Pub Date : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011967
Changda Tian, Yue Gao
Hexapod robots are good at traversing on complex terrains, yet its capability is challenging to define. The robot's traverse ability varies due to its structure, topology, and locomotion controller. The existing motion planner rarely considers the robot's traverse ability, causing higher failure risk when it gives motion commands that do not match the robot's capability. In this paper, we present CapPlanner, a hierarchical motion control and planning system which can do long-range locomotion control and planning according to the learned traverse capability of the robot in different topologies. It consists of two layers, the bottom-level controller computes the trajectory of the body and the feet according to the terrain, local target and current feets' positions. Besides, it controls the motors to track the calculated trajectory. The top-level controller learns the traverse ability of the robot with its bottom-level controller by simulating locomotion tasks on various terrains and in different topologies. Hence our CapPlanner can guide the robot to reach a long-term destination with a much higher success rate. In the experiment, we test CapPlanner in simulation and on our real hexapod robot, Qingzhui. The results show that CapPlanner is able to accomplish long distance and tough terrain locomotion planning for hexapod robot.
{"title":"CapPlanner: Adaptable to Various Topology and Locomotion Capability for Hexapod Robots","authors":"Changda Tian, Yue Gao","doi":"10.1109/ROBIO55434.2022.10011967","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011967","url":null,"abstract":"Hexapod robots are good at traversing on complex terrains, yet its capability is challenging to define. The robot's traverse ability varies due to its structure, topology, and locomotion controller. The existing motion planner rarely considers the robot's traverse ability, causing higher failure risk when it gives motion commands that do not match the robot's capability. In this paper, we present CapPlanner, a hierarchical motion control and planning system which can do long-range locomotion control and planning according to the learned traverse capability of the robot in different topologies. It consists of two layers, the bottom-level controller computes the trajectory of the body and the feet according to the terrain, local target and current feets' positions. Besides, it controls the motors to track the calculated trajectory. The top-level controller learns the traverse ability of the robot with its bottom-level controller by simulating locomotion tasks on various terrains and in different topologies. Hence our CapPlanner can guide the robot to reach a long-term destination with a much higher success rate. In the experiment, we test CapPlanner in simulation and on our real hexapod robot, Qingzhui. The results show that CapPlanner is able to accomplish long distance and tough terrain locomotion planning for hexapod robot.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126994791","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 : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011867
Qigao Fan, Jiawei Lu, Jie-hua Jia, Juntian Qu
This paper demonstrates the control system of magnetic microrobot driven by combined coils. The combined coils consist of three pairs of Helmholtz coils and three pairs of Maxwell coils. The rotating magnetic field, gradient magnetic field and combined magnetic field model of the combined coils were analyzed. A discrete-time optimal controller based on auto disturbance rejection control technology is used to realize fast response of output magnetic field to current. We have designed a closed-loop controller based on position servo. The control system consists of closed-loops of direction and position. As the sampling frequency of vision based position feedback of microrobot is not high enough, the actual position cannot be transmitted to the control system in time. Kalman filter algorithm is used to predict the position of microrobot in the movement process to improve the accuracy of control. Combined with the magnetic drive device and the proposed microrobot control method, simulation and experiment are carried out to verify the proposed scheme. The results show that the magnetic field driving microrobot is effective and the proposed method can improve the magnetic field response ability and the accuracy of the motion control of microrobot.1
