Abstract
Path tracing or simulation in robotics Operating System ROS is very popular in highly dynamic research projects. The Matplotlib’s 3D path tracing is a graphical 3-axis plotting tool with real- time data visualization over a very long range. The data’s are generated in all 3 dimensions from accelerometer readings for visualizing the ground station navigation system. The communication between the two radio modules are established and the data’s are transmitted between the two modules that are placed remotely. The data can be plotted in real-time in 3 Dimensions and then, visualized. The main goal of path tracing is to make a comparative study between 3 axis displacement as any kind of irregular plot or trace can be a sign of a dangerous situations that can occur. There are many other significances of this concept that can be used in navigation systems.
References
- S. -J. Lee and H. -S. Kim, "Applying the Kalman filter to increase accuracy of location measurement," 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand, 2019, pp. 1-3, doi: 10.23919/ELINFOCOM.2019.8706347.
- Kulakova et al. SINS/GNSS Aided by Autonomous AHRS for a Small UAV. In Proceedings of the 2020 European Navigation Conference (ENC), Online, 23–24 November 2020; pp. 1–10.[Google Scholar]
- A. Hassan et al., "Statistical scheme for fault detection using Arduino and MPU 6050," 2019 Prognostics and System Health Management Conference (PHM- Qingdao), Qingdao, China, 2019, pp. 1-7, doi: 10.1109/PHM- Qingdao46334.2019.8942922.
- Duong Quoc, Dung & Sun, Jinwei & Nguyen, Tuan & Luo, Lei. (2016). Attitude estimation by using MEMS IMU with Fuzzy Tuned Complementary Filter. 372-378. 10.1109/ICEICT.2016.7879720.
- Ludwig, S.A. Genetic algorithm based Kalman filter adaptation algorithm for magnetic and inertial measurement unit. In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–7.[Google Scholar]
- A. Jain et al., “Data mining approach to analyse the road accidents in India,” in Proc. IEEE 5th Int. Conf. on Reliability, Infocom Technologies and Optimization (ICRITO), 2016, pp. 175–179.
- M. Peden et al., “World report on road traffic injury prevention,” 2004.
- P. Gui, L. Tang and S. Mukhopadhyay, "MEMS based IMU for tilting measurement: Comparison of complementary and kalman filter based data fusion," 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand, 2015, pp. 2004-2009, doi: 10.1109/ICIEA.2015.7334442.
- M. J. McGuffin, R. Servera and M. Forest, "Path Tracing in 2D, 3D, and Physicalized Networks," in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2023.3238989.
- C. Uluisik and L. Sevgi, "A MATLAB-based visualization package for complex functions, and their mappings and integrals," in IEEE Antennas and Propagation Magazine, vol. 54, no. 1, pp. 243-253, Feb. 2012, doi: 10.1109/MAP.2012.6209520.
- F. Qin, X. Zhan, and G. Du, “Performance improvement of receivers based on ultra-tight integration in GNSS-challenged environments,” Sensors, vol. 13, no. 12, pp. 16406–16423, 2013.View at: Publisher Site | Google Scholar
- A. Budiyono, “Principles of GNSS, inertial, and multi-sensor integrated navigation systems,” Industrial Robot, vol. 39, no. 3, pp. 191-192, 2012.View at: Publisher Site | Google Scholar
- G. Xia and G. Wang, “INS/GNSS tightly-coupled integration using quaternion-based AUPF for USV,” Sensors, vol. 16, no. 8, pp. 1215–1230, 2016. View at: Publisher Site | Google Scholar
- A. Noureldin, A. El-Shafie, and M. Bayoumi, “GPS/INS integration utilizing dynamic neural networks for vehicular navigation,” Information Fusion, vol. 12, no. 1, pp. 48–57, 2011. View at: Publisher Site | Google Scholar
- T. Li, H. Zhang, X. Niu, and Z. Gao, “Tightly-coupled integration of multi-GNSSf single-frequency RTK and MEMS-IMU for enhanced positioning performance,” Sensors, vol. 17, no. 11, p. 2462, 2017. View at: Publisher Site | Google Scholar
