UAV-assisted task offloading system using dung beetle optimization algorithm & deep reinforcement learning

Abstract

The current technologies such as computer vision and artificial intelligence are continuously being applied to disaster rescue robots, enabling their adaptability to different environments. However, the challenge arising from this is that these robots will generate computationally intensive tasks, constrained by the size and energy supply of the robots. This limitation leads to a reduction in device endurance and an increase in processing latency, becoming a bottleneck for the application of artificial intelligence technologies. To address this concern, we propose a novel edge offloading system based on unmanned aerial vehicles (UAVs) and mobile edge service vehicles (MESVs) to assist with communication and computation tasks, aiming to minimize the overall computational burden of rescue robots in extreme scenarios where communication infrastructure is unavailable due to sudden natural disasters or accidents. Leveraging the high maneuverability of UAVs, we utilize an improved dung beetle optimization (DBO) algorithm to optimize the UAV’s flight positions and assist robots in task processing and data relay. Moreover, we address the optimization problem of robot offloading strategies by employing a deep reinforcement learning-based Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which effectively reduces processing latency and energy consumption. Through simulation experiments and real-world simulation tests, our proposed method, compared to other UAV position optimization algorithms and computational strategy offloading methods, can effectively reduce the computational energy consumption of robots. In comparison with other methods presented in this paper, it can also reduce latency by nearly 40%.