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Abstract

Rat robots have great potential in rescue and search tasks because of their excellent motion ability. However, most of the current rat-robot systems relay on human guidance due to variable voluntary motor behaviour of rats, which limits their application. In this study, we developed a real-time system to detect a rat robot’s transient motion states, as the prerequisite for further study of automatic navigation. We built the detection model by using a wearable inertial sensor to capture acceleration and angular velocity data during the control of a rat robot. Various machine learning algorithms, including Decision Trees, Random Forests, Logistic Regression, and SupportVector Machines,were employed to performthe classification of motion states. This detection system was tested in manual navigation experiments, with detection accuracy achieving 96.70%. The sequence of transient motion states could be further used as a promising reference for offline behaviour analysis.
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Authors and Affiliations

Yuxin Chen
1
Haoze Xu
2 3
Wei Yang
1 4
Canjun Yang
1 4
Kedi Xu
2 5

  1. Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China
  2. Zhejiang University, Qiushi Academy for Advanced Studies (QAAS), Hangzhou, China
  3. Zhejiang University, Key Laboratory of Biomedical Engineering of Education Ministry, Hangzhou, China
  4. Zhejiang University, Ningbo Research Institute, Ningbo, China
  5. Zhejiang Lab, Hangzhou, China
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Abstract

This paper proposes a new approach called the Predictive Kalman Filter (PKF) which predicts and compensates model errors of inertial sensors to improve the accuracy of static alignment without the use of external assistance. The uncertain model error is the main problem in the field as the Micro Electro Mechanical System (MEMS) inertial sensors have bias which change over time, and these errors are not all observable. The proposed filter determines an optimal equivalent model error by minimizing a quadratic penalty function without augmenting the system state space. The optimization procedure enables the filter to decrease both model uncertainty and external disturbances. The paper first presents the complete formulation of the proposed filter. Then, a nonlinear alignment model with a large misalignment angle is considered. Experimental results demonstrate that the new method improves the accuracy and rapidness of the alignment process as the convergence time is reduced from 550 s to 50 s, and the azimuth misalignment angle correctness is decreased from 52" 47" to 4" 0:02".
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Bibliography

[1] Britting, K. R. (1971). Inertial navigation systems analysis. Wiley Interscience.
[2] Chang, L., Li, J., & Li, K. (2016). Optimization-based alignment for strapdown inertial navigation system: Comparison and extension. IEEE Transactions on Aerospace and Electronic Systems, 52(4), 1697–1713. https://doi.org/10.1109/TAES.2016.130824
[3] Xue, H., Guo, X., & Zhou, Z. (2016). Parameter identification method for SINS initial alignment under inertial frame. Mathematical Problems in Engineering, 2016, 5301242. https://doi.org/10.1155/2016/5301242
[4] Wang, D., Dong, Y., Li, Q., Wu, J., & Wen, Y. (2018). Estimation of small UAV position and attitude with reliable in-flight initial alignment for MEMSinertial sensors. Metrology and Measurement Systems, 25(3), 603–616. https://doi.org/10.24425/123904
[5] Ghanbarpourasl, H. (2020). A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 234(12), 1913–1925. https://doi.org/10.1177/0954410020920473
[6] Guo, S., Chang, L., Li, Y., & Sun, Y. (2020). Robust fading cubature Kalman filter and its application in initial alignment of SINS. Optik, 202, 163593. https://doi.org/10.1016/j.ijleo.2019.163593
[7] Zhang, T., Wang, J., Jin, B., & Li, Y. (2019). Application of improved fifth-degree cubature Kalman filter in the nonlinear initial alignment of strapdown inertial navigation system. Review of Scientific Instruments, 90(1), 015111. https://doi.org/10.1063/1.5061790
[8] Xing, H., Chen, Z.,Wang, C., Guo, M., & Zhang, R. (2019). Quaternion-based Complementary Filter for Aiding in the Self-Alignment of the MEMS IMU. 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), USA, 1–4. https://doi.org/10.1109/ISISS.2019.8739728
[9] Yang, B., Xu, X., Zhang, T., Sun, J., & Liu, X. (2017). Novel SINS initial alignment method under large misalignment angles and uncertain noise based on nonlinear filter. Mathematical Problems in Engineering, 2017, 5917917. https://doi.org/10.1155/2017/5917917
[10] Sun, J., Xu, X., Liu, Y., Zhang, T., & Li, Y. (2015). Initial alignment of large azimuth misalignment angles in SINS based on adaptive UPF. Sensors, 15(9), 21807–21823. https://doi.org/10.3390/s150921807
[11] Han, H., Wang, J., & Du, M. (2017). A fast SINS initial alignment method based on RTS forward and backward resolution. Journal of Sensors, 2017, 7161858. https://doi.org/10.1155/2017/7161858
[12] Kaygısız, B. H., & Sen, B. (2015). In-motion alignment of a low-cost GPS/INS under large heading error. The Journal of Navigation, 68(2), 355–366. https://doi.org/10.1017/S0373463314000629
[13] Xia, X.,&Sun, Q. (2018). Initial alignment algorithm based on theDMCSmethod in single-axis RSINS with large azimuth misalignment angles for submarines. Sensors, 18(7), 1807–2123. https://doi.org/10.3390/s18072123
[14] Li, J., Gao, W., Zhang, Y., & Wang, Z. (2018). Gradient Descent Optimization-Based Self-Alignment Method for Stationary SINS. IEEE Transactions on Instrumentation and Measurement, 68(9), 3278– 3286. https://doi.org/10.1109/TIM.2018.2878071
[15] Camacho, E. F., Ramírez, D. R., Limón, D., De La Peña, D. M., & Alamo, T. (2010). Model predictive control techniques for hybrid systems. Annual Reviews in Control, 34(1), 21–31. https://doi.org/10.1016/j.arcontrol.2010.02.002
[16] Titterton, D., Weston, J. L., & Weston, J. (2004). Strapdown inertial navigation technology. IET. https://doi.org/10.1049/PBRA017E
[17] Analog Devices. (2018). Tactical Grade Ten Degrees of Freedom Inertial Sensor – ADIS16488A. [Datasheet, Rev. F]. https://www.analog.com/media/en/technical-documentation/data-sheets/ADIS16488A.pdf
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Authors and Affiliations

