A search and rescue robot design based on LiDAR technology

Research Article
Open access

A search and rescue robot design based on LiDAR technology

Yichen Ding 1* , Fei Ye 2
  • 1 Keystone Academy    
  • 2 Shanghai Jiao Tong University, Shanghai, China    
  • *corresponding author yichendingacademic@163.com
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/30/20230108
ACE Vol.30
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-285-5
ISBN (Online): 978-1-83558-286-2

Abstract

For a long time, search and rescue operations during natural disasters and man-made catastrophes have been a major challenge. Due to the rapidly changing environment in disasters, deploying rescue teams for search missions entails significant risks. With advancements in technology, the latest innovations can be applied to search and rescue tasks to reduce these risks. LiDAR (Light Detection and Ranging) sensors can be installed on unmanned search and rescue vehicles to explore the space. This article utilizes solid-state LiDAR technology, along with various algorithms like SLAM (Simultaneous Localization and Mapping) and EKF (Extended Kalman Filter), to design a remotely controlled unmanned exploration vehicle. By capturing point cloud data, it enables modelling and recording of indoor or outdoor spaces, allowing for space exploration and the identification of trapped individuals and other important rescue-related information before rescue personnel enter the premises. This significantly reduces the risks and time involved in search and rescue operations. The prototype vehicle designed in this paper possesses the advantages of low cost and high flexibility, making it feasible for direct deployment after minor optimization. Finally, the author provides a summary and outlook for this research.

Keywords:

LiDAR, rescue robotics, point-cloud, EKF, SLAM

Ding,Y.;Ye,F. (2024). A search and rescue robot design based on LiDAR technology. Applied and Computational Engineering,30,238-254.
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References

[1]. A. Hakami, A. Kumar, S. J. Shim, and Y. A. Nahleh. Application of soft systems methodology in solving disaster emergency logistics problems. International Journal of Industrial and Manufacturing Engineering, 7(12):2470–2477, 2013.

[2]. W. Wang and F. Tai. Research progress on bat audition and echo localization in china. Journal of Shaanxi Normal University: Natural Science Edition, 34(B03):121–127, 2006.

[3]. N. Li. Theoretical modeling and experimental study on noise of laser radar receiver. 2016.

[4]. W. Wei. Research on Positioning and Navigation System for Substation Inspection Robot based on Differential GPS. PhD thesis, Harbin: Harbin Institute of Technology, 2015.

[5]. T. Shen, W. Liu, and J. Wang. Object ranging system based on binocular stereovision. Electronic Measurement Technology, (4):52–54, 2015.

[6]. T. Thueer and R. Siegwart. Mobility evaluation of wheeled all-terrain robots. Robotics and Autonomous Systems, 58(5):508–519, 2010.

[7]. H. Wang, C. Wang, and L. Xie. Lightweight 3-d localization and mapping for solid-state lidar. IEEE Robotics and Automation Letters, 6(2):1801–1807, 2021.

[8]. Intel. Intel® realsense lidar camera l515 datasheet. https : / / www . intelrealsense . com / download / 7691/, 2021.

[9]. Velodyne. Hdl-32e high resolution real-time 3d lidar sensor. https://velodynelidar.com/products/hdl-32e/.

[10]. J. S. Dai. Euler–rodrigues formula variations, quaternion conjugation and intrinsic connections. Mechanism and Machine Theory, 92:144–152, 2015.

[11]. J. Wang, J. Boyer, and M. G. Genton. A skewsymmetric representation of multivariate distributions. Statistica Sinica, pages 1259–1270, 2004.

[12]. K. Zhou, Q. Hou, R. Wang, and B. Guo. Real-time kdtree construction on graphics hardware. ACM Transactions on Graphics (TOG), 27(5):1–11, 2008.

[13]. C. Hertzberg, R. Wagner, U. Frese, and L. Schröder. Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds. Information Fusion, 14(1):57–77, 2013.

[14]. M. Yermo, F. F. Rivera, J. C. Cabaleiro, D. L. Vilariño, and T. F. Pena. A fast and optimal pathfinder using airborne lidar data. ISPRS Journal of Photogrammetry and Remote Sensing, 183:482–495, 2022.

[15]. J. Zhao, Q. Xu, S. Zlatanova, L. Liu, C. Ye, and T. Feng. Weighted octree-based 3d indoor pathfinding for multiple locomotion types. International Journal of Applied Earth Observation and Geoinformation, 112:102900, 2022.

Cite this article

Ding,Y.;Ye,F. (2024). A search and rescue robot design based on LiDAR technology. Applied and Computational Engineering,30,238-254.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-285-5(Print) / 978-1-83558-286-2(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.30
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. A. Hakami, A. Kumar, S. J. Shim, and Y. A. Nahleh. Application of soft systems methodology in solving disaster emergency logistics problems. International Journal of Industrial and Manufacturing Engineering, 7(12):2470–2477, 2013.

[2]. W. Wang and F. Tai. Research progress on bat audition and echo localization in china. Journal of Shaanxi Normal University: Natural Science Edition, 34(B03):121–127, 2006.

[3]. N. Li. Theoretical modeling and experimental study on noise of laser radar receiver. 2016.

[4]. W. Wei. Research on Positioning and Navigation System for Substation Inspection Robot based on Differential GPS. PhD thesis, Harbin: Harbin Institute of Technology, 2015.

[5]. T. Shen, W. Liu, and J. Wang. Object ranging system based on binocular stereovision. Electronic Measurement Technology, (4):52–54, 2015.

[6]. T. Thueer and R. Siegwart. Mobility evaluation of wheeled all-terrain robots. Robotics and Autonomous Systems, 58(5):508–519, 2010.

[7]. H. Wang, C. Wang, and L. Xie. Lightweight 3-d localization and mapping for solid-state lidar. IEEE Robotics and Automation Letters, 6(2):1801–1807, 2021.

[8]. Intel. Intel® realsense lidar camera l515 datasheet. https : / / www . intelrealsense . com / download / 7691/, 2021.

[9]. Velodyne. Hdl-32e high resolution real-time 3d lidar sensor. https://velodynelidar.com/products/hdl-32e/.

[10]. J. S. Dai. Euler–rodrigues formula variations, quaternion conjugation and intrinsic connections. Mechanism and Machine Theory, 92:144–152, 2015.

[11]. J. Wang, J. Boyer, and M. G. Genton. A skewsymmetric representation of multivariate distributions. Statistica Sinica, pages 1259–1270, 2004.

[12]. K. Zhou, Q. Hou, R. Wang, and B. Guo. Real-time kdtree construction on graphics hardware. ACM Transactions on Graphics (TOG), 27(5):1–11, 2008.

[13]. C. Hertzberg, R. Wagner, U. Frese, and L. Schröder. Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds. Information Fusion, 14(1):57–77, 2013.

[14]. M. Yermo, F. F. Rivera, J. C. Cabaleiro, D. L. Vilariño, and T. F. Pena. A fast and optimal pathfinder using airborne lidar data. ISPRS Journal of Photogrammetry and Remote Sensing, 183:482–495, 2022.

[15]. J. Zhao, Q. Xu, S. Zlatanova, L. Liu, C. Ye, and T. Feng. Weighted octree-based 3d indoor pathfinding for multiple locomotion types. International Journal of Applied Earth Observation and Geoinformation, 112:102900, 2022.

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