{"title":"Two-dimensional Path Tracking Control of Microrobot Driven by Combined Magnetic Field","authors":"Qigao Fan, Jiawei Lu, Jie-hua Jia, Juntian Qu","doi":"10.1109/ROBIO55434.2022.10011867","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011867","url":null,"abstract":"This paper demonstrates the control system of magnetic microrobot driven by combined coils. The combined coils consist of three pairs of Helmholtz coils and three pairs of Maxwell coils. The rotating magnetic field, gradient magnetic field and combined magnetic field model of the combined coils were analyzed. A discrete-time optimal controller based on auto disturbance rejection control technology is used to realize fast response of output magnetic field to current. We have designed a closed-loop controller based on position servo. The control system consists of closed-loops of direction and position. As the sampling frequency of vision based position feedback of microrobot is not high enough, the actual position cannot be transmitted to the control system in time. Kalman filter algorithm is used to predict the position of microrobot in the movement process to improve the accuracy of control. Combined with the magnetic drive device and the proposed microrobot control method, simulation and experiment are carried out to verify the proposed scheme. The results show that the magnetic field driving microrobot is effective and the proposed method can improve the magnetic field response ability and the accuracy of the motion control of microrobot.1","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121583537","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}
Breathing plays an important part for human beings in our daily life. Besides physical parameters like tidal volume or respiratory rate, biomedical signals like electromyography (EMG) signals can be a potential candidate for breathing activity monitoring. In this work, we propose a novel scheme for breathing activity pattern recognition by fusing features extracted from both EMG and acceleration signals. The EMG signals and acceleration signals during four breathing activities usually used in our daily life, including normal breathing, fast breathing, coughing, and deep breathing, are captured. The raw data is preprocessed, feature extracted by several hand-crafted features, and pattern classified. The performance of five EMG feature sets, five acceleration feature sets, and two machine learning algorithms are evaluated. The best result achieves an accuracy of 82.20% using an EMG feature and an acceleration feature with a support vector machine (SVM) classifier. It shows that fusing EMG and acceleration data is better than EMG signals alone or acceleration signals alone, and it also raises the problem of finding the best features to reach higher performance. To the best of our knowledge, this is the first time that EMG signals are combined with acceleration signals for human breathing activity classification. The proposed approach is effective and explores a new way of human breathing monitoring.
{"title":"Breathing Pattern Recognition By the Fusion of EMG and Acceleration Signals","authors":"Dezhen Xiong, Daohui Zhang, Xingang Zhao, Yaqi Chu, Yiwen Zhao","doi":"10.1109/ROBIO55434.2022.10012002","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10012002","url":null,"abstract":"Breathing plays an important part for human beings in our daily life. Besides physical parameters like tidal volume or respiratory rate, biomedical signals like electromyography (EMG) signals can be a potential candidate for breathing activity monitoring. In this work, we propose a novel scheme for breathing activity pattern recognition by fusing features extracted from both EMG and acceleration signals. The EMG signals and acceleration signals during four breathing activities usually used in our daily life, including normal breathing, fast breathing, coughing, and deep breathing, are captured. The raw data is preprocessed, feature extracted by several hand-crafted features, and pattern classified. The performance of five EMG feature sets, five acceleration feature sets, and two machine learning algorithms are evaluated. The best result achieves an accuracy of 82.20% using an EMG feature and an acceleration feature with a support vector machine (SVM) classifier. It shows that fusing EMG and acceleration data is better than EMG signals alone or acceleration signals alone, and it also raises the problem of finding the best features to reach higher performance. To the best of our knowledge, this is the first time that EMG signals are combined with acceleration signals for human breathing activity classification. The proposed approach is effective and explores a new way of human breathing monitoring.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665149","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}
Surgical robots have been widely used in tumor resection, but they still have shortcomings such as the lack of tactile perception, which may lead to inaccurate intraoperative tumor identification and resection. A piezoresistive tactile array sensor is proposed in this paper, which features small size (10 mm X 10 mm) as well as the high-efficiency array detection mode. The sensing principle is simply applying a constant voltage to each tactile element and measuring the current to generate a tactile image. Its effectiveness and performance are verified by finite element simulations. In addition, a deep reinforcement learning-based autonomous detection algorithm is developed to further improve the detection efficiency and facilitate the planning of the resection path, which provides an effective guarantee for accurate tumor resection in future autonomous robotic surgeries.
