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SCIENTIA SINICA Informationis
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SCIENTIA SINICA Informationis, Volume 49 , Issue 5 : 520-537(2019) https://doi.org/10.1360/N112018-00318

UAV sense and avoidance: concepts, technologies, and systems

Yang LYU , Tongna KANG , Quan PAN *, Chunhui ZHAO , Jinwen HU
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  • ReceivedNov 30, 2018
  • AcceptedFeb 28, 2019
  • PublishedMay 15, 2019
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Abstract


Funded by

国家自然科学基金(61603303,61473230)


References

[1] Hu Z P, Tian B C. Battlefield application and development trend of drones under informatization conditions. Aerod Missile J, 2011, 10: 63--65. Google Scholar

[2] Li D R, Li M. Research advance and application prospect of unmannedaerial vehicle remote sensing system. Geomat Inform Sci Wuhan Univ, 2014, 39: 505--513. Google Scholar

[3] Guo S Y. Research on earth observation coverage for multiple unmanned rotorcraft. Dissertation for Master Degree. Hangzhou: Zhejiang University, 2014. Google Scholar

[4] Gao J S, Zou Q Y, Chen S D. Research on anti-terrorism of foreign UAV system. In: Proceedings of 2006 China UAV Congress, 2006. Google Scholar

[5] Chen T. Design and research on visual positioning and tracking system for the small unmanned helicopter. Dissertation for Master Degree. Hangzhou: Zhejiang University, 2013. Google Scholar

[6] Huerta M. Integration of civil unmanned aircraft systems (UAS) in the national airspace system (NAS) roadmap. Federal Aviation Administration, Retrieved Dec. 2013, 19: 2013. Google Scholar

[7] Li Y J, Pan Q, Yang F, et al. Multi-source information fusion for sense and avoidance of UAV. In: Proceedings of the 29th Chinese Control Conference, Beijing, 2010. Google Scholar

[8] Part FAR. 91, General Operating and Flight Rules. Federal Aviation Administration. 1991. Google Scholar

[9] Weatherington D, Deputy U. Unmanned aircraft systems roadmap, 2005-2030. Deputy, UAV Planning Task Force, OUSD (AT&L). 2005. Google Scholar

[10] Verstraeten J, Stuip M, van Birgelen T. Assessment of detect and avoid solutions for use of unmanned aircraft systems in nonsegregated airspace. In: Handbook of Unmanned Aerial Vehicles. Berlin: Springer, 2015. 1955--1979. Google Scholar

[11] Angelov P. Sense and Avoid in UAS: Research and Applications. Hoboken: Wiley, 2012. Google Scholar

[12] Billingsley T B. Safety analysis of TCAS on Global Hawk using airspace encounter models. Cambridge: Massachusetts Institute of Technology, 2006. Google Scholar

[13] Sahawneh L R, Duffield M O, Beard R W. Detect and Avoid for Small Unmanned Aircraft Systems Using ADS-B. Air Traffic Control Q, 2015, 23: 203-240 CrossRef Google Scholar

[14] Fasano G, Accardo D, Moccia A. Multi-Sensor-Based Fully Autonomous Non-Cooperative Collision Avoidance System for Unmanned Air Vehicles. J Aerospace Computing Inf Communication, 2008, 5: 338-360 CrossRef Google Scholar

[15] Lyu Y, Pan Q, Zhao C H, et al. Autonomous stereo vision based collision avoid system for small UAV. In: Proceedings of AIAA Information Systems, 2017. Google Scholar

[16] Ozaslan T, Loianno G, Keller J. Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels With MAVs. IEEE Robot Autom Lett, 2017, 2: 1740-1747 CrossRef Google Scholar

[17] Lester T, Cook S, Noth K. USAF Airborne Sense and Avoid (ABSAA) Airworthiness and Operational Approval Approach. 2014. http://www.mitre.org/sites/default/files/publications/usaf-airborne-sense-avoid-13-3116.pdf. Google Scholar

[18] Dalamagkidis K, Valavanis K P, Piegl L A. Current Status and Future Perspectives for Unmanned Aircraft System Operations in the US. J Intell Robot Syst, 2008, 52: 313-329 CrossRef Google Scholar

