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Yogesh Girdhar and Jonathan P. How. Jamieson, Stewart Christopher. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2020-09-03T17:45:21Z 2020-09-03T17:45:21Z 2020 2020 https://hdl.handle.net/1721.1/127067 1191819112 Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 93-104). Contemporary scientific exploration most often takes place in highly remote and dangerous environments, such as in the deep sea and on other planets. These environments are very hostile to humans, which makes robotic exploration the first and often the only option. However, they also impose restrictive limits on how much communication is possible, creating challenges in implementing remote command and control. We propose an approach to enable more efficient autonomous robot-based scientific exploration of remote environments despite these limits on human-robot communication. We find this requires the robot to have a spatial observation model that can predict where to find various phenomena, a reward model which can measure how relevant these phenomena are to the scientific mission objectives, and an adaptive path planner which can use this information to plan high scientific value paths. We identified and addressed two main gaps: the lack of a general-purpose means for spatial observation modelling, and the challenge in learning a reward model based on images online given the limited bandwidth constraints. Our first key contribution is enabling general-purpose spatial observation modelling through spatio-temporal topic models, which are well suited for unsupervised scientific exploration of novel environments. Our next key contribution is an active learning criterion which enables learning an image-based reward model during an exploration mission by communicating with the science team efficiently. We show that using these together can result in a robotic explorer collecting up to 230% more scientifically relevant observations in a single mission than when using lawnmower trajectories. by Stewart Christopher Jamieson. S.M. S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics 104 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Aeronautics and Astronautics. Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments Thesis application/pdf 2020-09-03T17:45:20Z Publication Master Aero unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="600c31ed-8c64-48d2-84d7-45f80a1910b2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Enabling human-robot cooperation in scientific exploration of bandwidth-limited environments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Jamieson, Stewart Christopher.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>Contemporary scientific exploration most often takes place in highly remote and dangerous environments, such as in the deep sea and on other planets. These environments are very hostile to humans, which makes robotic exploration the first and often the only option. However, they also impose restrictive limits on how much communication is possible, creating challenges in implementing remote command and control. We propose an approach to enable more efficient autonomous robot-based scientific exploration of remote environments despite these limits on human-robot communication. We find this requires the robot to have a spatial observation model that can predict where to find various phenomena, a reward model which can measure how relevant these phenomena are to the scientific mission objectives, and an adaptive path planner which can use this information to plan high scientific value paths. We identified and addressed two main gaps: the lack of a general-purpose means for spatial observation modelling, and the challenge in learning a reward model based on images online given the limited bandwidth constraints. Our first key contribution is enabling general-purpose spatial observation modelling through spatio-temporal topic models, which are well suited for unsupervised scientific exploration of novel environments. Our next key contribution is an active learning criterion which enables learning an image-based reward model during an exploration mission by communicating with the science team efficiently. We show that using these together can result in a robotic explorer collecting up to 230% more scientifically relevant observations in a single mission than when using lawnmower trajectories.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>