DeukDeuk's Stars
libai1943/UAV-Path-Planning
Unmanned Combat Aerial Vehicles Path Planning Using a Novel Probability Density Model Based on Artificial Bee Colony Algorithm
jrgenerative/fixed-wing-sim
Matlab implementation to simulate the non-linear dynamics of a fixed-wing unmanned areal glider. Includes tools to calculate aerodynamic coefficients using a vortex lattice method implementation, and to extract longitudinal and lateral linear systems around the trimmed gliding state.
maazmb/UAV-waypoint-guidance-matlab
"4D TRAJECTORY GENERATION FOR GUIDANCE MODULE OF A UAV FOR A GATE TO GATE FLIGHT IN PRESENCE OF TURBULENCE", International Journal of Advanced Robotic Systems, 2016, DOI: 10.5772/64063. publication description: Robotic air vehicles are used increasingly in delivering goods especially for safety-of-life applications. This paper discusses a guidance module for trajectory generation of such vehicles. An offline algorithm is developed using a navigation model to produce the required trajectory in the form of time-tagged longitude, latitude and altitude. The process is an essential requirement when an operator has to program a robotic vehicle to travel on the desired course. This problem is addressed scarcely in the relevant literature. The waypoints are generated for all phases of flight and then modified to cater for the wind disturbance parameters obtained from current meteorological information. The waypoints are uploaded to the vehicle’s flight control system memory and reside there for the vehicle to follow. This paper also renders the generated trajectory on Google Earth® using Matlab/Simulink® to test the closed-loop performance. Furthermore, a Dryden wind model is utilized to generate a modified trajectory for turbulent conditions. An operator can make adjustments in the required initial heading angle so the vehicle lands at its destination even in turbulent weather. An empirical formula is also proposed for this purpose. Further work includes design of a control system to follow the generated waypoints.
wasswashafik/UAV-Performance
abhineet-gupta/NonlinearModel_Minimal
The repository contains a nonlinear simulation model of the mAEWing1 series of aircraft developed at University of Minnesota as part of the `Performance Adaptive Aeroelastic Wing' project.
HHH-YYY/uavSwarm_EKF
XiUN1/Cross-Domain-Cooperative-Control
Search-and-Track of ROVs using UAV Swarms
zhang373/UAV-swarm-positioning-and-scheduling-method
venezia-antonio/Consensus-based-Algorithm-for-Swarm-UAVs
zzycoder/Cooperative-Attack-Algorithm-for-UAVs
Cooperative Attack Algorithm for UAVs is focusing on the cooperative attack problem of UAV swarm system with flight time and attack angle constraints. It includes an efficient attack framework for real-time planning and control of drones.
Rajshah05/Behaviour-Optimization-for-Swarm-Navigation-in-Cluttered-Environment
Optimized settling time in formation control of UAVs swarm navigation in the presence of obstacles
yangbin-xd/UAV-positioning
source code for IEEE IoTJ paper "Distributed and Collaborative Localization for Swarming UAVs"
olwyk/FYP2019
FYP on path planning for swarming fixed-wing aircraft using distributed model predictive control . Written in MATLAB using IPOPT.
yltzdhbc/Swarm_Sim_Matlab
集群机器人Matlab仿真
BAN-JY/PSO
Vehicle scheduling optimization of battery electric bus based on Partical Swarm Optimization
WilliamFun/UAV_swarm_3d_simulation
Simulation of coordinated formation control of UAV based on leader-follower and artificial potential
jullyjelly/Intelligent_Algorithm
Optimization problem solving: genetic algorithm, ant colony algorithm, tabu search algorithm, simulated annealing algorithm, particle swarm optimization
Rajshah05/UAV-swarm-control-optimization
Minimized settling time in the formation control of UAVs swarm navigation in the presence of obstacles by optimizing feedback control gains/parameters. Designed and simulated a model of swarm navigation in MATLAB
sabinaya/Resource-Allocation-VMS
Application of Particle Swarm Optimization Technique for dynamic resource allocation in a cloud computing environment
duongpm/MPSO
Motion-Encoded Particle Swarm Optimization Algorithm
mlpi-unipi/drones-swarm
Adaptive exploration of a UAVs swarm for distributed targets detection and tracking
stxupengyu/PSO-RBF-NN
使用粒子群算法优化的RBF神经网络进行预测。RBF neural network optimized by particle swarm optimization is used for prediction.
zegangYang/PSO_PathPlaningNew
This open source project is a matlab GUI project,is a Robot Path Planing Demo use Particle Swarm Optimization(PSO) algorithm
ShangruZhong/Firefly_Algorithm_WSN
Swarm Intelligence Algorithm for WSN problems
duongpm/SPSO
Spherical Vector-based Particle Swarm Optimization
cuntou0906/Route-Planning
use some algorithm to solve the Route Planning. Including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),ant colony optimization(ACO).
Mohamadnet/MOPSO-WSN
Routing in Wireless Sensor Network using Multiobjective Particle Swarm Optimization
cvar-upm/cvg_quadrotor_swarm
Software framework for vision-based quadrotor multi-robot systems
UPatras-ANeMoS/UAV_coverage
Simulation for planar area coverage by a swarm of UAVs
SajadAHMAD1/Chaotic-GSA-for-Engineering-Design-Problems
All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.