Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053889
He Zhong, Jiangtao Liu, Mingwei Tang, Jianlei Zhang, Kai Liu, Song Xiao, Yujun Guo
During the operational process of the metro lines, the traction current discharged from the working grounding wheels flows back to the terrestrial traction substations through the steel rail, as the DC stray current may be generated due to the poor insulating condition between the rail and the ground. The characteristics of the DC stray current invading the transformer settled at the terrestrial substation is affected by a series elements including the train's operational condition, the ‘rail -ground’ transition resistance, the soil structure and power grid topology. Exploring the influence factors of the stray current lays the foundations for further preventing the negative impact brought from stray current. In order to analyze the influence of the power grid topology on the stray current invading transformers, a coupling model involving the up and down metro lines with the power grid is built, based on the finite element method (FEM). Based on this FEM model, the variation of stray current invading the transformer is analyzed along with the relative position between the power grid single circuit and the metro line varying, meanwhile the variation of the power grid's topology is also considered. It is found that with the complexity of the power grid topology, the total amount of stray current invading the power grid increases, whereas the stray current flowing through the majority of the transmission lines decreases. In addition, no matter the two transformers constituting the power grid circuit are on the same side or on the opposite side of the metro line, the stray current invading the grid tends to increase with the reduction of the angle between the metro line and the power grid circuit.
{"title":"The Influence Analysis of the Power Grid Topology on the Stray Current Invading Transformers","authors":"He Zhong, Jiangtao Liu, Mingwei Tang, Jianlei Zhang, Kai Liu, Song Xiao, Yujun Guo","doi":"10.1109/ICCR55715.2022.10053889","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053889","url":null,"abstract":"During the operational process of the metro lines, the traction current discharged from the working grounding wheels flows back to the terrestrial traction substations through the steel rail, as the DC stray current may be generated due to the poor insulating condition between the rail and the ground. The characteristics of the DC stray current invading the transformer settled at the terrestrial substation is affected by a series elements including the train's operational condition, the ‘rail -ground’ transition resistance, the soil structure and power grid topology. Exploring the influence factors of the stray current lays the foundations for further preventing the negative impact brought from stray current. In order to analyze the influence of the power grid topology on the stray current invading transformers, a coupling model involving the up and down metro lines with the power grid is built, based on the finite element method (FEM). Based on this FEM model, the variation of stray current invading the transformer is analyzed along with the relative position between the power grid single circuit and the metro line varying, meanwhile the variation of the power grid's topology is also considered. It is found that with the complexity of the power grid topology, the total amount of stray current invading the power grid increases, whereas the stray current flowing through the majority of the transmission lines decreases. In addition, no matter the two transformers constituting the power grid circuit are on the same side or on the opposite side of the metro line, the stray current invading the grid tends to increase with the reduction of the angle between the metro line and the power grid circuit.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"29 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":"126735410","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/ICCR55715.2022.10053897
Zhaofeng Chen
Animals control is an important task for the safe operation of substations. Aiming at the problems existing in the control of small animals, with the superior prediction ability of machine learning, a prediction model of small animals hazard grade is proposed, which combines gradient boosting decision (GBDT) and logistic regression (LR) algorithm. The model combined substation operation and maintenance data with local meteorological data, performs features screening by calculating the variance value, and achieves classes balance by using sampling technology. And finally the model achieves the prediction of small animals hazard grade in substation. By using different data sets and not using GBDT algorithm to train the model, the prediction results are compared and analyzed. The proposed model is better in all prediction performance indicators, which verifies the validity of the method.
