This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment as well as simulated missions over a 10m×10m coral reef using real data show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantify how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model. I. INTRODUCTIONThe challenges of exploration in remote and extreme environments such as the deep seas [1], [2], cave systems [3], outer space [4] and during or after a natural disaster [5], [6] have much in common. It is expensive and inherently dangerous for humans to explore such locations directly; hence, the use of mobile robots is desirable. However, if communication bottlenecks exist in the environment, prohibiting live streaming of video or other sensor data, then direct control of the robots is generally not possible. This paper describes a novel approach to co-robotic exploration in communication starved environments, and presents a system implementation of an under-sea exploration robot for corobotic exploration of marine environments.Although physically controlling a robot can be achieved over relatively low bandwidth, it is difficult to transmit the scene information necessary for an operator or scientist to make high level navigational decisions. We propose a spatially correlated Chinese Restaurant Process (CRP)-based [7] scene understanding model, that can be tuned to operate with
We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very highdimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion.
None of this work would have been possible without the guidance and support of my co-supervisors, Dr. Yogesh Girdhar and Prof. Jonathan How. They have been exceedingly generous in giving their time, encouragement, and advice in matters both technical and related to professional development. I feel very grateful for the combined breadth and depth of their backgrounds, experiences, and technical knowledge, as this has been instrumental in allowing me to pursue such a cross-disciplinary and complex topic as co-robotic scientific exploration. I would next like to thank all of my collaborators and peers involved in the research and discussion of the work and ideas presented in this thesis. My fellow members of the WARPLab and the Autonomous Controls Lab always created a positive, supportive, and productive atmosphere to work in, one I missed when working from home due to current world affairs. I'd like to particularly thank Vv, John, Genevieve, Victoria, Mike, Kaveh, Kasra, and Kevin for many long discussions and useful advice on the topics and ideas presented in this work. I'd also like to thank Nathan, Brian, and Stefano for their invaluable technical advice and support. Alongside them are all the others at MIT, WHOI, and in the MIT-WHOI Joint Program who have been excellent friends to me since coming to MA and made me feel at home in a new country. Lastly, I would like to thank my family. In particular, my partner Victoria who provides me with constant support and inspires me with her incredible work ethic and dedication, as well my mom and dad and my brother Chris, whose love and encouragement have been ever-present and invaluable. I'd also like to thank Suzy and the rest of my extended family, who have likewise been major sources of support in both the best and worst of times. Finally, I dedicate this thesis in memory of my grandparents Chris & Molly, who were each deeply loved and are deeply missed; they always brought out the very best in me.
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