Dynamic Tasks Scheduling Model for UAV Cluster Flexible Network Architecture

Duan, Ting; Wang, Weiping; Wang, Tao; Huang, Meigen; Li, Xiaobo; He, Hua

doi:10.1109/icus50048.2020.9275008

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…A flexible network is based on the use of the resources of network nodes within the current architecture and the practical resources of other nodes in the whole network [27] to achieve rapid adaptation of the failure node requirements in order to have the ability to adapt to current sudden environmental changes. The flexible network is mainly used in the adaptive mechanism [28]. When the cluster network changes, it can be adjusted autonomously in a short time, respond in time or find out countermeasures in time, and reduce losses as much as possible.…”

Section: Partially Observable Markov Decision Processes or The Hidden...mentioning

confidence: 99%

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

Duan

Wang

et al. 2023

Electronics

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The primary purpose of task allocation is to build each equipment platform and quickly complete integration planning at the actual combat speed to achieve efficient management of the entire task. In this process, higher requirements are put forward for dynamic, cooperative, and highly adaptive drone colony organization. In this paper, the scheduling problem of hybrid unmanned aerial vehicle (UAV) systems is studied under an uncertain environment. First, the system-capability-task organizational structure is defined and quantified, which lays a foundation for dynamic adjustment of the organizational structure in the future. Then, combined with the theory of flexible network and elastic network management, the model is calculated, and the linear transformation function and fuzzy theory are used to stratify and cluster the capability layers. On this basis, four motif structures are introduced for abnormal nodes in the process of dynamic adjustment, and a dynamic group reconstruction algorithm (DRA-M) is established. Finally, the time and communication load indexes are determined, and the alternative strategy is designed for the failure point. The performance of the classical scheduling algorithm is evaluated by benchmarking it under different conditions. The results show that the algorithm has a good dynamic adjustment ability in the event of a UAV swarm emergency, which is a bright light for the future study of highly adaptive UAV cluster organization.

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Section: Partially Observable Markov Decision Processes or The Hidden...mentioning

confidence: 99%

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

Duan

Wang

et al. 2023

Electronics

Self Cite

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“…The main challenge is to achieve the optimal allocation, maximize the completion of the original task sequence and minimize the harm of the accident. Duan T et al [29] proposed a dynamic fault-tolerant task scheduling model (DSMFNA) to solve the problems of node failure and inability to dynamically provide capacity requirements during UAV task execution,and proposed a flexible network structure dynamic scheduling algorithm(FDSA).And a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed. However, these architectures are not suited to the problems of interest in this paper (EDEI problems)as here.…”

Section: Related Workmentioning

confidence: 99%

Decision-making of Emergent Incident based on P-MADDPG

Guo¹,

Hou²,

Li³

et al. 2022

Preprint

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In recent years, human casualties and damage to resources caused by emergent incidents have become a serious problem worldwide. In this paper, we model the emergency decision-making problem and use Multi-agent System (MAS) to solve the problem that the decision speed cannot keep up with the spreading speed. MAS can play an important role in the automated execution of these tasks to reduce mission completion time. In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and makes decisions before the incident occurs, thus solving the problem that the decision speed cannot keep up with the spreading speed. A simulation environment was established for realistic scenarios, and three scenarios were selected to test the performance of P-MADDPG in emergency decision-making problems for emergent incidents: unmanned storage, factory assembly line, and civil airport baggage transportation. Simulation results using the P-MADDPG algorithm are compared with the greedy algorithm and the MADDPG algorithm, and the final experimental results show that the P-MADDPG algorithm converges faster and better than the other algorithms in scenarios of different sizes. This shows that the P-MADDP algorithm is effective for emergency decision-making in emergent incident.

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