A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs

Wang, Jingjing; Zhang, Yunfeng; Geng, Lin; Fuh, Jerry Ying Hsi; Teo, S. H.

doi:10.1142/s2301385015500132

Cited by 22 publications

(7 citation statements)

References 14 publications

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“…Some existing studies assume that UAVs only can perform tasks at the target points to ensure the effective execution of tasks. 24,29 The detection scope of surveillance UAVs is not considered in these task assignment model, which may lead to unnecessary resource consumption of UAV system. 30 In Jia et al, 25 the detection scope of UAV is represented by detection radius.…”

Section: Problem Formulationmentioning

confidence: 99%

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A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

Han

Fan

et al. 2021

Measurement and Control

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This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.

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Section: Problem Formulationmentioning

confidence: 99%

“…Therefore, take f as the example, with the increase of f, E m increases slowly in the early stage and increases greatly when f increases to nearly its maximum value. Thus, we construct new type exponential function in equation (29) to define E m of the i-th individual.…”

Section: Fuzzy Elite Degreementioning

confidence: 99%

A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

Han

Fan

et al. 2021

Measurement and Control

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“…Mixed Integer Linear Programming (MILP) approaches have shown success in solving UAV tasking problems [6][7][8]. Additional heuristic approaches [9,10] and taboo search algorithms [11] have been used to task a homogeneous fleet of fixed wing UAVs in a multi-objective mission. On the other hand, MILP is often undesired due to the long computational time required to solve larger problems making it unsuitable for real-time applications [12].…”

Section: Multiple Aircraft Path Planning Literaturementioning

confidence: 99%

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

et al. 2017

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The focus of this study was to examine an automated mission planner that utilized a heterogeneous set of small aerial assets simultaneously surveying several Points of Interest (POI). The concept mission was to aerially search for an unknown Target of Interest (TOI) located amongst a set of POIs. In order to develop a planning system that is capable of meeting mission requirements, an adaptive mission planner, based on a Genetic Algorithm (GA), was investigated that seeks to task a heterogeneous set of unmanned aerial vehicles. Initially, a set of fixed wing UAVs were tasked to survey a set of POIs in search of a TOI, a POI that requires additional or long term surveillance. Once a TOI was located, a multi-rotor UAV was deployed to visit the TOI and additional POIs. Remaining POIs that were not tasked to the multirotor UAV were then re-tasked amongst the fixed wing aircraft set. The mission planner was implemented using a GA that planned initial and post TOI identification UAV paths. Mission simulations were conducted and the mission time increase was analyzed against different TOI locations and number of UAVs searching. Simulation results indicated that the deployment of a multi-rotor UAV not only provided additional surveillance of the TOI, but reduced overall mission times as well.

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“…In ref. [18], an optimization method combining VRP and ILP was proposed to conduct different UAV cruise tasks of various time windows for a fleet of UAVs. In ref.…”

Section: Introductionmentioning

confidence: 99%

Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

Liu

Ma²,

Chen³

et al. 2020

Robotica

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SUMMARY Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

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A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs

Cited by 22 publications

References 14 publications

A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

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