Article
Stochastic Trajectory Generation Using Particle
Swarm Optimization for Quadrotor Unmanned
Aerial Vehicles (UAVs)
Babak Salamat * and Andrea M. Tonello
EcoSys Lab, Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65-67, 9020 Klagenfurt, Austria;
andrea.tonello@aau.at
* Correspondence: babaksa@edu.aau.at; Tel.: +43-(0)-463-2700-3661
Academic Editors: David Anderson, Javaan Chahl and Michael Wing
Received: 22 March 2017; Accepted: 4 May 2017; Published: 8 May 2017
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.
Keywords:
stochastic trajectory; unmanned aerial vehicle (UAV); multi-objective optimization;
obstacle avoidance; particle swarm optimization (PSO)
1. Introduction
In the last decade, unmanned aerial vehicles (UAVs), mostly known as an autonomous aerial
vehicles, have been used in numerous military, aerial photography, agricultural and surveillance
applications. UAVs can be classified into three significant groups: fixed-wing UAVs, rotary-wing UAVs
and hybrid-layout UAVs [1].
The advantages of fixed-wing UAVs are the high-speed and the ability to fly for long distances.
However, mechanical systems for landing and take-off, for example the landing gear, have to be
installed. Furthermore, spacious structures, e.g., landing strips, have to be built. In small areas
with obstacles, vertical take-off and landing (VTOL) and the ability to hover in a motionless spot
are crucial. Therefore, rotary-wing UAVs have more applications. In addition, VTOL UAVs have
greater maneuverability in indoor flight. Hybrid-layout UAVs have both long distance flight and
VTOL properties. On the other hand, they have complex mechanisms for changing from rotary wing
to fixed wing during the flight.
Considering harsh scenarios characterized by limited area [
2
], the presence of obstacles and
possible dynamic constraints, rotary-wing UAVs have been shown to give the best performance [
3
].
This is why we consider these specific types in this paper. In particular, a quadrotor UAV is assumed.
Its model structure is depicted in Figure 1. The aim is to derive a model to represent realistic trajectories
that such a UAV can follow. In particular, there exist several methods to generate trajectories, including
Aerospace 2017, 4, 27; doi:10.3390/aerospace4020027 www.mdpi.com/journal/aerospace
