Abstract
UAV path planning in 3D cluttered and uncertain environments centers on finding an optimal / sub-optimal collision-free path, considering in parallel geometric, physical and temporal constraints, fox example, obstacles, infrastructure, physical or artificial landmarks, etc. This paper introduces a novel node-based algorithm, called Energy Efficient A* (EEA*), which is based on the A* search algorithm, but overcomes some of its key limitations. The EEA* deals with 3D environments, it is robust converging fast to the solution, it is energy efficient and it is real-time implementable and executable. In addition to the EEA*, a local path planner is also derived to cope with unknown dynamic threats within the working environment. The EEA* and the local path planner are first implemented and evaluated via simulated experiments using a fixed-wing UAV operating in mountain-like 3D environments, and in the presence of unknown dynamic obstacles. This is followed by evaluating a set up where three UAVs are commanded to follow their respective paths in a safe way. The energy efficiency of EEA* is also tested and compared with the conventional A* algorithm.
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09 October 2022
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Acknowledgements
This paper is an extended and enhanced version of the conference paper ”3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs”, published in Proceedings, 2021 International Conference on Unmanned Aircraft Systems, June 2021, Athens, Greece.
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Open access funding provided by Politecnico di Torino within the CRUI-CARE Agreement. A.R. acknowledges partial funding by Compagnia di San Paolo and the 2019 Amazon Research Award.
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K.V. and A.R. conceived and supervised the research. G.A. designed and implemented the simulation studies and drafted a preliminary version of the paper. All the authors concurred to the analysis of data, revised, and approved the final version of the manuscript.
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Aiello, G., Valavanis, K.P. & Rizzo, A. Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments. J Intell Robot Syst 105, 60 (2022). https://doi.org/10.1007/s10846-022-01608-1
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DOI: https://doi.org/10.1007/s10846-022-01608-1