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dc.contributor.authorCarlone, Luca
dc.contributor.authorPinciroli, Carlo
dc.date.accessioned2021-11-03T17:38:31Z
dc.date.available2021-11-03T17:38:31Z
dc.date.issued2019-05
dc.identifier.urihttps://hdl.handle.net/1721.1/137260
dc.description.abstract© 2019 IEEE. Recent advances in 3D printing and manufacturing of miniaturized robotic hardware and computing are paving the way to build inexpensive and disposable robots. This will have a large impact on several applications including scientific discovery (e.g., hurricane monitoring), search-and-rescue (e.g., operation in confined spaces), and entertainment (e.g., nano drones). The need for inexpensive and task-specific robots clashes with the current practice, where human experts are in charge of designing hardware and software aspects of the robotic platform. This makes the robot design process expensive and time consuming, and ultimately unsuitable for small-volumes low-cost applications. This paper considers the computational robot co-design problem, which aims to create an automatic algorithm that selects the best robotic modules (sensing, actuation, computing) in order to maximize the performance on a task, while satisfying given specifications (e.g., maximum cost of the resulting design). We propose a binary optimization formulation of the co-design problem and show that such formulation generalizes previous work based on strong modeling assumptions. We show that the proposed formulation can solve relatively large co-design problems in seconds and with minimal human intervention. We demonstrate the proposed approach in two applications: the co-design of an autonomous drone racing platform and the co-design of a multi-robot system.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/icra.2019.8793926en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleRobot Co-design: Beyond the Monotone Caseen_US
dc.typeArticleen_US
dc.identifier.citationCarlone, Luca and Pinciroli, Carlo. 2019. "Robot Co-design: Beyond the Monotone Case." Proceedings - IEEE International Conference on Robotics and Automation, 2019-May.
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.relation.journalProceedings - IEEE International Conference on Robotics and Automationen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-04-16T17:21:43Z
dspace.orderedauthorsCarlone, L; Pinciroli, Cen_US
dspace.date.submission2021-04-16T17:21:44Z
mit.journal.volume2019-Mayen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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