International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 13, No. 4, 8 Aug. 2021
Cover page and Table of Contents: PDF (size: 306KB)
A Potrace-based Tracing Algorithm for Prescribing Two-dimensional Trajectories in Inertial Sensors Simulation Software
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Author(s)
Index Terms
Inertial Sensor, Simulation, Bezier Curve, Tracing, Two-dimensional Trajectory, Potrace, Accelerometer
Abstract
Inertial measurement units based on microelectromechanical systems are perspectives for motion capture applications due to their numerous advantages. A motion trajectory is restored using a well-known navigation algorithm, which assumes integration of the signals from accelerometers and gyroscopes. Readings of both sensors contain errors, which quickly accumulate due to integration. The applicability of an inertial measurement unit for motion capture depends on the trajectory being tracked and can be predicted due to the simulation of signals from inertial sensors. The first simulation step is prescribing a motion trajectory and corresponding velocities. The existing simulation software provides no user-friendly graphical tools for the completion of this step. This work introduces an algorithm for the simulation of accelerometer signals upon a two-dimensional trajectory drawn with a computer mouse and then vectorized. We propose a modification of the Potrace algorithm for tracing motion trajectories. Thus, a trajectory and velocities can be set simultaneously. The obtained results form a basis for simulating three-dimensional motion trajectories since the latter can be represented by three mutually orthogonal two-dimensional projections.
Cite This Paper
Bohdan R. Tsizh, Tetyana A. Marusenkova, " A Potrace-based Tracing Algorithm for Prescribing Two-dimensional Trajectories in Inertial Sensors Simulation Software ", International Journal of Modern Education and Computer Science(IJMECS), Vol.13, No.4, pp. 42-54, 2021.DOI: 10.5815/ijmecs.2021.04.04
Reference
[1] M. El-Gohary, "Joint Angle Tracking with Inertial Sensors", Ph.D. dissertation, Portland State University, Portland, USA, 2013. doi: 10.15760/etd.661.
[2] A. Bulling, U. Blanke, and B. Schiele, "A tutorial on human activity recognition using body-worn inertial sensors", ACM Computing Surveys, vol. 46, no. 3, pp. 1-33, 2014. doi: 10.1145/2499621.
[3] T. Ngo, Y. Makihara, H. Nagahara, Y. Mukaigawa, and Y. Yagi, "Similar gait action recognition using an inertial sensor", Pattern Recognition, vol. 48, no. 4, pp. 1289-1301, 2015. doi: 10.1016/j.patcog.2014.10.012.
[4] C. Chen, R. Jafari and N. Kehtarnavaz, "A survey of depth and inertial sensor fusion for human action recognition", Multimedia Tools and Applications, vol. 76, no. 3, pp. 4405-4425, 2015. doi: 10.1007/s11042-015-3177-1.
[5] S. Daroogheha, T. Lasky, and B. Ravani, "Position Measurement Under Uncertainty Using Magnetic Field Sensing", IEEE Transactions on Magnetics, vol. 54, no. 12, pp. 1-8, 2018. doi: 10.1109/tmag.2018.2873158.
[6] Y. Shmaliy, S. Zhao, and C. Ahn, "Optimal and Unbiased Filtering with Colored Process Noise Using State Differencing", IEEE Signal Processing Letters, vol. 26, no. 4, pp. 548–551, 2019. doi: 10.1109/LSP.2019.2898770.
[7] X. Lin, Y. Jiao, and D. Zhao, "An improved Gaussian filter for dynamic positioning ships with colored noises and random measurements loss", IEEE Access, vol. 6, pp. 6620–6629, 2018. doi: 10.1109/ACCESS.2018.2789336
[8] Dmytro V. Fedasyuk, Tetyana A. Marusenkova, "An Algorithm for Detecting the Minimal Sample Frequency for Tracking a Preset Motion Scenario", International Journal of Intelligent Systems and Applications, Vol.12, No.4, pp.1-12, 2020.
[9] A. Ravankar, A. Ravankar, Y. Kobayashi, Y. Hoshino, and C. Peng "Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges", Sensors, Vol. 18, 3170, 2018. DOI: doi:10.3390/s18093170.
[10] S. Gim. Flexible and Smooth Trajectory Generation based on Parametric Clothoids for Nonholonomic Car-like Vehicles. Automatic. Université Clermont Auvergne; Sung Kyun Kwan university (Séoul), 2017.
