UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment

Maaji, Salim Sulaiman; Landa-Silva, Dario

Abstract

This paper proposes a model for unmanned aerial vehicles (UAV) grid-based coverage path planning, considering coverage completeness and energy consumption in complex environments with multiple obstacles. The work is inspired by the need for more efficient approaches to oil and gas exploration, but other application areas where UAVs can be used to explore unknown environments can also benefit from this work. An energy consumption model is proposed that considers acceleration, deceleration, and turning manoeuvres, as well as the distance to obstacles, to more accurately simulate the UAV’s movement in different environments. Three different environments are modelled: desert, forest, and jungle. The energy-aware coverage path planning algorithm implemented seeks to reduce the energy consumption of a single drone while increasing coverage completeness. The model implementation and experiments were performed in the ROS/Gazebo simulation software. Obtained results show that the algorithm performs very well, with the drone able to manoeuvre itself in a combination of hills, valleys, rugged terrain, and steep topography while balancing coverage and energy consumption.

Citation

Maaji, S. S., & Landa-Silva, D. (2023, September). UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment. Presented at 14th International Conference on Computational Logistics, ICCL 2023, Berlin, Germany

• Presentation Conference Type: Edited Proceedings
• Conference Name: 14th International Conference on Computational Logistics, ICCL 2023
• Start Date: Sep 6, 2023
• End Date: Sep 8, 2023
• Acceptance Date: Sep 6, 2023
• Online Publication Date: Sep 7, 2023
• Publication Date: 2023
• Deposit Date: Sep 22, 2023
• Publicly Available Date: Sep 8, 2024
• Publisher: Springer Verlag
• Volume: 14239
• Pages: 467-481
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 1611-3349
• Book Title: Computational Logistics. ICCL 2023
• ISBN: 9783031436116
• DOI: https://doi.org/10.1007/978-3-031-43612-3_29
• Public URL: https://nottingham-repository.worktribe.com/output/25081725
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-031-43612-3_29
• Additional Information: First Online: 7 September 2023; Conference Acronym: ICCL; Conference Name: International Conference on Computational Logistics; Conference City: Berlin; Conference Country: Germany; Conference Year: 2023; Conference Start Date: 6 September 2023; Conference End Date: 8 September 2023; Conference Number: 14; Conference ID: iccl22023; Conference URL: https://www.iccl2023.uni-hamburg.de/en.html