IEEE Trans Cybern. 2022 May;52(5):3971-3983. doi: 10.1109/TCYB.2020.3019255. Epub 2022 May 19.
This article focuses on the target search problem in a curve-shape area using multiple unmanned aerial vehicles (UAVs), with the demand for obtaining the maximum cumulative detection reward, as well as the constraint of maneuverability and obstacle avoidance. First, the prior target probability map of the curve-shape area, generated by Parzen windows with Gaussian kernels, is approximated by the 1-D Gaussian mixture model (GMM) in order to extract some high-value curve segments corresponding to Gaussian components. Based on the parameterized curve segments from GMM, the self-organizing map (SOM) neural network is then established to achieve the coverage search. The step of winner neuron selection in SOM will prioritize and allocate the curve segments to UAVs, with the comprehensive consideration of multiple evaluation factors and allocation balance. The following step of neuron weight update will plan the UAV paths under the constraint of maneuverability and obstacle avoidance, using the modified Dubins guidance vector field. Finally, the good performance of GMM-SOM is evaluated on a coastline map.
本文针对在曲线区域使用多架无人机（UAV）进行目标搜索的问题进行研究，要求获得最大的累计检测奖励，同时还要满足机动性和避障的约束条件。首先，使用基于高斯核的 Parzen 窗口生成曲线区域的先验目标概率图，然后通过 1-D 高斯混合模型（GMM）对其进行近似，以提取与高斯分量相对应的一些高值曲线段。基于 GMM 的参数化曲线段，然后建立自组织映射（SOM）神经网络来实现覆盖搜索。SOM 中的获胜神经元选择步骤将根据多个评估因素和分配平衡来优先分配和分配曲线段给 UAV。神经元权重更新的下一步将在机动性和避障的约束下，使用改进的 Dubins 制导向量场来规划 UAV 路径。最后，在海岸线地图上评估 GMM-SOM 的良好性能。
