Li Lili, Zhang Xiaoyong, Yue Wei, Liu Zhongchang
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China.
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China.
ISA Trans. 2021 Aug;114:230-241. doi: 10.1016/j.isatra.2020.12.055. Epub 2021 Jan 2.
This paper studies the problem of cooperative searching for dynamical moving targets by multiple unmanned aerial vehicles (UAVs). The environmental information possessed by UAVs is inconsistent due to packet losses of shared environmental information in communication channels and the discrepancies of detected information among different UAVs. To unify the environmental information among UAVs, the lost information is compensated for by an improved Least Square Method (LSM) which incorporates the target location model into the fitting function to enhance data fitting precision. The Weighted Averaging Method (WAM) is used to merge multiple source information where the weight coefficients are set based on the uncertain values of environmental information. To search for dynamic targets and then automatically re-enter into search areas for UAVs, a Modified Genetic Algorithm (MGA) and rolling optimization techniques are utilized to generate real-time paths for UAVs. Simulation results and comparison studies with existing methods validate the effectiveness of the above cooperative searching strategy.
本文研究了多架无人机协同搜索动态移动目标的问题。由于通信信道中共享环境信息的数据包丢失以及不同无人机检测信息的差异，无人机所拥有的环境信息不一致。为了统一无人机之间的环境信息，采用一种改进的最小二乘法（LSM）对丢失的信息进行补偿，该方法将目标位置模型纳入拟合函数以提高数据拟合精度。使用加权平均法（WAM）合并多个源信息，其中权重系数根据环境信息的不确定值来设置。为了搜索动态目标并使无人机自动重新进入搜索区域，利用改进的遗传算法（MGA）和滚动优化技术为无人机生成实时路径。仿真结果以及与现有方法的对比研究验证了上述协同搜索策略的有效性。
