Pub Date : 2017-12-01DOI: 10.1109/ROBIO.2017.8324701
Dongliang Zheng, Hesheng Wang, Zheyuan Xie, Weidong Chen, X. Kong
In this paper, the perception, planning, and control of a quadrotor are studied to navigate the quadrotor through unknown confined environments. To deal with the limited sensing and computation capability of the quadrotor, the perception, planning, and control are designed in a coupled manner. The basic idea is to design a method that is less demanding on perception and planning by leveraging the Model Predictive Control (MPC). More specifically, the waypoints and trajectories are generated in real-time using the point-cloud information from a range sensor. The trajectories are generated with respect to a moving reference frame, and the constantly regenerated trajectories act as feedback to guide the quadrotor. Then, a model predictive controller is introduced for trajectory tracking. With the coupled design, no map of the environment needs to be built, and the position of the quadrotor is not needed. Multiple simulations are conducted in the ROS and gazebo environment. The results show that the quadrotor can navigate in unknown, confined environment successfully.
{"title":"Autonomous navigation of a quadrotor in unknown environments","authors":"Dongliang Zheng, Hesheng Wang, Zheyuan Xie, Weidong Chen, X. Kong","doi":"10.1109/ROBIO.2017.8324701","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324701","url":null,"abstract":"In this paper, the perception, planning, and control of a quadrotor are studied to navigate the quadrotor through unknown confined environments. To deal with the limited sensing and computation capability of the quadrotor, the perception, planning, and control are designed in a coupled manner. The basic idea is to design a method that is less demanding on perception and planning by leveraging the Model Predictive Control (MPC). More specifically, the waypoints and trajectories are generated in real-time using the point-cloud information from a range sensor. The trajectories are generated with respect to a moving reference frame, and the constantly regenerated trajectories act as feedback to guide the quadrotor. Then, a model predictive controller is introduced for trajectory tracking. With the coupled design, no map of the environment needs to be built, and the position of the quadrotor is not needed. Multiple simulations are conducted in the ROS and gazebo environment. The results show that the quadrotor can navigate in unknown, confined environment successfully.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898455","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324674
Bhavik Patel, Yaodong Pan, Usman Ahmad
This paper presents a novel adaptive backstepping controller for n + m degrees of freedom (DOF) mobile manipulator with the aim of simultaneous control of the velocity of the mobile platform and the motion of the end-effector. Using the idea of kinematic backstepping control and adaptive torque control, a two-step controller is presented for the nonholonomic mobile manipulator. A kinematic velocity control is designed in the first step such that all the desired trajectories are achieved. In the second step, the adaptive torque controller based on the dynamics of the mobile manipulator is designed such that the mobile platform velocity and the end-effector position converge to the reference trajectories designed in the first step. This control scheme provides an efficient solution to the motion control problem of mobile manipulators and the simulation results verify the effectiveness of the proposed control design.
{"title":"Adaptive backstepping control approach for the trajectory tracking of mobile manipulators","authors":"Bhavik Patel, Yaodong Pan, Usman Ahmad","doi":"10.1109/ROBIO.2017.8324674","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324674","url":null,"abstract":"This paper presents a novel adaptive backstepping controller for n + m degrees of freedom (DOF) mobile manipulator with the aim of simultaneous control of the velocity of the mobile platform and the motion of the end-effector. Using the idea of kinematic backstepping control and adaptive torque control, a two-step controller is presented for the nonholonomic mobile manipulator. A kinematic velocity control is designed in the first step such that all the desired trajectories are achieved. In the second step, the adaptive torque controller based on the dynamics of the mobile manipulator is designed such that the mobile platform velocity and the end-effector position converge to the reference trajectories designed in the first step. This control scheme provides an efficient solution to the motion control problem of mobile manipulators and the simulation results verify the effectiveness of the proposed control design.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131407891","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324743
Kewen Tang, Fan Hu, Wentao Liu, Yian Deng, Xihong Wu, D. Luo
Workpiece recognition is vital and essential for robot manipulation which is one of the most important skills for robot. In this paper, we present a corner detection based strategy to recognize the type of workpiece so as to offer important visual cues for robot manipulation. Our framework works by three steps. Firstly, the bounding-box of workpiece is detected using a multi-scale convolutional neural network according to the closed regions (enclosed by edges). Secondly, three types of corners (Y-type, A-type and L-type) are detected by employing a simple neural network and the results are further refined according to both the detection probability and geometric relationship between corners. Finally, the type of workpiece is recognized on the basis of the relative position of corners. Due to that the workpiece detection step greatly reduces the searching space for the corner detection, a real-time process is achieved. Another important characteristic of our method lies in that the detected corners can be further used as basic modelling element in reconstructing the 3D structure of the workpiece, which is beneficial for the robot to decide the grasping position and pose. With the PKU-HR6.0 platform, a manipulation controller is established based on the proposed approach. Experimental results show that our approach is comparable with some state-of-the-art work in precision of recognition, and our PKU-HR6.0 robot is able to precisely recognize and locate the Tetris-shaped building blocks so that to well accomplish the manipulation tasks.
