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IEEE International Conference on robotics and Biomimetics

作者： Yanchao Zhao Weichao Guo Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Meta Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Proximity-pressure dual-mode capacitive sensor is a novel sensing form to simultaneously detect distance and pressure information through a single device. In recent years, much effort has been devoted to improve its performance. However, it still suffers from robustness issues such as crosstalk and noise in practical applications. Electromagnetic coupling crosstalk caused by the outward electric fields as well as the response from unexpected directions also limit its distributed or array arrangements. In this regard, this paper proposes 3D-shielding structure as well as isolated iontronic dielectric designs to reduce the effects of electromagnetic/mechanical crosstalk. Through simulations and experiments, we demonstrate the effectiveness of the proposed design and optimise the structural parameters. The finally obtained sensing skin has an excellent crosstalk rejection ratio (15.68 dB, approximately three times improvement) while maintaining high response sensitivity.

关键词： Performance evaluation Sensitivity Crosstalk Robot sensing systems Skin Robustness Sensors Dielectrics Structural engineering Electric fields

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Open-Source Reinforcement Learning Environments Implemented in MuJoCo with Franka Manipulator

arXiv

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arXiv 2023年

作者： Xu, Zichun Li, Yuntao Yang, Xiaohang Zhao, Zhiyuan Zhuang, Lei Zhao, Jingdong The State Key Laboratory of Robotics and Systems Harbin Institute of Technology Heilongjiang Province Harbin150001 China

This paper presents three open-source reinforcement learning environments developed on the MuJoCo physics engine with the Franka Emika Panda arm in MuJoCo Menagerie. Three representative tasks, push, slide, and pick-and-place, are implemented through the Gymnasium robotics API, which inherits from the core of Gymnasium. Both the sparse binary and dense rewards are supported, and the observation space contains the keys of desired and achieved goals to follow the Multi-Goal Reinforcement Learning framework. Three different off-policy algorithms are used to validate the simulation attributes to ensure the fidelity of all tasks, and benchmark results are also given. Each environment and task are defined in a clean way, and the main parameters for modifying the environment are preserved to reflect the main difference. The repository, including all environments, is available at https://***/zichunxx/panda_mujoco_gym. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Enhancing Household Service Robots with a Dual-Arm Mobile Manipulator and Multimodal Large Language Models

Enhancing Household Service Robots with a Dual-Arm Mobile Ma...

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IEEE International Conference on robotics and Biomimetics

作者： Yang Liu Yanchao Zhao Weichao Guo Xinjun Sheng Han Ding State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Meta Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This research aims to enhance the practicality of household service robots by integrating multimodal large language models (MLLMs). Utilizing a dual-arm mobile manipulator platform, our focus is on advancing autonomous mobile manipulation in home environments. By leveraging the advanced text understanding and visual processing capabilities inherent in MLLMs, the proposed system adeptly converts user dialogues and environmental visual inputs into precise robotic plans. Among the suite of tested models, GPT-4o mini and Gemini 1.5 flash perform exceptionally well in generating accurate robotic plans. These results underscore a significant enhancement in the robot's ability to manipulate objects within complex home environments, thereby contributing to more seamless human-robot interactions and markedly improved task execution. We anticipate that our findings will inspire future advances in the field of human-robot interaction for home robots.

关键词： Visualization Translation Service robots Target recognition Large language models Human-robot interaction Grasping Manipulators Software Plastics

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Attitude control of flapping wing aircraft based on energy optimization and ESO

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Biomimetic Intelligence & robotics 2021年第1期1卷 36-41页

作者： Luo Li Hongwei Wang Long Cui State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110016China University of Chinese Academy of Sciences Beijing 100049China

Aiming at the problem of insufficient endurance performance of flapping wing aircraft,a stable attitude control algorithm based on energy optimization and ESO(extended state observer)is designed,which effectively reduces the energy consumption in cruise ***,the longitudinal dynamic model of flapping wing aircraft is established,and then the uncertain part of the system and various unknown external disturbances are taken as the total *** module is introduced to observe and track the total disturbance in real ***,the system is transformed into a series integral system through the total disturbance feedback,and then the energy optimal control law is designed on the base of the transformed *** numerical simulation results show that,compared with the traditional PID control method,the designed energy optimal control method reduces the average energy consumption by 35.28%.