{"title":"Autonomous tumor palpation and resection path planning using tactile array sensor and deep reinforcement learning for surgical robot","authors":"Feng Ju, Haoran Ye, Dongming Bai, Yingxuan Zhang, Chengjun Zhu, Yanfei Cao, Wenchao Yue","doi":"10.1109/ROBIO55434.2022.10012022","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10012022","url":null,"abstract":"Surgical robots have been widely used in tumor resection, but they still have shortcomings such as the lack of tactile perception, which may lead to inaccurate intraoperative tumor identification and resection. A piezoresistive tactile array sensor is proposed in this paper, which features small size (10 mm X 10 mm) as well as the high-efficiency array detection mode. The sensing principle is simply applying a constant voltage to each tactile element and measuring the current to generate a tactile image. Its effectiveness and performance are verified by finite element simulations. In addition, a deep reinforcement learning-based autonomous detection algorithm is developed to further improve the detection efficiency and facilitate the planning of the resection path, which provides an effective guarantee for accurate tumor resection in future autonomous robotic surgeries.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124537288","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 : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011894
Qingshuai Zhao, Haiyan Shao, Weixin Yang, Bin Chen, Zhiquan Feng, Hao Teng, Qi Li
This paper presents a fusion scheme to estimate the state of the quadruped robot dog using the pose estimation of the leg odometer and ORB-SLAM3 algorithm, which is continuous research to provide solutions to the existing problems of internal sensor-based pose state estimation. The problems are described as 1) electromagnetic interference and inaccurate zero position of the motor leading to the accumulation of integral errors in the IMU, and 2) low efficiency and instability of the compensation solutions for the IMU's yaw angular velocity. Aiming at the above problems, the advantages and disadvantages of pose estimation schemes of binocular cameras based on different algorithms are compared and analyzed through data sets experiments and real environment experiments. The Error-State Kalman Filter (ESKF) based fusion framework and formulas are proposed. The comparison fusion experiments using internal and external sensors are conducted with angular velocity compensation and without. The experimental results show a significant improvement in the accuracy and robustness of the pose estimation system, which is and the endpoint error accuracy of the fusion scheme without angular velocity compensation is improved by about 73.5 %.
{"title":"A Sensor Fusion Algorithm: Improving State Estimation Accuracy for a Quadruped Robot Dog","authors":"Qingshuai Zhao, Haiyan Shao, Weixin Yang, Bin Chen, Zhiquan Feng, Hao Teng, Qi Li","doi":"10.1109/ROBIO55434.2022.10011894","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011894","url":null,"abstract":"This paper presents a fusion scheme to estimate the state of the quadruped robot dog using the pose estimation of the leg odometer and ORB-SLAM3 algorithm, which is continuous research to provide solutions to the existing problems of internal sensor-based pose state estimation. The problems are described as 1) electromagnetic interference and inaccurate zero position of the motor leading to the accumulation of integral errors in the IMU, and 2) low efficiency and instability of the compensation solutions for the IMU's yaw angular velocity. Aiming at the above problems, the advantages and disadvantages of pose estimation schemes of binocular cameras based on different algorithms are compared and analyzed through data sets experiments and real environment experiments. The Error-State Kalman Filter (ESKF) based fusion framework and formulas are proposed. The comparison fusion experiments using internal and external sensors are conducted with angular velocity compensation and without. The experimental results show a significant improvement in the accuracy and robustness of the pose estimation system, which is and the endpoint error accuracy of the fusion scheme without angular velocity compensation is improved by about 73.5 %.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128082079","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 : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011879
Xiaofan Li, Xiang Luo, Kunhong Dou
To improve the control accuracy of robot joints under the influence of coupling, this paper uses a decoupling control method based on the coupling principle analysis to solve the kinematic and dynamic coupling caused by a three-axis concentric hip joint structure, taking the right leg of a 23 degree-of-freedom bipedal humanoid robot as the research object. Simscape is chosen to simulate the physical model of the right leg. And the Active Disturbance Rejection Decoupled Controller with a nonlinear Extended State Observer is designed, in which the derived gravity compensation is added to reduce the control difficulty and improve the observation accuracy. The initial value peaking is avoided by clipping the observer output. The simulation results show that, compared with the PID control with gravity feedback and coupling compensation, ADRDC has better dynamic and steady-state performance, higher position tracking accuracy and stronger anti-interference ability.
