Full length articleImmune genetic algorithm based multi-UAV cooperative target search with event-triggered mechanism
Introduction
Cooperative target searching (CTS) using multiple Unmanned Aerial Vehicles (multi-UAV) is an important problem in both military and civilian field [1]. It involves technologies such as communication topology, cooperative path planning and optimization, obstacle and threat avoidance, cooperative control, etc. [2], [3], [4]. Now it attracts great attentions and many research results are obtained [5], [6], [7]. However, many researches only considered the CTS problem and neglect when the UAV should quit the target search mode and enter into the mode of base-returning. Considering the triggering events such as damage caused by enemy aircraft attack, flight failure or insufficient fuel, UAV should return to the closest base with the shortest flight path. In this paper, We consider a closed loop cooperative target search for UAVs with CTS phase and flight path planning phase (FPP) in a designated area.
For the problem of multi-UAV CTS, [8] uses the Beta distribution to model the uncertainty of the search environment and calculate the minimum range of UAV search target. But it only deals with the single UAV target search problem. Riehl proposes a rolling time domain optimization algorithm to optimize the route of mobile target search in a closed bounded search area [9], and it not consider the steering constraint factors of UAV. In [10], Tianjing establishes a hexagonal grid environment model, and introduces a multi-UAV cooperative search algorithm based on model predictive control (MPC) theory and Genetic Algorithm (GA). It effectively combines MPC algorithm’s prediction of UAV search route and GA’s judgment of target search. [11] demonstrates a dynamic two-stage closed search scheme that satisfies the distance and direction constraint of UAV. In the path planning part, the path tracking algorithm is adopted in local obstacle-free environment, it cannot guarantee to obtain the optimal solution. Currently, most of the existing approaches such as [12], [13] only consider the target search problem without consider the base returning path planning problem of UAV when some event triggered.
In the CTS phase, the CTS efficiency of UAV is improved via Immune Genetic Algorithm (IGA) in the uncertain environment by introducing an immune operator based on GA [14]. GA has high robustness to handle optimization problems [15], but it has some imperfections such as early maturity, slow convergent speed, etc. [16]. By incorporating a problem specified heuristic immune operator, IGA can generate and preserve the excellent antibodies and keep the antibody diversity in a new population. IGA can effectively avoid the defect with general GA of falling into local optimum [17]. In the FPP phase, in order to respond to the event trigger in the CTS process, we design a Divide-and-conquer and Deterministic Path Optimization Algorithm (DDPOA), which is based on an angular joint force method to determine the position of the obstacle in search environment. By simplifying the global FPP problem, the DDPOA can obtain an optimal return flight path for UAV from the return event trigger position to a nearest base. Considering the turn making constrain of UAV, Dubins curve [18] method is employed to smooth the path obtained by the DDPOA and finally generate a feasible returning path for UAV.
The rest of this paper is organized as follows. Section 2 establish the search environment model. In Section 3, IGA is proposed and the algorithm is designed for multi-UAV CTS. In Section 4, DDPOA is designed to address the FPP problem. Comparative simulations are performed and analyses are conducted on the results in Section 5. Conclusion is given in the last section.
Section snippets
Problem description
Multi-UAV cooperative combat is a new effective combat mode with the development of UAV technology. Compared with single UAV, multiple UAVs can cooperate to carry a mission with a self organized manner and distributed control method. Even if one UAV is shot down, it will not affect other UAVs to continue to perform tasks. With a cooperative way, UAVs can expand the detection scope to the environment, shorten the task completion time, and improve the combat efficiency. Multiple UAVs can detect
Multi-UAV CTS based on IGA
The environment is rasterized into cells. It is assumed that each UAV flies at a constant speed, and visits one cell at one time step. UAV uses the predictive flight strategy in Section 2.6 to determine the next flight path and avoid obstacles in the environment. Each detection flight route is encoded as an antibody in IGA, and the UAV flight angle increment value as the gene on the antibody. Antibodies perform crossover, mutation and memory cells differentiation operations to obtain a
UAV’s return FPP based on DDPOA
In the actual battlefield, many emergencies may occur in the environmental cell during UAV searching flying, which can be expressed as , where means flight failure, denotes damage caused by enemy aircraft, is insufficient fuel and means return instruction. Then the UAV need to fly to the nearest base with the shortest flight path. DDPOA is based on an angular joint force method, and combines with the Dubins curve to plan a shortest return path for
Simulation experiment
In this section, the effectiveness of IGA and DDPOA is verified by three experiments: probability-based CTS, FPP of UAV, and CTS of multi-UAV based on event trigger mechanism. All the experiments are coded in Matlab R2016a, and ran on Intel Core I5-3470 3.20 GHz personal computer with 10 GB memory which running in Windows 10 × 64.
Conclusions
For the CTS problem, we propose the IGA approach, which enables the UAV can search for the targets quickly and avoid obstacle areas accurately. First, the rasterized environment model and the probability map model are established. And then, we define the search reward function and adopt an idea of predicting flight. In FPP mission, with the corner joint force method, DDPOA can plan an optimal track for UAVs to the return base with initial and terminal constraints. In order to meet the minimum
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work is supported by National Natural Science Foundation of China (61673327) and jointly supported by Science and Technology on Avionics Integration Laboratory, PR China and Aviation Science Foundation of China (20185568005).
Zhenwen Zhou received his B.S. degrees from University of Science and Technology LiaoNing. He is currently pursuing the M.S. degree with School of Aerospace Engineering, Xiamen University from 2018. His research interests is multiple UAVs cooperative region search. E-mail: [email protected].
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Cited by (0)
Zhenwen Zhou received his B.S. degrees from University of Science and Technology LiaoNing. He is currently pursuing the M.S. degree with School of Aerospace Engineering, Xiamen University from 2018. His research interests is multiple UAVs cooperative region search. E-mail: [email protected].
DeLin Luo received the B.S. degree in control engineering from Harbin Institute of Technology, China, in 1991, and the M.S and Ph.D. degrees in navigation, guidance and control from Nanjing University of Aeronautics and Astronautics, China, in 2002 and 2006, respectively. He was a visiting scholar at National University of Singapore from Sept. 2011 to Jan. 2013. He is currently a professor with School of Aerospace Engineering, Xiamen University. His research interests include guidance and control, UAV cooperative decision and control, and computational intelligence. E-mail: [email protected].
Jiang Shao received his B.S. degrees from Guilin University of Aerospace Technology. He is currently pursuing the M.S. degree with School of Aerospace Engineering, Xiamen University from 2017. He published six journal and conference papers and won 2018 IEEE CGNCC’’Feng Ru’’ best paper finalist award. He is broadly interested in the cooperative guidance and control theory of multiple robotic systems. E-mail: [email protected].
Yang Xu (M’19) received his B.Sc. and M.Sc. degrees from the College of Automation, Nanjing University of Aeronautics and Astronautics. He has been an exchange Ph.D. student at National University of Singapore from 2016 to 2018, and obtained Ph.D. degree at Xiamen University in 2019. Nowadays, he is a research fellow with School of Engineering of Westlake University. His research interests lie in control theory and application of multiple robotic systems. E-mail: [email protected].
Yancheng You received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics and then became a research scientist in German Aerospace Center (DLR). He is currently a full professor and the dean of the School of Aerospace Engineering at Xiamen University. His main research interest is flight control theory and application, numerical turbulence modeling and aerodynamic design of hypersonic vehicles. E-mail: [email protected].