Natural, Robust, and Multi-Modal Human-Robot Interaction For Underwater Robots
2023-01
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Natural, Robust, and Multi-Modal Human-Robot Interaction For Underwater Robots
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2023-01
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In the mid-twentieth century, robots began to swim in the oceans, lakes, rivers, and waterways of the world. Over the seventy years that have passed since then, autonomous underwater vehicles (AUVS) have slowly been evolving, becoming smaller, more intelligent, and more capable. As they have begun to be deployed in a wider variety of locations and for increasingly complex purposes, excitement over the idea of a collaborative AUV (co-AUV) has begun to grow, with the continued development of the field. Now we stand upon the cusp of a revolution in the world of underwater work. Thousands of divers the world over could be aided in their work by a co-AUV in the coming years, helping humans to better understand and protect the critical water resources of our planet. However, for this dream to come to fruition, these co-AUVs must be capable of natural, robust communication, rich and accurate perception of their human partners and adaptive operation in an ever-changing environment. Though researchers have been making steps toward this goal, this thesis marks a new stage in the development of the co-AUV. In the following chapters, we present three novel methods of communication, two state-of-the-art perception capabilities, a new capability for diver approach, a new methodology for gestural AUV control, a modular software ecosystem for UHRI, and an adaptive communication controller. Additionally, seven human studies evaluating these systems are presented, five of which were conducted in underwater environments with an unprecedented number of participants. The communication methods presented in Part I are a new direction for the field, emphasizing non-text communication which is easily perceived at a distance, natural and intuitive design over information complexity, and introducing new vectors of communication using motion and sound that have not been previously studied underwater. The perception methods of Part II are more traditional, but push the boundaries of previously developed capabilities in numerous ways: developing a new capability in terms of diver motion prediction, creating a method for estimating the relative distance to a diver using only monocular vision, and creating reconfigurable and dynamic gestural control in a way that has not previously been attempted for AUVs. The capstone of the thesis in Part III is the PROTEUS underwater HRI software system, which could serve as a foundation for a great deal of future research, as well as the first adaptive communication system for AUVs, ACVS. ACVS uses the perception capabilities presented in Part II to determine which of the communication vectors introduced in Part I should be utilized given the context of an interaction, with all of the components implemented within the PROTEUS framework. The research contained in this thesis is highly multidisciplinary, encompassing interaction design, software development, hardware fabrication, the design and administration of human studies, quantitative and qualitative analysis of study results, deep learning system design, training and deployment of neural networks, robot design, and general robotics development. The results of these investigations into UHRI reveal an exciting potential for the field. Nearly every method presented in this thesis has achieved sufficient success in testing to indicate that it could be effectively applied in field environments, especially given some further development. The dream of co-AUVs helping divers in their work is already beginning to come to life, and the algorithms and systems presented in this document have brought us ever closer to that goal. The work that is done by divers is critical for human society and the health of our planet's ecosystems and the aid that collaborative AUVs could render in these environments is invaluable, greatly increasing diver safety and task success rates. This thesis provides novel communication methods, a new state of the art in diver perception, an adaptive communication system, and a software architecture that ties them all together, improving the flexibility and robustness of underwater human-robot interaction and providing a basis for further development along these exciting avenues.
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University of Minnesota Ph.D. dissertation. January 2023. Major: Computer Science. Advisor: Junaed Sattar. 1 computer file (PDF); xiii, 321 pages.
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Fulton, Michael. (2023). Natural, Robust, and Multi-Modal Human-Robot Interaction For Underwater Robots. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/254125.
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