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

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Bio-inspired visual relative localization for large swarms of UAVs

Bio-inspired visual relative localization for large swarms o...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Martin Křížek Matouš Vrba Antonella Barišić Kulaš Stjepan Bogdan Martin Saska Faculty of Electrical Engineering Czech Technical University in Prague Prague Laboratory for Robotics and Intelligent Control Systems (LARICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We propose a new approach to visual perception for relative localization of agents within large-scale swarms of Unmanned Aerial Vehicles (UAVs). Inspired by biological perception utilized by schools of sardines, swarms of bees, and other large groups of animals capable of moving in a decentralized yet coherent manner, our method does not rely on detecting individual neighbors by each agent and estimating their relative position, but rather we propose to regress a neighbor density over distance. This allows for a more accurate distance estimation as well as better scalability with respect to the number of neighbors. Additionally, a novel swarm control algorithm is proposed to make it compatible with the new relative localization method. We provide a thorough evaluation of the presented methods and demonstrate that the regressing approach to distance estimation is more robust to varying relative pose of the targets and that it is suitable to be used as the main source of relative localization for swarm stabilization.

关键词： Location awareness Visualization Scalability Estimation Object detection Autonomous aerial vehicles Robustness

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Performance Comparison of Teleoperation Interfaces for Ultra-Lightweight Anthropomorphic Arms

Performance Comparison of Teleoperation Interfaces for Ultra...

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

作者： Filip Zorić Alejandro Suarez Goran Vasiljević Matko Orsag Zdenko Kovačić Anibal Ollero Laboratory for Robotics and Intelligent Control Systems (LARICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia GRVC Robotics Lab University of Seville Sevilla Spain

This paper presents a comparative performance evaluation of three different teleoperation interfaces for very low weight (<3 kg) anthropomorphic dual arms intended to conduct complex manipulation tasks involving a certain level of dexterity, accuracy and agility, either in ground service or in aerial manipulation applications. A visual human pose estimation system is developed to obtain the Cartesian and joint values of the user, which are mapped to the corresponding pose of the dual arm manipulator exploiting the equivalent human-robot kinematics. A leader-follower scheme is also presented, using a reduced scale dual arm that can replicate directly the joint positions of the leader arms to the follower arms. A 6-DOF (degrees of freedom) joystick is proposed to generate linear motions more accurately. A total of 60 ground tests were conducted involving 10 participants to determine the accuracy and time performance in two benchmarks (box edges and S contour tracking). Finally, the visual and leader-follower interfaces were evaluated with the dual arm aerial manipulator on flight tests, reporting several findings derived from the system evaluation.

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A distributed variable density path search and simplification method for industrial manipulators with end-effector's attitude constraints

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Frontiers of Information Technology & Electronic engineering 2023年第4期24卷 536-552页

作者： Jin WANG Shengjie LI Haiyun ZHANG Guodong LU Yichang FENG Peng WANG Jituo LI State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering.Zhejiang UniversityHangzhou 310027China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China School of Robotics Ningbo University of TechnologyNingbo 31521lChina

In many robot operation scenarios, the end-effector's attitude constraints of movement are indispensable for the task process, such as robotic welding, spraying, handling, and stacking. Meanwhile, the inverse kinematics, collision detection, and space search are involved in the path planning procedure under attitude constraints, making it difficult to achieve satisfactory efficiency and effectiveness in practice. To address these problems, we propose a distributed variable density path planning method with attitude constraints(DVDP-AC) for industrial robots. First, a position – attitude constraints reconstruction(PACR) approach is proposed in the inverse kinematic solution. Then, the distributed signed-distance-field(DSDF) model with single-step safety sphere(SSS) is designed to improve the efficiency of collision detection. Based on this, the variable density path search method is adopted in the Cartesian space. Furthermore, a novel forward sequential path simplification(FSPS) approach is proposed to adaptively eliminate redundant path points considering path accessibility. Finally, experimental results verify the performance and effectiveness of the proposed DVDP-AC method under end-effector's attitude constraints, and its characteristics and advantages are demonstrated by comparison with current mainstream path planning methods.

关键词： Path planning Industrial robots Distributed signed-distance-field Attitude constraints Path simplification

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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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2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Zhang, Shuo Zou, Yupeng Guo, Xinqiang Li, Changyu Wang, Jingxin Kobsiriphat, Worawarit Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China Jining Zhongke Intelligent Technology Co. Ltd. China Zhengzhou Central Hospital Zhengzhou University Department of Rehabilitation Medicine Zhengzhou China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

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. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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A Novel Transfer Learning Method for Robot Bearing Fault Diagnosis Based on Deep Convolutional Residual Wasserstein Adversarial Network 16th

A Novel Transfer Learning Method for Robot Bearing Fault Dia...

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

作者： Pan, Bing Xiong, Xin Hu, Hailiang He, Jun Yang, Shixi The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Hangzhou Steam Turbine Power Group Co. Ltd. Hangzhou China

ISBN: (纸本)9789819965007

In the process of robot bearing fault diagnosis based on data-driven, transfer learning is an effective method to solve the lack of labeled data, and data distribution difference between source domain data and target domain data will lead to domain drifts under variable working conditions. In this paper, a robot bearing fault diagnosis method based on deep convolutional residual Wasserstein adversarial network (DCRWAN) is proposed to reduce domain offsets and improve the robot bearing fault diagnosis accuracy. In the proposed method, a deep convolutional residual network is constructed to extract the transferable features of source domain and target domain. Then, a domain discrimination module is constructed and the domain adversarial training is carried out to minimize the distribution discrepancy of cross-domain by the Wasserstein loss function. At the same time, a domain distribution discrepancy metrics module is applied to estimate the difference of features distribution between two domains, and further fine-tuning the network parameters to obtain domain-invariant and class-separable features for the classification. The average accuracy of the two transfer tasks using this method is 95.65% and 93.75%, respectively, and the results show that the method effectively reduces the domain differences caused by varying working conditions and improves the fault diagnosis accuracy of robot bearings under variable working conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Failure analysis

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method 5

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Shen, Cuifeng Deng, Zhaokun Wang, Jinzhi Wang, Shuangyi Chen, Chen Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong

ISBN: (纸本)9798350307320

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer's skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation. © 2023 IEEE.

