{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T16:21:38Z","timestamp":1726762898568},"reference-count":101,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"67","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2022,6,29]]},"abstract":"\n Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platform is needed to accelerate research and let practitioners focus on the core problems. To this end, we present Agilicious, a codesigned hardware and software framework tailored to autonomous, agile quadrotor flight. It is completely open source and open hardware and supports both model-based and neural network\u2013based controllers. Also, it provides high thrust-to-weight and torque-to-inertia ratios for agility, onboard vision sensors, graphics processing unit (GPU)\u2013accelerated compute hardware for real-time perception and neural network inference, a real-time flight controller, and a versatile software stack. In contrast to existing frameworks, Agilicious offers a unique combination of flexible software stack and high-performance hardware. We compare Agilicious with prior works and demonstrate it on different agile tasks, using both model-based and neural network\u2013based controllers. Our demonstrators include trajectory tracking at up to 5\n g<\/jats:italic>\n and 70 kilometers per hour in a motion capture system, and vision-based acrobatic flight and obstacle avoidance in both structured and unstructured environments using solely onboard perception. Last, we demonstrate its use for hardware-in-the-loop simulation in virtual reality environments. Because of its versatility, we believe that Agilicious supports the next generation of scientific and industrial quadrotor research.\n <\/jats:p>","DOI":"10.1126\/scirobotics.abl6259","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T17:55:55Z","timestamp":1655920555000},"source":"Crossref","is-referenced-by-count":54,"title":["Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight"],"prefix":"10.1126","volume":"7","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9585-1278","authenticated-orcid":true,"given":"Philipp","family":"Foehn","sequence":"first","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6094-5901","authenticated-orcid":true,"given":"Elia","family":"Kaufmann","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7977-7802","authenticated-orcid":true,"given":"Angel","family":"Romero","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8549-4932","authenticated-orcid":true,"given":"Robert","family":"Penicka","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8539-3979","authenticated-orcid":true,"given":"Sihao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5790-9982","authenticated-orcid":true,"given":"Leonard","family":"Bauersfeld","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8551-0370","authenticated-orcid":true,"given":"Thomas","family":"Laengle","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3964-8552","authenticated-orcid":true,"given":"Giovanni","family":"Cioffi","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6352-3744","authenticated-orcid":true,"given":"Yunlong","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8410-3933","authenticated-orcid":true,"given":"Antonio","family":"Loquercio","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3831-6778","authenticated-orcid":true,"given":"Davide","family":"Scaramuzza","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Zurich, Switzerland."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911434236"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2016.2633290"},{"key":"e_1_3_2_4_2","first-page":"690","article-title":"Beauty and the beast: Optimal methods meet learning for drone racing","author":"Kaufmann E.","year":"2018","unstructured":"E. Kaufmann, M. Gehrig, P. Foehn, R. Ranftl, A. Dosovitskiy, V. Koltun, D. Scaramuzza, Beauty and the beast: Optimal methods meet learning for drone racing. IEEE Int. Conf. Robot. Autom. (ICRA) 690\u2013696 (2018).","journal-title":"IEEE Int. Conf. Robot. Autom. (ICRA)"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21774"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3071527"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-021-10011-y"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2020.103621"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","unstructured":"H. Nguyen M. Kamel K. Alexis R. Siegwart Model predictive control for micro aerial vehicles: A survey in 2021 