大规模无线传感网络数据收集的无人机路径规划

针对部署在地表交通困难的大规模无线传感网络,采用目前可控无人机(unmanned aerial vehicles, UAV)进行数据收集能够达到更好的效果. 然而,考虑到无人机自身有限的资源,以及网络中存在大量传感器节点的情况,无人机飞行路径规划对于顺利完成数据收集任务具有重要作用. 无人机路径规划可以看作经典的旅行商问题(traveling salesman problem,TSP). 针对部署具有均匀性特点的大规模无线传感网络,提出了一种规则化快速路径规划(fast path planning with rules, FPPWR)算法. 该算法通过网格划分,将全局区域飞行路径的求解划分到多个较小的方格中进行,并通过成对算子路径优化算法在初等飞行路径上将方格区域中的路径合并为全局路径. 实验证明,该算法在保证了较高精度的同时,显著提升了路径规划的效率. 

Path Planning for UAV to Collect Sensor Data in Large-Scale WSNs

For a large-scale WSNs deployed in an environment with poor ground transport, it will be better to collect sensor data from WSNs with unmanned aerial vehicle (UAV). However, considering that the UAV has limited resources and there are a large number of sensor nodes in the network, the path planning for UAV is an important factor in whether the sensor data can be collected successfully. This process can be taken as the classical traveling salesman problem (TSP). Considering the large-scale WSNs was deployed homogeneously, an algorithm based on the grid division for the UAV path planning was proposed, which was named as fast path planning with rules (FPPWR). Through the division by a suitable grid, the global path planning can be achieved by merging the path which has been planned according to the primary flight path in the square area. Before merging square path, the paired-operator algorithm will optimize the global path. The experiment indicated that the FPPWR has higher efficiency, besides it also ensures high accuracy.