REM-Based Trajectory Optimization for Proactive Communications Reliability of Indoor Robotics

Future wireless communication systems are foreseen to provide powerful enhancements such as high-precision localization, allowing the radio environment to be characterized with high spatial resolution. On the other hand, future industrial ultra-reliable low-latency communications (URLLC) scenarios will place extreme reliability requirements on communication systems. In this work, we present the idea of using high-resolution radio environment maps (REMs) that contain received power levels per location as well as information about spatially occurring fading patterns for the trajectory optimization of mobile robotics to proactively increase communications reliability. To find the optimal trajectory, we show how to apply the Dijkstra path-finding algorithm to a two-dimensional REM. Simulations using a high-resolution REM from a previous measurement campaign indicate that small trajectory adaptations may increase the minimum received power (and hence the reliability) by orders of magnitude. By penalizing trajectory adaptations during optimization, we analyze the tradeoff between the number of lane adaptations and the communications reliability. Appropriate penalty parameters enable to drastically reduce the number of path adaptations without compromising reliability. Similar results are found for a ray-tracing-based REM of the same environment which suggests the use of synthetic REMs for effective investigation of other environments. This encourages follow-up investigations on reliability gains for radio propagation conditions in other environments.