Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053852
Yajie Tang, Xisheng Li, Jia You, Ran Hong
The performance parameters and long-term operation stability of the photodetector in radiation temperature measurement will change with the change of working temperature. In order to meet the high precision temperature control demand of the photodetector, a temperature control system based on the thermoelectric refrigeration control chip ADN8831 is designed. The control system uses the thermistor as the temperature sensing element, and uses the analog proportional integral differential (PID) circuit to control the temperature closed-loop. The experimental results show that the Thermoelectric cooler (TEC) inside the photodetector can stabilize at the set temperature within 11 s under the drive of the temperature control system, and the temperature control accuracy is as low as ± 0.01 °C.
{"title":"Design of Temperature Control System of Photodetector Based on ADN8831","authors":"Yajie Tang, Xisheng Li, Jia You, Ran Hong","doi":"10.1109/ICCR55715.2022.10053852","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053852","url":null,"abstract":"The performance parameters and long-term operation stability of the photodetector in radiation temperature measurement will change with the change of working temperature. In order to meet the high precision temperature control demand of the photodetector, a temperature control system based on the thermoelectric refrigeration control chip ADN8831 is designed. The control system uses the thermistor as the temperature sensing element, and uses the analog proportional integral differential (PID) circuit to control the temperature closed-loop. The experimental results show that the Thermoelectric cooler (TEC) inside the photodetector can stabilize at the set temperature within 11 s under the drive of the temperature control system, and the temperature control accuracy is as low as ± 0.01 °C.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134006520","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 : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053884
Yuchen Ma, Yancai Xu
Path planning is one of the most essential for unmanned aerial vehicle (UAV) autonomous navigation. The deep Q-network (DQN) method is widely used for solving the path planning problem, but most researchers simplify the scene into the 2D environment with a single UAV and ignore the fact that there are always multi-UAVs working in 3D environments. Therefore, a double deep Q-network (DDQN) based global path planning algorithm for multi-UAVs in a 3D indoor environment is proposed in this paper. Firstly, the double deep Q-network was designed to approximate the action of multi-UAVs. The 3D space is discretized into grids while each gird is a basic unit of path planning and the whole grid map is the input for the neural network. Then, a continual reward function generated by building an artificial potential field was determined to replace the traditional sparse reward function. Moreover, the action selection strategy is used to determine the current optimal action so that multi-UAVs are able to find the path to reach target points in a simulated indoor environment and avoid crashing into each other and obstacles at the same time. Finally, the experiment verifies the effectiveness of the proposed method. The simulation result demonstrates that the agents can effectively avoid local optimal solution and correctly predict the global optimal action.
{"title":"A DDQN-Based Path Planning Method for Multi-UAVs in a 3D Indoor Environment","authors":"Yuchen Ma, Yancai Xu","doi":"10.1109/ICCR55715.2022.10053884","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053884","url":null,"abstract":"Path planning is one of the most essential for unmanned aerial vehicle (UAV) autonomous navigation. The deep Q-network (DQN) method is widely used for solving the path planning problem, but most researchers simplify the scene into the 2D environment with a single UAV and ignore the fact that there are always multi-UAVs working in 3D environments. Therefore, a double deep Q-network (DDQN) based global path planning algorithm for multi-UAVs in a 3D indoor environment is proposed in this paper. Firstly, the double deep Q-network was designed to approximate the action of multi-UAVs. The 3D space is discretized into grids while each gird is a basic unit of path planning and the whole grid map is the input for the neural network. Then, a continual reward function generated by building an artificial potential field was determined to replace the traditional sparse reward function. Moreover, the action selection strategy is used to determine the current optimal action so that multi-UAVs are able to find the path to reach target points in a simulated indoor environment and avoid crashing into each other and obstacles at the same time. Finally, the experiment verifies the effectiveness of the proposed method. The simulation result demonstrates that the agents can effectively avoid local optimal solution and correctly predict the global optimal action.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116815557","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 : 2022-12-02DOI: 10.1109/iccr55715.2022.10053866
{"title":"Table of Contentes","authors":"","doi":"10.1109/iccr55715.2022.10053866","DOIUrl":"https://doi.org/10.1109/iccr55715.2022.10053866","url":null,"abstract":"","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132316987","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}
Autonomous underwater vehicles (AUVs) with bearing target tracking capability are fundamental and essential. However, they face the problems of high nonlinearity, difficult target trajectory initialization, and poor multi-target tracking (MTT) performance. Consequently, we adopt a novel MTT method for multi-AUV based on the fast Labeled Multi-Bernoulli (LMB) filter. In this method, the LMB filter uses belief propagation (BP) to solve the data association problem quickly and effectively approximate the LMB during the update step. And a Gaussian mixture approximation is used to determine the new potential target trajectory based on individual AUV-bearing measurements. Furthermore, we employ the iterator-corrector strategy to perform the fast LMB filter for multi-AUV. The simulation results show that the method performs well in MTT for multi-AUV using bearing-only measurements.
