scholarly journals A Deep Learning Model Based on Multi-Objective Particle Swarm Optimization for Scene Classification in Unmanned Aerial Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 135383-135393
Author(s):  
Aghila Rajagopal ◽  
Gyanendra Prasad Joshi ◽  
A. Ramachandran ◽  
R. T. Subhalakshmi ◽  
Manju Khari ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Learning Model ◽  
Scene Classification ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Aerial Vehicles ◽  
Deep Learning Model

scholarly journals Pareto Optimal PID Tuning for Px4-Based Unmanned Aerial Vehicles by Using a Multi-Objective Particle Swarm Optimization Algorithm

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 71
Author(s):  
Victor Gomez ◽  
Nicolas Gomez ◽  
Jorge Rodas ◽  
Enrique Paiva ◽  
Maarouf Saad ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Pid Tuning ◽  
Aerial Vehicles ◽  
The Stability ◽  
Selection Of

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study.

scholarly journals A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

2022 ◽  
Vol 40 (1) ◽  
pp. 223-235
Author(s):  
Adi Alhudhaif ◽  
Ammar Saeed ◽  
Talha Imran ◽  
Muhammad Kamran ◽  
Ahmed S. Alghamdi ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Learning Model ◽  
Vehicle Classification ◽  
Swarm Optimization ◽  
Deep Learning Model

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

2009 ◽  
Vol 6 (4) ◽  
pp. 271-290 ◽  
Author(s):  
Jung Leng Foo ◽  
Jared Knutzon ◽  
Vijay Kalivarapu ◽  
James Oliver ◽  
Eliot Winer
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
B Splines ◽  
Aerial Vehicles

Multi-objective quantum tunicate swarm optimization with deep learning model for intelligent dystrophinopathies diagnosis

2022 ◽  
Author(s):  
Fahd N. Al-Wesabi ◽  
Marwa Obayya ◽  
Anwer Mustafa Hilal ◽  
Oscar Castillo ◽  
Deepak Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Deep Learning Model
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