Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning

TL;DR

This paper presents a multi-UAV platform for mobile edge computing that uses reinforcement learning to optimize path planning and service quality in complex environments with obstacles.

Contribution

It introduces a novel integrated reinforcement learning framework that optimizes service quality, path planning, and risk considerations for multi-UAV edge computing.

Findings

Reinforcement learning effectively improves UAV path planning and service quality.

The platform demonstrates high feasibility and effectiveness in simulations.

It balances service quality, risk, and cost in UAV operations.

Abstract

Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers. However, there are significant challenges to use UAVs in complex environments with obstacles and cooperation between UAVs. We introduce a new multi-UAV Mobile Edge Computing platform, which aims to provide better Quality-of-Service and path planning based on reinforcement learning to address these issues. The contributions of our work include: 1) optimizing the quality of service for mobile edge computing and path planning in the same reinforcement learning framework; 2) using a sigmoid-like function to depict the terminal users' demand to ensure a higher quality of service; 3) applying synthetic considerations of the terminal users' demand, risk and geometric distance in reinforcement learning reward matrix to ensure the quality of service, risk avoidance, and the cost-savings. Simulations have shown the effectiveness and feasibility of our platform, which can help advance related researches.