{"title":"Decoupling Control for Hip Joint of Humanoid Robot Based on ADRC","authors":"Xiaofan Li, Xiang Luo, Kunhong Dou","doi":"10.1109/ROBIO55434.2022.10011879","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011879","url":null,"abstract":"To improve the control accuracy of robot joints under the influence of coupling, this paper uses a decoupling control method based on the coupling principle analysis to solve the kinematic and dynamic coupling caused by a three-axis concentric hip joint structure, taking the right leg of a 23 degree-of-freedom bipedal humanoid robot as the research object. Simscape is chosen to simulate the physical model of the right leg. And the Active Disturbance Rejection Decoupled Controller with a nonlinear Extended State Observer is designed, in which the derived gravity compensation is added to reduce the control difficulty and improve the observation accuracy. The initial value peaking is avoided by clipping the observer output. The simulation results show that, compared with the PID control with gravity feedback and coupling compensation, ADRDC has better dynamic and steady-state performance, higher position tracking accuracy and stronger anti-interference ability.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127967568","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 : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011859
Yucheng Zhu, Yang Zhou, Wei Song
We propose a 6-DOF pose measurement method for industrial parts with complex shape based on monocular vision. According to the CAD file information, the 3D model of an industrial part is established. Then, an offline template library is obtained by the 3D model under different observation views, to reduce the actual online measurement time. The similarity function between the image and the template is established by a Canny-based improved Chamfer distance matching algorithm. The Chamfer distance image is divided into four layers by using the direction angles of the edge gradient, to improve the sensitivity of the matching function. Genetic algorithm (GA) is used to search for the optimal matching result, which combined with the hill-climbing method to make the searching process converge quickly. The experimental results show that our proposed method can measure the targets with known complex shapes in a 3D working environment, with the position error is within 2mm and the rotation error is within 2°. For dynamic parts, our proposed method can achieve fast matching, and the matching is applicable to different dynamic target parts, the model matching is only related to the shape of the part.
{"title":"An Evolutionary CAD model-based Pose Measurement Method for Industrial Parts based on Monocular Vision","authors":"Yucheng Zhu, Yang Zhou, Wei Song","doi":"10.1109/ROBIO55434.2022.10011859","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011859","url":null,"abstract":"We propose a 6-DOF pose measurement method for industrial parts with complex shape based on monocular vision. According to the CAD file information, the 3D model of an industrial part is established. Then, an offline template library is obtained by the 3D model under different observation views, to reduce the actual online measurement time. The similarity function between the image and the template is established by a Canny-based improved Chamfer distance matching algorithm. The Chamfer distance image is divided into four layers by using the direction angles of the edge gradient, to improve the sensitivity of the matching function. Genetic algorithm (GA) is used to search for the optimal matching result, which combined with the hill-climbing method to make the searching process converge quickly. The experimental results show that our proposed method can measure the targets with known complex shapes in a 3D working environment, with the position error is within 2mm and the rotation error is within 2°. For dynamic parts, our proposed method can achieve fast matching, and the matching is applicable to different dynamic target parts, the model matching is only related to the shape of the part.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508715","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 : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011834
Lei Xia, C. Shao, Huiyun Li, Yunduan Cui
This paper explores the potential of Lyapunov function approximated by neural networks in unmanned surface vehicles (USV) control problem. A novel model-based reinforcement learning method, Lyapunov filtered probabilistic model predictive control (LFPMPC) is proposed to explore the USV control policy under the guidance of Lyapunov neural networks. The USV system based on LFPMPC is developed and evaluated by a USV simulator driven by real boat data in position-keeping task with various environmental disturbances. Taking the output of Lyapunov neural networks as one metric of the system robustness in the cost function, the proposed approach demonstrated significant superiorities in not only control stability against disturbances but also learning capabilities of the system model compared with the baseline approach without Lyapunov neural networks.
{"title":"Robust Model-based Reinforcement Learning USV System Guided by Lyapunov Neural Networks","authors":"Lei Xia, C. Shao, Huiyun Li, Yunduan Cui","doi":"10.1109/ROBIO55434.2022.10011834","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011834","url":null,"abstract":"This paper explores the potential of Lyapunov function approximated by neural networks in unmanned surface vehicles (USV) control problem. A novel model-based reinforcement learning method, Lyapunov filtered probabilistic model predictive control (LFPMPC) is proposed to explore the USV control policy under the guidance of Lyapunov neural networks. The USV system based on LFPMPC is developed and evaluated by a USV simulator driven by real boat data in position-keeping task with various environmental disturbances. Taking the output of Lyapunov neural networks as one metric of the system robustness in the cost function, the proposed approach demonstrated significant superiorities in not only control stability against disturbances but also learning capabilities of the system model compared with the baseline approach without Lyapunov neural networks.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130424061","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 : 2022-12-05DOI: 10.1109/ROBIO55434.2022.10011724
Zhaoyu Zhang, H. Duan, Yang Yuan
Carrier aircraft is a commonly concerned issue in scientific research due to its extensive military use. Turbofan engine has equipped nearly every carrier aircraft to provide propulsion and gas flow. Gas path parameter identification is performed to establish a mathematical component model for dynamic in-loop simulation. In this paper, the identification is transformed into a two-stage optimization problem, solving by bionic intelligent computation and adaptive Newton Raphson (NR) Iteration. Adaptive step-size adjustment is applied in NR and dynamic scale coefficient in cost function brings convergence to the steady state equation of component model. To reduce the difficulty of deciding the initial status, typical mutation mechanism is utilized to enhance the exploitation characteristic of Pigeon-Inspired Optimization, which is effective in searching for the suitable initial value of NR method. Finally, comparative simulation is put forward to prove the satisfactory performance of the novel optimization method towards other typical swarm intelligence algorithm.
