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Ellipsoidal Path Planning for Unmanned Aerial Vehicles

Department of Computer Science, University of Guadalajara, 1421 Marcelino García Barragán, Guadalajara 44430, Mexico
Department of Artificial Intelligence, Neural10 S de RL de CV, Av. Aviación 5051, Zapopan 45019, Mexico
Author to whom correspondence should be addressed.
Academic Editor: Grzegorz Dudek
Appl. Sci. 2021, 11(17), 7997;
Received: 4 June 2021 / Revised: 23 August 2021 / Accepted: 26 August 2021 / Published: 29 August 2021
(This article belongs to the Special Issue Applied Machine Learning Ⅱ)
The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost. View Full-Text
Keywords: path planning; unmanned aerial vehicles; neural networks; evolutionary algorithms path planning; unmanned aerial vehicles; neural networks; evolutionary algorithms
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MDPI and ACS Style

Villaseñor, C.; Gallegos, A.A.; Lopez-Gonzalez, G.; Gomez-Avila, J.; Hernandez-Barragan, J.; Arana-Daniel, N. Ellipsoidal Path Planning for Unmanned Aerial Vehicles. Appl. Sci. 2021, 11, 7997.

AMA Style

Villaseñor C, Gallegos AA, Lopez-Gonzalez G, Gomez-Avila J, Hernandez-Barragan J, Arana-Daniel N. Ellipsoidal Path Planning for Unmanned Aerial Vehicles. Applied Sciences. 2021; 11(17):7997.

Chicago/Turabian Style

Villaseñor, Carlos, Alberto A. Gallegos, Gehova Lopez-Gonzalez, Javier Gomez-Avila, Jesus Hernandez-Barragan, and Nancy Arana-Daniel. 2021. "Ellipsoidal Path Planning for Unmanned Aerial Vehicles" Applied Sciences 11, no. 17: 7997.

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