Introduction to UAV swarm utilization for communication on the move terminals tracking evaluation with reinforcement learning technique

Abstract

As the growth of communication and satellite industry, the demand of satellite antenna evaluation is increasing. Particularly Communication On The Move (COTM) terminal antenna, including electronically steerable antennas (ESA) and for the communication between new constellations on LEO and MEO, requires tracking accuracy test for the communication on moving vehicles. The measurement capability of conventional methodologies have been limited due to their location fixed facilities and non-adjustable sensor's positions during the measurement. To overcome this drawbacks, we will present how multi-agent system of UAVs could be utilized for COTM tracking accuracy evaluation. This measurement needs instant actions for UAVs to keep them navigating in order to achieve accurate and stable measurement. Reinforcement learning (RL) techniques are investigated for this purpose in this paper. The performance improvement is demonstrated with the system using RL technique to adjust UAVs with sensors during the measurement.

Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review

“…Some maps oversimplify the environment representation: the map is divided into a grid with equally-sized smaller cells that store information about the environment [68,73,93]. Others oversimplify the environment's structure by simplifying objects representation or by using 1D/2D to represent the environment [34,36,39,41,47,55,65,67,74,86,87] . The UAV has to plan a safe and optimal path over the cells to avoid cells containing obstacles until it reaches its destination and has to plan its stopover at the charging stations based on the battery level and path length.…”