in reduced team flexibility among other concerns. Likewise,
present robot-to-human communications suffer from under- or overdisplay
of information, diminishing the ability of the human-machine
team to form a suitable shared understanding of the tactical picture,
and reducing operator trust in the system. We propose a novel solution
co-developed across industry and academic partners. Our system
is based on the intertwining of state-of-the-art machine learning algorithms
and modern edge-processing capabilities to deliver a platform
agnostic gesture-based control suite with augmented-reality feedback
loop. This solution is poised to disrupt the human-machine teaming
space by significantly reducing the workload costs of controlling machine
team-mates while simultaneously enhancing the shared mental
model, improving predictability and trust between human and machine
team-mates.
Original languageEnglish
PublisherEdith Cowan University
Commissioning bodyDefence Science Centre
Number of pages32
Publication statusPublished - Apr 2022

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