{"title":"A Multi-AUV Bearings-Only Multi-target Tracking Method Based on the Fast LMB Filter","authors":"Yuexing Zhang, Yiping Li, Shuo Li, J. Zeng, Liang Li, Gaopeng Xu, Peiyan Gao","doi":"10.1109/ICCR55715.2022.10053872","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053872","url":null,"abstract":"Autonomous underwater vehicles (AUVs) with bearing target tracking capability are fundamental and essential. However, they face the problems of high nonlinearity, difficult target trajectory initialization, and poor multi-target tracking (MTT) performance. Consequently, we adopt a novel MTT method for multi-AUV based on the fast Labeled Multi-Bernoulli (LMB) filter. In this method, the LMB filter uses belief propagation (BP) to solve the data association problem quickly and effectively approximate the LMB during the update step. And a Gaussian mixture approximation is used to determine the new potential target trajectory based on individual AUV-bearing measurements. Furthermore, we employ the iterator-corrector strategy to perform the fast LMB filter for multi-AUV. The simulation results show that the method performs well in MTT for multi-AUV using bearing-only measurements.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128838561","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 : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053913
Sheng Yang, Lan Cheng, Jiaqi Yin
As an important research topic in the field of robotics, the optical flow method is widely used for motion estimation in visual simultaneous location and mapping (VSLAM). However, the optical flow method is based on the gray-invariant assumption, which restricts its application in the case of drastic luminosity variation. Moreover, the optical flow method can speed up the processing speed in motion estimation, but it cannot work effectively in scenarios with missing features. As a velocity measurement technology in the field of flow field and fluid, the particle image velocimetry (PIV) can overcome the aforementioned disadvantages of the optical flow method, and achieve motion estimation since it considers points in an image uniformly and can achieve sub-pixel accuracy for positional estimation. To this end, an improved optical flow method based on PIV is proposed by adopting the FFT cross-correlation matching algorithm and the sub-pixel displacement matching algorithm to estimate the image pixel displacement in the field of missing features. Experiments on the EUROC data-set show that the proposed method can not only track the motion of more pixels compared with that the multi-layer optical flow method, but also run in higher accuracy in the areas with missing features.
{"title":"Motion Estimation Based on an Improved Optical Flow Method Using PIV for VSLAM","authors":"Sheng Yang, Lan Cheng, Jiaqi Yin","doi":"10.1109/ICCR55715.2022.10053913","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053913","url":null,"abstract":"As an important research topic in the field of robotics, the optical flow method is widely used for motion estimation in visual simultaneous location and mapping (VSLAM). However, the optical flow method is based on the gray-invariant assumption, which restricts its application in the case of drastic luminosity variation. Moreover, the optical flow method can speed up the processing speed in motion estimation, but it cannot work effectively in scenarios with missing features. As a velocity measurement technology in the field of flow field and fluid, the particle image velocimetry (PIV) can overcome the aforementioned disadvantages of the optical flow method, and achieve motion estimation since it considers points in an image uniformly and can achieve sub-pixel accuracy for positional estimation. To this end, an improved optical flow method based on PIV is proposed by adopting the FFT cross-correlation matching algorithm and the sub-pixel displacement matching algorithm to estimate the image pixel displacement in the field of missing features. Experiments on the EUROC data-set show that the proposed method can not only track the motion of more pixels compared with that the multi-layer optical flow method, but also run in higher accuracy in the areas with missing features.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115857378","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 : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053874
Jing Chen, Linkai Wang, Wen Wang, Ruizhuo Song
Smart transportation is an important part of building a smart city, and accurate traffic forecasting is crucial for citizen travel and urban construction. Aiming at the temporal and spatial dimensions in traffic forecasting, we focus on the extraction methods of the correlation between the two dimensions, and propose a new prediction model of the spatio-temporal graph attention network from the temporal correlation and the spatial correlation. The structure of the model is studied and analyzed. Finally, experiments are carried out on the mainstream traffic data sets, and by comparing with other prediction models, it is concluded that the evaluation indicators of the prediction model are better than other models.
