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arXiv 2022年

作者： Miki, Takahiro Lee, Joonho Hwangbo, Jemin Wellhausen, Lorenz Koltun, Vladlen Hutter, Marco Robotic Systems Lab ETH Zurich Zurich Switzerland Robotics and Artificial Intelligence Lab KAIST Daejeon Korea Republic of Intelligent Systems Lab Intel JacksonWY United States

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into under-explored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: perceiving the terrain before making contact with it enables planning and adaptation of the gait ahead of time to maintain speed and stability. However, utilizing exteroceptive perception robustly for locomotion has remained a grand challenge in robotics. Snow, vegetation, and water visually appear as obstacles on which the robot cannot step – or are missing altogether due to high reflectance. Additionally, depth perception can degrade due to difficult lighting, dust, fog, reflective or transparent surfaces, sensor occlusion, and more. For this reason, the most robust and general solutions to legged locomotion to date rely solely on proprioception. This severely limits locomotion speed, because the robot has to physically feel out the terrain before adapting its gait accordingly. Here we present a robust and general solution to integrating exteroceptive and proprioceptive perception for legged locomotion. We leverage an attention-based recurrent encoder that integrates proprioceptive and exteroceptive input. The encoder is trained end-to-end and learns to seamlessly combine the different perception modalities without resorting to heuristics. The result is a legged locomotion controller with high robustness and speed. The controller was tested in a variety of challenging natural and urban environments over multiple seasons and completed an hour-long hike in the Alps in the time recommended for human hikers. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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A Sensing Platform to Monitor Sleep Efficiency 11th

A Sensing Platform to Monitor Sleep Efficiency

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11th Italian Forum on Ambient Assisted Living, ForItAAL 2020

作者： Crivello, Antonino La Rosa, Davide Wilhelm, Elisabeth Palumbo, Filippo 1 Via G. Moruzzi Pisa56124 Italy Department of Health Sciences and Technology Istitute of Robotics and Intelligent Systems Sensory Motor Systems Lab ETH Zurich Zurich8092 Switzerland Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9783031088377

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sleep research

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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ViT-based Terrain Recognition System for wearable soft exosuit

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Biomimetic Intelligence & robotics 2023年第1期3卷 57-64页

作者： Fangliang Yang Chunjie Chen Zhuo Wang Hui Chen Yao Liu Gang Li Xinyu Wua Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhen 518055China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055China SIASUN Robot and Automation CO. LtdShenyang 110169China

Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist forces that can easily injure the *** of these problems,we introduced a depth camera into the exosuit system,perform classification of terrain based on a Vision Transformer(ViT),and optimized the control algorithm,which is known as a ViT-Based Terrain Recognition System(TTRS).First,we used the Transformer algorithm to achieve a considerable classification effect in terrain *** also introduced terrain recognition as prior knowledge into the force assistance strategy of the exosuit,providing different force assistance to the exosuit in different ***,we performed human experiments with seven able-bodied people(six males and one female).The promising results demonstrate that our classification accuracy can reach 99.2%under six different terrains and that it can smoothly switch the force–assist curve in different terrains to better adapt to the complex terrain and improve the walking *** aforementioned terrain recognition algorithms and force–assist strategies may positively influence the study of soft exosuit,powered prostheses,and orthotics.

关键词： Vision Transformer(ViT) Depth camera Wearable soft exosuit Force assistance strategy Terrain recognition

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SIN: A Programmable Platform for Swarm robotics

SIN: A Programmable Platform for Swarm Robotics

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International CSI Computer Conference, CSICC

作者： Ali Foroutannia Milad Shoryabi Amirali Alizadeh Anaraki Alireza Rowhanimanesh Intelligent Systems & Robotics Lab University of Neyshabur Neyshabur Iran

Swarm robotics is an inspiration from nature and incorporates swarm intelligence to help collective robotics. This recent technology is usually characterized by a swarm of simple, low-cost, and small robots instead of a complicated and expensive robot. Designing optimal and reliable swarm intelligence algorithms require real-world test environments. As a practical solution, physical platforms can efficiently address this issue. In this paper, a programmable physical platform, called SIN, is introduced for swarm robotics. Different design parameters such as communication range, signaling pattern, types of sensors and actuators, cooperation rules, and degree of uncertainty and noise can be simply adjusted by user. The building blocks of each agent has been developed in a modular form to improve the hardware flexibility. To illustrate the efficiency of the proposed platform, a cooperative multi-robot target tracking problem is implemented on this platform as a case study, where the robots interact by artificial attraction-repulsion forces based on short-range and noisy optical communication. The results demonstrate how the details of swarm behaviors such as decentralized aggregation and collective target tracking can be successfully implemented on the proposed platform.

