Pérez-Cutiño, Miguel Angel (Universidad de Sevilla), Rodríguez, Fabio (Universidad de Sevilla), Pascual Callejo, Luis David (Universidad de Sevilla), Díaz-Báñez, José-Miguel (Universidad de Sevilla)

Neural Networks Algorithms for Ornithopter Trajectory Optimization

Scheduled for presentation during the Poster Session "Poster Papers Session" (FrPS), Friday, June 18, 2021, 11:00−11:15, Foyer, Mezzanine Level

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

Trajectory optimization has recently been addressed to compute energy-efficient routes for ornithopter navigation, but its online application remains a challenge. To overcome the high computation time of traditional approaches, this paper proposes algorithms that recursively generate trajectories based on the output of neural networks. To this end, we create a novel data set composed by energy-efficient trajectories obtained by running a competitive planner. We present two methods to compute low-cost trajectories: a classification approach to learn maneuvers and an alternative regression method that predicts new states. Both approaches are tested inseveral scenarios, including the landing case. The effectiveness and efficiency of the proposed algorithms are demonstrated through simulation, which show that neural networks can beused to compute the flight path of the ornithopter in real time.