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In particular, Micro Aerial Vehicles (MAVs) have seen a resurgence in activity. With multiple use cases, such as surveillance, search and rescue, package delivery, and more, these unmanned aerial systems are on the cusp of demonstrating their full potential. Despite such promises, these systems face many challenges, one of the most prominent of which is their low endurance caused by their limited onboard energy. Since the success of a mission depends on whether the drone can finish it within such duration and before it runs out of battery, improving both the time and energy associated with the mission are of high importance. Such improvements have traditionally been arrived at through the use of better algorithms. But our premise is that more powerful and efficient onboard compute can also address the problem. In this article, we investigate how the compute subsystem, in a cyber-physical mobile machine such as a Micro Aerial Vehicle, can impact mission time (time to complete a mission) and energy. Specifically, we pose the question as\n            <jats:italic>what is the role of computing for cyber-physical mobile robots?<\/jats:italic>\n            We show that compute and motion are tightly intertwined, and as such\n            <jats:italic>a close examination of cyber and physical processes and their impact on one another is necessary.<\/jats:italic>\n            We show different \u201cimpact paths\u201d through which compute impacts mission metrics and examine them using a combination of analytical models, simulation, and micro and end-to-end benchmarking. To enable similar studies, we open sourced\n            <jats:italic>MAVBench<\/jats:italic>\n            , our tool-set, which consists of (1) a closed-loop real-time feedback simulator and (2) an end-to-end benchmark suite composed of state-of-the-art kernels. By combining MAVBench, analytical modeling, and an understanding of various compute impacts, we show up to 2X and 1.8X improvements for mission time and mission energy for two optimization case studies, respectively. Our investigations, as well as our optimizations, show that\n            <jats:italic>cyber-physical co-design, a methodology with which both the cyber and physical processes\/quantities of the robot are developed with consideration of one another, similar to hardware-software co-design, is necessary for arriving at the design of the optimal robot.<\/jats:italic>\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3511210","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T19:46:22Z","timestamp":1644867982000},"page":"1-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["The Role of Compute in Autonomous Micro Aerial Vehicles: Optimizing for Mission Time and Energy Efficiency"],"prefix":"10.1145","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3655-266X","authenticated-orcid":false,"given":"Behzad","family":"Boroujerdian","sequence":"first","affiliation":[{"name":"University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan","family":"Genc","sequence":"additional","affiliation":[{"name":"University of Texas at Austin and University of California Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srivatsan","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bardienus Pieter","family":"Duisterhof","sequence":"additional","affiliation":[{"name":"Harvard University and Delft University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Plancher","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayvan","family":"Mansoorshahi","sequence":"additional","affiliation":[{"name":"University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelino","family":"Almeida","sequence":"additional","affiliation":[{"name":"University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhi","family":"Cui","sequence":"additional","affiliation":[{"name":"University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3268-8685","authenticated-orcid":false,"given":"Aleksandra","family":"Faust","sequence":"additional","affiliation":[{"name":"Robotics at Google"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay Janapa","family":"Reddi","sequence":"additional","affiliation":[{"name":"Harvard University and University of Texas at Austin"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"3DR Robotics. 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