We present a novel approach to maximize the communication-aware coverage for robots operating over large-scale geographical regions of interest (ROIs). Our approach complements the underlying network topology in neighborhood selection and control, rendering it highly robust in dynamic environments. We formulate the coverage as a multi-stage, cooperative graphical game and employ Variational Inference (VI) to reach the equilibrium. We experimentally validate our approach in an mobile ad-hoc wireless network scenario using Unmanned Aerial Vehicles (UAV) and User Equipment (UE) robots. We show that it can cater to ROIs defined by stationary and moving User Equipment (UE) robots under realistic network conditions.
翻译:我们提出了一种新颖的方法,以最大限度地扩大对大型感兴趣地理区域作业的机器人的通信意识覆盖。我们的方法补充了社区选择和控制的基本网络地形,使其在动态环境中高度稳健。我们把覆盖设计成多阶段合作图形游戏,并采用变式推论(VI)来达到平衡。我们实验性地验证了我们在使用无人驾驶航空飞行器和用户设备机器人的移动专用无线网络情景中采用的方法。我们证明它能够在现实的网络条件下满足固定和移动用户设备机器人定义的ROI。