Deep reinforcement learning-based routing framework for bidirectional communication in UAV-UGV networks

Abstract

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) performs crucial function in many applications like military operations, disaster management, hazardous operations and surveillance. Efficient bidirectional communication between UAVs and UGVs is necessary for effective coordination and successful task completion. Traditional routing protocols facilitate communication either between UAVs or between UGVs, but not efficiently across both platforms. Moreover traditional routing protocol often fail to adapt dynamically to varying network conditions, such as mobility, interference, and congestion. To overcome these challenges, this paper presents a design, implementation, and optimization of adaptive routing protocol engineered for specific requirements of coordinated network consisting of UAV and UGV. This novel protocol design integrates the Greedy Perimeter Stateless Routing (GPSR) and Deep Reinforcement Learning (DRL) to optimize packet routing based on real-time network states and ensuring obstacle avoidance, enhanced throughput, minimal latency and reduced packet loss. Simulations are conducted in python to evaluate the performance of the proposed protocol. The results shows that the DRL-based routing protocol enables communication between UAVs and UGVs through the shortest and most efficient path. This research contributes to the advancement of AI enabled communication architecture for co-ordinated UAV-UGV networks, for robust and efficient mission-critical operations.