Similar articles

Abstract: Target tracking is one of the hottest topics in the field of drone research. In this paper, we study the multiple unmanned aerial vehicles (multi-UAV) collaborative target tracking problem. We propose a novel tracking method based on intention estimation and effective cooperation for UAVs with inferior tracking capabilities to track the targets that may have agile, uncertain, and intelligent motion. For three classic target motion modes, we first design a novel trajectory feature extraction method with the least dimension and maximum coverage constraints, and propose an intention estimation mechanism based on the environment and target trajectory features. We propose a novel Voronoi diagram, called MDA-Voronoi, which divides the area with obstacles according to the minimum reachable distance and the minimum steering angle of each UAV. In each MDA-Voronoi region, the maximum reachable region of each UAV is defined, the upper and lower bounds of the trajectory coverage probability are analyzed, and the tracking strategies of the UAVs are designed to effectively reduce the tracking gaps to improve the target sensing time. Then, we use the Nash Q-learning method to design the UAVs’ collaborative tracking strategy, considering factors such as collision avoidance, maneuvering constraints, tracking cost, sensing performance, and path overlap. By designing the reward mechanism, the optimal action strategies are obtained as the control input of the UAVs. Finally, simulation analyses are provided to validate our method, and the results demonstrate that the algorithm can improve the collaborative target tracking performance for multiple UAVs with inferior tracking capabilities.

一种跟踪性能不占优的多无人机协同目标跟踪方法

[1]Adepegba A, Miah MS, Spinello D, 2016. Multi-agent area coverage control using reinforcement learning. Proc 29^th Int Florida Artificial Intelligence Research Society Conf, p.368-373.

[2]Bordonaro SV, Willett P, Bar-Shalom Y, et al., 2019. Converted measurement sigma point Kalman filter for bistatic sonar and radar tracking. IEEE Trans Aerosp Electron Syst, 55(1):147-159.

[3]Cai J, Huang CQ, Guo HF, 2012. Multi-sensor cooperative tracking using distributed Nash Q-learning. Adv Mater Res, 591-593:1475-1478.

[4]Di B, Zhou R, Dong ZN, 2016. Cooperative localization and tracking of multiple targets with the communication-aware unmanned aerial vehicle system. Contr Dec, 31(4):616-622 (in Chinese).

[5]Douthwaite JA, Zhao SY, Mihaylova LS, 2019. Velocity obstacle approaches for multi-agent collision avoidance. Unmann Syst, 7(1):55-64.

[6]Gao GQ, Xin B, 2019. A-STC: auction-based spanning tree coverage algorithm for motion planning of cooperative robots. Front Inform Technol Electron Eng, 20(1):18-31.

[7]Goldhoorn A, Garrell A, Alqu�zar R, et al., 2018. Searching and tracking people with cooperative mobile robots. Auton Robot, 42(4):739-759.

[8]Hu JL, Wellman MP, 2003. Nash Q-learning for general-sum stochastic games. J Mach Learn Res, 4:1039-1069.

[9]Jiang H, Liang YQ, 2018. Online path planning of autonomous UAVs for bearing-only standoff multi-target following in threat environment. IEEE Access, 6:22531-22544.

[10]Khalkhali MB, Vahedian A, Yazdi HS, 2020. Multi-target state estimation using interactive Kalman filter for multi-vehicle tracking. IEEE Trans Intell Transp Syst, 21(3):1131-1144.

[11]Li TC, 2019. Single-road-constrained positioning based on deterministic trajectory geometry. IEEE Commun Lett, 23(1):80-83.

[12]Li TC, Su JY, Liu W, et al., 2017. Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front Inform Technol Electron Eng, 18(12):1913-1939.

[13]Li TC, Chen HM, Sun SD, et al., 2019. Joint smoothing and tracking based on continuous-time target trajectory function fitting. IEEE Trans Autom Sci Eng, 16(3):1476-1483.

[14]Liu YS, Wang QX, Hu HS, et al., 2019. A novel real-time moving target tracking and path planning system for a quadrotor UAV in unknown unstructured outdoor scenes. IEEE Trans Syst Man Cybern Syst, 49(11):2362-2372.

[15]Meng W, He ZR, Su R, et al., 2017. Decentralized multi-UAV flight autonomy for moving convoys search and track. IEEE Trans Contr Syst Technol, 25(4):1480-1487.

[16]Quintero SAP, Copp DA, Hespanha JP, 2015. Robust UAV coordination for target tracking using output-feedback model predictive control with moving horizon estimation. American Control Conf, p.3758-3764.

[17]Ragi S, Chong EKP, 2012. Dynamic UAV path planning for multitarget tracking. American Control Conf, p.3845-3850.

[18]Ragi S, Chong EKP, 2013. Decentralized control of unmanned aerial vehicles for multitarget tracking. Int Conf on Unmanned Aircraft Systems, p.260-268.

[19]Ruan WY, Duan HB, 2020. Multi-UAV obstacle avoidance control via multi-objective social learning pigeon-inspired optimization. Front Inform Technol Electron Eng, 21(5):740-748.

[20]Shao Y, Zhao ZF, Li RP, et al., 2020. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments. Front Inform Technol Electron Eng, 21(5):796-808.

[21]Skorobogatov G, Barrado C, Salamí E, 2020. Multiple UAV systems: a survey. Unmann Syst, 8(2):149-169.

[22]Song WH, Wang JA, Zhao SY, et al., 2019. Event-triggered cooperative unscented Kalman filtering and its application in multi-UAV systems. Automatica, 105(3):264-273.

[23]Sutton RS, Barto AG, 1998. Introduction to Reinforcement Learning. MIT Press, Cambridge, MA, USA.

[24]Vanegas F, Campbell D, Eich M, et al., 2016. UAV based target finding and tracking in GPS-denied and cluttered environments. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2307-2313.

[25]Wang DB, Wang Y, Jiang WY, et al., 2015. Unmanned aerial vehicles cooperative path planning for ground target tracking via chemical reaction optimization. Sci Sin Technol, 45(6):583-594.

[26]Wang L, Peng H, Zhu HY, et al., 2011. Cooperative tracking of ground moving target using unmanned aerial vehicles in cluttered environment. Contr Theor Appl, 28(3):300-308 (in Chinese).

[27]Wang T, Qin RX, Chen Y, et al., 2019. A reinforcement learning approach for UAV target searching and tracking. Multim Tools Appl, 78(4):4347-4364.

[28]Yao P, Wang HL, Su ZK, 2015. Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment. Aerosp Sci Technol, 47(6):269-279.

[29]Yu HL, Meier K, Argyle M, et al., 2015. Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles. IEEE/ASME Trans Mech, 20(2):541-552.

[30]Zollars MD, Cobb RG, Grymin DJ, 2019. Optimal SUAS path planning in three-dimensional constrained environments. Unmann Syst, 7(2):105-118.