{"title":"异构无人飞行器合作分配任务,同时多方位攻击移动目标","authors":"Sami Shahid , Ziyang Zhen , Umair Javaid","doi":"10.1016/j.engappai.2024.109595","DOIUrl":null,"url":null,"abstract":"<div><div>Multiple unmanned aerial vehicle (UAV) attacks, on moving targets with directional priorities, need careful task allocation especially when the UAVs have variable attacking power. In addition, the constraints of simultaneous attack from different directions multi-folds the complexity of the problem. In this work, an extended contract net protocol (ECNP) based autonomous and cooperative task assignment method is proposed to deal with the time-sensitive position assignment for multi-directional attack and uniform resource allocation. Initially, an optimization problem is formulated using distances between each UAV and expected attack positions (APs), arrival time, and attack power. In addition, for uniform resource allocation, a variable is introduced to monitor available resources at a given time. An agent-based model is built with UAV information, such as position and speed, along with the direction of the high-value moving target (HVMT). Each UAV identifies the possible arrival points based on its speed constraints, and current position, and the position and velocity of HVMT. Moreover, distance and expected arrival time to all APs are computed. Finally, the agents make attack point allocations using the proposed ECNP after making a consensus about simultaneous attack time. The proposed method ensures uniform resource allocation. The simulation results show the superiority of the proposed method (ECNP) in comparison with classical contract net protocol (CNP) and Genetic Algorithms (GA) in terms of uniform resource allocation and mission accomplishment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109595"},"PeriodicalIF":7.5000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cooperative task assignment of heterogeneous unmanned aerial vehicles for simultaneous multi-directional attack on a moving target\",\"authors\":\"Sami Shahid , Ziyang Zhen , Umair Javaid\",\"doi\":\"10.1016/j.engappai.2024.109595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Multiple unmanned aerial vehicle (UAV) attacks, on moving targets with directional priorities, need careful task allocation especially when the UAVs have variable attacking power. In addition, the constraints of simultaneous attack from different directions multi-folds the complexity of the problem. In this work, an extended contract net protocol (ECNP) based autonomous and cooperative task assignment method is proposed to deal with the time-sensitive position assignment for multi-directional attack and uniform resource allocation. Initially, an optimization problem is formulated using distances between each UAV and expected attack positions (APs), arrival time, and attack power. In addition, for uniform resource allocation, a variable is introduced to monitor available resources at a given time. An agent-based model is built with UAV information, such as position and speed, along with the direction of the high-value moving target (HVMT). Each UAV identifies the possible arrival points based on its speed constraints, and current position, and the position and velocity of HVMT. Moreover, distance and expected arrival time to all APs are computed. Finally, the agents make attack point allocations using the proposed ECNP after making a consensus about simultaneous attack time. The proposed method ensures uniform resource allocation. The simulation results show the superiority of the proposed method (ECNP) in comparison with classical contract net protocol (CNP) and Genetic Algorithms (GA) in terms of uniform resource allocation and mission accomplishment.</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"139 \",\"pages\":\"Article 109595\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197624017536\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624017536","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
多架无人飞行器(UAV)攻击具有方向优先权的移动目标时,需要谨慎分配任务,尤其是当无人飞行器的攻击力不稳定时。此外,从不同方向同时发起攻击的约束条件也使问题的复杂性成倍增加。本研究提出了一种基于扩展契约网协议(ECNP)的自主合作任务分配方法,用于处理多方向攻击的时间敏感位置分配和统一资源分配问题。首先,利用每架无人机与预期攻击位置(AP)之间的距离、到达时间和攻击功率,提出一个优化问题。此外,在统一资源分配方面,还引入了一个变量来监控给定时间内的可用资源。利用无人机的位置和速度等信息以及高价值移动目标(HVMT)的方向,建立了一个基于代理的模型。每个无人机根据其速度限制、当前位置以及高价值移动目标(HVMT)的位置和速度,确定可能的到达点。此外,还要计算到所有 AP 的距离和预计到达时间。最后,代理在就同时攻击时间达成共识后,使用提议的 ECNP 进行攻击点分配。建议的方法确保了资源的统一分配。仿真结果表明,与经典的契约网协议(CNP)和遗传算法(GA)相比,建议的方法(ECNP)在统一资源分配和完成任务方面更具优势。
Cooperative task assignment of heterogeneous unmanned aerial vehicles for simultaneous multi-directional attack on a moving target
Multiple unmanned aerial vehicle (UAV) attacks, on moving targets with directional priorities, need careful task allocation especially when the UAVs have variable attacking power. In addition, the constraints of simultaneous attack from different directions multi-folds the complexity of the problem. In this work, an extended contract net protocol (ECNP) based autonomous and cooperative task assignment method is proposed to deal with the time-sensitive position assignment for multi-directional attack and uniform resource allocation. Initially, an optimization problem is formulated using distances between each UAV and expected attack positions (APs), arrival time, and attack power. In addition, for uniform resource allocation, a variable is introduced to monitor available resources at a given time. An agent-based model is built with UAV information, such as position and speed, along with the direction of the high-value moving target (HVMT). Each UAV identifies the possible arrival points based on its speed constraints, and current position, and the position and velocity of HVMT. Moreover, distance and expected arrival time to all APs are computed. Finally, the agents make attack point allocations using the proposed ECNP after making a consensus about simultaneous attack time. The proposed method ensures uniform resource allocation. The simulation results show the superiority of the proposed method (ECNP) in comparison with classical contract net protocol (CNP) and Genetic Algorithms (GA) in terms of uniform resource allocation and mission accomplishment.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.