Finite-Time Formation Control for Unmanned Aerial Vehicle Swarm System With Time-Delay and Input Saturation

Zhang, An; Ding, Zhiying; Mi, Yang; Yang, Pan

doi:10.1109/access.2018.2889858

Cited by 24 publications

(14 citation statements)

References 46 publications

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“…Therefore, in the case of satisfying various special conditions (such as the total task cannot be greater than a certain time or the total flight time is as small as possible, etc. ), the overall reconnaissance reward is maximized [31,32]. The schematic diagram of multi-UAVs reconnaissance mission allocation is shown in Figure 1.…”

Section: Problem Descriptionmentioning

confidence: 99%

Multi-UAV Mission Allocation under Constraint

Xie

Zhang

et al. 2019

Applied Sciences

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Featured Application: The improved model can effectively adapt to the problem of UAV swarm reconnaissance task allocation. On the basis of ensuring the optimization of reconnaissance reward, the UAV resources should be distributed as much as possible to reasonably complete the overall combat mission.Abstract: This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method. Appl. Sci. 2019, 9, 2184 2 of 17 an improved genetic algorithm for the UAV mission planning problem with limited resources, and the simulation results were analyzed and verified [14]. Lanah et al. presented a maximum coverage stochastic orienteering problem with time windows (MCS-OPTW) planning approach for mission planning and showed the simulation data [15]. Zhu et al. proposed a new reconnaissance mission planning model and used a genetic algorithm with the UAVs' task sequence to solve the model [16]. The existing reconnaissance mission models used the shortest path as the objective function and only considered a single task sequence assignment without considering the reconnaissance time allocation of the UAV. In reality, the allocation of reconnaissance time is often the decisive factor in determining the success of the reconnaissance mission and the core indicator that the commander is concerned about. The new model proposed in this paper considers the reconnaissance reward maximization as the objective function. By considering the allocation of each UAV's reconnaissance time resource, the reconnaissance time allocation and task sequence allocation are considered simultaneously.The UAV mission planning problem is the extended multiple Dubins traveling salesmen problem (MDTSP), which is a typical NP-hard combinatorial optimization problem. Some studies translated related models into a mixed integer linear programming (MILP) and achieved good results [17][18][19]. The time window was first applied to the vehicle routing problem (VRP) problem model. Righini and Salani systematically introduced the VRP with time wind...

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Section: Problem Descriptionmentioning

confidence: 99%

Multi-UAV Mission Allocation under Constraint

Xie

Zhang

et al. 2019

Applied Sciences

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“…Recent work in [21] provides initial investigations to the use of fully distributed control algorithms in a UAV swarm, taking into account the battery of the UAVs [22], [23]. The efficiency of the algorithms and frameworks proposed are tested in simulations.…”

Section: Background and Related Workmentioning

confidence: 99%

A Unified Framework for Reliable Multi-Drone Tasking in Emergency Response Missions

Terzi

Kolios

Panayiotou

et al. 2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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In this paper a unified framework is presented for coordinated multi-drone tasking in emergency response missions. As elaborated in this work, response missions can be broken into a number of distinct tasks that can be allocated among the available drone agents to expedite the response operations. The proposed framework enables the development and execution of algorithms that jointly schedule and route drone agents across the field to complete their tasks and successfully address the mission goals considering the agent limitations. The key design challenges of implementing the proposed framework are discussed. Finally, initial simulation and experimental results are presented providing evidence of the real life applicability and reliability of the proposed framework.

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“…In the past few decades, coordination control has been studied with great attention due to its range of applications in areas, such as formation control in robotic systems [1], exploration of hazardous environments [2], unmanned aerial vehicle formation [3], and distributed optimisation [4]. As a fundamental research topic of coordination control, the consensus is a typical collective behaviour, which develops a distributed algorithm such that a group of agents can achieve an agreement on states by communicating with their neighbours [5, 6].…”

Section: Introductionmentioning

confidence: 99%