{"title":"Corner detection based real-time workpiece recognition for robot manipulation","authors":"Kewen Tang, Fan Hu, Wentao Liu, Yian Deng, Xihong Wu, D. Luo","doi":"10.1109/ROBIO.2017.8324743","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324743","url":null,"abstract":"Workpiece recognition is vital and essential for robot manipulation which is one of the most important skills for robot. In this paper, we present a corner detection based strategy to recognize the type of workpiece so as to offer important visual cues for robot manipulation. Our framework works by three steps. Firstly, the bounding-box of workpiece is detected using a multi-scale convolutional neural network according to the closed regions (enclosed by edges). Secondly, three types of corners (Y-type, A-type and L-type) are detected by employing a simple neural network and the results are further refined according to both the detection probability and geometric relationship between corners. Finally, the type of workpiece is recognized on the basis of the relative position of corners. Due to that the workpiece detection step greatly reduces the searching space for the corner detection, a real-time process is achieved. Another important characteristic of our method lies in that the detected corners can be further used as basic modelling element in reconstructing the 3D structure of the workpiece, which is beneficial for the robot to decide the grasping position and pose. With the PKU-HR6.0 platform, a manipulation controller is established based on the proposed approach. Experimental results show that our approach is comparable with some state-of-the-art work in precision of recognition, and our PKU-HR6.0 robot is able to precisely recognize and locate the Tetris-shaped building blocks so that to well accomplish the manipulation tasks.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127390135","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324540
Hong Liu, Yongqing Jin, Chenyang Zhao
Ground plane segmentation is quite a challenging fundamental problem for monocular mobile robot navigation due to the dynamic unknown environments and the initialization of coordinate system which induces outliers to the bottom region of interest. Current geometric-based methods are mostly limited to deal with multiple plane segmentation in stationary known scene from depth sensor. In this paper, we propose a robust realtime trust region ground plane segmentation method to handle the unknown environments with a single camera. The proposed method utilizes Radius Outlier Removal filter to exclude the outliers of candidate points generated by the state-of-the-art method, Direct Sparse Odometry (DSO), then candidate points in the trust region are provided to fit the ground plane. The coefficients of fitted plane will be used to remove the outliers and to compensate omissive points. Therefore the ground plane segmentation is refined iteratively. Comprehensive experiments on the TUM monoVO dataset demonstrate that our method outperforms the random sample consensus (RANSAC) methods on time consumption and robustness in the unknown scenes, even when the initial coordinate system is pitched and rolled.