{"title":"Traffic Prediction Model Based on Spatio-temporal Graph Attention Network","authors":"Jing Chen, Linkai Wang, Wen Wang, Ruizhuo Song","doi":"10.1109/ICCR55715.2022.10053874","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053874","url":null,"abstract":"Smart transportation is an important part of building a smart city, and accurate traffic forecasting is crucial for citizen travel and urban construction. Aiming at the temporal and spatial dimensions in traffic forecasting, we focus on the extraction methods of the correlation between the two dimensions, and propose a new prediction model of the spatio-temporal graph attention network from the temporal correlation and the spatial correlation. The structure of the model is studied and analyzed. Finally, experiments are carried out on the mainstream traffic data sets, and by comparing with other prediction models, it is concluded that the evaluation indicators of the prediction model are better than other models.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124887738","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 : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053898
Xingfang Fu, Hongjun Jiang
With the rapid development of China's economy and the wide application of Internet technology, the trend of intelligent development of logistics warehouses is becoming increasingly obvious, and it is particularly prominent to solve the problem of the shortest path of intelligent logistics warehouse robots. This paper proposes Ant colony algorithm, Dijkstra algorithm and $mathrm{A}^{star}$ algorithm to analyze the optimization problem of intelligent warehouse sorting path. According to the actual situation of intelligent warehouse sorting, simulation is carried out in three situations, such as the change of the placement position of inventory logistics spare parts and the transformation of the scale of intelligent warehouse, and the advantages and disadvantages of various algorithms in practical applications are compared and analyzed, so as to provide decision-making reference for the selection of intelligent warehouse sorting path optimization methods in different situations.
{"title":"Research on the Optimal Sorting Path of Intelligent Logistics Warehouse","authors":"Xingfang Fu, Hongjun Jiang","doi":"10.1109/ICCR55715.2022.10053898","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053898","url":null,"abstract":"With the rapid development of China's economy and the wide application of Internet technology, the trend of intelligent development of logistics warehouses is becoming increasingly obvious, and it is particularly prominent to solve the problem of the shortest path of intelligent logistics warehouse robots. This paper proposes Ant colony algorithm, Dijkstra algorithm and $mathrm{A}^{star}$ algorithm to analyze the optimization problem of intelligent warehouse sorting path. According to the actual situation of intelligent warehouse sorting, simulation is carried out in three situations, such as the change of the placement position of inventory logistics spare parts and the transformation of the scale of intelligent warehouse, and the advantages and disadvantages of various algorithms in practical applications are compared and analyzed, so as to provide decision-making reference for the selection of intelligent warehouse sorting path optimization methods in different situations.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476960","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 : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053899
Xueyuan Zhang, Chunzhe Wang, Han Du, Li Quan, Jin Shi, Yirong Ma
Human use their visual systems to perceive the interest objects in the images and videos with the past experience including shapes, textures, spatial knowledge and other subconscious information. In this paper, we develop an end-to-end object detection framework, combining with salient knowledge of objects. Firstly, we use the convolutional neural networks(CNNs) to extract the multi-scales feature maps representing the normal knowledge of objects in the images and videos. Then, the candidate feature map is selected from the extracted feature maps to encode the salient knowledge of objects using the mathematical strategy, and the new feature map is generated using the candidate feature map and the salient knowledge of objects. Thirdly, we use the feature map combining with salient knowledge and other feature maps at different scales to identify and localize the objects in the images and videos. The results show that our proposed approach can achieve better performance than other attention-based object detectors on PASCAL VOC 2007 and PASCAL VOC 2012, and this indicates the predicted results of our approach have a good consistency with the object's perception of human brains. At the same time, our approach can process 43 frames per second on the device NVIDIA GTX1080, and is more practical from the efficiency of running time.