关键词： Diagnosis

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Accelerating Visual Reinforcement Learning with Separate Primitive Policy for Peg-in-Hole Tasks

arXiv

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arXiv 2025年

作者： Xu, Zichun Wang, Zhaomin Li, Yuntao Zhuang, Lei Zhao, Zhiyuan Yang, Guocai Zhao, Jingdong State Key Laboratory of Robotics and Systems Harbin Institute of Technology Heilongjiang Province Harbin150001 China School of Mechanical Engineering Shandong University Jinan250061 China

For peg-in-hole tasks, humans rely on binocular visual perception to locate the peg above the hole surface and then proceed with insertion. This paper draws insights from this behavior to enable agents to learn efficient assembly strategies through visual reinforcement learning. Hence, we propose a Separate Primitive Policy (S2P) to simultaneously learn how to derive location and insertion actions. S2P is compatible with model-free reinforcement learning algorithms. Ten insertion tasks featuring different polygons are developed as benchmarks for evaluations. Simulation experiments show that S2P can boost the sample efficiency and success rate even with force constraints. Real-world experiments are also performed to verify the feasibility of S2P. Ablations are finally given to discuss the generalizability of S2P and some factors that affect its performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Robot Embodied Dynamic Tactile Perception of Liquid in Containers 23

Robot Embodied Dynamic Tactile Perception of Liquid in Conta...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Xu, Yingtian Ma, Yichen Lin, Waner Sun, Zhenglong Zhang, Tianwei Wang, Ziya The Chinese Univ of Hong Kong School of Science and Engineering Shenzhen518172 China Shenzhen University Key Laboratory of Optoelectronic Devices and Systems Ministry of Education School of Physics and Optoelectronic Engineering Shenzhen518060 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Department of Micro-Nano Electronics Shanghai200240 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China

ISBN: (纸本)9798350373578

Humans perceive objects and environments actively and dynamically using tactile sensing facilitated by the skin. However, due to the gap in sensing capabilities between electronic skin and human skin, it remains a challenge for robots to achieve intricate tactile perception. For example, the task of liquid properties estimation within containers, demands sensing and comprehension of complex dynamic tactile signals during contact. This paper introduces a novel tactile fingertip inspired by human skin enabling both static tactile sensing facilitated by a layer of porous piezoresistive elastomer and dynamic tactile sensing enabled by condenser microphones. A data-driven methodology is employed to analyze liquids through multimodal signals devoid of physical modelling. The proposed robotic system engages in shaking various bottles filled with different volumes of water, allowing the tactile fingertip to detect changes in grasping force caused by shaking and small vibrations caused by the collision of the container with liquid. Five machine learning models trained on these tactile signals are analyzed and compared. Experimental results demonstrate the proficiency of the tactile system in distinguishing liquid fill percentages across bottles of different shapes and capacities with a remarkable accuracy of 98%. This paper holds promise for advancing the embodied intelligence of robots, enhancing their ability to perceive mixed tactile signals dynamically, thereby facilitating tasks such as liquid estimation and other intricate tactile operations in environments like kitchens and hospitals. © 2024 IEEE.

关键词： Bottles

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Multi-aspect Optimal Sliding Mode Controller for a Mecanum Wheeled Robot

Multi-aspect Optimal Sliding Mode Controller for a Mecanum W...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Sohrab Allahyari Hamid Rahmanei S. Ali A. Moosavian Dept. of Mechanical Engineering Center of Excellence in Robotics & Control Advanced Robotics & Automated Systems (ARAS) Laboratory K. N. Toosi University of Technology Tehran

Four-wheel drive Mecanum robots have gained attention due to their ability to move in all directions, which allows them to work in tight and complex environments. For this reason, ensuring precise control of these robots along predefined trajectories is crucial. This work presents a comparative study on the effects of parametric uncertainties, external disturbances, measurement noises, and initial deviations on the trajectory-tracking performance of a 4-wheel drive Mecanum robot. First, the kinematic and kinetic models of the Mecanum robot are introduced. Then, the integral sliding mode control is designed, and proof of stability is provided. In addition, we explore other control strategies, including PID, feedback linearization, sliding mode, and adaptive sliding mode control, that are designed for this robot. Furthermore, the particle swarm optimization algorithm is hired for the controllers with a cost function as the total sum of average error, maximum error, and final error. Optimization has been done on all controllers to compare the results fairly. To appraise the effectiveness of the proposed control optimization methods, simulations are carried out in MATLAB/Simulink. The results reveal that the proposed ISMC method performs best in both defined scenarios compared to other controllers. However, it must be noted that the ISMC controller exhibits limitations when facing significant uncertainties in the dynamic model. This study underscores the effectiveness of our proposed control method and provides valuable insights into the practical implementation controller for the Mecanum robot in real-world applications.

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