Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385598
K. Ohno, H. Fujimoto, Yoshihiro Isaoka, Yuki Terada
Monitoring cutting force generated during the machining process is crucial to prevent tool breakage and chattering. The cutting force observer, which considers the machine tool as the two-inertia system, has been proposed to estimate cutting forces in wide bandwidth using multiple encoders. However, modeling errors and the parameter variation during machining can deteriorate estimation accuracy in such a model-based observer. Previous studies solved some modeling error issues, but inertia, friction, and other parameters that belong to the moving stage had rarely considered. Therefore, the adaptive cutting force observer is proposed in this paper. The proposal consists of online stage parameter identification and updating algorithm. The effectiveness of the proposed adaptive observer is demonstrated through the experiments using the simplified experimental setup.
{"title":"Adaptive Cutting Force Observer for Machine Tool Considering Stage Parameter Variation","authors":"K. Ohno, H. Fujimoto, Yoshihiro Isaoka, Yuki Terada","doi":"10.1109/ICM46511.2021.9385598","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385598","url":null,"abstract":"Monitoring cutting force generated during the machining process is crucial to prevent tool breakage and chattering. The cutting force observer, which considers the machine tool as the two-inertia system, has been proposed to estimate cutting forces in wide bandwidth using multiple encoders. However, modeling errors and the parameter variation during machining can deteriorate estimation accuracy in such a model-based observer. Previous studies solved some modeling error issues, but inertia, friction, and other parameters that belong to the moving stage had rarely considered. Therefore, the adaptive cutting force observer is proposed in this paper. The proposal consists of online stage parameter identification and updating algorithm. The effectiveness of the proposed adaptive observer is demonstrated through the experiments using the simplified experimental setup.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129652746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385648
N. Dirkx, T. Oomen
Structural deformations resulting from exogenous disturbances limit the control performance in high-precision positioning systems. The aim of this paper is to identify these limitations and mitigate these through multivariable inferential control. A systematic analysis and control design framework is established. Herein, additional sensors and actuators are exploited to achieve control performance beyond conventional limits. Successful performance enhancement using the presented methods is shown on an identified wafer stage model.
{"title":"Suppressing spatially distributed disturbances by exploiting additional sensors and actuators in inferential motion control","authors":"N. Dirkx, T. Oomen","doi":"10.1109/ICM46511.2021.9385648","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385648","url":null,"abstract":"Structural deformations resulting from exogenous disturbances limit the control performance in high-precision positioning systems. The aim of this paper is to identify these limitations and mitigate these through multivariable inferential control. A systematic analysis and control design framework is established. Herein, additional sensors and actuators are exploited to achieve control performance beyond conventional limits. Successful performance enhancement using the presented methods is shown on an identified wafer stage model.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128118735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385665
Xulei Liu, Ge Jin, Yafei Wang, Chengliang Yin
Forecasting the motion of surrounding vehicles is a key issue for autonomous vehicles to assess potential risks and avoid collisions. Among them, the sharp lane change of vehicle in adjacent lane has a greater impact on the ego vehicle. In this paper, we propose a deep learning-based approach to predict the lane change maneuver of adjacent vehicles and quantitatively estimate the position and time to line crossing point (PTLC). In order to distinguish the real lane change from an unintentional drifting between lane boundaries and make accurate prediction of the line crossing point, the features of vehicle kinematics and the driver's driving style as well as the interaction with surrounding vehicle are extracted. Furthermore, a deep neural network is used to process and fuse these features to obtain the probability distribution of PTLC, in which a gated recurrent units (GRU) is adopted as an improvement to robustly learn the historical trajectory of the adjacent target vehicle. Experiments using the data collected from highways show that the proposed method can achieve a reliable prediction of the driver's intention and line crossing point.
