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IEEE International Conference on Robotics and Biomimetics

作者： Yidi Wang Xuan Zhao Qiuyu Shi Haisu Liao Xinyu Wu Fei Gao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

ISBN: (纸本)9781665481106

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W.

关键词： Legged locomotion Biomechanics Wearable computers Bending Optical fiber devices Piezoelectric devices Optical fiber testing

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A method of cliff detection in robot navigation based on multi-sensor

A method of cliff detection in robot navigation based on mul...

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

作者： Zhilong Su Can Wang Yuxiao Li Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technolog Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper proposed a method to detect cliff edge by using an RGB-D camera, and then to fuse the cliff information with LiDAR, finally to provide navigation information for robot autonomous exploration and autonomous movement. Compared with the prior methods which set sonar or infra-red sensor in the bottom of the robot, our method could detect cliff earlier by set the RGB-D camera forward. In cliff detection, we use region growing algorithm and the normal vector of plane to segment out ground plane in point cloud, then extract the boundary points of ground, and we proposed an Edge Point Filter (EPF) to get the edge points of cliff, then use the edge points to simulate LiDAR data and combine with real LiDAR data to mark the untraversable area. We verified the practicability of this method in simulation, and verified the accuracy with real sensors.

关键词： Laser radar Robots Three-dimensional displays Robot sensing systems Indexes Cameras Image edge detection

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A Learning Human-Based Method For Peg-In-Hole Assembly Using ELM

A Learning Human-Based Method For Peg-In-Hole Assembly Using...

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

作者： Juyi Sheng Sheng Liu Bing Zhu Chi Zhu Yongsheng Ou Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Maebashi Institute of Technology Japan Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems (#2019B121205007)

This paper focuses on using sensor information to adjust the position for assembly in the presence of uncertainty error in vision. Firstly, a human demonstration of the peg-in-hole assembly is provided, and the data is collected for the next step of training. We start with a human demonstration and collect training data. A network is trained with 6D Force/Torque sensor (F/T sensor) as input and a strategy of which direction the robot end-effector needs to move as output. An algorithm using Extreme Learning machines (ELM) search is proposed. This is a human-like method of searching for holes. We validated the effectiveness of our method on a KUKA iiwa 7 robot arm.

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Robust Bayesian optimization with reinforcement learned acquisition functions

arXiv

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arXiv 2022年

作者： Liu, Zijing Qu, Xiyao Liu, Xuejun Lyu, Hongqiang Ministry of Industry and Information Technology Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Key Laboratory of Aerodynamic Noise Control State Key Laboratory of Aerodynamics Mianyang621000 China

In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima. Prevailing AFs usually rely on artificial experiences in terms of preferences for exploration or exploitation, which runs a risk of a computational waste or traps in local optima and resultant re-optimization. To address the crux, the idea of data-driven AF selection is proposed, and the sequential AF selection task is further formalized as a Markov decision process (MDP) and resort to powerful reinforcement learning (RL) technologies. Appropriate selection policy for AFs is learned from superior BO trajectories to balance between exploration and exploitation in real time, which is called reinforcement-learning-assisted Bayesian optimization (RLABO). Competitive and robust BO evaluations on five benchmark problems demonstrate RL's recognition of the implicit AF selection pattern and imply the proposal's potential practicality for intelligent AF selection as well as efficient optimization in expensive black-box problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Interaction Design between Train Master control Unit and ETBN and SDTv2 Application

Interaction Design between Train Master Control Unit and ETB...

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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Zhaoling Gao Wei Wang Haoshang Zhou Demin Wang School of Intelligence and Electronic Engineering Dalian Neusoft University of Information DaLian China State Key Laboratory for Traction and Control System of EMU and Locomotive CRRC Dalian Electric Traction R&D Center Co. Ltd. DaLian China

At present, the train level network control communication mostly adopts the twisted Wire Train Bus (WTB) cable transmission. But its speed is slow and the amount of transmitted data is small, so it is difficult to meet the current requirements. Therefore, the high-speed and large amount of industrial Ethernet communication will become the mainstream of the future train level network control communication. It will research the train level network control communication based on the Train Real-time Data Protocol (TRDP) in combination with the requirements of the research project-200 km/h power centralized Electric Multiple Unit (EMU), and compare the international standards and the national standards of China in this paper. It will adopt the method of combining process data and message data to realize the data interaction requirements between the central control unit and the Ethernet Train Backbone Node (ETBN), acquire relevant data of Ethernet Train Backbone (ETB) nodes and vehicle formation information of the whole vehicle, and further acquire control data of the multiple connected vehicles. At the same time, transmit the control data and message data of the own vehicle to the multiple connected vehicles, so as to realize interconnection and mutual control between multiple connected vehicles. In order to improve the correctness and real-time of data transmission, it introduced the Secure Data Transmission protocol version2(SDTv2) for communication between the central control unit and other equipment in this project, and completed the interconnection test with the Ethernet card of Swiss Duagon company to ensure the safety and reliability of important data.

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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A Hierarchical Fusion Strategy Based on EEG and sEMG for Human-Exoskeleton system

A Hierarchical Fusion Strategy Based on EEG and sEMG for Hum...