European Control Conference (ECC) (IEEE 2022).","DOI":"10.23919\/ECC54610.2021.9654841"},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","unstructured":"E. Kaufmann A. Loquercio R. Ranftl M. M\u00fcller V. Koltun D. Scaramuzza Deep drone acrobatics in RSS: Robotics Science and Systems (Robotics: Science and Systems Foundation 2020).","DOI":"10.15607\/RSS.2020.XVI.040"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abh1221"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11370-018-00271-6"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"J. A. Cocoma-Ortega J. Mart\u00ednez-Carranza Towards high-speed localisation for autonomous drone racing in Mexican International Conference on Artificial Intelligence (Springer 2019).","DOI":"10.1007\/978-3-030-33749-0_59"},{"key":"e_1_3_2_14_2","unstructured":"R. Madaan N. Gyde S. Vemprala M. Brown K. Nagami T. Taubner E. Cristofalo D. Scaramuzza M. Schwager A. Kapoor Airsim drone racing lab in Proceedings of the NeurIPS 2019 Competition and Demonstration Track (PMLR 2020)."},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"W. Guerra E. Tal V. Murali G. Ryou S. Karaman FlightGoggles: Photorealistic sensor simulation for perception-driven robotics using photogrammetry and virtual reality in Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2019).","DOI":"10.1109\/IROS40897.2019.8968116"},{"key":"e_1_3_2_16_2","unstructured":"CORDIS - European Comission AgileFlight; https:\/\/cordis.europa.eu\/project\/id\/864042 [accessed 30 July 2021]."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-012-9281-4"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2017.2771326"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2017.2776353"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21950"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-021-01383-5"},{"key":"e_1_3_2_22_2","unstructured":"G. Hattenberger M. Bronz M. Gorraz Using the paparazzi uav system for scientific research in International Micro Air Vehicle Conference and Competition (Delft University of Technology 2014) pp. 247\u2013252."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364912455954"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14542"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2020.3001117"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920908331"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"L. Meier D. Honegger M. Pollefeys Px4: A node-based multithreaded open source robotics framework for deeply embedded platforms. in Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2015) pp. 6235\u20136240.","DOI":"10.1109\/ICRA.2015.7140074"},{"key":"e_1_3_2_28_2","unstructured":"P. D. SAS Parrot ANAFI ai; www.parrot.com\/en\/drones\/anafi-ai [accessed 20 July 2021]."},{"key":"e_1_3_2_29_2","unstructured":"DJI DJI digital FPV system; www.dji.com\/fpv [accessed 20 July 2021]."},{"key":"e_1_3_2_30_2","unstructured":"Skydio (2021)."},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"W. Giernacki M. Skwierczy\u0144ski W. Witwicki P. Wro\u0144ski P. Kozierski Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering in Proceedings of the International Conference on Methods and Models in Automation and Robotics (MMAR) (IEEE 2017) pp. 37\u201342.","DOI":"10.1109\/MMAR.2017.8046794"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","unstructured":"D. Falanga P. Foehn P. Lu D. Scaramuzza Pampc: Perception-aware model predictive control for quadrotors in Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2018) pp. 1\u20138.","DOI":"10.1109\/IROS.2018.8593739"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abg5810"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3048875"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2918689"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Y. Song M. Steinweg E. Kaufmann D. Scaramuzza Autonomous drone racing with deep reinforcement learning in Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2021).","DOI":"10.1109\/IROS51168.2021.9636053"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"J. Delmerico T. Cieslewski H. Rebecq M. Faessler D. Scaramuzza Are we ready for autonomous drone racing? the uzh-fpv drone racing dataset in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2019).","DOI":"10.1109\/ICRA.2019.8793887"},{"key":"e_1_3_2_38_2","unstructured":"The Betaflight Open Source Flight Controller Firmware Project Betaflight; https:\/\/github.com\/betaflight\/betaflight [accessed 20 July 2021]."