Hassan Majed Alhassan
1
Nemat Allah Ghahremani
1

  1. Malek Ashtar University of Technology, Faculty of Electrical & Computer Engineering, Tehran 15875-1774, Iran
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Abstract

Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight, and less power consumption than their more technologically advanced counterparts. However, current low-grade MEMS gyroscopes have poor performance and cannot compete with quality sensors in high accuracy navigational and guidance applications. The main focus of this paper is to investigate performance improvements by fusing multiple homogeneous MEMS gyroscopes. These gyros are transformed into a virtual gyro using a feedback weighted fusion algorithm with dynamic sensor bias correction. The gyroscope array combines eight homogeneous gyroscope units on each axis and divides them into two layers of differential configuration. The algorithm uses the gyroscope array estimation value to remove the gyroscope bias and then correct the gyroscope array measurement value. Then the gyroscope variance is recalculated in real time according to the revised measurement value and the weighted coefficients and state estimation of each gyroscope are deduced according to the least square principle. The simulations and experiments showed that the proposed algorithm could further reduce the drift and improve the overall accuracy beyond the performance limitations of individual gyroscopes. The maximum cumulative angle error was - 2:09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.
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Authors and Affiliations

Ding Yuan
1
Yongyuan Qin
1
Xiaowei Shen
2
Zongwei Wu
2

  1. School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
  2. Xi’an Research Institute of High Technology, Hongqing Town, Xi’an 710025, China
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Abstract

This article presents a wearable system that localizes people in the indoor environment, using data from inertial sensors. The sensors measure the parameters of human motion, tracking the movements of the torso and foot. For this purpose, they were integrated with shirt and the shoe insole. The values of acceleration measured by the sensors are sent via Bluetooth to a smartphone. The localization algorithm implemented on the smartphone, presented here, merges data from the shirt and the shoe to track the steps made by the user and filter out the localization errors caused by movements the shirt and torso. The experimental verification of the algorithm is also presented.

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Authors and Affiliations

Jaroslaw Kawecki
Pawel Oleksy
Lukasz Januszkiewicz
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Abstract

Monitoring head movements is important in many aspects of life from medicine and rehabilitation to sports, and VR entertainment. In this study, we used recordings from two sensors, i.e. an accelerometer and a gyroscope, to calculate the angles of movement of the gesturing person’s head. For the yaw motion, we proposed an original algorithm using only these two inertial sensors and the detected motion type obtained from a pre-trained SVM classifier. The combination of the gyroscope data and the detected motion type allowed us to calculate the yaw angle without the need for other sensors, such as a magnetometer or a video camera. To verify the accuracy of our algorithm, we used a robotic arm that simulated head gestures where the angle values were read out from the robot kinematics. The calculated yaw angles differed from the robot’s readings with a mean absolute error of approx. 1 degree and the rate of differences between these values exceeding 5 degrees was significantly below 1 percent except for one outlier at 1.12%. This level of accuracy is sufficient for many applications, such as VR systems, human-system interfaces, or rehabilitation.
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Authors and Affiliations

Anna Borowska-Terka
1
Paweł Strumiłło
1

  1. Łódz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering, Institute of Electronics, Al. Politechniki 10, 93-590 Łódz, Poland

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