{"title":"Application of Small Animals Control in Substation Based on GBDT and LR Fusion Algorithm","authors":"Zhaofeng Chen","doi":"10.1109/ICCR55715.2022.10053897","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053897","url":null,"abstract":"Animals control is an important task for the safe operation of substations. Aiming at the problems existing in the control of small animals, with the superior prediction ability of machine learning, a prediction model of small animals hazard grade is proposed, which combines gradient boosting decision (GBDT) and logistic regression (LR) algorithm. The model combined substation operation and maintenance data with local meteorological data, performs features screening by calculating the variance value, and achieves classes balance by using sampling technology. And finally the model achieves the prediction of small animals hazard grade in substation. By using different data sets and not using GBDT algorithm to train the model, the prediction results are compared and analyzed. The proposed model is better in all prediction performance indicators, which verifies the validity of the method.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"22 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":"127962202","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/ICCR55715.2022.10053887
Shibo Na, Ruizhuo Song
In this paper, we proposed an adaptive dynamic programming (ADP) algorithm for discrete time stochastic linear quadratic game without system dynamics. Firstly, we described the problem and converted it into a deterministic form. Then, we solved the Bellman equation to obtain the control gain matrix and disturbance gain matrix when the system dynamics were known. After that, we implemented the ADP algorithm with unknown system through neural networks. Model network, action network, disturbance network and critic network were used to approximate the system model, control gain matrix, disturbance gain matrix and value function respectively. Finally, a simulation example was given to verify the effectiveness of the algorithm.
{"title":"Stochastic Linear Quadratic Game for Discrete-time Systems Based-on Adaptive Dynamic Programming","authors":"Shibo Na, Ruizhuo Song","doi":"10.1109/ICCR55715.2022.10053887","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053887","url":null,"abstract":"In this paper, we proposed an adaptive dynamic programming (ADP) algorithm for discrete time stochastic linear quadratic game without system dynamics. Firstly, we described the problem and converted it into a deterministic form. Then, we solved the Bellman equation to obtain the control gain matrix and disturbance gain matrix when the system dynamics were known. After that, we implemented the ADP algorithm with unknown system through neural networks. Model network, action network, disturbance network and critic network were used to approximate the system model, control gain matrix, disturbance gain matrix and value function respectively. Finally, a simulation example was given to verify the effectiveness of the algorithm.","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":"128009250","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/ICCR55715.2022.10053905
Eneo Petoku, G. Capi
Brain-Computer Interface research aims to build systems that can connect the brain to computer or a certain robotic application. The brain activity is solely used to generate commands that can be recognized by the computer. To generate a recognizable brain activity, usually the subject imagines the movements of one's limbs without performing any real movement. In the literature, this paradigm is called Motor Imagery (MI). The subject provides data through a particular recording technology, such as EEG, in a certain time frame, in which the subject forces himself/herself into the feeling of performing a particular action. Each recorded data is linked to a label, and different techniques are used to learn patterns, in order to map them correctly. The goal of this paper is to investigate, whether it is possible to generate similar results as in the case of imagining the movement of limbs, by imagining the movement of an external object. To investigate this, we compare the performance of Motor Imagery and Object Motor Imagery. In the first case the mental task consists of imagining the movements of arms, while in the second the imagining of moving an external box through solely brain activity. A video of a box that moves through a plane in two directions, right, left, is used as visual feedback in both cases. The recorded EEG data are split into training and testing subsets, and are fed to a deep neural network, that tries to learn the different patterns and to classify them. The results show that Object Motor Imagery can achieve better results compared to MI, despite the lack of embodiment and congruity with any daily neural command. The trained architecture is used to control a mobile humanoid, investigating the implementation of Object Motor Movement in robotic application.
