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Sensors 2019, 19(8), 1758;

Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310018, China
Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
Author to whom correspondence should be addressed.
Received: 22 February 2019 / Revised: 30 March 2019 / Accepted: 10 April 2019 / Published: 12 April 2019
(This article belongs to the Section Intelligent Sensors)
PDF [10644 KB, uploaded 14 April 2019]


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. View Full-Text
Keywords: UAVs; path planning; obstacle avoidance; MTS; optimization algorithms UAVs; path planning; obstacle avoidance; MTS; optimization algorithms

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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.

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