[19] Zeitlin A, Lacher A, Kuchar J, et al. Collision avoidance for unmanned aircraft: proving the safety case. JAIDS J Acq Immun Def Synd, 2006, 21: 49--57. Google Scholar

[20] Accardo D, Fasano G, Forlenza L. Flight Test of a Radar-Based Tracking System for UAS Sense and Avoid. IEEE Trans Aerosp Electron Syst, 2013, 49: 1139-1160 CrossRef ADS Google Scholar

[21] Owen M P, Duffy S M, Edwards M W. Unmanned aircraft sense and avoid radar: surrogate flight testing performance evaluation. In: Proceedings of IEEE Radar Conference, 2014. Google Scholar

[22] Newmeyer L, Wilde D, Nelson B, et al. Efficient processing of phased array radar in sense and avoid application using heterogeneous computing. In: Proceedings of the 26th International Conference on Field Programmable Logic and Applications (FPL), 2016. Google Scholar

[23] Yu H B. Research on the key technology of MMW radar for power line detection. Dissertation for Master Degree. Nanjing: Nanjing University of Science and Technology, 2015. Google Scholar

[24] Geyer C M, Dey D, Singh S. Prototype Sense-and-Avoid System for UAVs. Technical Report CMU-RI-TR-09-09. 2009. Google Scholar

[25] Li J P, Liu K, Li J M, et al. The invention relates to a multi-rotor uav ultrasonic anti-collision system. CN205353698U, 2016-06-29. Google Scholar

[26] Dey D, Geyer C, Singh S, et al. Passive, long-range detection of aircraft: towards a field deployable sense and avoid system. In: Field and Service Robotics. Berlin: Springer, 2010. 113--123. Google Scholar

[27] Lai J, Mejias L, Ford J J. Airborne vision-based collision-detection system. J Field Robotics, 2011, 28: 137-157 CrossRef Google Scholar

[28] Song K T, Huang J H. Fast optical flow estimation and its application to real-time obstacle avoidance. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2001. 2891--2896. Google Scholar

[29] Mori T, Scherer S. First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2013. 1750--1757. Google Scholar

[30] Fu C, Olivares-Mendez M A, Suarez-Fernandez R. Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers. J Intell Robot Syst, 2014, 73: 513-533 CrossRef Google Scholar

[31] Heng L, Meier L, Tanskanen P, et al. Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2011. 2472--2477. Google Scholar

[32] Hart P, Nilsson N, Raphael B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Trans Syst Sci Cyber, 1968, 4: 100-107 CrossRef Google Scholar

[33] Li J, Sun X X. A route planning's method for unmanned aerial vehicles based on improved A-Star algorithm. Acta Armament Arii, 2008, 29: 788--792. Google Scholar

[34] Van T J. Development of an autonomous avoidance algorithm for UAVs in general airspace. In: Proceedings of the 1st CEAS European Air and Space Conference, 2007. Google Scholar

[35] Wang X, Yadav V, Balakrishnan S N. Cooperative UAV Formation Flying With Obstacle/Collision Avoidance. IEEE Trans Contr Syst Technol, 2007, 15: 672-679 CrossRef Google Scholar

[36] Saunders J B, Call B, Curtis A, et al. Static and dynamic obstacle avoidance in miniature air vehicles. In: Proceedings of AIAA Infotech, 2005. Google Scholar

[37] Lin Y, Saripalli S. Sense and avoid for unmanned aerial vehicles using ADSB. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2015. 6402--6407. Google Scholar

[38] Tu J, Yang S X. Genetic algorithm based path planning for a mobile robot. In: Proceedings of IEEE International Conference on Robotics and Automation, 2003. 1221--1226. Google Scholar

[39] Durand N. Neural nets trained by genetic algorithms for collision avoidance. Appl Intelligence, 2000, 13: 205-213 CrossRef Google Scholar

[40] Ma Y H, Zhou D Y. A chaotic genetic algorithm (CGA) for path planning of UAVs. J Northwestern Polytechnical Univ, 2006, 24: 468--471. Google Scholar

[41] Duan H, Zhang X, Wu J. Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments. J Bionic Eng, 2009, 6: 161-173 CrossRef Google Scholar

