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From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching (2403.00128v1)

Published 29 Feb 2024 in cs.RO, cs.LG, cs.SY, and eess.SY

Abstract: Inverted landing is a routine behavior among a number of animal fliers. However, mastering this feat poses a considerable challenge for robotic fliers, especially to perform dynamic perching with rapid body rotations (or flips) and landing against gravity. Inverted landing in flies have suggested that optical flow senses are closely linked to the precise triggering and control of body flips that lead to a variety of successful landing behaviors. Building upon this knowledge, we aimed to replicate the flies' landing behaviors in small quadcopters by developing a control policy general to arbitrary ceiling-approach conditions. First, we employed reinforcement learning in simulation to optimize discrete sensory-motor pairs across a broad spectrum of ceiling-approach velocities and directions. Next, we converted the sensory-motor pairs to a two-stage control policy in a continuous augmented-optical flow space. The control policy consists of a first-stage Flip-Trigger Policy, which employs a one-class support vector machine, and a second-stage Flip-Action Policy, implemented as a feed-forward neural network. To transfer the inverted-landing policy to physical systems, we utilized domain randomization and system identification techniques for a zero-shot sim-to-real transfer. As a result, we successfully achieved a range of robust inverted-landing behaviors in small quadcopters, emulating those observed in flies.

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References (78)
  1. D. N. Lee, M. N. Davies, P. R. Green, and F. . Van Der Weel, “Visual control of velocity of approach by pigeons when landing,” Journal of experimental biology, vol. 180, no. 1, pp. 85–104, 1993.
  2. W. R. Roderick, D. D. Chin, M. R. Cutkosky, and D. Lentink, “Birds land reliably on complex surfaces by adapting their foot-surface interactions upon contact,” Elife, vol. 8, p. e46415, 2019.
  3. A. C. Carruthers, A. L. Thomas, S. M. Walker, and G. K. Taylor, “Mechanics and aerodynamics of perching manoeuvres in a large bird of prey,” The Aeronautical Journal, vol. 114, no. 1161, pp. 673–680, 2010.
  4. M. V. Srinivasan, S.-W. Zhang, J. S. Chahl, E. Barth, and S. Venkatesh, “How honeybees make grazing landings on flat surfaces,” Biological cybernetics, vol. 83, no. 3, pp. 171–183, 2000.
  5. E. Baird, N. Boeddeker, M. R. Ibbotson, and M. V. Srinivasan, “A universal strategy for visually guided landing,” Proceedings of the National Academy of Sciences, vol. 110, no. 46, pp. 18 686–18 691, 2013.
  6. P. Tichit, I. Alves-dos Santos, M. Dacke, and E. Baird, “Accelerated landings in stingless bees are triggered by visual threshold cues,” Biology letters, vol. 16, no. 8, p. 20200437, 2020.
  7. ——, “Accelerated landing in a stingless bee and its unexpected benefits for traffic congestion,” Proceedings of the Royal Society B, vol. 287, no. 1921, p. 20192720, 2020.
  8. P. Goyal, J. L. van Leeuwen, and F. T. Muijres, “Bumblebees land rapidly by intermittently accelerating and decelerating towards the surface during visually guided landings,” iScience, p. 104265, 2022.
  9. P. Goyal, G. de Croon, J. van Leeuwen, and F. Muijres, “Flight control model of landing maneuver in bumblebees,” in Zoology 2019, 2019.
  10. P. Liu, S. P. Sane, J.-M. Mongeau, J. Zhao, and B. Cheng, “Flies land upside down on a ceiling using rapid visually mediated rotational maneuvers,” Science advances, vol. 5, no. 10, p. eaax1877, 2019.
  11. S. Balebail, S. K. Raja, and S. P. Sane, “Landing maneuvers of houseflies on vertical and inverted surfaces,” PloS one, vol. 14, no. 8, p. e0219861, 2019.
  12. A. Borst, “How do flies land?” BioScience, vol. 40, no. 4, pp. 292–299, 1990.
  13. D. N. Lee, J. A. Simmons, P. A. Saillant, and F. Bouffard, “Steering by echolocation: a paradigm of ecological acoustics,” Journal of Comparative Physiology A, vol. 176, no. 3, pp. 347–354, 1995.
  14. D. K. Riskin, J. W. Bahlman, T. Y. Hubel, J. M. Ratcliffe, T. H. Kunz, and S. M. Swartz, “Bats go head-under-heels: the biomechanics of landing on a ceiling,” Journal of Experimental Biology, vol. 212, no. 7, pp. 945–953, 2009.
  15. A. Restas et al., “Drone applications for supporting disaster management,” World Journal of Engineering and Technology, vol. 3, no. 03, p. 316, 2015.
