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Dynamic Trajectory Design for Multi-UAV-Assisted Mobile Edge Computing
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AI
DOI：10.1109/TVT.2024.3485182.png)
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Abstract
En 中文
The trajectory design for unpiloted aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks has become a hot research topic. In the UAV-assisted MEC scenario, the UAV is required to frequently adjust its flight trajectory due to dynamic factors such as time-varying task offloading requirements, user mobility, and transmission environment variation. In this paper, with consideration of the constraint induced by the UAV flight dynamics, the dynamic trajectory design challenge within the blockchain-based multi-UAV-assisted MEC framework is investigated. An intelligent algorithm that integrates multi-agent deep deterministic policy gradient (MADDPG), linear quadratic regulator (LQR), and CVXPY solver, named MADDPG-LC, is proposed to achieve real-time joint optimization of dynamic trajectory control and resource allocation with respect to minimizing weighted energy consumption and delays. Numerical simulation results demonstrate the efficacy of the proposed MADDPG-LC algorithm in addressing the UAV flight dynamics constraint, which has generally overlooked in existing works.
Keywords:
Autonomous aerial vehicles
Trajectory
Vehicle dynamics
Optimization
Heuristic algorithms
Resource management
Dynamic scheduling
Security
Real-time systems
Trajectory planning
Dynamic trajectory
flight dynamics
intelligent design
unpiloted aerial vehicle (UAV)-assisted MEC
Journal
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7.1
Papers:
1.8W
Citations:
6.6W