DESIGN AND DEVELOPMENT OF LiDAR INTEGRATED DRONE

Authors

  • V. Bharath Simha Reddy
  • Srikanth Ghodke
  • M. Habeeb

DOI:

https://doi.org/10.46243/jst.2023.v8.i05.pp117-124

Keywords:

Aerial mapping, Drone, Six-propeller layout, RPLiDAR A1M8-R6 sensor

Abstract

This research is focused on the design and development of a cutting-edge drone equipped with a cutting-edge RPLiDAR A1M8-R6 sensor. The drone's unique characteristic is its six-propeller layout, which sets it apart from regular drones. The major goal of this project is to use advanced sensor technology to revolutionize aerial mapping and surveillance capabilities. This novel approach intends to alleviate standard drone limitations, particularly in terms of obstacle recognition and mapping accuracy. We anticipate that by using the RPLiDAR A1M8-R6 sensor with a six-propeller arrangement, the drone's navigation and mapping capabilities will be considerably improved. The suggested system includes a custom-built drone that has been precisely manufactured to be lightweight and agile, allowing for improved flying efficiency and maneuverability. The integration of the RPLiDAR A1M8-R6 sensor, known for its outstanding lidar capabilities, is critical to this system. With a 360-degree scan capability and a range of up to 12 meters, this sensor gives highly accurate distance readings and enables efficient obstacle identification. As a result, the integrated drone can easily navigate complex areas while avoiding collisions. The experimental results unambiguously show that the integrated drone outfitted with the RPLiDAR A1M8-R6 sensor, and six propellers outperforms conventional drones in terms of obstacle avoidance, mapping accuracy, and overall flight performance.

References

Omari, S.; Hua, M.; Ducard, G.; Hamel, T. Hardware and Software Architecture for Nonlinear Control of Multirotor Helicopters. IEEE/ASME Trans. Mech. 2013, 18, 1724–1736

Giribet, J.I.; Sanchez-Pena, R.S.; Ghersin, A.S. Analysis and Design of a Tilted Rotor Hexacopter for Fault Tolerance. IEEE Trans. Aerosp. Electron. Syst. 2016, 52, 1555–1567

Lee, H.; Kim, H.; Kim, H.J. Planning and Control for Collision-Free Cooperative Aerial Transportation. IEEE Trans. Autom. Sci. Eng. 2018, 15, 189–201

Li, Z.; Tan, J.; Liu, H. Rigorous Boresight Self-Calibration of Mobile and UAV LiDAR Scanning Systems by Strip Adjustment. Remote Sens. 2019, 11, 442

Lei, L.; Qiu, C.; Li, Z.; Han, D.; Han, L.; Zhu, Y.; Wu, J.; Xu, B.; Feng, H.; Yang, H.; Yang, G. Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV–LiDAR Data. Remote Sens. 2019, 11, 1067

Yang, Q.; Su, Y.; Jin, S.; Kelly, M.; Hu, T.; Ma, Q.; Li, Y.; Song, S.; Zhang, J.; Xu, G.; Wei, J.; Guo, Q. The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data. Remote Sens. 2019, 11, 2880

Wallace, L.; Lucieer, A.; Watson, C.; Turner, D. Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sens. 2012, 4, 1519–1543

Wallace, L.; Lucieer, A.; Watson, C.S. Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data. IEEE Trans. Geosci. Remote Sens. 2014, 52, 7619–7628

Chen, N.; Ni, N.; Kapp, P.; Chen, J.; Xiao, A.; Li, H. Structural Analysis of the Hero Range in the Qaidam Basin, Northwestern China, Using Integrated UAV, Terrestrial LiDAR, Landsat 8, and 3-D Seismic Data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2015, 8, 4581–4591

Hening, S.; Ippolito, C.; Krishnakumar, K. 3D LiDAR SLAM Integration with GPS/INS for UAVs in Urban GPS-Degraded Environments. In Proceedings of the AIAA Infotech@Aerospace Conference, Grapevine, TX, USA, 9–13 January 2017

Zheng, L.; Zhang, P.; Tan, J.; Li, F. The Obstacle Detection Method of UAV Based on 2D Lidar. IEEE Access 2019, 7, 163437–163448

Cherubini, A.; Spindler, F.; Chaumette, F. Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance. IEEE Trans. Intell. Transp. Syst. 2014, 15, 2101–2110

Ji, J.; Khajepour, A.; Melek, W.W.; Huang, Y. Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints. IEEE Trans.Veh. Technol. 2017, 66, 952–964

Malone, N.; Chiang, H.; Lesser, K.; Oishi, M.; Tapia, L. Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field. IEEE Trans. Robot. 2017, 33, 1124–1138

Li, S.; Li, Z.; Yu, Z.; Zhang, B.; Zhang, N. Dynamic Trajectory Planning and Tracking for Autonomous Vehicle With Obstacle Avoidance Based on Model Predictive Control. IEEE Access 2019, 7, 132074–132086

Zhang, H.; Jin, H.; Liu, Z.; Liu, Y.; Zhu, Y.; Zhao, J. Real-Time Kinematic Control for Redundant Manipulators in a Time-Varying Environment: Multiple-Dynamic Obstacle Avoidance and Fast Tracking of a Moving Object. IEEE Trans. Ind. Inform. 2020, 16, 28–41

Mujumdar, A.; Padhi, R. Reactive Collision Avoidance Using Nonlinear Geometric and Differential Geometric Guidance. J. Guid. Control Dyn. 2011, 34, 303–310

Wang, J.; Xin, M. Integrated Optimal Formation Control of Multiple Unmanned Aerial Vehicles. IEEE Trans. Control Syst. Technol. 2013, 21, 1731–1744

Yang, X.; Alvarez, L.M.; Bruggemann, T. A 3D Collision Avoidance Strategy for UAVs in a Non-Cooperative Environment. J. Intell. Robot. Syst. 2013, 70, 315–327

Published

2023-05-18

How to Cite

V. Bharath Simha Reddy, Srikanth Ghodke, & M. Habeeb. (2023). DESIGN AND DEVELOPMENT OF LiDAR INTEGRATED DRONE. Journal of Science & Technology , 8(5), 117–124. https://doi.org/10.46243/jst.2023.v8.i05.pp117-124