  16. B. Mishra, D. Garg, P. Narang, and V. Mishra, “Drone-surveillance for search and rescue in natural disaster,” Computer Communications, vol. 156, pp. 1–10, 2020.
  17. S. J. Kim and G. J. Lim, “Drone-aided border surveillance with an electrification line battery charging system,” Journal of Intelligent & Robotic Systems, vol. 92, no. 3, pp. 657–670, 2018.
  18. J. Seo, L. Duque, and J. Wacker, “Drone-enabled bridge inspection methodology and application,” Automation in Construction, vol. 94, pp. 112–126, 2018.
  19. J. Irizarry, M. Gheisari, and B. N. Walker, “Usability assessment of drone technology as safety inspection tools,” Journal of Information Technology in Construction (ITcon), vol. 17, no. 12, pp. 194–212, 2012.
  20. K. Cesare, R. Skeele, S.-H. Yoo, Y. Zhang, and G. Hollinger, “Multi-uav exploration with limited communication and battery,” in 2015 IEEE international conference on robotics and automation (ICRA).   IEEE, 2015, pp. 2230–2235.
  21. J. Thomas, M. Pope, G. Loianno, E. W. Hawkes, M. A. Estrada, H. Jiang, M. R. Cutkosky, and V. Kumar, “Aggressive flight with quadrotors for perching on inclined surfaces,” Journal of Mechanisms and Robotics, vol. 8, no. 5, p. 051007, 2016.
  22. F. Kendoul, “Four-dimensional guidance and control of movement using time-to-contact: Application to automated docking and landing of unmanned rotorcraft systems,” The International Journal of Robotics Research, vol. 33, no. 2, pp. 237–267, 2014.
  23. F. Van Breugel and M. H. Dickinson, “The visual control of landing and obstacle avoidance in the fruit fly drosophila melanogaster,” Journal of Experimental Biology, vol. 215, no. 11, pp. 1783–1798, 2012.
  24. B. Habas, B. AlAttar, B. Davis, J. W. Langelaan, and B. Cheng, “Optimal inverted landing in a small aerial robot with varied approach velocities and landing gear designs,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 2003–2009.
  25. J. Mao, G. Li, S. Nogar, C. Kroninger, and G. Loianno, “Aggressive visual perching with quadrotors on inclined surfaces,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 5242–5248.
  26. M. Kovac, “Learning from nature how to land aerial robots,” Science, vol. 352, no. 6288, pp. 895–896, 2016.
  27. R. Pfeifer, M. Lungarella, and F. Iida, “Self-organization, embodiment, and biologically inspired robotics,” science, vol. 318, no. 5853, pp. 1088–1093, 2007.
  28. W. G. Hyzer, “Flight behavior of a fly alighting on a ceiling,” Science, vol. 137, no. 3530, pp. 609–610, 1962.
  29. A. Borst, “Drosophila’s view on insect vision,” Current biology, vol. 19, no. 1, pp. R36–R47, 2009.
  30. J. Mao, S. Nogar, C. Kroninger, and G. Loianno, “Robust active visual perching with quadrotors on inclined surfaces,” arXiv preprint arXiv:2204.02458, 2022.
  31. K. Hang, X. Lyu, H. Song, J. A. Stork, A. M. Dollar, D. Kragic, and F. Zhang, “Perching and resting—a paradigm for uav maneuvering with modularized landing gears,” Science Robotics, vol. 4, no. 28, p. eaau6637, 2019.
  32. W. Chi, K. Low, K. H. Hoon, and J. Tang, “An optimized perching mechanism for autonomous perching with a quadrotor,” in 2014 IEEE international conference on robotics and automation (ICRA).   IEEE, 2014, pp. 3109–3115.
  33. J. A. Dougherty and T. Lee, “Monocular estimation of ground orientation for autonomous landing of a quadrotor,” Journal of Guidance, Control, and Dynamics, vol. 39, no. 6, pp. 1407–1416, 2016.
  34. M. T. Alkowatly, V. M. Becerra, and W. Holderbaum, “Bioinspired autonomous visual vertical control of a quadrotor unmanned aerial vehicle,” Journal of Guidance, Control, and Dynamics, vol. 38, no. 2, pp. 249–262, 2015.
  35. S. Yang, S. A. Scherer, and A. Zell, “An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle,” Journal of Intelligent & Robotic Systems, vol. 69, no. 1, pp. 499–515, 2013.
  36. H. Das, K. Sridhar, and R. Padhi, “Bio-inspired landing of quadrotor using improved state estimation,” IFAC-PapersOnLine, vol. 51, no. 1, pp. 462–467, 2018.
  37. C. Luo, L. Yu, and P. Ren, “A vision-aided approach to perching a bioinspired unmanned aerial vehicle,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 3976–3984, 2017.