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

作者： Mengyao Li Shengcai Duan Yao Dong Can Wang Wei Feng Xinyu Wu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Most of the human spinal cord injuries result in lower-limb disability. To integrate the brain's holistic motion intention with sEMG's control reliability for lower-limb motion neurorehabilitation with the exoskeleton, an ingenious hierarchical fusion strategy based on EEG and sEMG for ambulation with obstacle crossing task was proposed. Specifically, EEG delivers senior task-specific commands to execute refined locomotion while the specific implementation is determined by sEMG. The practical feasibility of ridge regression-based EEG decoding was proved firstly via offline and online methods involving 3 subjects, with subsequent online verification of the proposed strategy yielding higher than 85% of average accuracy. Furthermore, one subject was invited to wear the exoskeleton for the practical application of the strategy.

关键词： Electroencephalography Task analysis Exoskeletons Switches Legged locomotion Feature extraction Eigenvalues and eigenfunctions

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Intelligent Vision-Assisted Obstacle Avoidance and Unknown Seasonal Products Navigation in Farmer's Market for the Visually Impaired

SSRN

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SSRN 2023年

作者： Liu, Mei Chen, Yunhua Djoulde, Aristide He, Mengfan Rao, Jinjun Giernacki, Wojciech Wang, Zhiming Chen, Jinbo Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

Abstract: Shopping at a farmer's market is a considerable daily challenge for the visually impaired. General indoor obstacle avoidance and navigation algorithms are not fully applicable to the crowded and dynamic shopping environment of farmer's market. To overcome these challenges and provide precise assistance to visually impaired, this paper proposes an intelligent vision-assisted (IVA) algorithm for navigating narrow and crowded farmer's market aisles. The algorithm employs a pedestrian detection camera based on deep learning as the hardware to intelligently detect the type and number of pedestrians in front, and guides a navigation robot to adopt corresponding obstacle avoidance strategies in time according to the detection results. Different from general obstacle avoidance methods, IVA doesn't have issues of guiding robot to make frequent turns and change routes when avoiding obstacles in a crowded and narrow environment, making it more suitable for the visually impaired. Additionally, this paper proposes an unknown seasonal product navigation (USPN) algorithm for robots to automatically search for navigation information that has changed. When used in conjunction with a camera driven by deep learning, USPN successfully guided a navigation robot to identify the names and coordinates of newly listed products. Compared with general navigation algorithms, USPN can guide robots to update product information timely and intelligently. Experimental tests conducted in a farmer's market demonstrated that IVA is more intelligent and efficient than the dynamic window approach (DWA) in obstacle avoidance performance, and it is more suitable for guiding the visually impaired;furthermore, USPN successfully guides the robot to automatically acquire products information that changes with the seasons, and the acquired product categories and locations are precise. Particularly, IVA and USPN can be used as references when designing guide robots for use in public spaces like super

关键词： Navigation

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AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models

arXiv

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arXiv 2024年

作者： Yao, Yifei He, Wentao Gu, Chenyu Du, Jiaheng Tan, Fuwei Zhu, Zhen Lu, Junguo All authors are with Machine Vision and Autonomous System Laboratory Department of Automation School of Electrical Information and Electronic Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China

Training and deploying reinforcement learning (RL) policies for robots, especially in accomplishing specific tasks, presents substantial challenges. Recent advancements have explored diverse reward function designs, training techniques, simulation-to-reality (sim-to-real) transfers, and performance analysis methodologies, yet these still require significant human intervention. This paper introduces an end-to-end framework for training and deploying RL policies, guided by Large Language Models (LLMs), and evaluates its effectiveness on bipedal robots. The framework consists of three interconnected modules: an LLM-guided reward function design module, an RL training module leveraging prior work, and a sim-to-real homomorphic evaluation module. This design significantly reduces the need for human input by utilizing only essential simulation and deployment platforms, with the option to incorporate human-engineered strategies and historical data. We detail the construction of these modules, their advantages over traditional approaches, and demonstrate the framework’s capability to autonomously develop and refine controlling strategies for bipedal robot locomotion, showcasing its potential to operate independently of human intervention. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments

arXiv

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arXiv 2024年

作者： Li, Haoang Meng, Xiangqi Zuo, Xingxing Liu, Zhe Wang, Hesheng Cremers, Daniel Guangzhou China School of Computation Information and Technology Technical University of Munich Munich Germany MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Shanghai China

Simultaneous localization and mapping (SLAM) has achieved impressive performance in static environments. However, SLAM in dynamic environments remains an open question. Many methods directly filter out dynamic objects, resulting in incomplete scene reconstruction and limited accuracy of camera localization. The other works express dynamic objects by point clouds, sparse joints, or coarse meshes, which fails to provide a photo-realistic representation. To overcome the above limitations, we propose a photo-realistic and geometry-aware RGB-D SLAM method by extending Gaussian splatting. Our method is composed of three main modules to 1) map the dynamic foreground including non-rigid humans and rigid items, 2) reconstruct the static background, and 3) localize the camera. To map the foreground, we focus on modeling the deformations and/or motions. We consider the shape priors of humans and exploit geometric and appearance constraints of humans and items. For background mapping, we design an optimization strategy between neighboring local maps by integrating appearance constraint into geometric alignment. As to camera localization, we leverage both static background and dynamic foreground to increase the observations for noise compensation. We explore the geometric and appearance constraints by associating 3D Gaussians with 2D optical flows and pixel patches. Experiments on various real-world datasets demonstrate that our method outperforms state-of-the-art approaches in terms of camera localization and scene representation. Source codes will be publicly available upon paper acceptance. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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