Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization

被引:1
作者
Roberge, Vincent [1 ]
Labonte, Gilles [2 ]
Tarbouchi, Mohammed [1 ]
机构
[1] Royal Mil Coll Canada, Dept Elect & Comp Engn, Kingston, ON K7K 7B4, Canada
[2] Royal Mil Coll Canada, Dept Math & Comp Sci, Kingston, ON K7K 7B4, Canada
关键词
unmanned aerial vehicle; surveillance; particle swarm optimization; fuel consumption; equation of motion; optimization; POWER;
D O I
10.3390/s24020408
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67x compared to a sequential implementation on the CPU.
引用
收藏
页数:19
相关论文
共 50 条
  • [11] Autonomous Patrol and Surveillance System using Unmanned Aerial Vehicles
    Seng, Lee Kian
    Ovinis, Mark
    Nagarajan, T.
    Seulin, Ralph
    Morel, Olivier
    [J]. 2015 IEEE 15TH INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING (IEEE EEEIC 2015), 2015, : 1291 - 1297
  • [12] Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms
    Feng, Siling
    Chen, Yinjie
    Huang, Mengxing
    Shu, Feng
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 75 (02): : 4341 - 4355
  • [13] Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles
    Xin, Junfeng
    Li, Shixin
    Sheng, Jinlu
    Zhang, Yongbo
    Cui, Ying
    [J]. SENSORS, 2019, 19 (14)
  • [14] Surveillance of Uneven Surface with Unmanned Aerial Vehicles
    Saha, Dibakar
    Pattanayak, Debasish
    Mandal, Partha Sarathi
    [J]. PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING (ICDCN 2020), 2020,
  • [15] Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization
    Xu Zhen
    Zhang Enze
    Chen Qingwei
    [J]. JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2020, 31 (01) : 130 - 141
  • [16] Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization
    XU Zhen
    ZHANG Enze
    CHEN Qingwei
    [J]. JournalofSystemsEngineeringandElectronics, 2020, 31 (01) : 130 - 141
  • [17] Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks
    Liu, Wentao
    Niu, Guanchong
    Cao, Qi
    Pun, Man-On
    Chen, Junting
    [J]. IEEE ACCESS, 2020, 8 : 174342 - 174352
  • [18] Fault-Tolerant Control of a Multirotor Unmanned Aerial Vehicle applying Particle Swarm Optimization
    Mazeh, Hussein
    Sahili, Jihad
    [J]. 2019 7TH INTERNATIONAL CONFERENCE ON ROBOTICS AND MECHATRONICS (ICROM 2019), 2019, : 619 - 624
  • [19] Swarm of Public Unmanned Aerial Vehicles as a Queuing Network
    Kirichek, R.
    Paramonov, A.
    Koucheryavy, A.
    [J]. DISTRIBUTED COMPUTER AND COMMUNICATION NETWORKS, 2016, 601 : 111 - 120
  • [20] Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path
    Md, Abdul Quadir
    Agrawal, Divyank
    Mehta, Monark
    Sivaraman, Arun Kumar
    Tee, Kong Fah
    [J]. FUTURE INTERNET, 2021, 13 (12):