关键词： Flapping wing Energy optimal control ESO(Extended state Observer)

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Reliability Design of Manipulator Joint for China Space Station 2

Reliability Design of Manipulator Joint for China Space Stat...

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2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Li, Delun Zhu, Chao Zhang, Yun Bai, Mei Huang, Zhipeng Xie, Zongwu Liu, Teng Fu, Hao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering CAST Beijing100094 China Beijing Satellite Manufacturing Co. Ltd. Beijing100094 China Beijing Institute of Space Environment Engineering Beijing100094 China

ISBN: (纸本)9781510657717

The TianHe manipulator is responsible for major tasks such as China Space Station construction, operation, and supporting astronaut's extravehicular operations. The joint is the direct execution part of the manipulator to realize flexible movement, and it is the key component to ensure the core functions and performance of the China Space Station manipulator, The composition of the joint is complex, and the experience of its reliability design is very poor. So the research of reliability design method for the joint is of great significance. Starting from the joint reliability design method, this paper uses various methods such as redundant design, margin design, and environmental adaptability design to complete the joint reliability design comprehensively. The ideas and methods for reliability design for the joint of the China Space Station manipulator introduced in this paper have important reference value for the reliability design for the space complicated electromechanical products. © 2022 SPIE. All rights reserved.

关键词： Space stations

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A simulation framework for ultrasound-guided minimally invasive robotic breast surgery

A simulation framework for ultrasound-guided minimally invas...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Le Ye, Yanchen Niu, Baoshan Xiong, Genliang Yang, Dapeng State Key Laboratory of Robotics and System Harbin Institute of Technology Yikuang Street NanGang District HeilongJiang Harbin China School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Longteng Rd 333 Shanghai201620 China

ISBN: (纸本)9781665481090

Surgical simulation plays an essential role for surgeons to familiarize themselves with surgical procedures and improve the success rate of surgery. In this paper, a flexible and easy-extendable intervention surgery simulation framework was presented, which can perform virtual simulation training and realize synchronous interaction between the real and virtual environment. In particular for breast surgery, the simulation framework was exemplified for an ultrasound-guided minimally invasive robotic breast surgery. The framework used Visual Studio®(Microsoft, USA) and Chai3D open graphics library to realize the virtual surgery environment, and integrated the anti-collision warning of the robotic arms and teleoperation communication support. A layered deformation model of breast was established based on the Mass-Spring Model to simulate the real-time deformation of breast during puncture surgery. In addition, due to the hyperelasticity and great deformation characteristics of breast soft tissue, we further used an improved meshless Radial Point Interpolation Method to improve the simulation accuracy of breast soft tissue deformation model. Finally, experiments on robot pressing a rectangular parallelepiped breast phantom were carried out to verify the simulation accuracy of the deformation model. © 2022 IEEE.

关键词： Tissue

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Cooperative Control of Dual-Arm Robot of Adaptive Impedance Controller Based on RBF Neural Network 16th

Cooperative Control of Dual-Arm Robot of Adaptive Impedance ...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Zhang, Tongyan Wang, Ting Shao, Shiliang Cao, Zonghan Dou, Xinke Qin, Hongwei School of Artificial Intelligence Shenyang University of Technology Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Software Shenyang University of Technology Shenyang China School of Information Engineering Shenyang University of Chemical Technology Shenyang China