},{"key":"e_1_3_2_39_2","unstructured":"S. Sun A. Romero P. Foehn E. Kaufmann D. Scaramuzza Quadrotor accurate agile trajectory tracking: Differential-flatness vs. model-predictive control. arXiv e-prints (2021)."},{"key":"e_1_3_2_40_2","unstructured":"Laird connectivity; www.lairdconnect.com\/ [accessed 20 July 2021]."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2021.XVII.042"},{"key":"e_1_3_2_42_2","unstructured":"A. Romero S. Sun P. Foehn D. Scaramuzza Model predictive contouring control for near-time-optimal quadrotor flight. arXiv:2108.13205 [cs.RO] (30 August 2021)."},{"key":"e_1_3_2_43_2","unstructured":"F. Nan S. Sun P. Foehn D. Scaramuzza Nonlinear mpc for quadrotor fault-tolerant control. arXiv:2109.12886 [cs.RO] (27 September 2021)."},{"key":"e_1_3_2_44_2","unstructured":"A. I. Automation Ati mini40-si-20-1; www.ati-ia.com\/products\/ft\/ft_models.aspx?id=Mini40 [accessed 30 November 2021]."},{"key":"e_1_3_2_45_2","unstructured":"Y. Song S. Naji E. Kaufmann A. Loquercio D. Scaramuzza Flightmare: A flexible quadrotor simulator in Conference on Robot Learning [Proceedings of Machine Learning Research (PMLR) 2020]."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2623335"},{"key":"e_1_3_2_47_2","unstructured":"\u201cSvo Pro: Semi-direct visual-inertial odometry and SLAM for monocular stereo and wide angle cameras; http:\/\/rpg.ifi.uzh.ch\/svo_pro.html [accessed 30 November 2021]."},{"key":"e_1_3_2_48_2","doi-asserted-by":"crossref","unstructured":"J. Rehder J. Nikolic T. Schneider T. Hinzmann R. Siegwart Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2016).","DOI":"10.1109\/ICRA.2016.7487628"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Z. Zhang D. Scaramuzza A tutorial on quantitative trajectory evaluation for visual(\u2212inertial) odometry in IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2018).","DOI":"10.1109\/IROS.2018.8593941"},{"key":"e_1_3_2_50_2","unstructured":"SevenSense Alphasense; www.sevensense.ai\/product\/alphasense-position [accessed 30 November 2021]."},{"key":"e_1_3_2_51_2","unstructured":"M. EYE S210; www.mynteye.com\/pages\/s210 [accessed 30 November 2021]."},{"key":"e_1_3_2_52_2","unstructured":"M. Vision Bluefox; www.matrix-vision.com\/en\/products\/areas\/MA08 [accessed 30 November 2021]."},{"key":"e_1_3_2_53_2","unstructured":"ArtiSense Artislam; www.artisense.ai\/vinspro-2020 [accessed 30 November 2021]."},{"key":"e_1_3_2_54_2","unstructured":"SLAMcore Spatial intelligence sdk; www.slamcore.com\/spatial-intelligence-sdk [accessed 30 November 2021]."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2018.2853729"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"P. Geneva K. Eckenhoff W. Lee Y. Yang G. Huang Openvins: A research platform for visual-inertial estimation in Proceedings of the IEEE International Conference on Robots and Automation (ICRA) (IEEE 2020).","DOI":"10.1109\/ICRA40945.2020.9196524"},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","unstructured":"J. Delmerico D. Scaramuzza A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots in Proceedings of the IEEE International Conference on Robots and Automation (ICRA) (IEEE 2018) pp. 2502\u20132509.","DOI":"10.1109\/ICRA.2018.8460664"},{"key":"e_1_3_2_58_2","unstructured":"Intel realsense d400 series product; www.intel.com\/content\/dam\/support\/us\/en\/documents\/emerging-technologies\/intel-realsense-technology\/Intel-RealSense-D400-Series-Datasheet.pdf."},{"key":"e_1_3_2_59_2","unstructured":"Roboception rc_visard; https:\/\/roboception.com\/en\/rc_visard-en\/."},{"key":"e_1_3_2_60_2","unstructured":"ModalAI Voxl cam; www.modalai.com\/pages\/voxl-cam-perception-engine [accessed 30 November 2021]."},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","unstructured":"F. Furrer M. Burri M. Achtelik and R. Siegwart Rotors\u2014A modular gazebo mav simulator framework in Robot Operating System (ROS) (Springer 2016) pp. 595\u2013625.","DOI":"10.1007\/978-3-319-26054-9_23"},{"key":"e_1_3_2_62_2","unstructured":"A. Juliani V.-P. Berges E. Vckay Y. Gao H. Henry M. Mattar D. Lange Unity: A general platform for intelligent agents. arXiv:1809.02627 [cs.LG] (7 September 2018)."