Next Article in Journal
An Efficiently Parallelized High-Order Aeroacoustics Solver Using a Characteristic-Based Multi-Block Interface Treatment and Optimized Compact Finite Differencing
Next Article in Special Issue
Nonlinear Model Predictive Control for Unmanned Aerial Vehicles
Previous Article in Journal
Good Code Sets from Complementary Pairs via Discrete Frequency Chips
Previous Article in Special Issue
Direct Entry Minimal Path UAV Loitering Path Planning
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle
Aerospace 2017, 4(2), 27; doi:10.3390/aerospace4020027

Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)

EcoSys Lab, Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65-67, 9020 Klagenfurt, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: David Anderson, Javaan Chahl and Michael Wing
Received: 22 March 2017 / Revised: 21 April 2017 / Accepted: 4 May 2017 / Published: 8 May 2017
(This article belongs to the Collection Unmanned Aerial Systems)

Abstract

The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajectories for these scenarios are implemented with quintic B-splines that grant smoothness in the second-order derivatives of Euler angles and accelerations. In order to tune the parameters of the quintic B-spline in the search space, a multi-objective optimization method called particle swarm optimization (PSO) is used. The proposed technique satisfies the constraints imposed by the configuration of the unmanned aerial vehicle (UAV). Further particular constraints can be introduced such as: obstacle avoidance, speed limitation, and actuator torque limitations due to the practical feasibility of the trajectories. Finally, the standard rapidly-exploring random tree (RRT*) algorithm, the standard (A*) algorithm and the genetic algorithm (GA) are simulated to make a comparison with the proposed algorithm in terms of execution time and effectiveness in finding the minimum length trajectory. View Full-Text
Keywords: stochastic trajectory; unmanned aerial vehicle (UAV); multi-objective optimization; obstacle avoidance; particle swarm optimization (PSO) stochastic trajectory; unmanned aerial vehicle (UAV); multi-objective optimization; obstacle avoidance; particle swarm optimization (PSO)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Salamat, B.; Tonello, A.M. Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs). Aerospace 2017, 4, 27.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Aerospace EISSN 2226-4310 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top