{"title":"Real-time trust region ground plane segmentation for monocular mobile robots","authors":"Hong Liu, Yongqing Jin, Chenyang Zhao","doi":"10.1109/ROBIO.2017.8324540","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324540","url":null,"abstract":"Ground plane segmentation is quite a challenging fundamental problem for monocular mobile robot navigation due to the dynamic unknown environments and the initialization of coordinate system which induces outliers to the bottom region of interest. Current geometric-based methods are mostly limited to deal with multiple plane segmentation in stationary known scene from depth sensor. In this paper, we propose a robust realtime trust region ground plane segmentation method to handle the unknown environments with a single camera. The proposed method utilizes Radius Outlier Removal filter to exclude the outliers of candidate points generated by the state-of-the-art method, Direct Sparse Odometry (DSO), then candidate points in the trust region are provided to fit the ground plane. The coefficients of fitted plane will be used to remove the outliers and to compensate omissive points. Therefore the ground plane segmentation is refined iteratively. Comprehensive experiments on the TUM monoVO dataset demonstrate that our method outperforms the random sample consensus (RANSAC) methods on time consumption and robustness in the unknown scenes, even when the initial coordinate system is pitched and rolled.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398782","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324679
Fangzhou Zhao, Junyao Gao, Jingchao Zhao, Chunlei Zhang, Zhe Xu, Xuanyang Shi, Yi Liu, Chuzhao Liu, Cunqiu Liu
It is a new research hotspot to improve the jumping and crawling ability of humanoid robot. A novel anti-impact and lightweight humanoid waist joint structure is presented. The degree of freedom of roll and pitch is provided by sphere-pin pair, and driven by two lead screws in the back. When the robot is in the upright state, the rotation axis of DoF of roll is not in the horizontal direction. It is vertical to the rotation axis of DoF of pitch, but not vertical to that of yaw. A kinematic model and corresponding rotation matrix calculation method is proposed for this structure. The motion space and attitude changes are numerically analyzed. The lengths of the screw rod with different attitudes are calculated by inverse kinematics calculation using the rotation matrix. The numerical results show that the structure can adapt to the requirements of the robot jumping and crawling on the motion space and attitude, and all the poses can be smoothly controlled.
{"title":"Structure design and motion analysis of waist of humanoid robot for jumping and crawling","authors":"Fangzhou Zhao, Junyao Gao, Jingchao Zhao, Chunlei Zhang, Zhe Xu, Xuanyang Shi, Yi Liu, Chuzhao Liu, Cunqiu Liu","doi":"10.1109/ROBIO.2017.8324679","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324679","url":null,"abstract":"It is a new research hotspot to improve the jumping and crawling ability of humanoid robot. A novel anti-impact and lightweight humanoid waist joint structure is presented. The degree of freedom of roll and pitch is provided by sphere-pin pair, and driven by two lead screws in the back. When the robot is in the upright state, the rotation axis of DoF of roll is not in the horizontal direction. It is vertical to the rotation axis of DoF of pitch, but not vertical to that of yaw. A kinematic model and corresponding rotation matrix calculation method is proposed for this structure. The motion space and attitude changes are numerically analyzed. The lengths of the screw rod with different attitudes are calculated by inverse kinematics calculation using the rotation matrix. The numerical results show that the structure can adapt to the requirements of the robot jumping and crawling on the motion space and attitude, and all the poses can be smoothly controlled.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115421623","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}
The RoboCup Middle Size League (MSL) robot soccer competition is a standard test platform for distributed multi-robot systems. There are many challenges in the vision system for MSL soccer robots. For example, huge amount of data from the Kinect v2 sensor leads to heavy computation burden for the robot's onboard industrial computer, the obstacle-detection algorithm is mainly dependent on the obstacle' colors, the omnidirectional vision system is not able to detect the ball above the camera and get the objects' height information. In this paper, we proposed an algorithm for object detection based on GPU parallel computing employing Kinect v2 and Jetson TX1 as the hardware platform. Parallel computing is utilized throughout all the steps of the object detection algorithm, so the speed and accuracy of the algorithm are greatly improved. We test the real-time performance and the accuracy of the algorithm using our NuBot soccer robots. The experimental results show that objects can be detected and their 3-D information can be obtained accurately, satisfying the real-time requirements of the MSL competition and decreasing the robot's onboard computer's CPU burden. In addition, the proposed algorithm for obstacle detection is not dependent on a specific color.
