Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs) has been selected as the best research article published in 2017 in the MDPI Aerospace journal.
In the domain of aerial robotics, there is a large body of literature on path planning and flight control. However, to assess performance, for instance of navigation algorithms, the trajectories followed by the moving aerial vehicle must be generated with a statistically representative emulator. In this paper, we have provided a new seminal idea on how to do so, and we believe that the results can open the door to a novel methodology to develop stochastic trajectory generators.
The paper is co-authored by Babak Salamat and Andrea Tonello. It provides a realistic stochastic trajectory generation method for unmanned aerial vehicles. It offers a tool for the emulation of trajectories in typical flight scenarios, for instance, flight level, takeoff-mission-landing, and collision avoidance with complex maneuvering. The trajectories for these scenarios are implemented with quintic B-splines, which grants smoothness in the second-order derivatives of the 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 UAV. Further constraints can be introduced such as: obstacle avoidance, speed limitation, and actuator torque limitations due to the practical feasibility of the trajectories.
Babak Salamat and Andrea M. Tonello. Stochastic trajectory generation using particle swarm optimization for quadrotor unmanned aerial vehicles (UAVs). Aerospace 2017, 4(2), 27.
Aerospace best paper awards 2017 – Editorial. Aerospace 2018, 5(2), 61.