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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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Bionic Vibrotactile Feedback for Stiffness Recognition Towards the Pinch of a Prosthetic Hand

Bionic Vibrotactile Feedback for Stiffness Recognition Towar...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Yun Fang Yujun Wu 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)9798350386523

ISBN: (纸本)9798350386530

The prosthetic hand has access to abundant hand-object information with equipped sensors. Nevertheless, there is a gap between high-dimensional sensory signals and effective tactile feedback for functional tasks with intuitive strategies. Here, we proposed a bionic vibrotactile interface providing stiffness feedback during a prosthetic hand pinching. The compact and flexible device consisted of four independently addressable eccentric rotating mass (ERMs). We designed a bionic coding (BC) strategy referenced from the physiological state of the force applied on skin. The proposed interface was evaluated on ten subjects with perceptual experiments, and compared with two traditional coding methods, i.e., circular coding (CC) and intensity coding (IC). The experiment results showed that CC and IC were limited by ERM pitch and the number of patterns for absolute information identification. Behavioral and psycho-physiological assessments demonstrated the superiority of BC in terms of classification accuracy $({85.94}\% \pm\ {3.29}\%)$ and cognitive burden $({4.0167} \pm {1.6376})$ . Specifically, CC with a pitch of 50 mm achieved best performance $({95.47}\% \pm\ {3.57}\%)$ for four-stiffness recognition and performed similarly to BC in psychophysical evaluation, whereas this configuration was not favorable for wearability and extendability. Moreover, subjects indicated that BC was a more natural representation for stiffness which they preferred in practical applications. The outcomes of this study indicated that the proposed vibrotactile interface can be intuitively exploited by subjects for stiffness recognition, proving the potential for extended multi-finger configuration of high-dimensional information feedback.

关键词： Sockets Force Tactile sensors Biomechatronics Encoding Skin Physiology Sensors Object recognition Prosthetic hand

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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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Tsun: A Traversability-Based Safety-Ensured Uneven Terrain Navigation system

SSRN

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SSRN 2024年

作者： Wang, Han Wang, Gongcheng Zhu, Hongbiao Du, Chengjin Bai, Hua Ding, Pengchao Xu, Wenda Du, Zhijiang Wang, Weidong The State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China Istituto Italiano di Tecnologia The BioRobotics Institute Scuola Superiore Sant’Anna Genova16163 Italy

The application of autonomous navigation in mobile robots has seen notable success in indoor, structured 2D environments. However, applying this technology to outdoor, 3D unstructured environments — especially on uneven terrains, presents significant challenges. To address this, we propose a navigation framework for uneven terrain based on continuous traversability analysis. Our methodology unfolds in three stages: First, Bayesian generalized kernel inference is employed to estimate attributes of the unobserved point cloud, enabling the computation of continuous terrain traversability values by using dispersed point cloud data. Second, an improved A* (TRA-A*) algorithm is proposed for global path determination. In the final stage, nonlinear model predictive control (NMPC) is utilized for path following. This involves integrating the traversable exponents and uncertainty into the cost function, resulting in a safety-focused motion planning algorithm that encourages the navigation stability of the robot. The effectiveness of this method was verified through simulation and actual scene experiments. This algorithm improves the robot's navigation success rate by 8% while sacrificing a small amount of 9.83% of the path length in the actual environment, preventing the robot from tipping over or getting stuck in rough terrain, and improving the stationarity of the robot during driving. It reduces the cost of the terrain the robot travels on by 42.6%, reduces the robot's longitudinal acceleration fluctuation and lateral angular velocity fluctuation by 28.52% and 28.9% respectively, and improves the robot's driving stability. © 2024, The Authors. All rights reserved.

关键词： Cost functions

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter 8

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Lin, Qiguang Yan, Chaojie Li, Qiang Ling, Yonggen Zheng, Yu Lee, Wangwei Wan, Zhaoliang Huang, Bidan Liu, Xiaofeng College of IoT Engineering Hohai University Jiangsu Key Laboratory of Special Robotic Technology Jiangsu Changzhou213022 China Zhejiang University Institute of Cyber-System and Control State Key Laboratory of Industrial Control and Technology Hangzhou China Bielefeld33619 Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350300178

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks. © 2023 IEEE.

关键词： Extended Kalman filters

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Human-like Walking of Biped Robot with Foot Rotation Using Passive Metatarsophalangeal Joint 12

Human-like Walking of Biped Robot with Foot Rotation Using P...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Dai, Xiaolin Liu, Yixiang Liu, Xinyu Zang, Xizhe Song, Rui Li, Yibin School of Control Science and Engineering Shandong University Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Jiangsu Automation Research Institute Lianyungang222000 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In the process of walking, humans are accompanied by the important action of foot rotation. The foot rotates taking the heel and toe as the axis of rotation when landing on and leaving the ground, respectively. Although having high similarity to humans, many biped robots do not reproduce this important feature. This paper describes a method for adding foot rotation into biped robots using the passive metatarsophalangeal joint. Firstly, the biomechanics of human metatarsophalangeal joint during walking is analyzed. Then, the control strategy based on finite state machine for human-like biped walking with foot rotation is proposed for a simplified biped robot model that is composed of eight joints. Finally, the simulation results of the walking gait of the biped robot in MATLAB demonstrate the effectiveness of the control strategy. © 2022 IEEE.