[11] M. Brezak, and I. Petrović, "Path Smoothing Using Clothoids for Differential Drive Mobile Robots", in Proceedings of the 18th World Congress The International Federation of Automatic Control, Milano, Italy, 2011, pp. 1133–1138. DOI: 10.3182/20110828-6-IT-1002.02944.
[12] C. Xiong, D. Chen, D. Lu, Z. Zeng, and L. Lian, "Path planning of multiple autonomous marine vehicles for adaptive sampling using Voronoi-based ant colony optimization", Robotics and Autonomous Systems, Vol. 115, 2019, pp. 90–103. DOI: 10.1016/j.robot.2019.02.002.
[13] X. Chen, J. Zhang, M. Yang, L. Zhong, J. Dong, "Continuous-Curvature Path Generation Using Fermat’s Spiral for Unmanned Marine and Aerial Vehicles", in Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 2018; pp. 4911–4916.
[14] K. Kawabata, L. Ma, J. Xue, C. Zhu, N. Zheng, "A Path Generation for Automated Vehicle Based on Bezier Curve and Via-points", Robotics and Autonomous Systems, No. 74. pp. 243–252, 2015. DOI: 10.1016/j.robot.2015.08.001.
[15] Z. Liang, G. Zheng, J. Li, "Automatic Parking Path Optimization based on Bezier Curve Fitting", in Proceedings of the 2012 IEEE International Conference on Automation and Logistics, Zhengzhou, China, 2012, pp. 583–587.
[16] Ashutosh Kumar Tiwari, Sandeep Varma Nadimpalli, " New Fusion Algorithm Provides an Alternative Approach to Robotic Path Planning", International Journal of Information Engineering and Electronic Business, Vol.12, No.3, pp. 1-7, 2020.
[17] Vanitha Aenugu,Peng-Yung Woo,"Mobile Robot Path Planning with Randomly Moving Obstacles and Goal", International Journal of Intelligent Systems and Applications, vol.4, no.2, pp.1-15, 2012.
[18] Roudabe Seif, Mohammadreza Asghari Oskoei,"Mobile Robot Path Planning by RRT* in Dynamic Environments", International Journal of Intelligent Systems and Applications, vol.7, no.5, pp.24-30, 2015.
[19] K. Liu, W. Wu, K. Tang, and L. He, "IMU Signal Generator Based on Dual Quaternion Interpolation for Integration Simulation", Sensors, Vol. 18, 2721, 2018. DOI: doi:10.3390/s18082721.
[20] T. Brunner, J. Lauffenburger, S. Changey, and M. Basset, "Magnetometer-Augmented IMU Simulator: In-Depth Elaboration", Sensors, Vol. 15, pp. 5293–5310, 2015. DOI: doi:10.3390/s150305293.
[21] M. Parés, J. Rosales, I. Colomina, “Yet Another IMU Simulator: Validation and Applications” [online]: http://www.isprs.org/proceedings/2008/euroCOW08/euroCOW08_files/papers/20.pdf, last accessed January 2021.
[22] T. Kröger, J. Padial, "Simple and robust visual servo control of robot arms using an on-line trajectory generator", in Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, 2012, pp. 4862–4869. DOI: 10.1109/ICRA.2012.6225175.
[23] R. Gonzalez, J. Giribet, H. Patiño, "NaveGo: A simulation framework for low-cost integrated navigation systems", Control Engineering and Applied Informatics, Vol. 17(2), 2015, pp. 110-120.
[24] R. Gonzalez, C. Catania, P. Dabove, J. Taffernaberry, M. Piras, "Model Validation of an Open-source Framework for Post-processing INS/GNSS Systems", in Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), Porto, Portugal, 2017, pp. 201-208. DOI: 10.5220/0006313902010208.
[25] F. Poiesi, and A. Cavallaro, "MTTV - An Interactive Trajectory Visualization and Analysis Tool" in Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pp. 157–162. DOI: 10.5220/0005311001570162.
[26] Y. Li, K. Yao and G. Zweig, "Feedback-based handwriting recognition from inertial sensor data for wearable devices", in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, QLD, Australia, 2015. doi: 10.1109/icassp.2015.7178375.
[27] P. Selinger, "Potrace: a polygon-based tracing algorithm" [online]: http://potrace.sourceforge.net/potrace.pdf, last accessed November 2020.