{"title":"Mobile Humanoid Robot Control through Object Movement Imagery","authors":"Eneo Petoku, G. Capi","doi":"10.1109/ICCR55715.2022.10053905","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053905","url":null,"abstract":"Brain-Computer Interface research aims to build systems that can connect the brain to computer or a certain robotic application. The brain activity is solely used to generate commands that can be recognized by the computer. To generate a recognizable brain activity, usually the subject imagines the movements of one's limbs without performing any real movement. In the literature, this paradigm is called Motor Imagery (MI). The subject provides data through a particular recording technology, such as EEG, in a certain time frame, in which the subject forces himself/herself into the feeling of performing a particular action. Each recorded data is linked to a label, and different techniques are used to learn patterns, in order to map them correctly. The goal of this paper is to investigate, whether it is possible to generate similar results as in the case of imagining the movement of limbs, by imagining the movement of an external object. To investigate this, we compare the performance of Motor Imagery and Object Motor Imagery. In the first case the mental task consists of imagining the movements of arms, while in the second the imagining of moving an external box through solely brain activity. A video of a box that moves through a plane in two directions, right, left, is used as visual feedback in both cases. The recorded EEG data are split into training and testing subsets, and are fed to a deep neural network, that tries to learn the different patterns and to classify them. The results show that Object Motor Imagery can achieve better results compared to MI, despite the lack of embodiment and congruity with any daily neural command. The trained architecture is used to control a mobile humanoid, investigating the implementation of Object Motor Movement in robotic application.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"7 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114011899","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/ICCR55715.2022.10053929
Chenxi Guan, Shuying Wang
When the traditional A* algorithm is applied to robot path planning, it has the problems of low efficiency and unable to avoid obstacles dynamically. In order to solve the above problems, a fusion algorithm based on improved A* algorithm and DWA algorithm is proposed. The A* algorithm is improved in three aspects: reducing the search direction of A* algorithm to reduce the search time, adding path information parameters to dynamically adjust the weight of heuristic function, and introducing important node extraction strategy to reduce the number of turns and shorten the path. Finally, the improved A* algorithm is fused with DWA algorithm. The experimental results show that the improved fusion algorithm can realize global optimal path planning and local real-time obstacle avoidance.
{"title":"Robot Dynamic Path Planning Based on Improved A* and DWA Algorithms","authors":"Chenxi Guan, Shuying Wang","doi":"10.1109/ICCR55715.2022.10053929","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053929","url":null,"abstract":"When the traditional A* algorithm is applied to robot path planning, it has the problems of low efficiency and unable to avoid obstacles dynamically. In order to solve the above problems, a fusion algorithm based on improved A* algorithm and DWA algorithm is proposed. The A* algorithm is improved in three aspects: reducing the search direction of A* algorithm to reduce the search time, adding path information parameters to dynamically adjust the weight of heuristic function, and introducing important node extraction strategy to reduce the number of turns and shorten the path. Finally, the improved A* algorithm is fused with DWA algorithm. The experimental results show that the improved fusion algorithm can realize global optimal path planning and local real-time obstacle avoidance.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"29 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":"131467692","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/ICCR55715.2022.10053879
Lulu Zhou, Chen Peng, Z. Cao
This paper is concerned with the collaborative design of networked control systems (NCSs) subject to DoS attacks, scheduling protocols, and time-varying delays. First, to save limited network resources and prevent data collisions, the Try-Once-Discard (TOD) protocol is introduced to orchestrate the node access assignment in the sensor-to-controller channel. Then, denial-of-service (DoS) attacks that can cause communication blockages are addressed. Additionally, sufficient conditions are derived to guarantee the exponential mean-square stability of the resulting hybrid system based on which the controller gain and weighted matrix of scheduling protocols are co-designed. Finally, two simulation examples are used to illustrate the validity of the proposed method.
{"title":"Communication and Control Co-design for Networked Control Systems under DoS Attacks and Time-varying Delays","authors":"Lulu Zhou, Chen Peng, Z. Cao","doi":"10.1109/ICCR55715.2022.10053879","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053879","url":null,"abstract":"This paper is concerned with the collaborative design of networked control systems (NCSs) subject to DoS attacks, scheduling protocols, and time-varying delays. First, to save limited network resources and prevent data collisions, the Try-Once-Discard (TOD) protocol is introduced to orchestrate the node access assignment in the sensor-to-controller channel. Then, denial-of-service (DoS) attacks that can cause communication blockages are addressed. Additionally, sufficient conditions are derived to guarantee the exponential mean-square stability of the resulting hybrid system based on which the controller gain and weighted matrix of scheduling protocols are co-designed. Finally, two simulation examples are used to illustrate the validity of the proposed method.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"20 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":"132394862","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/ICCR55715.2022.10053931
Yongxin Liu, Qiang He, Junhui Wang, Zhiliang Wang, Tianheng Chen, Shichen Jin, Chi Zhang, Zhiqiang Wang
In order to reduce the cost of human resources and material resources and improve the power line inspection efficiency, unmanned ground vehicle (UGV), which utilizes the modern artificial intelligence such as deep learning and reinforcement learning, is commonly introduced to replace of human to inspect power lines in the grid system. This paper provides a deep Q network (DQN) and convolutional neural network (CNN) based end-to-end control model to drive UGV to inspect automatically, and meanwhile to avoid obstacles. Specifically, we utilize the preprocessed grayscale image as the input of the CNN, and output the final Q value. This model simulates human learning behavior by interaction between UGV and the environment. Through repeated self-learning and reward value increasing in a simulation environment, the UGV successfully reaches the target position in a shortest time and meanwhile avoiding a variety of obstacles.