{"title":"A Deep Learning-based Approach to Line Crossing Prediction for Lane Change Maneuver of Adjacent Target Vehicles","authors":"Xulei Liu, Ge Jin, Yafei Wang, Chengliang Yin","doi":"10.1109/ICM46511.2021.9385665","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385665","url":null,"abstract":"Forecasting the motion of surrounding vehicles is a key issue for autonomous vehicles to assess potential risks and avoid collisions. Among them, the sharp lane change of vehicle in adjacent lane has a greater impact on the ego vehicle. In this paper, we propose a deep learning-based approach to predict the lane change maneuver of adjacent vehicles and quantitatively estimate the position and time to line crossing point (PTLC). In order to distinguish the real lane change from an unintentional drifting between lane boundaries and make accurate prediction of the line crossing point, the features of vehicle kinematics and the driver's driving style as well as the interaction with surrounding vehicle are extracted. Furthermore, a deep neural network is used to process and fuse these features to obtain the probability distribution of PTLC, in which a gated recurrent units (GRU) is adopted as an improvement to robustly learn the historical trajectory of the adjacent target vehicle. Experiments using the data collected from highways show that the proposed method can achieve a reliable prediction of the driver's intention and line crossing point.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128084900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385670
Kentaro Yokota, H. Fujimoto, Hiroshi Kobayashi
Research and development have been very active in electric vertical takeoff and landing (eVTOL) aircraft. Tilt-Wing aircraft especially receive significant attention as one of the most efficient configurations; however, they are apt to be unstable during the transition from hover to cruise. The angle of attack (AoA) is a critical parameter for aircraft motion, and with its real-time data, Tilt-Wing aircraft would achieve a more robust transition. Conventional methods of obtaining AoA require either additional sensors or an aircraft model, which is not robust to propeller slipstreams and unsuitable for Tilt-Wing aircraft. In this paper, a new AoA estimation method for Tilt-Wing aircraft is proposed. The proposed method is based on the propeller dynamics model and requires only an existing pitot tube. Wind tunnel tests verify its effectiveness.
{"title":"Observer-based Angle of Attack Estimation for Tilt-Wing eVTOL Aircraft","authors":"Kentaro Yokota, H. Fujimoto, Hiroshi Kobayashi","doi":"10.1109/ICM46511.2021.9385670","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385670","url":null,"abstract":"Research and development have been very active in electric vertical takeoff and landing (eVTOL) aircraft. Tilt-Wing aircraft especially receive significant attention as one of the most efficient configurations; however, they are apt to be unstable during the transition from hover to cruise. The angle of attack (AoA) is a critical parameter for aircraft motion, and with its real-time data, Tilt-Wing aircraft would achieve a more robust transition. Conventional methods of obtaining AoA require either additional sensors or an aircraft model, which is not robust to propeller slipstreams and unsuitable for Tilt-Wing aircraft. In this paper, a new AoA estimation method for Tilt-Wing aircraft is proposed. The proposed method is based on the propeller dynamics model and requires only an existing pitot tube. Wind tunnel tests verify its effectiveness.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121003052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385682
Michael Schneider, R. Amann, C. Mitsantisuk
The classification of waste with neural networks is already a topic in some scientific papers. An application in the embedded systems area with current AI processors to accelerate the inference has not yet been discussed. Therefore a prototype is created which classifies waste objects and automatically opens the appropriate container for the object. The area of application is in the public space. For the classification a dataset with 25.681 images and 11 classes was created to retrain the CNNs EfficentNet-B0, MobileNet-v2 and NASNet-Mobile. These CNNs run on the current Edge AI -accelerator processors from Google, Intel and Nvidia and are compared for performance, consumption and accuracy. The result of these comparisons and shows the advantages and disadvantages of the respective processors and the CNNs. For the prototype, the most suitable combination of hardware and AI architecture is used and exhibited at the university fair KasetFair2020.
