Deep Reinforcement Learning Based Collaborative Optimization of Communication Resource and Route for UAV Cluster

Huang, Lizhen; Liu, Chunhui; Dong, Zanliang

doi:10.1109/icus52573.2021.9641386

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…As such, in recent years, UAVs have been widely implemented in the logistics field to perform several tasks, including warehouse management, cargo inspection and logistics distribution. In particular, the efficiency of logistics distribution cannot keep up with the growing shopping demand, which has become one of the significant bottlenecks for the development of e-commerce [2]. Compared with traditional delivery vehicles, UAVs can greatly improve the efficiency of logistics distribution and make up for the lack of ground transportation, especially in remote and rural areas.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Approach of Joint Route Planning and Resource Allocation for Multiple UAVs in Unmanned Logistics Distribution

2022

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Unmanned aerial vehicles (UAVs) can serve as means of delivery to enhance the effectiveness and accuracy of logistics distribution in various scenarios, such as emergency items delivery for disaster areas and logistics services for remote areas. This paper focuses on the problem of route planning for UAVs, which is the basis for UAVs to complete distribution tasks. Besides, to enhance the collaboration of UAVs during the delivery mission, this work also studies wireless resource (spectrum, power) allocation problem for multiple UAVs enabled communication networks. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for joint route planning and resource allocation issues within a finite time. To this end, the paths of UAVs are planned by the simulated annealing (SA) method in the first stage. Based on the preplanned paths, a double deep Q network (DDQN) based resource allocation method is proposed to maximize the average sum capacity of UAV-to-UAV (U2U) links and reduce transmission delay while minimizing the interference to UAV-to-infrastructure (U2I) links. Multi-UAV communication networks are reconnected according to the positions of UAVs changing in each time slot. Then each U2U link acting as an agent learns to improve spectrum and power allocation policy with imperfect knowledge of the environment. Simulation results demonstrate that the proposed DDQN-based resource management scheme can achieve higher system communication capacity than a random scheme. Moreover, the successful transmission probability of U2U links obtained by the DDQN-based method is much higher than a Particle Swarm Optimization (PSO) based method.

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Section: Introductionmentioning

confidence: 99%

A Two-Stage Approach of Joint Route Planning and Resource Allocation for Multiple UAVs in Unmanned Logistics Distribution

2022

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Spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search

Zheng¹,

Liu

Huang

2022

J. Phys.: Conf. Ser.

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A spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search is proposed in this paper, named STC-MARL, which is based on multi-agent reinforcement learning and allows several UAVs equipped with two types of sensors to learn to complete full view of a field of interest (FOI). The proposed STC-MARL algorithm can make participating UAVs to learn from the environment to complete the full coverage of a specific FOI while minimizing field of views (FOVs) interacted with each other. The experimental results show in detail with simulation that the UAVs in the mission can effectively accomplish the task of spatio-temporal coverage planning.

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A Scale-Independent Deep Reinforcement Learning Framework for Multi-UAV Communication Resource Allocation in Unmanned Logistics

Huang,

Wang,

Liu

2023

2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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Deep Reinforcement Learning Based Collaborative Optimization of Communication Resource and Route for UAV Cluster

Cited by 4 publications

References 3 publications

A Two-Stage Approach of Joint Route Planning and Resource Allocation for Multiple UAVs in Unmanned Logistics Distribution

A Two-Stage Approach of Joint Route Planning and Resource Allocation for Multiple UAVs in Unmanned Logistics Distribution

Spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search

A Scale-Independent Deep Reinforcement Learning Framework for Multi-UAV Communication Resource Allocation in Unmanned Logistics

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