Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81195
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Title: Intelligent beetle antennae search for UAV sensing and avoidance of obstacles
Authors: Wu, Q
Shen, X
Jin, Y
Chen, Z
Li, S 
Khan, AH 
Chen, D
Issue Date: 2019
Source: Sensors (Switzerland), 2019, v. 19, no. 8, 1758
Abstract: Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.
Keywords: MTS
Obstacle avoidance
Optimization algorithms
Path planning
UAVs
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors (Switzerland) 
ISSN: 1424-8220
DOI: 10.3390/s19081758
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Wu, Q.; Shen, X.; Jin, Y.; Chen, Z.; Li, S.; Khan, A.H.; Chen, D. Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles. Sensors 2019, 19, 1758 is available at https://doi.org/10.3390/s19081758
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