The paper proposes a framework to identify and avoid the coverage hole in an indoor industry environment. We assume an edge cloud co-located controller that followers the Automated Guided Vehicle (AGV) movement on a factory floor over a wireless channel. The coverage holes are caused due to blockage, path-loss, and fading effects. An AGV in the coverage hole may lose connectivity to the edge-cloud and become unstable. To avoid connectivity loss, we proposed a framework that identifies the position of coverage hole using a Support- Vector Machine (SVM) classifier model and constructs a binary coverage hole map incorporating the AGV trajectory re-planning to avoid the identified coverage hole. The AGV's re-planned trajectory is optimized and selected to avoid coverage hole the shortest coverage-hole-free trajectory. We further investigated the look-ahead time's impact on the AGV's re-planned trajectory performance. The results reveal that an AGV's re-planned trajectory can be shorter and further optimized if the coverage hole position is known ahead of time

翻译：本文提出了一种框架，用于识别并避免室内工业环境中的覆盖空洞。我们假设存在一个与边缘云共址的控制器，它通过无线信道跟踪工厂车间内自动导引车（AGV）的运动。覆盖空洞由阻塞、路径损耗和衰落效应引起。处于覆盖空洞中的AGV可能失去与边缘云的连接，从而变得不稳定。为避免连接丢失，我们提出了一种框架，该框架使用支持向量机（SVM）分类器模型识别覆盖空洞的位置，并构建一个二进制覆盖空洞地图，结合对AGV轨迹进行重新规划以避开已识别的覆盖空洞。对AGV的重新规划轨迹进行优化和选择，以在避开覆盖空洞的同时获得最短的无覆盖空洞轨迹。我们进一步研究了前视时间对AGV重新规划轨迹性能的影响。结果表明，如果提前获知覆盖空洞的位置，AGV的重新规划轨迹可以更短，并能得到进一步优化。