Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization

2013 ◽  
Vol 43 (6) ◽  
pp. 1451-1465 ◽  
Author(s):  
Yangguang Fu ◽  
Mingyue Ding ◽  
Chengping Zhou ◽  
Hanping Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Route Planning ◽  
Swarm Optimization ◽  
Aerial Vehicle

scholarly journals Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881523 ◽  
Author(s):  
Yohanes Khosiawan ◽  
Sebastian Scherer ◽  
Izabela Nielsen
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Task Assignment ◽  
Computation Time ◽  
Task Sequence ◽  
Swarm Optimization ◽  
Assignment Algorithm ◽  
Aerial Vehicle ◽  
Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

scholarly journals Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Giang Thi - Huong Dang ◽  
Quang - Huy Vuong ◽  
Minh Hoang Ha ◽  
Minh - Trien Pham
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Planning Problem ◽  
Swarm Optimization ◽  
3D Environment ◽  
Aerial Vehicle ◽  
Improved Pso

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

scholarly journals Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 174342-174352
Author(s):  
Wentao Liu ◽  
Guanchong Niu ◽  
Qi Cao ◽  
Man-On Pun ◽  
Junting Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicle

scholarly journals PERANCANGAN SISTEM KONTROL NAVIGASI BEARING PADA QUADCOPTER DENGAN METODE PID (PROPORTIONAL, INTEGRAL, DERIVATIVE) SELF TUNING PSO (PARTICLE SWARM OPTIMIZATION)

TRANSIENT ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 323
Author(s):  
Muhammad Surya Sulila ◽  
Sumardi Sumardi ◽  
Munawar Agus Riyadi
Keyword(s):  
Particle Swarm Optimization ◽  
Global Position System ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Settling Time ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Self Tuning ◽  
Aerial Vehicle

Unmanned Aerial Vehicle (UAV) adalah pesawat tanpa awak yang dapat dikendalikan secara manual ataupun otomatis dari jarak jauh. Sistem navigasi UAV quadcopter salah satunya adalah membuat sistem kontrol quadcopter agar dapat stabil menghadap ke arah koordinat yang dituju dengan mengatur sudut putar sumbu vertikal (yaw) atau disebut navigasi bearing sehingga pada Penelitian ini dirancang sistem kontrol Proportional Integral Derivative self tuning Particle Swarm Optimization. Perancangan sistem navigasi bearing digunakan input berupa Global Position System untuk mengetahui koordinat quadcopter, sedangkan sensor kompas HMC5883L digunakan untuk mengetahui kondisi aktual sudut arah hadap quadcopter. Berdasarkan hasil pengujian respon sistem quadcopter, untuk dapat mengarah ke koordinat yang dituju dengan koordinat quadcopter tetap, settling time dicapai pada detik ke 6,4 dan error setelah settling time sebesar 5,4⁰. Berdasarkan pengujian dengan perubahan koordinat, didapatkan error rata-rata sebesar 7,9⁰. Berdasarkan pengujian dengan diberi gangguan didapatkan error offset rata-rata sebesar 1,89⁰ dan mencapai settling time pada detik ke 4,1. Batasan nilai self tuning PSO yang terbaik didapat pada nilai Kp = 0,15 sampai 0,3, Ki = 0,06 sampai 0,6, dan Kd = 0,005 sampai Kd = 0,1. Nilai koefisien PSO yang digunakan adalah C1 = 1,5,  C2 = 2 dan bobot inersia dari 0,7 sampai 1,2.

Particle Swarm Optimization Based UHF Band Rectangular Loop Antenna for Unmanned Aerial Vehicle Application

2021 ◽  
Author(s):  
Mohamad Ridwan ◽  
Farida Gamar ◽  
Maretha Ruswiansari ◽  
Hanif Abdillah ◽  
Dea Fitriani Ilma ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Loop Antenna ◽  
Swarm Optimization ◽  
Aerial Vehicle ◽  
Uhf Band
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