Path planning for multi-UAV-assisted mobile edge computing framework using reinforcement learning in urban environment disaster scenarios

Unmanned aerial vehicles (UAVs) are significantly used in a various field, including disaster relief and rescue operations. The goal of path planning is to determine the shortest or most efficient route while using the least amount of energy and resources possible. In environmental disaster scenarios, multiple UAVs can be deployed in search and rescue operations to look for individuals that are trapped in the disaster areas. To address the issue of multi-UAV path planning, numerous academics have recently proposed heuristic, sampling, and machine learning-based techniques. However, deploying UAVs in dynamic environments with moving obstacles and inter-UAV interaction presents considerable problems. This paper presents the framework for path planning optimization for multi-UAV-assisted Mobile Edge Computing (MEC) network using Reinforcement Learning (RL) strategy during disaster relief operations in an urban environment. To tackle these challenges, we provide a novel multi-UAV Mobile Edge Computing architecture that uses reinforcement learning to improve Quality-of-Service and path planning. Our primary contributions include: 1) maximizing the quality of service for UAV-assisted mobile edge computing and path planning strategy in the reinforcement learning framework; 2) illustrating the computational demand of terminal users to secure higher level quality-of-service using a sigmoid-like function; 3) applying a reinforcement learning incentive matrix that incorporates synthetic considerations of the geometric distance, risk level, and demand of the terminal users to guarantee cost savings, risk avoidance, and the service quality. In this paper, the proposed framework considers the configuration of urban surroundings, including critical operational constraints, such as obstacle mobility and obstacle avoidance. From the simulation testing results, it shows that the proposed strategy is more appropriate in the disaster scenario since it is effective and performs better than the benchmark systems.

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