[42] Liu S, Mao L, Yu J. Path planning based on ant colony algorithm and distributed local navigation for multi-robot systems. In: Proceedings of IEEE International Conference on Mechatronics and Automation, 2006. 1733--1738. Google Scholar

[43] Lu L, Gong D. Robot path planning in unknown environments using particle swarm optimization. In: Proceedings of 4th International Conference on Natural Computation, 2008. 422--426. Google Scholar

[44] Richards A, How J P. Aircraft trajectory planning with collision avoidance using mixed integer linear programming. In: Proceedings of American Control Conference, 2002. 1936--1941. Google Scholar

[45] Wang Y, Zhu X P, Zhou Z, et al. UAV path following in 3-D dynamic environment. Robot, 2014, 36: 83--91. Google Scholar

[46] Neunert M, de Crousaz C, Furrer F, et al. Fast nonlinear model predictive control for unified trajectory optimization and tracking. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2016. 1398--1404. Google Scholar

[47] Zhu Y, Zhang T, Song J Y. Path planning for nonholonomic mobile robots using artificial potential field method. Control Theory Appl, 2010, 27: 152--158. Google Scholar

[48] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous robot vehicles. Berlin: Springer, 1986. 396--404. Google Scholar

[49] Dong Z N, Chen Z L, Zhou R, et al. A hybrid approach of virtual force and $A^*$ search algorithm for UAV path replanning. In: Proceedings of the 6th IEEE Conference on Industrial Electronics and Applications, 2011. 1140--1145. Google Scholar

[50] Li W. Behavior based control of a mobile robot in unknown environments using fuzzy logicp. Control Theory Appl, 1996, 2: 153--162. Google Scholar

[51] Han S C, Bang H, Yoo C S. Proportional navigation-based collision avoidance for UAVs. Int J Control Autom Syst, 2009, 7: 553-565 CrossRef Google Scholar

[52] Mujumdar A, Padhi R. Nonlinear geometric guidance and differential geometric guidance of UAVs for reactive collision avoidance. In: Proceedings of AIAA Guidance, Navigation, and Control Conference, 2010. Google Scholar

[53] Chakravarthy A, Ghose D. Obstacle avoidance in a dynamic environment: a collision cone approach. IEEE Trans Syst Man Cybern A, 1998, 28: 562-574 CrossRef Google Scholar

[54] Carbone C, Ciniglio U, Corraro F, et al. A novel 3D geometric algorithm for aircraft autonomous collision avoidance. In: Proceedings of IEEE Conference on Decision and Control, 2006. 1580--1585. Google Scholar

[55] Shanmugavel M, Tsourdos A, White B. Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs. Control Eng Practice, 2010, 18: 1084-1092 CrossRef Google Scholar

[56] Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 1998. Google Scholar

[57] La H M, Lim R, Sheng W. Multirobot Cooperative Learning for Predator Avoidance. IEEE Trans Contr Syst Technol, 2015, 23: 52-63 CrossRef Google Scholar

[58] Hung S M, Givigi S N. A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment.. IEEE Trans Cybern, 2017, 47: 186-197 CrossRef PubMed Google Scholar

[59] Long P, Liu W, Pan J. Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation. IEEE Robot Autom Lett, 2017, 2: 656-663 CrossRef Google Scholar

[60] van den Berg J, Lin M, Manocha D. Reciprocal velocity obstacles for real-time multiagent navigation. In: Proceedings of IEEE International Conference on Robotics and Automation, 2008. 1928--1935. Google Scholar

[61] Chen Y F, Liu M, Everett M, et al. Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2017. 285--292. Google Scholar

[62] Lyu Y, Pan Q, Hu J W, et al. Multi-vehicle flocking control with deep deterministic policy gradient method. 2018,. arXiv Google Scholar

[63] Liang B, Hong B R, Shu G. Research on vision-action model of autonomous robot and obstacle avoiding. Acta Electron Sin, 2003, 31: 2197--2200. Google Scholar

[64] Aguilar W G, Casaliglla V P, Pólit J L, et al. Obstacle avoidance for flight safety on unmanned aerial vehicles. In: Proceedings of International Work-Conference on Artificial Neural Networks, 2017. 575--584. Google Scholar