{"title":"Convolutional Neural Network Based Unmanned Ground Vehicle Control via Deep Reinforcement Learning","authors":"Yongxin Liu, Qiang He, Junhui Wang, Zhiliang Wang, Tianheng Chen, Shichen Jin, Chi Zhang, Zhiqiang Wang","doi":"10.1109/ICCR55715.2022.10053931","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053931","url":null,"abstract":"In order to reduce the cost of human resources and material resources and improve the power line inspection efficiency, unmanned ground vehicle (UGV), which utilizes the modern artificial intelligence such as deep learning and reinforcement learning, is commonly introduced to replace of human to inspect power lines in the grid system. This paper provides a deep Q network (DQN) and convolutional neural network (CNN) based end-to-end control model to drive UGV to inspect automatically, and meanwhile to avoid obstacles. Specifically, we utilize the preprocessed grayscale image as the input of the CNN, and output the final Q value. This model simulates human learning behavior by interaction between UGV and the environment. Through repeated self-learning and reward value increasing in a simulation environment, the UGV successfully reaches the target position in a shortest time and meanwhile avoiding a variety of obstacles.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"27 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":"125168325","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/ICCR55715.2022.10053885
Shujun Huang, Zhihua Zhang, Ruofeng Xie
The use of wireless sensor networks can achieve effective protection of the monitored area. The node deployment and energy saving optimization of wireless sensor network is important due to the constraints of limited battery capacity and short life span of nodes, and a node deployment and energy saving optimization method is proposed based on reinforcement learning. The Q-learning algorithm is used to screen the nodes that can detect the range of small animals, deploy the nodes autonomously and achieve effective energy saving optimization. Simulation results show that the method can reduce energy consumption by 30% to 35% with shorter convergence time.
{"title":"Node Deployment and Energy Saving Optimization Method for Wireless Sensor Networks Based on Q-learning","authors":"Shujun Huang, Zhihua Zhang, Ruofeng Xie","doi":"10.1109/ICCR55715.2022.10053885","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053885","url":null,"abstract":"The use of wireless sensor networks can achieve effective protection of the monitored area. The node deployment and energy saving optimization of wireless sensor network is important due to the constraints of limited battery capacity and short life span of nodes, and a node deployment and energy saving optimization method is proposed based on reinforcement learning. The Q-learning algorithm is used to screen the nodes that can detect the range of small animals, deploy the nodes autonomously and achieve effective energy saving optimization. Simulation results show that the method can reduce energy consumption by 30% to 35% with shorter convergence time.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"8 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":"114343136","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/ICCR55715.2022.10053863
X. Liu, Lei Zhang, Yunjian Peng
In this paper, an adaptive optimal control is investigated for a stochastic linear discrete-time system with multiplicative state-dependent noise and control-dependent noise without knowledge of the system dynamics. With the framework of Q-learning, an off-policy state feedback solution for stochastic linear quadratic tracking (SLQT) problem has been proposed. First, an augmented system of the original system and the reference command generator is established to solve SLQT problem. Then, we present an optimal control by solving stochastic algebraic Riccati equation (SARE). Next, we present the on-policy and off-policy algorithms to achieve an adaptive optimal control without knowing the system dynamics. Finally, a simulation test is finally setup to verify the performance of the proposed adaptive optimal control.