关键词： Rotation

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Viia-hand: a Reach-and-grasp Restoration system Integrating Voice interaction, Computer vision and Auditory feedback for Blind Amputees

arXiv

引用

arXiv 2023年

作者： Peng, Chunhao Yang, Dapeng Cheng, Ming Dai, Jinghui Zhao, Deyu Jiang, Li State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China Artificial Intelligence Laboratory Harbin Institute of Technology Harbin150080 China

Visual feedback plays a crucial role in the process of amputation patients completing grasping in the field of prosthesis control. However, for blind and visually impaired (BVI) amputees, the loss of both visual and grasping abilities makes the "easy" reach-and-grasp task a feasible challenge. In this paper, we propose a novel multi-sensory prosthesis system helping BVI amputees with sensing, navigation and grasp operations. It combines modules of voice interaction, environmental perception, grasp guidance, collaborative control, and auditory/tactile feedback. In particular, the voice interaction module receives user instructions and invokes other functional modules according to the instructions. The environmental perception and grasp guidance module obtains environmental information through computer vision, and feedbacks the information to the user through auditory feedback modules (voice prompts and spatial sound sources) and tactile feedback modules (vibration stimulation). The prosthesis collaborative control module obtains the context information of the grasp guidance process and completes the collaborative control of grasp gestures and wrist angles of prosthesis in conjunction with the user's control intention in order to achieve stable grasp of various objects. This paper details a prototyping design (named viia-hand) and presents its preliminary experimental verification on healthy subjects completing specific reach-and-grasp tasks. Our results showed that, with the help of our new design, the subjects were able to achieve a precise reach and reliable grasp of the target objects in a relatively cluttered environment. Additionally, the system is extremely user-friendly, as users can quickly adapt to it with minimal training. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer vision

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An Origami-Based Miniature Jumping Robot with Adjustable Jumping Trajectory and Enhanced Intermittent Jumps

An Origami-Based Miniature Jumping Robot with Adjustable Jum...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhipeng Xiong Lingqi Tang Longlong Hu Songlin Yang Xiaojun Yang Yao Li Bing Li School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

A small-scale jumping robot can reach obstacles much larger than its size. It is important for a jumping robot to perform intermittent jumps to cross through rough terrains. However, the limitations of conventional structures hinder the further integration of functions to a miniature (sub-50 g) jumping robot. No sub-50 g jumpers could perform intermittent jumps with adjustable jumping trajectories. In this work, we proposed an origami-based miniature jumper, which performed intermittent jumps with adjustable omni-directional trajectories. The intermittent jumps were achieved by the jumping and self-righting mechanisms, which were actuated by a single motor. The clockwise and counterclockwise rotation of the motor actuated the loading, self-righting and triggering process, respectively. The jumping height was adjustable by adjusting the rotation angle of the motor. Meanwhile, the take-off pitch & yaw angle adjustment methods were integrated into the robot. Therefore, we demonstrated a 9 cm, 13.5 g prototype with functions of re-loading, self-righting, jumping height adjustment and take-off pitch & yaw angle adjustment. The robot could adjust jumping height from 16 to 34 cm and self-right for the next jump. The results revealed that our robot could jump across different obstacles with different scales and directions. The mobility was greatly increased compared with other miniature jumping robots.

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Research on Multi-sensor Fusion Localization of Wheel-Legged Robots in Dynamic Scenarios

Research on Multi-sensor Fusion Localization of Wheel-Legged...

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Automation, Control and Robots (ICACR), International Conference on

作者： Xu Li Yili Fu Zuolun Cheng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Intelligent Robot Research Center Zhejiang Laboratory Hangzhou 311100 China

With the rapid development of human beings in motion control and localization perception, intelligent mobile robots are widely used in various scenarios, such as industrial production, medical care, logistics and distribution. Among them, wheel-legged robots have both the high mobility of wheeled robots and the high adaptability of legged robots, which can not only move fast on platform terrain, but also adapt to rugged and complex ground with the multiple degrees of freedom of their legs. Wheel-legged robots face the following main problems in high-precision robust localization in multiple scenarios: there are a lot of dynamic factors in daily indoor and outdoor scenarios, such as vehicles, pedestrians, etc., which require specific processing of dynamic environment in the visual front-end part to improve the effect of visual factor constraints; as ground mobile robots, the localization framework of visual inertial fusion (VIO) will show scale degeneration phenomenon, resulting in long-distance localization accuracy decline. In addition, as a two wheeled inverted pendulum robot, the ground constraint form will undergo corresponding changes. Aiming at the above problems, this paper takes the hydraulic-driven wheel-legged robot as the object and proposes a multi-sensor fusion localization method in dynamic scenarios.

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Learning to Identify Footholds from Geometric Characteristics for a Six-legged Robot over Rugged Terrain

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Journal of Bionic Engineering 2020年第3期17卷 512-522页

作者： Jie Chen Chong Liu Haibin Zhao Yanhe Zhu Jie Zhao School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

Foothold identification is a key ability for legged robots that allows generating terrain adaptive behaviors(e.g.,gait and control parameters)and thereby improving mobility in complex *** this end,this paper addresses the issue of foothold characterization and identification over rugged terrain,from the terrain geometry point of *** a terrain region that might be a potential foothold of a robotic leg,the characteristic features are extracted as two first-order partial derivatives and two curvature parameters of a quadric regression surface at this *** features are able to give an intuitive and,more importantly,accurate characterization towards the specific geometry of the ground *** this basis,a supervised learning technique,Support Vector Machine(SVM),is employed,seeking to Ieam a foothold identification policy from human expert *** a result,an SVM classifier is leamt using the extracted features and human-demonstrated labels,which is able to identify whether or not a certain ground location is suited as a safe foot support for a robotic *** is shown that over 90%identification rate can be achieved with the proposed ***,preliminary experiment is implemented with a six-legged robot to demonstrate the effectiveness of the proposed approach.

关键词： six-legged robot foothold characterization foothold identification geometric characteristics support vector machine

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