  38. J. Thomas, J. Polin, K. Sreenath, and V. Kumar, “Avian-inspired grasping for quadrotor micro uavs,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 55935.   American Society of Mechanical Engineers, 2013, p. V06AT07A014.
  39. J. Thomas, G. Loianno, K. Daniilidis, and V. Kumar, “Visual servoing of quadrotors for perching by hanging from cylindrical objects,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 57–64, 2016.
  40. P. Yu and K. Wong, “An implementation framework for vision-based bat-like inverted perching with bi-directionalthrust quadrotor,” International Journal of Micro Air Vehicles, vol. 14, p. 17568293211073672, 2022.
  41. L. Bai, H. Wang, X. Chen, J. Zheng, L. Xin, Y. Deng, and Y. Sun, “Design and experiment of a deformable bird-inspired uav perching mechanism,” Journal of Bionic Engineering, vol. 18, no. 6, pp. 1304–1316, 2021.
  42. C. E. Doyle, J. J. Bird, T. A. Isom, J. C. Kallman, D. F. Bareiss, D. J. Dunlop, R. J. King, J. J. Abbott, and M. A. Minor, “An avian-inspired passive mechanism for quadrotor perching,” IEEE/ASME Transactions On Mechatronics, vol. 18, no. 2, pp. 506–517, 2012.
  43. H. Hsiao, J. Sun, H. Zhang, and J. Zhao, “A mechanically intelligent and passive gripper for aerial perching and grasping,” IEEE/ASME Transactions on Mechatronics, 2022.
  44. H. Zhang, E. Lerner, B. Cheng, and J. Zhao, “Compliant bistable grippers enable passive perching for micro aerial vehicles,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 5, pp. 2316–2326, 2020.
  45. A. Kalantari, K. Mahajan, D. Ruffatto, and M. Spenko, “Autonomous perching and take-off on vertical walls for a quadrotor micro air vehicle,” in 2015 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2015, pp. 4669–4674.
  46. Q. He, X. Xu, Z. Yu, K. Huo, Z. Wang, N. Chen, X. Sun, G. Yin, P. Du, Y. Li et al., “Optimized bio-inspired micro-pillar dry adhesive and its application for an unmanned aerial vehicle adhering on and detaching from a ceiling,” Journal of Bionic Engineering, vol. 17, no. 1, pp. 45–54, 2020.
  47. H. Hsiao, F. Wu, J. Sun, and J. Zhao, “A novel passive mechanism for flying robots to perch onto surfaces,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 1183–1189.
  48. H. W. Wopereis, T. Van Der Molen, T. Post, S. Stramigioli, and M. Fumagalli, “Mechanism for perching on smooth surfaces using aerial impacts,” in 2016 IEEE international symposium on safety, security, and rescue robotics (SSRR).   IEEE, 2016, pp. 154–159.
  49. M. T. Pope, C. W. Kimes, H. Jiang, E. W. Hawkes, M. A. Estrada, C. F. Kerst, W. R. Roderick, A. K. Han, D. L. Christensen, and M. R. Cutkosky, “A multimodal robot for perching and climbing on vertical outdoor surfaces,” IEEE Transactions on Robotics, vol. 33, no. 1, pp. 38–48, 2016.
  50. Z. Huang, S. Li, J. Jiang, Y. Wu, L. Yang, and Y. Zhang, “Biomimetic flip-and-flap strategy of flying objects for perching on inclined surfaces,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5199–5206, 2021.
  51. J. Kim, M. C. Lesak, D. Taylor, D. J. Gonzalez, and C. M. Korpela, “Autonomous quadrotor landing on inclined surfaces using perception-guided active asymmetric skids,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7877–7877, 2021.
  52. X. Ni, Q. Yin, X. Wei, P. Zhong, and H. Nie, “Research on landing stability of four-legged adaptive landing gear for multirotor uavs,” Aerospace, vol. 9, no. 12, p. 776, 2022.
  53. D. J. Dunlop and M. A. Minor, “Modeling and simulation of perching with a quadrotor aerial robot with passive bio-inspired legs and feet,” ASME Letters in Dynamic Systems and Control, vol. 1, no. 2, 2021.
  54. S. Liu, W. Dong, Z. Wang, and X. Sheng, “Hitchhiker: A quadrotor aggressively perching on a moving inclined surface using compliant suction cup gripper,” arXiv preprint arXiv:2203.02304, 2022.
  55. T. Baca, P. Stepan, V. Spurny, D. Hert, R. Penicka, M. Saska, J. Thomas, G. Loianno, and V. Kumar, “Autonomous landing on a moving vehicle with an unmanned aerial vehicle,” Journal of Field Robotics, vol. 36, no. 5, pp. 874–891, 2019.