{"title":"Waste object classification with AI on the edge accelerators","authors":"Michael Schneider, R. Amann, C. Mitsantisuk","doi":"10.1109/ICM46511.2021.9385682","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385682","url":null,"abstract":"The classification of waste with neural networks is already a topic in some scientific papers. An application in the embedded systems area with current AI processors to accelerate the inference has not yet been discussed. Therefore a prototype is created which classifies waste objects and automatically opens the appropriate container for the object. The area of application is in the public space. For the classification a dataset with 25.681 images and 11 classes was created to retrain the CNNs EfficentNet-B0, MobileNet-v2 and NASNet-Mobile. These CNNs run on the current Edge AI -accelerator processors from Google, Intel and Nvidia and are compared for performance, consumption and accuracy. The result of these comparisons and shows the advantages and disadvantages of the respective processors and the CNNs. For the prototype, the most suitable combination of hardware and AI architecture is used and exhibited at the university fair KasetFair2020.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385681
Masatsugu Nishihara, F. Asano
To achieve steady locomotion with simple control for a locomotion robot on a slippery surface, the authors have been developing a crawling-like locomotion robot positively utilizing sliding. The previous researches were clarified that motion of the center of mass mightily induces sliding motion; whereas, they does not elucidate a principle of sliding motion generation on slippery ground. Aiming at designing effective acceleration control to efficiently slide on a slippery level ground based on a locomotion principle, we investigate relation between acceleration of the center of mass and friction in this paper. First, we introduce a simple robot model with two orthogonal telescopic joints. Second, we derive the equation of motion. Third, we design the acceleration control for the center of mass. Fourth, we show numerical simulation. The robot steadily locomotes on the slippery ground with simple control. In addition, our model allowed us to choose appropriate spring parameters which improve the specific resistance of the robot to 0.2039 [-].
{"title":"Design of Acceleration Control for Center of Mass on Sliding Robot","authors":"Masatsugu Nishihara, F. Asano","doi":"10.1109/ICM46511.2021.9385681","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385681","url":null,"abstract":"To achieve steady locomotion with simple control for a locomotion robot on a slippery surface, the authors have been developing a crawling-like locomotion robot positively utilizing sliding. The previous researches were clarified that motion of the center of mass mightily induces sliding motion; whereas, they does not elucidate a principle of sliding motion generation on slippery ground. Aiming at designing effective acceleration control to efficiently slide on a slippery level ground based on a locomotion principle, we investigate relation between acceleration of the center of mass and friction in this paper. First, we introduce a simple robot model with two orthogonal telescopic joints. Second, we derive the equation of motion. Third, we design the acceleration control for the center of mass. Fourth, we show numerical simulation. The robot steadily locomotes on the slippery ground with simple control. In addition, our model allowed us to choose appropriate spring parameters which improve the specific resistance of the robot to 0.2039 [-].","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385645
Xingyi Liu, Yingqiang Liu, Fuxin Duan, Zheng Chen, B. Yao
Maintaining high precision tracking while considering both state and input constraints has always been a challenging issue, where most existing studies merely solve constrained issues and few take integrated performance into account. Thus, we put forwarded a direct optimization-based compensation adaptive robust control (DOCARC) approach for single input single output(SISO) nonlinear system, where the model compensation term of the control input is directly optimized and the reference is simultaneously replanned so as to conform to the constraints. Simulations are conducted in the single-axis linear motor system, and comparative results further verify the superiority and effectiveness of the proposed scheme.
{"title":"Precision Motion Control of Constrained SISO Nonlinear System via Direct Optimized Compensation","authors":"Xingyi Liu, Yingqiang Liu, Fuxin Duan, Zheng Chen, B. Yao","doi":"10.1109/ICM46511.2021.9385645","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385645","url":null,"abstract":"Maintaining high precision tracking while considering both state and input constraints has always been a challenging issue, where most existing studies merely solve constrained issues and few take integrated performance into account. Thus, we put forwarded a direct optimization-based compensation adaptive robust control (DOCARC) approach for single input single output(SISO) nonlinear system, where the model compensation term of the control input is directly optimized and the reference is simultaneously replanned so as to conform to the constraints. Simulations are conducted in the single-axis linear motor system, and comparative results further verify the superiority and effectiveness of the proposed scheme.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121952240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385637
Kazuya Ito, Ryosuke Suzuki, K. Yoshimoto, T. Yokoyama
This paper proposes MSDB (Multi Sampling Deadbeat control) of PMSM (Permanent Magnet Synchronous Motor) drive system for EVs (Electric Vehicles) and HEVs (Hybrid Electric Vehicles) using a FPGA (Field Programmable Gate Array) to use potential performance of motor control response. The electric motor drive system used in EVs and HEVs gives not only efficient powertrain, but also quick and smooth response as an advantage compared with ICE (Internal Combustion Engine). The proposed deadbeat control using a FPGA could show good response and robustness, especially for electric motor drive system using low carrier frequency of PWM (Pulse Width Modulation) inverter like as EVs and HEVs.
