In this paper, a new three-dimensional path planning approach with obstacle avoidance for UAVs is proposed. The aim is to provide a computationally-fast on-board sub-optimal solution for collision- free path planning in static environments. The optimal 3D path is an NP (non-deterministic polynomial-time) hard problem which may be solved numerically by global optimization algorithms such as the Particle Swarm Optimization (PSO). Application of PSO to the 3D path plan- ning class of problems faces typical challenges such slow convergence rate. It is shown that the performance may be improved markedly by imple- menting a novel parallel approach and incorporation of new termination conditions. Moreover, the exploration and exploitation parameters are optimized to nd a reasonably short, smooth, and safe path connecting the way-points. As an additional precaution to avoid collisions, obstacle dimensions are artificially slightly enlarged. To verify the robustness of the algorithm, several simulations are carried out by varying the num- ber of obstacles, their volume, and location in space. A certain number of simulations exploiting the random nature of PSO are performed to highlight the computational efficiency and the robustness of this new approach.

A 3D Path Planning Algorithm based on PSO for Autonomous UAVs Navigation: 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings / Mirshamsi, Alireza; Godio, Simone; Nobakhti, Amin; Primatesta, Stefano; Dovis, Fabio; Guglieri, Giorgio (THEORETICAL COMPUTER SCIENCE AND GENERAL ISSUES). - In: Bioinspired Optimization Methods and Their Applications / Mirshamsi A., Godio S., Nobakhti A., Primatesta S. , Dovis F, and Guglieri G.. - ELETTRONICO. - [s.l] : Springer, 2020. - ISBN 978-3-030-63709-5. [10.1007/978-3-030-63710-1]

A 3D Path Planning Algorithm based on PSO for Autonomous UAVs Navigation: 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings

Godio,Simone;Primatesta, Stefano;Dovis Fabio;Guglieri Giorgio
2020

Abstract

In this paper, a new three-dimensional path planning approach with obstacle avoidance for UAVs is proposed. The aim is to provide a computationally-fast on-board sub-optimal solution for collision- free path planning in static environments. The optimal 3D path is an NP (non-deterministic polynomial-time) hard problem which may be solved numerically by global optimization algorithms such as the Particle Swarm Optimization (PSO). Application of PSO to the 3D path plan- ning class of problems faces typical challenges such slow convergence rate. It is shown that the performance may be improved markedly by imple- menting a novel parallel approach and incorporation of new termination conditions. Moreover, the exploration and exploitation parameters are optimized to nd a reasonably short, smooth, and safe path connecting the way-points. As an additional precaution to avoid collisions, obstacle dimensions are artificially slightly enlarged. To verify the robustness of the algorithm, several simulations are carried out by varying the num- ber of obstacles, their volume, and location in space. A certain number of simulations exploiting the random nature of PSO are performed to highlight the computational efficiency and the robustness of this new approach.
978-3-030-63709-5
Bioinspired Optimization Methods and Their Applications
File in questo prodotto:
File Dimensione Formato  
BIOMA_final_paper_1.zip

non disponibili

Descrizione: File zip contenente tutti i documenti Latex
Tipologia: Altro materiale allegato
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 10.47 MB
Formato Zip File
10.47 MB Zip File   Visualizza/Apri   Richiedi una copia
Paper.pdf

Open Access dal 30/10/2021

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 968.94 kB
Formato Adobe PDF
968.94 kB Adobe PDF Visualizza/Apri
496171_1_En_21_Chapter_Author.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 4.68 MB
Formato Adobe PDF
4.68 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2850165