Distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urba
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ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555
Distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urban Environment Chaofang Hu*, Zhuo Meng, Ge Qu, Hyo-Sang Shin, and Antonios Tsourdos Abstract: In this paper, a real-time distributed path planning method is developed for cooperatively tracking ground moving target in urban by multiple fixed-wing unmanned aerial vehicles (UAVs). For reasons of changeable movement of target, the commanded speed and turning rate of each UAV are both taken as control input variables. In urban environment, buildings may occlude the line of sight of on-board sensor. Hence the target coverage degree is proposed as objective function instead of distance. To save energy of UAV system as much as possible, the control input cost and sensor energy consumption are also taken as objectives. For preemptive priority requirement, the objective functions are fuzzified and the satisfactory degree order is designed to model priority. To guarantee the feasibility of solution, the varying domain is introduced to replace the strict order constraint. On this basis, generalized varying domain (GVD) method is developed to balance optimization and priority. In terms of the maneuverability of UAVs, the diverse constraints are considered, including real speed and turning rate, control input saturation, collision avoidance between UAVs, and obstacle avoidance between UAV and buildings. Consequently, distributed model predictive control (DMPC) strategy is designed to calculate the optimal path of each UAV, where the state information in finite period of UAV is transferred to the adjacent ones. The simulations show the effectiveness of proposed method by comparing with hierarchical optimization (HO). Keywords: Distributed model predictive control, path planning, priority, unmanned aerial vehicle.
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INTRODUCTION
Unmanned aerial vehicles (UAVs) have been widely applied in both civilian and military fields due to its low cost, high security and flexibility. Recently, more and more attention has been paid to monitoring and tracking of the moving target [1], and many applications are addressed, such as target location [2], multi-sensor information fusion [3], prediction [4], task allocation [5] and path planning [6]. Target tracking modes by UAVs include standoff and persistent. The former means to closely orbit the target but maintain a safe range. Frew et al. [7] present a control structure based on Lyapunov guidance vector field (LGVF). In [8], a standoff tracking guidance law is proposed using differential geometry between UAV and target. Oh et al. [9] propose a two-phase method via the vector field guidance, where target assignment and cooperative tracking via real-time local replanning are involved.
Based on tangent guidance vector field and LGVF, Chen et al. [10] propose a dynamic path planning algorithm. Different from standoff, persistent tracking requires that UAVs always try to get close to the target as much as possible. T
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