[65] Wilson M. Ground-based sense and avoid support for unmanned aircraft systems. In: Proceedings of Congress of the International Council of the Aeronautical Sciences (ICAS), 2012. Google Scholar

[66] Chamberlain L J, Scherer S, Singh S. Self-Aware Helicopters: Full-Scale Automated Landing and Obstacle Avoidance in Unmapped Environments. In: Proceedings of AHS Forum, 2011. Google Scholar

[67] Utt J, McCalmont J, Deschenes M. Development of a sense and avoid system. In: Proceedings of AIAA, 2005. Google Scholar

[68] Zarandy A, Nagy Z, Vanek B, et al. A five-camera vision system for UAV visual attitude calculation and collision warning. In: Proceedigns of International Conference on Computer Vision Systems, 2013. 11--20. Google Scholar

[69] Miller P C. General atomics successfully tests UAS sense-and-avoid system. Unmanned Aircraft System Magazine, 2014. Google Scholar

[70] Chaumette F, Hutchinson S. Visual servo control. IEEE Robot Automat Mag, 2006, 13: 82-90 CrossRef Google Scholar

[71] Mnih V, Kavukcuoglu K, Silver D. Human-level control through deep reinforcement learning. Nature, 2015, 518: 529-533 CrossRef PubMed ADS Google Scholar

[72] Tian J, Shen L C. Research on multi-base multi-UAV cooperative reconnaissance problem. Acta Aeronaut Et Astron Autica Sin, 2007, 28: 913--921. Google Scholar

[73] Ye X F. A HRI method based on stereo vision and deep learning for UAV. Dissertation for Master Degree. Tianjin: Tianjin University, 2017. Google Scholar

[74] Alvarez H, Paz L M, Sturm J, et al. Collision avoidance for quadrotors with a monocular camera. In: Experimental Robotics. Berlin: Springer, 2016. 195--209. Google Scholar

[75] Dey D, Shankar K S, Zeng S, et al. Vision and learning for deliberative monocular cluttered flight. In: Field and Service Robotics. Berlin: Springer, 2016. 391--409. Google Scholar

[76] Lyu Y, Pan Q, Zhao C. Vision-based UAV collision avoidance with 2D dynamic safety envelope. IEEE Aerosp Electron Syst Mag, 2016, 31: 16-26 CrossRef Google Scholar

[77] Oleynikova H, Honegger D, Pollefeys M. Reactive avoidance using embedded stereo vision for MAV flight. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2015. 50--56. Google Scholar

[78] McGuire K, de Croon G, De Wagter C. Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone. IEEE Robot Autom Lett, 2017, 2: 1070-1076 CrossRef Google Scholar

[79] Iacono M, Sgorbissa A. Path following and obstacle avoidance for an autonomous UAV using a depth camera. Robotics Autonomous Syst, 2018, 106: 38-46 CrossRef Google Scholar

[80] Hu J, Niu Y F, Wang Z C. Obstacle avoidance methods for rotor UAVs using RealSense camera. In: Proceedings of Chinese Automation Congress (CAC), 2017. 7151--7155. Google Scholar

[81] Upton E, Halfacree G. Raspberry Pi User Guide. Hoboken: John Wiley and Sons, 2014. Google Scholar

[82] Weiss A, Rosenthal M, Mazloumian A. Realtime signal processing on NVIDIA TX2 using CUDA. In: Proceedings of Nvidia GPU Technology Conference (GTC), 2018. Google Scholar

[83] Rabl A, Salner P, Büchi L, et al. Implementation of a capacitive proximity sensor system for a fully maneuverable modular mobile robot to evade humans. In: Proceedings of Austrian Robotics Workshop, 2018. Google Scholar

[84] Grzonka S, Grisetti G, Burgard W. A Fully Autonomous Indoor Quadrotor. IEEE Trans Robot, 2012, 28: 90-100 CrossRef Google Scholar

[85] Mohta K, Watterson M, Mulgaonkar Y. Fast, autonomous flight in GPS-denied and cluttered environments. J Field Robotics, 2018, 35: 101-120 CrossRef Google Scholar