ISBN: (纸本)9789819964918

Traditional impedance control methods often fail to accurately track force signals in unknown or changing environments, resulting in failure or instability in tasks such as coordinated handling. To solve the above problems, this paper adds the RBF neural network strategy, based on the traditional impedance control. First a model for the contact force between the dual redundant robotic arms and the environment is constructed, and the RBF neural network is used to estimate the stiffness of the changing environment online. Then, a dynamic adaptive force control co-simulation model is established. The change in the contact force is adapted to adjust the parameters of the two-arm impedance model to compensate for unknown environmental changes. Simulation experiments showed that the enhanced impedance control strategy is appropriate for the force-interaction circumstances of the robotic arm within the positional environment, has a stronger durability, enhances the sturdiness of the two-armed working together robot in an environment that shifts, and has a more effective force control effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Robotic arms

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Multi-DoF Continuous Estimation for Wrist Torques Using Convolutional Neural Network

Multi-DoF Continuous Estimation for Wrist Torques Using Conv...

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IEEE International Conference on robotics and Biomimetics

作者： Yun Fang Yang Yu Weichao Guo Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Meta Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Using surface electromyography (sEMG) signals for simultaneous and proportional control (SPC) is appealing for natural control in human-machine interaction (HMI). Introducing spatial information across sEMG channels in the decoding process provides a more complete approach than methods that focus only on time and frequency domain features. In this article, we proposed an sEMG-based model for thesi-multaneous estimation of two degree-of-freedom (DoF) wrist torques, namely, Pro-Sup and Fle-Ext. Specifically, the 8 × 24 channels sEMG signals were transformed into images, which further served as inputs in a 29-layer convolutional neural network to estimate multi-DoF wrist torques. To prove the superiority of the CNN-based model, comparisons with other regression methods were performed on eight healthy subjects and two amputees in terms of the determination coefficient (R2) and normalized root mean square error (nRMSE). The results demonstrated that the CNN-based model (CNN-WAF) outperformed two conventional regression methods on estimation accuracy (R2: 0.851 ± 0.024 for Pro-Sup and 0.863 ± 0.016 for Fle-Ext; nRMSE: 6.933% ± 0.872% for Pro-Sup and 6.830% ± 0.856% for Fle-Ext). The average filtering in data preprocessing greatly improves (p < 0.05) the smoothness and performance of the estimation (CNN-WOAF: R2: 0.837 ± 0.015 for Pro-Sup and 0.853 ± 0.021 for Fle-Ext; nRMSE: 7.487% ± 0.870% for Pro-Sup and 7.370% ± 0.866% for Fle-Ext). Overall, the outcomes of this study provide meaningful methods to utilize spatiotemporal information from sEMG signals for SPC in practical applications.

关键词： Wrist Accuracy Torque Filtering Data preprocessing Estimation Decoding Convolutional neural networks Robots Surface treatment

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Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulation

Efficient Model Learning and Adaptive Tracking Control of Ma...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Yongyi Jia Shu Miao Junjian Zhou Niandong Jiao Lianqing Liu Xiang Li Department of Automation Tsinghua University China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object, or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.

关键词： Adaptation models Target tracking Navigation Trajectory tracking Neural networks Organisms Trajectory

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Design and Research for the Control system of a Micro Autonomous Underwater Vehicle 5

Design and Research for the Control System of a Micro Autono...

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5th International Conference on robotics, Control and Automation Engineering, RCAE 2022

作者： Quanshan, Wang Tao, Han Chuanzhi, Fan Yi, Yang Zhiqiang, Hu Shenyang Ligong University Liaoning Province Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Province Shenyang China

ISBN: (纸本)9781665459419

A three-inch diameter Micro Autonomous Underwater Vehicle(Micro AUV) control system was designed and developed, of which the hardware and software control platform based on the MOOS system and BeagleBone Black as the core microcontroller was built. Based on the structure and navigation features of the Micro AUV, a cascade PID controller was designed for the Micro AUV to sail underwater with a certain heading and depth. Finally, the validity and reliability of the control system and controller were verified by the underwater experiments in the lake. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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