关键词： Actuators Target tracking Uncertainty Heuristic algorithms Swarm robotics Robot sensing systems Transceivers

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Exoskeleton-Based Lower Limb Rehabilitation Robot Design and Simulation for Children with Cerebral Palsy

Exoskeleton-Based Lower Limb Rehabilitation Robot Design and...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Shuo Zhang Yupeng Zou Xinqiang Guo Changyu Li Jingxin Wang Worawarit Kobsiriphat Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems. SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China Jining Zhongke Intelligent Technology Co. Ltd. Department of Rehabilitation Medicine Zhengzhou Central Hospital Affiliated Zhengzhou University Zhengzhou China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Squat gait is the most common abnormal gait in children with cerebral palsy. Gait training with lower limb rehabilitation robots can improve rehabilitation efficiency and reduce manual labor for them. A novel exoskeleton-based lower limb rehabilitation robot for children with cerebral palsy is proposed, considering the squat gait characteristics of children with cerebral palsy. The structure of the lower limb rehabilitation robot, which combines hip and knee with 4 active degrees of freedom exoskeleton and mobile walker, is designed and optimized. Static analysis of the key mechanical structure of the exoskeleton is conducted using ANSYS Workbench, and local structures are optimized to ensure the safety factor of the mechanical structure is greater than 1, guaranteeing reasonable structural strength and stiffness design. Dynamic simulation analysis based on the ADAMS model validates the reliability and dynamic walking effect of the lower limb exoskeleton rehabilitation robot, obtaining the curve relationship between joint angles and motion cycles, thus verifying the effectiveness of the mechanical system motion.

关键词： Knee Training Cerebral palsy Exoskeletons Dynamics Static analysis Assistive robots Safety Finite element analysis Hip

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Transformer-Based Fusion of RGB and Depth Images for Terrain Recognition

Transformer-Based Fusion of RGB and Depth Images for Terrain...

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International Conference on Biomimetic Intelligence and robotics & Medical robotics Forum, ICBIR+MRF 2024

作者： Chen, Hui Chen, Chunjie Wang, Zhuo Yang, Fangliang Pan, Heng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation. © 2024 The Authors.

关键词： RGB color model

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Guided Exploration Leads to Faster Familiarization with a Wearable Robot: First Results of an Innovative Protocol

Guided Exploration Leads to Faster Familiarization with a We...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Gleb Koginov Peter Wolf Kai Schmidt Jaime E. Duarte Robert Riener Sensory-Motor Systems (SMS) Lab Institute of Robotics and Intelligent Systems (IRIS) ETH Zurich Switzerland MyoSwiss AG Zurich Switzerland Spinal Cord Injury Center Medical Faculty Balgrist University Hospital University of Zurich Switzerland

Wearable robots show promise in addressing physical and functional deficits in individuals with mobility impairments. However, the process of learning to use these devices can take a long time. In this study, we propose a novel protocol to support the familiarization process with a wearable robot (the Myosuit) and achieve faster walking speeds. The protocol involves applying an anterior pulling force while participants perform a series of 10-meter Walking Tests (10mWT) with or without the Myosuit under various experimental conditions. We hypothesized that guiding the exploration of novel walking patterns can help the users learn to exploit the Myosuit's assistance faster by leading to larger step lengths and ultimately higher walking speeds. In this paper, we present the preliminary results of the protocol with seven participants with lower-limb mobility impairments. Participants who were assisted by the Myosuit showed a continuous increase in walking speed over the course of the pulling part of the experiment with a maximum increase of 41.3% (10.4%) when compared to the baseline 10mWT. Following the removal of the pulling force, these participants continued to show an increased walking speed while being supported by the Myosuit. This higher walking speed was primarily due to a significant increase in step length of 24% (16.6%) and cadence of 11% (8.9%). The results of this study may help the development of familiarization techniques for wearable robots.

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Different Height Stairs Climbing Pattern Generation for Humanoids

Different Height Stairs Climbing Pattern Generation for Huma...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Ming Yang Dingkui Tian Minjia Xu Feng Li Jianquan Sun Weiwei Shang Xinyu Wu University of Science and Technology of China Hefei P. R. China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Daqing Vocational College Daqing China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen China

Climbing stairs has been an indispensable ability for humanoids. This paper presents a novel and practical problem: climbing stairs of varying heights. In this study, the humanoid is modeled as an inverted pendulum and set the pelvis trajectory on the vertical axis, representing the center of mass (CoM), as a quadratic function. We also use a polynomial to design the ZMP and CoM trajectory on the horizontal plane. To generate the CoM trajectory, the sliding mode controller is utilized to track the reference trajectory. To eliminate chattering of the sliding mode controller, the universal approximation property of the radial basis function (RBF) network is leveraged to approximate the switching law. Finally, simulation experiments are performed in CoppeliaSim, and the biped robot is capable of walking stably and quickly.

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Design of a Force-Controlled End-Effector with Slender Flexible Beams 16th

Design of a Force-Controlled End-Effector with Slender Fl...

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

作者： Chen, Yin Chen, Genliang Chai, Yuchen Wang, Hao Kong, Lingyu State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong University Shanghai200240 China Meta Robotics Institute Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Digital Manufacturing for Thin-Walled Structures Shanghai Jiao Tong University Shanghai200240 China Intelligent Robot Research Center Zhejiang Lab Hangzhou China

ISBN: (纸本)9789819965007

This paper proposes a force-controlled end-effector with slender flexible beams. On the one hand, it can actively control the deformation of slender flexible beams to output a constant force at the end-effector without using a force sensor. On the other hand, it serves as a passive compliant mechanism utilizing the large deformation characteristic of slender flexible beams to prevent structural damage. The kinetostatic modeling of the force-controlled end-effector has been derived to obtain the relationship between the output force and displacement. To conduct experimental tests, a prototype of the force-controlled end-effector has been fabricated. From the experimental results, it can be seen that the error of the force-controlled end-effector is within 0.5N. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Compliant mechanisms

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