[86] Gageik N, Benz P, Montenegro S. Obstacle Detection and Collision Avoidance for a UAV With Complementary Low-Cost Sensors. IEEE Access, 2015, 3: 599-609 CrossRef Google Scholar

  • Click to view Download high-quality image

    Figure 1

    (Color online) SAA function flow

  • Click to view Download high-quality image

    Figure 2

    UAV SAA environment sensing technology

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    Figure 3

    (Color online) The hierarchical SAA paradigm for large & middle size UAV

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    Figure 4

    (Color online) The reactive SAA paradigm for small & micro size UAV

  • Table 1   UAV SAA sensor configuration
    Platform T-CAS ADS-B Ground radar Airborne radar Lidar Man eye EO IR IRST Ultra-Sonic Stereo MWR
    HALE √* √* √* √ √ √ √
    MALE √* √* √* √ √ √ √
    TUAV √* √ √ √ √ √
    SUAV √* √ √ √ √ √
    MAV √* √ √* √ √ √ √ √

    a) represents the sensor applicable to the UAV; * represents the sensors that should be mounted according to the policies and regulations in the future application of UAV.

  • Table 2   Global path planning & reactive collision avoidance
    Global path planning Reactive collision avoidance
    Platform HALE & MALE UAV Small & micro UAV
    Operation space Sparse mid-air scenario Complex low-altitude scenario
    Sensing information Global, wide range target status Local, limited range target measurements
    Control output Global way-points Local control command
    Sensing & control Almost decoupled Coupled
    Loop frequency Low High
  • Table 3   Large & middle size UAV safety capacity level
    Safety level Description
    0 Does not have any security assurance capability and technical means
    1
    Local flight safety support under specific mission functions include:
    (a) limited perceptionability and partial communication ability in VMC environment;
    (b) decision rules and operation methodsin simple task environment;
    (c) and simple flight control operation method and command realization.
    2
    Wide range and long endurance flight safety support in a simple and sparse flightenvironment:
    (a) wide range sensing and multi-link communication capabilities in a VMC condition;
    (b) situation assessment and decision making in a wide range of mission environments;
    (c) and fully controllable task execution.
    3
    Safety flight insurance in complex meteorological conditions and flight environment:
    (a) perception ability and communication link under VMC and IMC;
    (b) environmental situation analysis and fault diagnosis under complex flight conditions;
    (c) and controllable task instruction execution under man's supervision function.
    4
    All-weather flight safety support under complex air traffic control system:
    (a) large-scale perception and cooperative air traffic information under VMC and IMC;
    (b) multi-information support for flight situation analysis and platform health management functions;
    (c) and access to ATC system, complete flight status acquisition and flight command execution.
    5
    Equivalent safe flight in shared airspace with man-machine:
    (a) large-scale perception andhighly reliable information interaction under VMC and IMC;
    (b) highly reliable situationanalysis and decision-making punder the support of multiple information
    sources (more thanpeople in the loop);
    (c) and seamless access to the ATM (air traffic management) system to achieve the mission flight
    under the ATCoperating rules.
  • Table 4   Small & micro UAV safety capacity level
    Safety level Description
    0 Visual line of sight flight with man in the loop remote control (100% man in the loop).
    1
    Partial perception and auxiliary control functions:
    (a) auxiliary navigation andenvironment perception in sparse environment;
    (b) low level auxiliary judgment and warning ability;
    (c) and people in the loop control, with auxiliary functions of the automatic driving system.
    2
    Man in the loop decision making (partial man in the loop):
    (a) auxiliary navigation, environment and target modeling in dense environment;
    (b) auxiliary decision-making function and safety warning ability;
    (c) man in the loop path planning and maneuver control.
    3
    Automatic mission execution with man in the loop supervision:
    (a) reliable navigation and data processing in the task environment;
    (b) supervised online data analysis and decision-making function;
    (c) online path planning and control.
    4
    Autonomous task execution:
    (a) intelligent information processing and environmental perception;
    (b) autonomous analysis and decision-making function;
    (c) optimal online path planning and maneuver control.
    5
    Collaborative task execution ability with multiple platforms:
    (a) multi-platform collaborative perceptionand data analysis;
    (b) collaborative situational awareness and decision-making;
    (c) distributed multi-platform path planning and control.