基于无人机/无人艇的最优动态覆盖观测技术
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摘要
针对无人机(unmanned aerial vehicle,UAV)/无人艇(unmanned surface vehicle,USV)对海面区域的最优动态覆盖观测问题,提出了一种以最大化观测收益为指标的航路优化方法。采用区域分解、子区域分配、航路规划相结合的分层求解思路:首先,根据信息密度等先验知识,采取基于高斯混合模型(Gaussian mixture model,GMM)的区域特征提取理论,从任务区域中提取出若干个子区域;然后,将子区域进行排序与分配,从而将复杂的协同优化问题转化为多个简单的单UAV或USV航路规划问题;最后,各UAV或USV在分配的子区域内采用并行滚动时域控制(receding horizon control,RHC)算法进行航路规划。仿真结果表明,本文提出的GMM-RHC方法具有更高的观测效率,可有效解决无人机/无人艇最优动态覆盖观测问题,具有重要的应用价值。
In this paper, we propose a path optimization method with the objective of maximum surveillance to solve the problem of achieving optimal dynamic coverage in the surveillance of the sea surface by unmanned aerial vehicles(UAVs) and unmanned surface vehicles(USVs). We propose a layer-based framework that includes regional decomposition, subregion allocation, and path planning. First, based on the previously obtained information density, we utilize the Gaussian mixture model(GMM) to extract regional features with respect to several subregions. Next, we sequence these subregions and allocate them to UAVs or USVs to simplify the complex cooperative optimization problem into several single-UAV/USV path planning problems. Then, we plan each agent's path using the concurrent receding horizon control(RHC) method. Simulation results indicate that the proposed GMM–RHC method achieves higher surveillance efficiency.
In this paper, we propose a path optimization method with the objective of maximum surveillance to solve the problem of achieving optimal dynamic coverage in the surveillance of the sea surface by unmanned aerial vehicles(UAVs) and unmanned surface vehicles(USVs). We propose a layer-based framework that includes regional decomposition, subregion allocation, and path planning. First, based on the previously obtained information density, we utilize the Gaussian mixture model(GMM) to extract regional features with respect to several subregions. Next, we sequence these subregions and allocate them to UAVs or USVs to simplify the complex cooperative optimization problem into several single-UAV/USV path planning problems. Then, we plan each agent's path using the concurrent receding horizon control(RHC) method. Simulation results indicate that the proposed GMM–RHC method achieves higher surveillance efficiency.
引文
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[11]严卫生,左磊,崔荣鑫,等.洋流干扰下的多自主水面无人船最优覆盖控制[J].西北工业大学学报,2014,32(5):769-774.Yan Weisheng,Zuo Lei,Cui Rongxin,et al.Coverage control of multiple autonomous underwater vehicle with disturbance by ocean currents considered[J].Journal of Northwestern Polytechnical University,2014,32(5):769-774.
[12]Li Y,Chen H,Er M J,et al.Coverage path planning for UAVs based on enhanced exact cellular decomposition method[J].Mechatronics,2011,21(5):876-885.
[13]Yao P,Wang H,Ji H.Gaussian mixture model and receding horizon control for multiple UAV search in complex environment[J].Nonlinear Dynamics,2017,88(2):903-919.
[14]Fraley C,Raftery A E.Model-based clustering,discriminant analysis,and density estimation[J].Journal of the American statistical Association,2002,97(458):611-631.
[2]Song C,Feng G,Fan Y,et al.Decentralized adaptive awareness coverage control for multi-agent networks[J].Automatica,2011,47(12):2749-2756.
[3]孙昌浩,段海滨.基于进化势博弈的多无人机传感器网络K-覆盖[J].中国科学:技术科学,2016,46(10):1016-1023.Sun Changhao,Duan Haibin.An evolutionary potential game theoretic approach for the K-COVER problem in multi-UAV sensor networks[J].Scientia Sinica Technologica,2016,46(10):1016-1023.
[4]Gupta V,Chung T H,Hassibi B,et al.On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage[J].Automatica,2006,42(2):251-260.
[5]宋程.移动传感器网络的动态覆盖与聚集[D].合肥:中国科学技术大学,2012.Song Cheng.Dynamic coverage and rendezvous for mobile sensor networks[D].Hefei:University of Science and Technology of China,2012.
[6]陈海,何开锋,钱炜祺.多无人机协同覆盖路径规划[J].航空学报,2016,37(3):928-935.Chen Hai,He Kaifeng,Qian Weiqi.Cooperative coverage path planning for multiple UAVs[J].Acta Aeronautica et Astronautica Sinica,2016,37(3):928-935.
[7]Acar E U,Choset H,Lee J Y.Sensor-based coverage with extended range detectors[J].IEEE Transactions on Robotics,2006,22(1):189-198.
[8]Choi M H.Optimal underwater coverage of a cellular region by autonomous underwater vehicle using line sweep motion[J].Journal of Electrical Engineering and Technology,2012,7(6):1023-1033.
[9]朱心科,俞建成,王晓辉.水下滑翔机自适应覆盖采样[J].机器人,2012,34(5):566-573.Zhu Xinke,Yu Jiancheng,Wang Xiaohui.Adaptive coverage sampling of underwater glider[J].Robot,2012,34(5):566-573.
[10]Lanillos P,Gan S K,Besada-Portas E,et al.Multi-UAV target search using decentralized gradient-based negotiation with expected observation[J].Information Sciences,2014,282:92-110.
[11]严卫生,左磊,崔荣鑫,等.洋流干扰下的多自主水面无人船最优覆盖控制[J].西北工业大学学报,2014,32(5):769-774.Yan Weisheng,Zuo Lei,Cui Rongxin,et al.Coverage control of multiple autonomous underwater vehicle with disturbance by ocean currents considered[J].Journal of Northwestern Polytechnical University,2014,32(5):769-774.
[12]Li Y,Chen H,Er M J,et al.Coverage path planning for UAVs based on enhanced exact cellular decomposition method[J].Mechatronics,2011,21(5):876-885.
[13]Yao P,Wang H,Ji H.Gaussian mixture model and receding horizon control for multiple UAV search in complex environment[J].Nonlinear Dynamics,2017,88(2):903-919.
[14]Fraley C,Raftery A E.Model-based clustering,discriminant analysis,and density estimation[J].Journal of the American statistical Association,2002,97(458):611-631.