{"title":"A Study of Multisampling Deadbeat Control for Low Carrier Frequency PMSM Drive System Used in EVs and HEVs","authors":"Kazuya Ito, Ryosuke Suzuki, K. Yoshimoto, T. Yokoyama","doi":"10.1109/ICM46511.2021.9385637","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385637","url":null,"abstract":"This paper proposes MSDB (Multi Sampling Deadbeat control) of PMSM (Permanent Magnet Synchronous Motor) drive system for EVs (Electric Vehicles) and HEVs (Hybrid Electric Vehicles) using a FPGA (Field Programmable Gate Array) to use potential performance of motor control response. The electric motor drive system used in EVs and HEVs gives not only efficient powertrain, but also quick and smooth response as an advantage compared with ICE (Internal Combustion Engine). The proposed deadbeat control using a FPGA could show good response and robustness, especially for electric motor drive system using low carrier frequency of PWM (Pulse Width Modulation) inverter like as EVs and HEVs.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124954553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385695
Jing Li, Yonghua Xiong, Jinhua She
As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.
{"title":"An improved ant colony optimization for path planning with multiple UAVs","authors":"Jing Li, Yonghua Xiong, Jinhua She","doi":"10.1109/ICM46511.2021.9385695","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385695","url":null,"abstract":"As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133001053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-07DOI: 10.1109/ICM46511.2021.9385627
Patrick Mesmer, Michael Neubauer, A. Lechler, A. Verl
Most industrial robots are still controlled with motor-side feedback. To increase the accuracy of industrial robots, controllers with joint-side feedback and explicit consideration of the joint elasticity, such as linearization-based controllers, are needed. The key issue for the performance of linearization-based controllers is a high-fidelity model. Today, the drivetrains installed in the joints of industrial robots of the high payload class usually consist of a permanent magnet synchronous machine and a cycloidal drive. Such robot joints are highly nonlinear due to effects like hysteresis, torque ripples and friction. Therefore, the drivetrain dynamics are crucial for the experimental performance of linearization-based controllers for industrial robots. This paper identifies the challenges in linearization-based control of industrial robots with such a drivetrain configuration based on experimental results on a KUKA KR-210-2. Using an exemplary approach, it is shown that a linearization-based controller does not provide the theoretical performance due to needed model simplifications. For this purpose, simulation and experimental results are compared to a linear robot controller with motor-side feedback. These results indicate why such controllers are still a valid alternative for the practical application of similar industrial robots.
{"title":"Challenges of Linearization-based Control of Industrial Robots with Cycloidal Drives","authors":"Patrick Mesmer, Michael Neubauer, A. Lechler, A. Verl","doi":"10.1109/ICM46511.2021.9385627","DOIUrl":"https://doi.org/10.1109/ICM46511.2021.9385627","url":null,"abstract":"Most industrial robots are still controlled with motor-side feedback. To increase the accuracy of industrial robots, controllers with joint-side feedback and explicit consideration of the joint elasticity, such as linearization-based controllers, are needed. The key issue for the performance of linearization-based controllers is a high-fidelity model. Today, the drivetrains installed in the joints of industrial robots of the high payload class usually consist of a permanent magnet synchronous machine and a cycloidal drive. Such robot joints are highly nonlinear due to effects like hysteresis, torque ripples and friction. Therefore, the drivetrain dynamics are crucial for the experimental performance of linearization-based controllers for industrial robots. This paper identifies the challenges in linearization-based control of industrial robots with such a drivetrain configuration based on experimental results on a KUKA KR-210-2. Using an exemplary approach, it is shown that a linearization-based controller does not provide the theoretical performance due to needed model simplifications. For this purpose, simulation and experimental results are compared to a linear robot controller with motor-side feedback. These results indicate why such controllers are still a valid alternative for the practical application of similar industrial robots.","PeriodicalId":373423,"journal":{"name":"2021 IEEE International Conference on Mechatronics (ICM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133380392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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