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2624754"},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","unstructured":"H. Oleynikova Z. Taylor M. Fehr R. Siegwart J. I. Nieto Voxblox: Incremental 3d euclidean signed distance fields for on-board MAV planning in IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2017) pp. 1366\u20131373.","DOI":"10.1109\/IROS.2017.8202315"},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","unstructured":"P. Florence J. Carter R. Tedrake Integrated perception and control at high speed: Evaluating collision avoidance maneuvers without maps in Algorithmic Foundations of Robotics XII (Springer 2020) pp. 304\u2013319.","DOI":"10.1007\/978-3-030-43089-4_20"},{"key":"e_1_3_2_66_2","doi-asserted-by":"crossref","unstructured":"D. Falanga E. Mueggler M. Faessler D. Scaramuzza Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision in IEEE International Conference on Robotica and Automation (ICRA) (IEEE 2017) pp. 5774\u20135781.","DOI":"10.1109\/ICRA.2017.7989679"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2927938"},{"key":"e_1_3_2_68_2","doi-asserted-by":"crossref","unstructured":"D. Falanga E. Mueggler M. Faessler D. Scaramuzza Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision in IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2017) pp. 5774\u20135781.","DOI":"10.1109\/ICRA.2017.7989679"},{"key":"e_1_3_2_69_2","doi-asserted-by":"crossref","unstructured":"W. Zeng W. Luo S. Suo A. Sadat B. Yang S. Casas R. Urtasun End-to-end interpretable neural motion planner in IEEE International Conference Computer Vision and Pattern Recognition (CVPR) (IEEE 2019) pp. 8660\u20138669.","DOI":"10.1109\/CVPR.2019.00886"},{"key":"e_1_3_2_70_2","doi-asserted-by":"crossref","unstructured":"W. Zeng S. Wang R. Liao Y. Chen B. Yang R. Urtasun Dsdnet: Deep structured self-driving network in European Conference on Computer Vision (ECCV) (Springer International Publishing 2020) pp. 156\u2013172.","DOI":"10.1007\/978-3-030-58589-1_10"},{"key":"e_1_3_2_71_2","first-page":"420","article-title":"Combining optimal control and learning for visual navigation in novel environments","volume":"100","author":"Bansal S.","year":"2019","unstructured":"S. Bansal, V. Tolani, S. Gupta, J. Malik, C. J. Tomlin, Combining optimal control and learning for visual navigation in novel environments. Proc. Mach. Learn. Res. 100, 420\u2013429 (2019).","journal-title":"Proc. Mach. Learn. Res."},{"key":"e_1_3_2_72_2","doi-asserted-by":"crossref","unstructured":"N. Homayounfar W.-C. Ma J. Liang X. Wu J. Fan R. Urtasun Dagmapper: Learning to map by discovering lane topology in IEEE International Conference on Computer Visual and Pattern Recognition (CVPR) (IEEE 2019) pp. 2911\u20132920.","DOI":"10.1109\/ICCV.2019.00300"},{"key":"e_1_3_2_73_2","doi-asserted-by":"crossref","unstructured":"Z. Zhang D. Scaramuzza Perception-aware receding horizon navigation for mavs in IEEE International Confernce on Robotics and Automation (ICRA) (IEEE 2018) pp. 2534\u20132541.","DOI":"10.1109\/ICRA.2018.8461133"},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","unstructured":"S. Ross N. Melik-Barkhudarov K. S. Shankar A. Wendel D. Dey J. A. Bagnell M. Hebert Learning monocular reactive UAV control in cluttered natural environments in IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2013) pp. 1765\u20131772.","DOI":"10.1109\/ICRA.2013.6630809"},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","unstructured":"F. Sadeghi S. Levine CAD2RL: Real single-image flight without a single real image in Robotics: Science and Systems RSS N. M. Amato S. S. Srinivasa N. Ayanian S. Kuindersma Eds. (Robotics: Science and Systems Foundation 2017).","DOI":"10.15607\/RSS.2017.XIII.034"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2795643"},{"key":"e_1_3_2_77_2","doi-asserted-by":"crossref","unstructured":"D. Gandhi L. Pinto A. Gupta Learning to fly by crashing in International Conference on Intelligent Robots and Systems IROS (IEEE 2017) pp. 3948\u20133955.","DOI":"10.1109\/IROS.2017.8206247"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2018.2886342"},{"key":"e_1_3_2_79_2","unstructured":"Intel Corporation Intel movidius myriad X vision processing unit; https:\/\/www.intel.com\/content\/www\/us\/en\/products\/details\/processors\/movidius-vpu\/movidius-myriad-x.html [accessed 2 August 2021]."