{"title":"Gas Path Parameter Identification of Turbofan Engine for Carrier Aircraft via Hybrid Mutated Pigeon-Inspired Optimization","authors":"Zhaoyu Zhang, H. Duan, Yang Yuan","doi":"10.1109/ROBIO55434.2022.10011724","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011724","url":null,"abstract":"Carrier aircraft is a commonly concerned issue in scientific research due to its extensive military use. Turbofan engine has equipped nearly every carrier aircraft to provide propulsion and gas flow. Gas path parameter identification is performed to establish a mathematical component model for dynamic in-loop simulation. In this paper, the identification is transformed into a two-stage optimization problem, solving by bionic intelligent computation and adaptive Newton Raphson (NR) Iteration. Adaptive step-size adjustment is applied in NR and dynamic scale coefficient in cost function brings convergence to the steady state equation of component model. To reduce the difficulty of deciding the initial status, typical mutation mechanism is utilized to enhance the exploitation characteristic of Pigeon-Inspired Optimization, which is effective in searching for the suitable initial value of NR method. Finally, comparative simulation is put forward to prove the satisfactory performance of the novel optimization method towards other typical swarm intelligence algorithm.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128590","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}
Rehabilitation training of patients who received total hip arthroplasty (THA) operation is necessary for their rebuilding of motor function. However, most existing exoskeleton devices for hip rehabilitation have an anthropomorphic structure. Misalignment between the mechanical and planted prothesis center is a problem that can cause additional stress in the hip for anthropomorphic exoskeletons, which are thus not applicable to THA rehabilitation training process. Also, the parasitic force due to the cable pulling of soft exoskeletons is also regarded as a shortcoming for users. To address these limitations and to provide training assistance for THA patients for better recovery, a novel hip exoskeleton with parallel structure is presented in this paper. The proposed exoskeleton has a remote actuation and Cable-conduit transmissions and is hence light in weight and can provide bidirectional assistive/resistive torque in the hip without generating stress in the hip, which is significant for THA patients having weak and sensitive planted hip joints. A controller is presented for a stable and safe human-machine interfacing during training with desired assistance delivered. Experiment results based on a benchmark platform verify the performance of the proposed exoskeletons system.
{"title":"Cable-Conduit-Driven Parallel Hip Exoskeleton and Its Implementation in Rehabilitation Training","authors":"Xiangyang Wang, Sheng Guo, Lianzheng Niu, Du-Xin Liu, Guangrong Chen","doi":"10.1109/ROBIO55434.2022.10011900","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011900","url":null,"abstract":"Rehabilitation training of patients who received total hip arthroplasty (THA) operation is necessary for their rebuilding of motor function. However, most existing exoskeleton devices for hip rehabilitation have an anthropomorphic structure. Misalignment between the mechanical and planted prothesis center is a problem that can cause additional stress in the hip for anthropomorphic exoskeletons, which are thus not applicable to THA rehabilitation training process. Also, the parasitic force due to the cable pulling of soft exoskeletons is also regarded as a shortcoming for users. To address these limitations and to provide training assistance for THA patients for better recovery, a novel hip exoskeleton with parallel structure is presented in this paper. The proposed exoskeleton has a remote actuation and Cable-conduit transmissions and is hence light in weight and can provide bidirectional assistive/resistive torque in the hip without generating stress in the hip, which is significant for THA patients having weak and sensitive planted hip joints. A controller is presented for a stable and safe human-machine interfacing during training with desired assistance delivered. Experiment results based on a benchmark platform verify the performance of the proposed exoskeletons system.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134093461","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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