{"title":"Salient Knowledge-Based Object Detection","authors":"Xueyuan Zhang, Chunzhe Wang, Han Du, Li Quan, Jin Shi, Yirong Ma","doi":"10.1109/ICCR55715.2022.10053899","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053899","url":null,"abstract":"Human use their visual systems to perceive the interest objects in the images and videos with the past experience including shapes, textures, spatial knowledge and other subconscious information. In this paper, we develop an end-to-end object detection framework, combining with salient knowledge of objects. Firstly, we use the convolutional neural networks(CNNs) to extract the multi-scales feature maps representing the normal knowledge of objects in the images and videos. Then, the candidate feature map is selected from the extracted feature maps to encode the salient knowledge of objects using the mathematical strategy, and the new feature map is generated using the candidate feature map and the salient knowledge of objects. Thirdly, we use the feature map combining with salient knowledge and other feature maps at different scales to identify and localize the objects in the images and videos. The results show that our proposed approach can achieve better performance than other attention-based object detectors on PASCAL VOC 2007 and PASCAL VOC 2012, and this indicates the predicted results of our approach have a good consistency with the object's perception of human brains. At the same time, our approach can process 43 frames per second on the device NVIDIA GTX1080, and is more practical from the efficiency of running time.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123481583","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}
In modern cities, with the continuous intensification of subway lines, the impact of the stray current generated on the power system is constantly expanding, becoming a potential threat to the stability of the power supply of the power system. As a coupling channel between the subway system and the power grid system, different soil structures have a profound impact on the distribution of stray currents. In this paper, a coupled finite element model (FEM) of a single subway line and a power system is constructed, and the distribution characteristics of stray currents under different soil structure conditions and traction conditions are studied. The results show that different soil structures affect the surface potential distribution around the subway and affect the neutral point current of the transformer.
{"title":"Analysis of Stray Current Intrusion into Grid Substation under Multiple Factors","authors":"Haiyan Gao, Ru Wei, Jiangtao Liu, Zijing Wang, Hemin Zhong, Ye Cao, Mingwei Tang, Jianlei Zhang","doi":"10.1109/ICCR55715.2022.10053908","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053908","url":null,"abstract":"In modern cities, with the continuous intensification of subway lines, the impact of the stray current generated on the power system is constantly expanding, becoming a potential threat to the stability of the power supply of the power system. As a coupling channel between the subway system and the power grid system, different soil structures have a profound impact on the distribution of stray currents. In this paper, a coupled finite element model (FEM) of a single subway line and a power system is constructed, and the distribution characteristics of stray currents under different soil structure conditions and traction conditions are studied. The results show that different soil structures affect the surface potential distribution around the subway and affect the neutral point current of the transformer.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128323154","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 : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053871
Zicong Chen, L. Wang, Hui Zhang, Jianqi Liu, Qin-ruo Wang
Aiming at the accuracy of the large inertia industrial robot dynamic model, a radial basis function neural networks (RBFNNs) weighted least square (WLS) identification scheme is proposed to further improve the accuracy of the dynamic model. Based on the dynamic linearization model of a large inertia industrial robot, the open-source toolbox Sympybotics is introduced to assist in obtaining the minimum inertia parameter set and observation matrix. The finite-term Fourier series is selected as the excitation trajectory while the condition number of the observation matrix is applied as the performance index for optimization. Its purpose is to ensure that the impact of external disturbances on the identification data is minimized while fully exciting the robot dynamics. Based on the actual operating data, the weighted least squares method is used to identify the kinetic parameters to obtain a rough solution of the kinetic parameters. Further, the accurate solution is obtained by nonlinear constraint function optimization and RBFNNs optimization. The experimental results show that the proposed method could guarantee the accuracy of the dynamic model of the large inertia industrial robot effectively, which provides an important technical support for its high-performance motion control.
{"title":"Research on Dynamic Parameter Identification of Large Inertia Industrial Robot Based on RBFNNs","authors":"Zicong Chen, L. Wang, Hui Zhang, Jianqi Liu, Qin-ruo Wang","doi":"10.1109/ICCR55715.2022.10053871","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053871","url":null,"abstract":"Aiming at the accuracy of the large inertia industrial robot dynamic model, a radial basis function neural networks (RBFNNs) weighted least square (WLS) identification scheme is proposed to further improve the accuracy of the dynamic model. Based on the dynamic linearization model of a large inertia industrial robot, the open-source toolbox Sympybotics is introduced to assist in obtaining the minimum inertia parameter set and observation matrix. The finite-term Fourier series is selected as the excitation trajectory while the condition number of the observation matrix is applied as the performance index for optimization. Its purpose is to ensure that the impact of external disturbances on the identification data is minimized while fully exciting the robot dynamics. Based on the actual operating data, the weighted least squares method is used to identify the kinetic parameters to obtain a rough solution of the kinetic parameters. Further, the accurate solution is obtained by nonlinear constraint function optimization and RBFNNs optimization. The experimental results show that the proposed method could guarantee the accuracy of the dynamic model of the large inertia industrial robot effectively, which provides an important technical support for its high-performance motion control.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121084442","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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