},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2917066"},{"key":"e_1_3_2_81_2","doi-asserted-by":"crossref","unstructured":"D. Palossi F. Conti L. Benini An open source and open hardware deep Learning-Powered visual navigation engine for autonomous Nano-UAVs in Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (IEEE 2019) pp. 604\u2013611.","DOI":"10.1109\/DCOSS.2019.00111"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abc2897"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aay1246"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2885575"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2021.3061819"},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","unstructured":"R. D\u2019Sa D. Jenson N. Papanikolopoulos Suav:q - a hybrid approach to solar-powered flight in Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2016) pp. 3288\u20133294.","DOI":"10.1109\/ICRA.2016.7487501"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3061307"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aaz9712"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3008413"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_3_2_91_2","doi-asserted-by":"crossref","unstructured":"J. Dupeyroux J. Hagenaars F. Paredes-Vall\u00e9s G. de Croon Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor. arxiv:2011.00534 [cs.RO] (1 November 2020).","DOI":"10.1109\/ICRA48506.2021.9560937"},{"key":"e_1_3_2_92_2","doi-asserted-by":"crossref","unstructured":"A. Vitale1 A. Renner C. Nauer D. Scaramuzza Y. Sandamirskaya Event-driven vision and control for uavs on a neuromorphic chip in Proceedings fo the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2021).","DOI":"10.1109\/ICRA48506.2021.9560881"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2017.2759700"},{"key":"e_1_3_2_94_2","unstructured":"M. Abadi A. Agarwal P. Barham E. Brevdo Z. Chen C. Citro G. S. Corrado A. Davis J. Dean M. Devin S. Ghemawat I. Goodfellow A. Harp G. Irving M. Isard Y. Jia R. Jozefowicz L. Kaiser M. Kudlur J. Levenberg D. Man\u00e9 R. Monga S. Moore D. Murray C. Olah M. Schuster J. Shlens B. Steiner I. Sutskever K. Talwar P. Tucker V. Vanhoucke V. Vasudevan F. Vi\u00e9gas O. Vinyals P. Warden M. Wattenberg M. Wicke Y. Yu X. Zheng TensorFlow: Large-scale machine learning on heterogeneous systems (2015); www.tensorflow.org\/."},{"key":"e_1_3_2_95_2","unstructured":"A. Paszke S. Gross F. Massa A. Lerer J. Bradbury G. Chanan T. Killeen Z. Lin N. Gimelshein L. Antiga A. Desmaison A. Kopf E. Yang Z. DeVito M. Raison A. Tejani S. Chilamkurthy B. Steiner L. Fang J. Bai S. Chintala Pytorch: An imperative style high-performance deep learning library in Advances in Neural Information Processing Systems 32 H. Wallach H. Larochelle A. Beygelzimer F. d Alch\u00e9-Buc E. Fox R. Garnett Eds. (Curran Associates Inc. 2019) pp. 8024\u20138035; http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf."},{"key":"e_1_3_2_96_2","unstructured":"Connect Tech Inc. Quasar carrier board; https:\/\/connecttech.com\/product\/quasar-carrier-nvidia-jetson-tx2\/ [accessed 20 July 2021]."},{"key":"e_1_3_2_97_2","unstructured":"M. Luessi radix; www.brainfpv.com\/radix2\/ [accessed 20 July 2021]."},{"key":"e_1_3_2_98_2","unstructured":"The Apache Software Foundation NuttX; https:\/\/nuttx.apache.org\/ [accessed 20 July 2021]."},{"key":"e_1_3_2_99_2","doi-asserted-by":"crossref","unstructured":"S. Shah D. Dey C. Lovett A. Kapoor Airsim: High-fidelity visual and physical simulation for autonomous vehicles in Field And Service Robotics (Springer 2018) pp. 621\u2013635.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"e_1_3_2_100_2","unstructured":"O. Ben-Kiki C. Evans I. D. Net YAML ain\u2019t markup language (YAML\u2122) version 1.2; https:\/\/yaml.org\/spec\/1.2\/spec.pdf [accessed 20 July 2021]."},{"key":"e_1_3_2_101_2","doi-asserted-by":"crossref","unstructured":"D. Mellinger V. Kumar Minimum snap trajectory generation and control for quadrotors in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2011) pp. 2520\u20132525.","DOI":"10.1109\/ICRA.2011.5980409"},{"key":"e_1_3_2_102_2","doi-asserted-by":"crossref","unstructured":"D. Hanover E. Kaufmann P. Foehn D. Scaramuzza Performance precision and payloads: Adaptive optimal control for quadrotors under uncertainty in Proceedings of the IEEE Robotics and Automation Letters (IEEE 2021).","DOI":"10.1109\/LRA.2021.3131690"}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.abl6259","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T12:51:48Z","timestamp":1705409508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.abl6259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":101,"journal-issue":{"issue":"67","published-print":{"date-parts":[[2022,6,29]]}},"alternative-id":["10.1126\/scirobotics.abl6259"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.abl6259","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}