{"title":"Object detection based on GPU parallel computing for RoboCup Middle Size League","authors":"Shan Luo, Weijia Yao, Qinghua Yu, Junhao Xiao, Huimin Lu, Zongtan Zhou","doi":"10.1109/ROBIO.2017.8324399","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324399","url":null,"abstract":"The RoboCup Middle Size League (MSL) robot soccer competition is a standard test platform for distributed multi-robot systems. There are many challenges in the vision system for MSL soccer robots. For example, huge amount of data from the Kinect v2 sensor leads to heavy computation burden for the robot's onboard industrial computer, the obstacle-detection algorithm is mainly dependent on the obstacle' colors, the omnidirectional vision system is not able to detect the ball above the camera and get the objects' height information. In this paper, we proposed an algorithm for object detection based on GPU parallel computing employing Kinect v2 and Jetson TX1 as the hardware platform. Parallel computing is utilized throughout all the steps of the object detection algorithm, so the speed and accuracy of the algorithm are greatly improved. We test the real-time performance and the accuracy of the algorithm using our NuBot soccer robots. The experimental results show that objects can be detected and their 3-D information can be obtained accurately, satisfying the real-time requirements of the MSL competition and decreasing the robot's onboard computer's CPU burden. In addition, the proposed algorithm for obstacle detection is not dependent on a specific color.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115561721","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}
The 3-RPS parallel mechanism with arc prismatic pairs is presented, this mechanism possesses a moving platform, a fixed platform, and three limb kinematic chains. Each limb kinematic chain consists of revolute pair R, a arc prismatic pair P, and a spherical joint S. The axis of revolute pairs are intersect each other and coplanar, the angle between two axes is 60°. Mobility, inverse solution and workspace of mechanism are analyzed via screw theory. This mechanism with three degrees of freedom, and the rotational center of moving platform is an intersection point of three straight lines, which passing through each center of arc rod and the center of spherical joint, and this intersection point changes with the posture of the moving platform. Furthermore, the Jacobian constraint matrix, the Jacobian actuation matrix and the Jacobian overall matrix are obtained, the singularity configurations of pose of moving platform are analyzed by a fixed rotation point or a fixed pose. The result provides a theoretical basis for kinematics and dynamics of this parallel mechanism.
{"title":"Kinematics and singularity analysis of a 3-RPS parallel mechanism","authors":"Yongfeng Wang, Shuncheng Fan, Xiaojun Zhang, Guangda Lu, Guoru Zhao","doi":"10.1109/ROBIO.2017.8324604","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324604","url":null,"abstract":"The 3-RPS parallel mechanism with arc prismatic pairs is presented, this mechanism possesses a moving platform, a fixed platform, and three limb kinematic chains. Each limb kinematic chain consists of revolute pair R, a arc prismatic pair P, and a spherical joint S. The axis of revolute pairs are intersect each other and coplanar, the angle between two axes is 60°. Mobility, inverse solution and workspace of mechanism are analyzed via screw theory. This mechanism with three degrees of freedom, and the rotational center of moving platform is an intersection point of three straight lines, which passing through each center of arc rod and the center of spherical joint, and this intersection point changes with the posture of the moving platform. Furthermore, the Jacobian constraint matrix, the Jacobian actuation matrix and the Jacobian overall matrix are obtained, the singularity configurations of pose of moving platform are analyzed by a fixed rotation point or a fixed pose. The result provides a theoretical basis for kinematics and dynamics of this parallel mechanism.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921146","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324463
Huan Yin, X. Ding, Li Tang, Yue Wang, R. Xiong
Loop closure detection in 3D LIDAR data is an essential but challenging problem in SLAM system. It is important to reduce global inconsistency or re-localize the robot that loses the localization, while is difficult for the lack of prior information. We present a semi-handcrafted representation learning method for LIDAR point cloud using siamese convolution neural network, which states the loop closure detection to a similarity modeling problem. With the learned representation, the similarity between two LIDAR scans is transformed as the Euclidean distance between the representations respectively. Based on it, we furthermore establish kd-tree to accelerate the searching of similar scans. To demonstrate the performance and effectiveness of the proposed method, the KITTI dataset is employed for comparison with other LIDAR loop closure detection methods. The result shows that our method can achieve both higher accuracy and efficiency.
