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International Automatic Control Conference

作者： Sheng-Wen Cheng Hsin-Ai Hung Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Mechanical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9781665496469

ISBN: (纸本)9781665496476

This paper presents an $\mathbf{H}_{\infty}$ control design for quadrotors to enhance robustness against unmodeled disturbances. In optimal control, the algebraic Riccati equation (ARE) frequently arises in topics like Linear Quadratic Regulator (LQR) or $\mathbf{H}_{\infty}$ control. The $\mathbf{H}_{\infty}$ control minimizes the maximal gain between the disturbance input and the control output, which can be solved by acquiring the solution of ARE while finding the minimal $\mathbf{H}_{\infty}$ norm of the corresponding transfer function. Thus, a simple bisection algorithm is also proposed for computing the stabilizing ARE solution of the $\mathbf{H}_{\infty}$ control, which combines a state-of-the-art algorithm for the ARE called the structure-preserving doubling algorithm (SDA).

关键词： Regulators Heuristic algorithms Simulation Quaternions Riccati equations Optimal control Transfer functions

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Using range of motion to examine the effects of deep brain stimulation on gait function of Parkinson’s disease patients with freezing of gait: a proof-of-concept study

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Journal of Biomechanical Science and Engineering 2021年第2期16卷 1-11页

作者： Nien, Ching Chen, Kai-Hsiang Wu, Yi-Ning Chuang, Shueh-Lin Yang, Bing-Shiang Department of Mechanical Engineering National Chiao Tung University Hsinchu30010 Taiwan Department of Neurology National Taiwan University Hospital Hsinchu Branch Hsinchu30059 Taiwan Department of Physical Therapy and Kinesiology University of Massachusetts Lowell LowellMA018514 United States Institute of Biomedical Engineering National Chiao Tung University Hsinchu30010 Taiwan Graduate Degree Program of Robotics National Chiao Tung University Hsinchu30010 Taiwan Department of Mechanical Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

The purpose of this study was to develop quantitative parameters using range of motion (ROM) of shanks, thighs and knees to evaluate the effect of deep brain stimulation (DBS) on gait performance and freezing of gait (FOG) of patients with advanced Parkinson’s disease (PD). Three patients with FOG due to advanced Parkinson’s disease who has received DBS were recruited. The recruited subjects were instructed to walk on a 100-meter path in three conditions: 60 Hz DBS (60 Hz), 130 Hz DBS (130 Hz) and no DBS (Off). Five inertial measurement unit sensors were attached to subjects’ sacrum, bilateral shanks and thighs respectively. Quantified parameters included (1) spatial parameters: shanks, thighs and knees ROM;(2) temporal parameters: stride time, stance time duration and double support time percentage;(3) FOG severity: the percentage of FOG duration during path walking. Three subjects’ ROM of right shank significantly increased in 60 Hz,130 Hz and Off order. Compared to 130 Hz, right shank ROM of subjects S1, S2 and S3 significantly increased 13.2%, 99.6% and 6.1% in 60 Hz, respectively. In temporal parameters results, only double support time percentage was significantly different between 60 Hz and 130 Hz in all three subjects. When compared to 130 Hz, the double support time percentage of S1 and S2 significantly decreased 6.3% and 18.4% in 60 Hz, and that of S3 significantly increased 4.4%. ROM of right shank and FOG severity were highly correlated (R2 = 0.71). Shank ROM could represent subjects’ gait performance under different stimulation conditions. Shank ROM could be treated as a reference for clinicians to evaluate gait performance and severity of FOG immediately when DBS frequency is adjusted. This study demonstrated the potential of using objective parameters to optimize the DBS through assessing the gait performance changes in the clinic. © 2021 The Japan Society of Mechanical Engineers

关键词： Freezing

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An Effective Seat Belt Detection System on the Bus

An Effective Seat Belt Detection System on the Bus

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hsiao-Chu Cheng Chin-Chuan Chang Wen-June Wang National Central University Taiwan Graduate Degree Program of Robotics National Chiao Tung University Taiwan

ISBN: (数字)9781728173993

ISBN: (纸本)9781728174006

According to the “Regulations of the Automobile Transportation” of Taiwan, any passenger on the bus who is over 4 years old must fasten seat belts when the bus is driving on the free high way. To enforce passengers to follow the regulation, this study designs and implements a smart seat belt warning system by which the driver of the bus can detect whether each passage fastens the seat belt and the detection result will be displayed on the smart cell phone. The implemented achievement includes a new seat belt buckle which includes a WIFI module and a designed APP. It only uses one WIFI module, and rewrite the module into STA mode. As long as the seat belt buckle is locked in, the WIFI modules on all seats can be connected such that the bus driver can know the belt of the seat is fastened from the APP of the cell phone. Otherwise, if any seat belt is not fastened, the driver also can find it. The advantages of the product are that it is very cheap and the original seat structure does not need to be changed.

关键词： Belts Wireless fidelity Vehicles Joining processes Sensors Seals Regulation

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A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

arXiv

引用

arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

引用

arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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