{"title":"Off-policy Q-learning-based Tracking Control for Stochastic Linear Discrete-Time Systems","authors":"X. Liu, Lei Zhang, Yunjian Peng","doi":"10.1109/ICCR55715.2022.10053863","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053863","url":null,"abstract":"In this paper, an adaptive optimal control is investigated for a stochastic linear discrete-time system with multiplicative state-dependent noise and control-dependent noise without knowledge of the system dynamics. With the framework of Q-learning, an off-policy state feedback solution for stochastic linear quadratic tracking (SLQT) problem has been proposed. First, an augmented system of the original system and the reference command generator is established to solve SLQT problem. Then, we present an optimal control by solving stochastic algebraic Riccati equation (SARE). Next, we present the on-policy and off-policy algorithms to achieve an adaptive optimal control without knowing the system dynamics. Finally, a simulation test is finally setup to verify the performance of the proposed adaptive optimal control.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"72 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":"114540501","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/ICCR55715.2022.10053894
Weibing Li, Biao Song, Yongping Pan
In minimally invasive surgery (MIS), a surgical endoscope is an essential instrument that provides visualization for the surgeon. One principal characteristic of surgical instruments is that remote center of motion (RCM) must be respected. To meet such a practical requirement, many physical RCM mechanisms and software-based RCM generation algorithms have been proposed. As compared with physical RCM mechanisms, RCM generation algorithms possess more flexibility due to the fact that the RCM point can be adjusted if required. This paper conducts comparisons of four typical RCM generation algorithms applied to a vision-controlled robotic endoscope under joint constraints. Kinematic models of the robotic endoscope and the four RCM generation algorithms are first briefly introduced. Then, a unified control formulation based on quadratic programming (QP) is constructed to incorporate kinematic, RCM, and physical constraints of the robotic endoscope. Based on the unified control scheme, comparative simulations and experiments are performed. The advantages and disadvantages of the four typical RCM generation algorithms are analyzed and discussed. When performing a same peg transfer task in the simulations, the RCM errors synthesized by RCM generation algorithms designed using a plane equation and an insertion equation are smaller. In the physical experiments, there are few differences in the RCM errors. Nevertheless, it is revealed that the joint velocities corresponding to the RCM generation algorithm based on a plane equation are the smallest, which means that the joint angles change more gently and it can be more friendly to MIS.
{"title":"Comparisons of RCM Generation Algorithms for Vision-Controlled Robotic Endoscope","authors":"Weibing Li, Biao Song, Yongping Pan","doi":"10.1109/ICCR55715.2022.10053894","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053894","url":null,"abstract":"In minimally invasive surgery (MIS), a surgical endoscope is an essential instrument that provides visualization for the surgeon. One principal characteristic of surgical instruments is that remote center of motion (RCM) must be respected. To meet such a practical requirement, many physical RCM mechanisms and software-based RCM generation algorithms have been proposed. As compared with physical RCM mechanisms, RCM generation algorithms possess more flexibility due to the fact that the RCM point can be adjusted if required. This paper conducts comparisons of four typical RCM generation algorithms applied to a vision-controlled robotic endoscope under joint constraints. Kinematic models of the robotic endoscope and the four RCM generation algorithms are first briefly introduced. Then, a unified control formulation based on quadratic programming (QP) is constructed to incorporate kinematic, RCM, and physical constraints of the robotic endoscope. Based on the unified control scheme, comparative simulations and experiments are performed. The advantages and disadvantages of the four typical RCM generation algorithms are analyzed and discussed. When performing a same peg transfer task in the simulations, the RCM errors synthesized by RCM generation algorithms designed using a plane equation and an insertion equation are smaller. In the physical experiments, there are few differences in the RCM errors. Nevertheless, it is revealed that the joint velocities corresponding to the RCM generation algorithm based on a plane equation are the smallest, which means that the joint angles change more gently and it can be more friendly to MIS.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"185 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":"131787790","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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