{"title":"Efficient 3D LIDAR based loop closing using deep neural network","authors":"Huan Yin, X. Ding, Li Tang, Yue Wang, R. Xiong","doi":"10.1109/ROBIO.2017.8324463","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324463","url":null,"abstract":"Loop closure detection in 3D LIDAR data is an essential but challenging problem in SLAM system. It is important to reduce global inconsistency or re-localize the robot that loses the localization, while is difficult for the lack of prior information. We present a semi-handcrafted representation learning method for LIDAR point cloud using siamese convolution neural network, which states the loop closure detection to a similarity modeling problem. With the learned representation, the similarity between two LIDAR scans is transformed as the Euclidean distance between the representations respectively. Based on it, we furthermore establish kd-tree to accelerate the searching of similar scans. To demonstrate the performance and effectiveness of the proposed method, the KITTI dataset is employed for comparison with other LIDAR loop closure detection methods. The result shows that our method can achieve both higher accuracy and efficiency.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241838","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324829
Limei Zhao, Qing Xiao, Zhengcai Cao, Ran Huang, Yili Fu
This paper proposes a new learning control method for snake-like robots to achieve trajectory tracking. Based on deterministic learning, an adaptive neural networks control algorithm is used to track the desired trajectory and approximate the unknown system dynamics of the snake-like robot. After that the learned knowledge from direct neural networks is stored as constant network weights. These weights improve the response speed and the accuracy of the system in repeating same or similar control tasks. By using the Lyapunov approach, the tracking error is proven to be uniformly ultimately bounded and converges to a residual set. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.
{"title":"Adaptive neural network tracking control of snake-like robots via a deterministic learning approach","authors":"Limei Zhao, Qing Xiao, Zhengcai Cao, Ran Huang, Yili Fu","doi":"10.1109/ROBIO.2017.8324829","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324829","url":null,"abstract":"This paper proposes a new learning control method for snake-like robots to achieve trajectory tracking. Based on deterministic learning, an adaptive neural networks control algorithm is used to track the desired trajectory and approximate the unknown system dynamics of the snake-like robot. After that the learned knowledge from direct neural networks is stored as constant network weights. These weights improve the response speed and the accuracy of the system in repeating same or similar control tasks. By using the Lyapunov approach, the tracking error is proven to be uniformly ultimately bounded and converges to a residual set. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124525445","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 : 2017-12-01DOI: 10.1109/ROBIO.2017.8324637
Qun Jia, Chao Zhou, Lu Deng, Zhangming Du, Zhiqiang Cao
This paper proposes the method of nanoscale micro-displacement modeling and control of the piezoelectric ceramic actuator (PCA) mounted on the end of a micro-nano robot. The robot works in the vacuum chamber of a Scanning Electron Microscope (SEM). We use the time-to-digital conversion (TDC) method to measure the displacement of the PCA. For the purpose of establishing the relationship of the applied voltage and the displacement of the PCA, we designed an online modeling and control system based on PC/104. Models with different order combinations are applied to fit the transfer function of the system and the least squares method is applied to identify the parameters of each model. In addition, the second-order transfer function model is used to approximate the open-loop transfer function model of piezoelectric ceramic by comparing the model fitting rate. Specially, some open-loop control experiments are performed to verify the accuracy of the model. Furthermore, a PID controller is proposed to achieve accurate position control for the PCA. In the end, simulation results demonstrate the feasibility and effectiveness of the closed-loop control system.
{"title":"Research on nanoscale displacement online modeling and control of PCA","authors":"Qun Jia, Chao Zhou, Lu Deng, Zhangming Du, Zhiqiang Cao","doi":"10.1109/ROBIO.2017.8324637","DOIUrl":"https://doi.org/10.1109/ROBIO.2017.8324637","url":null,"abstract":"This paper proposes the method of nanoscale micro-displacement modeling and control of the piezoelectric ceramic actuator (PCA) mounted on the end of a micro-nano robot. The robot works in the vacuum chamber of a Scanning Electron Microscope (SEM). We use the time-to-digital conversion (TDC) method to measure the displacement of the PCA. For the purpose of establishing the relationship of the applied voltage and the displacement of the PCA, we designed an online modeling and control system based on PC/104. Models with different order combinations are applied to fit the transfer function of the system and the least squares method is applied to identify the parameters of each model. In addition, the second-order transfer function model is used to approximate the open-loop transfer function model of piezoelectric ceramic by comparing the model fitting rate. Specially, some open-loop control experiments are performed to verify the accuracy of the model. Furthermore, a PID controller is proposed to achieve accurate position control for the PCA. In the end, simulation results demonstrate the feasibility and effectiveness of the closed-loop control system.","PeriodicalId":197159,"journal":{"name":"2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114643147","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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