Abstract
In recent years, multiple Unmanned Aerial Vehicle (UAV) formation flight has attracted worldwide research interest, for its potential benefits of scalability and flexibility. In complex urban environments, the successful operation of those UAVs requires the system to provide certain safety level. As one of the key requirements, collision avoidance improves the system’s ability to accommodate operational environment variations, and to perform multiple tasks. To achieve this, artificial potential field (APF) has been recognized as one of the most suitable methods along with drone control. Although there has been substantial relevant work on the APF for single UAV in static environment, more efforts are desired to address formation maneuvers in complex environments such as urban. Most importantly, the traditional APF algorithms do not account for random errors in navigation solutions, which can bring potential risk to the UAV system. In response, this paper proposes a new APF algorithm that employs navigation information in complex urban environments, and the goal is to realize UAV formation collision avoidance. By augmenting the APF algorithm with UAV navigation information, the potential risk caused by navigation uncertainty can be mitigated, especially in the Global Navigation Satellite System (GNSS) challenged environment. The principle of the new APF approach is adaptively estimating the parameters of potential field force function, using the variance of navigation information and user-defined confidence probability. This new approach is applied in the synchronized UAV formation collision avoidance control. As a result, the UAVs can achieve fast position and attitude adjustment with high safety confidence. To verify the algorithm, quadrotors with emulated GNSS receivers are used to generate observation data. These data are incorporated into a complex urban environment simulation, where multiple sets of virtual obstacles are injected. Results show that the proposed method can achieve safe and efficient collision avoidance for cooperative formation flight in urban GNSS challenged environment.
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References
Otto A, Agatz N, Campbell J, Golden B, Pesch E (2018) Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones. A survey, in networks 72–85.
Mohta K, Watterson M, Mulgaonkar Y et al (2018) Fast autonomous flight in GPS-denied and cluttered environments. J Field Robot 35:101–120
Hassa M, Abdelkefifi A (2017) Classifications, applications, and design challenges of drones. A review. Prog Aerosp Sci 91:99–131
Norman L, Liu HHT (2009) Multiple UAVs formation flight experiments using virtual structure and motion synchronization. AIAA guidance, navigation, and control conference. doi: 10.2514/6.2009-5887
Kumar K (2006) Review on dynamics and control of satellite systems. J Spacecr Rockets 43:705–720
Cledat E, Cucc DA (2017) Mapping GNSS restricted environments with a drone tandem and indirect position control. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 10:51–59
Ko N Y, Lee HKo B, Nak Yong B, Lee H (1996) Avoid-ability measure in moving obstacle avoidance problem and its use for robot motion planning. IEEE/RSJ international conference on intelligent robots and systems. doi:10.1109/IROS.1996.568984
Liu E, Wanxu Z (2013) Path planning for mobile robot based on improved artificial potential field method in complex environment. Comput Eng Appl 49(24):45–48
Elbanhawi M et al (2017) Enabling technologies for autonomous MAV operations. Prog Aerosp Sci 91:27–52
Khatib O. (1986) The potential field approach and operational space formulation in robot control. In Narendra KS (eds.). Adaptive and learning systems 9:90–98. doi: 10.1007/978–1–4757–1895–9_26.
Kownacki C, Ambroziak L (2017) Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs. Aerosp Sci Technol 68(9):465–474
Yong-shen LYU, Li-jia LIU, Xue-rong YANG et al (2019) Formation control of UAV swarm combining artificial potential field and virtual structure. Flight Dyn 37(03):43–47. https://doi.org/10.13645/j.cnki.f.d.20190123.001
Belkheiri M, Rabhi A, Hajjaji AE et al (2012) Different linearization control techniques for a quadrotor system. International conference on communications, computing and control applications. doi: 10.1109/CCCA.2012.6417914
Santana L, Vago AS, BrandaO S-F, M´ario. (2016) Navigation and cooperative control using the AR. Drone quadrotor. J Intell Robot Syst 84(1–4):327–350
Qian L, Liu HHT (2019) Path-following control of a quadrotor UAV with a cable-suspended payload under wind disturbances. IEEE Trans Industr Electron 67(3):2021–2029. https://doi.org/10.1109/TIE.2019.2905811
Shan J, Liu HT (2005) Close-formation flight control with motion synchronization. J Guid Control Dyn 28(6):1316–1320
Liu ZX, Yang LX, Wang JG (2012) Soccer robot path planning based on evolutionary artificial field. Adv Mater Res 562–564:955–958
He R, Wei R, Zhang Q (2017) UAV autonomous collision avoidance approach. Automatika 58(2):195–204
Vetrella A et al (2016) Differential GNSS and vision-based tracking to improve navigation performance in cooperative multi-UAV systems. Sensors 16:2164–2175
Guo K, Qiu Z, Meng W et al (2017) Ultra-wide band based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments. Int J Micro Air Veh 9(3):169–186
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This study was supported by Shanghai Jiao Tong University (SJTU) Global Strategic Partnership Fund (2019 SJTU – UoT).
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Liu, H., Liu, H.H.T., Chi, C. et al. Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight. AS 3, 229–241 (2020). https://doi.org/10.1007/s42401-020-00059-6
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DOI: https://doi.org/10.1007/s42401-020-00059-6