  56. H.-T. Zhang, B.-B. Hu, Z. Xu, Z. Cai, B. Liu, X. Wang, T. Geng, S. Zhong, and J. Zhao, “Visual navigation and landing control of an unmanned aerial vehicle on a moving autonomous surface vehicle via adaptive learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5345–5355, 2021.
  57. A. Paris, B. T. Lopez, and J. P. How, “Dynamic landing of an autonomous quadrotor on a moving platform in turbulent wind conditions,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 9577–9583.
  58. J. L. Paneque, J. R. Martínez-de Dios, A. Ollero, D. Hanover, S. Sun, A. Romero, and D. Scaramuzza, “Perception-aware perching on powerlines with multirotors,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3077–3084, 2022.
  59. J. S. Chahl, M. V. Srinivasan, and S.-W. Zhang, “Landing strategies in honeybees and applications to uninhabited airborne vehicles,” The International Journal of Robotics Research, vol. 23, no. 2, pp. 101–110, 2004.
  60. P. Chirarattananon, “A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and imu measurements,” Bioinspiration & biomimetics, vol. 13, no. 3, p. 036004, 2018.
  61. B. K. Horn and B. G. Schunck, “Determining optical flow,” Artificial intelligence, vol. 17, no. 1-3, pp. 185–203, 1981.
  62. B. K. Horn, Y. Fang, and I. Masaki, “Hierarchical framework for direct gradient-based time-to-contact estimation,” in 2009 IEEE Intelligent Vehicles Symposium.   IEEE, 2009, pp. 1394–1400.
  63. J. Wang, E. Uchibe, and K. Doya, “Em-based policy hyper parameter exploration: application to standing and balancing of a two-wheeled smartphone robot,” Artificial Life and Robotics, vol. 21, no. 1, pp. 125–131, 2016.
  64. B. Habas, J. W. Langelaan, and B. Cheng, “Inverted landing in a small aerial robot via deep reinforcement learning for triggering and control of rotational maneuvers,” arXiv preprint arXiv:2209.11043, 2022.
  65. F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation forest,” in 2008 eighth ieee international conference on data mining.   IEEE, 2008, pp. 413–422.
  66. Z. Cheng, C. Zou, and J. Dong, “Outlier detection using isolation forest and local outlier factor,” in Proceedings of the conference on research in adaptive and convergent systems, 2019, pp. 161–168.
  67. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: identifying density-based local outliers,” in Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp. 93–104.
  68. B. Schölkopf, R. C. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, “Support vector method for novelty detection,” Advances in neural information processing systems, vol. 12, 1999.
  69. J. Ma and S. Perkins, “Time-series novelty detection using one-class support vector machines,” in Proceedings of the International Joint Conference on Neural Networks, 2003., vol. 3.   IEEE, 2003, pp. 1741–1745.
  70. M. Amer, M. Goldstein, and S. Abdennadher, “Enhancing one-class support vector machines for unsupervised anomaly detection,” in Proceedings of the ACM SIGKDD workshop on outlier detection and description, 2013, pp. 8–15.
  71. T. Lee, M. Leok, and N. H. McClamroch, “Nonlinear robust tracking control of a quadrotor uav on se (3),” Asian Journal of Control, vol. 15, no. 2, pp. 391–408, 2013.
  72. J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS).   IEEE, 2017, pp. 23–30.
  73. C. Evangelista, P. Kraft, M. Dacke, J. Reinhard, and M. V. Srinivasan, “The moment before touchdown: landing manoeuvres of the honeybee Apis mellifera,” Journal of Experimental Biology, vol. 213, no. 2, pp. 262–270, 2010.
  74. W. R. Roderick, M. R. Cutkosky, and D. Lentink, “Touchdown to take-off: At the interface of flight and surface locomotion,” Interface Focus, vol. 7, no. 1, 2017.
  75. J. A. Preiss, W. Honig, G. S. Sukhatme, and N. Ayanian, “Crazyswarm: A large nano-quadcopter swarm,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2017, pp. 3299–3304.
  76. G. A. Garcia, A. R. Kim, E. Jackson, S. S. Keshmiri, and D. Shukla, “Modeling and flight control of a commercial nano quadrotor,” in 2017 International Conference on Unmanned Aircraft Systems (ICUAS).   IEEE, 2017, pp. 524–532.
  77. M. R. Jardin and E. R. Mueller, “Optimized measurements of unmanned-air-vehicle mass moment of inertia with a bifilar pendulum,” Journal of Aircraft, vol. 46, no. 3, pp. 763–775, 2009.
  78. J. Förster, “System identification of the crazyflie 2.0 nano quadrocopter,” B.S. thesis, ETH Zurich, 2015.

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