Research on PSO based multiple UAVs real-time task assignment

Lin, Lin; Sun, Qibo; Wang, Shangguang; Yang, Fangchun

doi:10.1109/ccdc.2013.6561171

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…TA with flexible demand (Alidaee et al, 2011), real-time and dynamic assignment (Kim et al, 2007;Lin et al, 2013), time-dependent TA (Kingston & Schumacher, 2005), and finally, TA under uncertainty (Alighanbari & How, 2008;Hu et al, 2015a). However, the UAVTAP differs from the VRP by allowing, for example, multiple visits and subtours.…”

Section: The Vehicle Routing Problemmentioning

confidence: 99%

The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

Coutinho

Battarra

Fliege

2018

Computers & Industrial Engineering

147

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Over the past few years, Unmanned Aerial Vehicles (UAVs) have become more and more popular. The complexity of routing UAVs has not been fully investigated in the literature. In this paper, we provide a formal definition of the UAV Routing and Trajectory Optimisation Problem (UAVRTOP). Next, we introduce a taxonomy and review recent contributions in UAV trajectory optimisation, UAV routing and articles addressing these problems, and their variants, simultaneously. We conclude with the identification of future research opportunities.

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Section: The Vehicle Routing Problemmentioning

confidence: 99%

The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

Coutinho

Battarra

Fliege

2018

Computers & Industrial Engineering

147

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“…Particle swarm optimization (PSO) is also selected for its fast searching speed in global optimization. MPC and PSO have been successfully applied in the controller design of real-time control systems [12][13][14][15][16][17]. In this work, MPC and PSO based control methods are proposed to realize the controlling aim of achieving good path following performance as well as high motion quality via vehicle steering control and independent force control at four wheels.…”

Section: Force Sensor Driving Unitmentioning

confidence: 99%

MPC and PSO Based Control Methodology for Path Tracking of 4WS4WD Vehicles

et al. 2018

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Four wheel steering and four wheel drive (4WS4WD) vehicles are over-actuated systems with superior performance. Considering the control problem caused by the system nonlinearity and over-actuated characteristics of the 4WS4WD vehicle, this paper presents two methods to enable a 4WS4WD vehicle to accurately follow a predefined path as well as its reference trajectories including velocity and acceleration profiles. The methodologies are based on model predictive control (MPC) and particle swarm optimization (PSO), respectively. The MPC method generates the virtual inputs in the upper controller and then allocates the actual inputs in the lower controller using sequential quadratic programming (SQP), whereas the PSO method is proposed as a fully optimization based method for comparison. Both methods achieve optimization of the steering angles and wheel forces for each of four independent wheels simultaneously in real time. Simulation results achieved by two different controllers in following the reference path with varying disturbances are presented. Discussion about two methodologies is provided based on their theoretical analysis and simulation results.

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“…At present, scholars have conducted a large body of research on task allocation and have achieved a series of results. The related research works mainly use intelligent algorithms (ant colony algorithms [12][13][14][15], genetic algorithms [16][17][18], particle swarm algorithms [19][20][21][22], artificial neural networks [23], etc. ), and contract network algorithms [24][25][26] to solve task coordination problems in a distributed architecture.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

et al. 2022

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With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.

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Research on PSO based multiple UAVs real-time task assignment

Cited by 12 publications

References 14 publications

The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

MPC and PSO Based Control Methodology for Path Tracking of 4WS4WD Vehicles

Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

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