research-article
Authors: Wenlong Zheng, Jianjun Yi, Haojie Xiang, Bo Zhou, Danwei Wang, and Changchun Zhao
CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
January 2021
Article No.: 154, Pages 1 - 7
Published: 17 May 2021 Publication History
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Abstract
Autonomous safe landing of UAV is an important function in many scenarios such as force landing and delivery. This paper proposes a method to autonomously select a safe landing site for vertical take-off and landing (VTOL) UAV based on point cloud, which can minimize combined risks posed during touch down at the chosen landing site. The most suitable landing site of a landing zone is selected according to the terrain complexity. In this paper, (1) fine-grained grid elevation map converted from the terrain point cloud is used to calculate the potential risk such as slope, roughness and maximum height difference. (2) A comprehensive risk model is designed to consider all above risks to recognize obstacles and risk areas, and combine the flight distance factors to obtain the final cost map. (3) We process cost map as image by OpenCV to accelerate the processing and reduce reaction time. Terrain point clouds of simulation scene and real world are used for experiments and experimental results show that the selected landing sites can meet the safety requirements, which demonstrate the effectiveness and feasibility of our proposed method.
References
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B. Ayhan and C. Kwan, "A Comparative Study of Two Approaches for UAV Emergency Landing Site Surface Type Estimation," IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 2018, pp. 5589-5593.
[2]
L. Yan, J. Qi, M. Wang, C. Wu and J. Xin, "A Safe Landing Site Selection Method of UAVs Based on LiDAR Point Clouds," 2020 39th Chinese Control Conference (CCC), Shenyang, China, 2020, pp. 6497-6502.
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Rabah, M., Rohan, A., Talha, M. Autonomous Vision-based Target Detection and Safe Landing for UAV. Int. J. Control Autom. Syst. 16, 3013–3025 (2018).
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J. Castagno, C. Ochoa and E. Atkins, "Comprehensive Risk-based Planning for Small Unmanned Aircraft System Rooftop Landing," 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, TX, 2018, pp. 1031-1040.
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Fankhauser P., Hutter M. (2016) A Universal Grid Map Library: Implementation and Use Case for Rough Terrain Navigation. In: Koubaa A. (eds) Robot Operating System (ROS). Studies in Computational Intelligence, vol 625. Springer, Cham.
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Ruikang Li, Qiwei Huang, Hui Feng, Bo Hu. Autonomous Safe Landing System of Unmanned Rotorcraft on Rugged Terrain. ROBOT, 2020,42(04):416-426.
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Bin Zhao. Research on Visual Analysis Method for Surface Terrain of Small Body. Harbin Institute of Technology, 2019.
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Zexu Zhang, Weidong Wang, Pingyuan Cui, Gongping Zhang. An algorithm of terrain hazard assessment for planetary soft landing. JOURNAL OF HARBIN INSTITUTE OF TECHNOLOGY, 2011,43(05):25-29.
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Xing Huang, Qunwei Ying.Obstacle Recognition Based on Lidar and Camera Information Fusion.Computer Measurement & Control, 2020,28(01):184-188+194.
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Haibo Li, Yunfeng Cao, Meng Ding, Likui Zhuang. A Method of Slope Estimation Based on Clustering of Three-dimensional Point Cloud. ACTA METROLOGICA SINICA,2018,39(03):304-309.
Index Terms
A Study for UAV Autonomous Safe Landing-Site Selection on Rough Terrain
Computer systems organization
Embedded and cyber-physical systems
Computing methodologies
Index terms have been assigned to the content through auto-classification.
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CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
January 2021
1142 pages
ISBN:9781450389570
DOI:10.1145/3448734
Copyright © 2021 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 May 2021
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Author Tags
- Elevation map
- Obstacle recognition
- Point Cloud
- Rough terrain
- Safe landing
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- Research-article
- Research
- Refereed limited
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CONF-CDS 2021
CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
January 28 - 30, 2021
CA, Stanford, USA
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