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arXiv 2024年

作者： Delehelle, Donatien Caldwell, Darwin Chen, Fei University of Genova Italy Istituto Italiano di Tecnologia Italy The Department of Mechanical and Automation Engineering T- Stone Robotics Institute The Chinese University of Hong Kong Hong Kong

While humans intuitively manipulate garments and other textiles items swiftly and accurately, it is a significant challenge for robots. A factor crucial to the human performance is the ability to imagine, a priori, the intended result of the manipulation intents and hence develop predictions on the garment pose. This allows us to plan from highly obstructed states, adapt our plans as we collect more information and react swiftly to unforeseen circumstances. Robots, on the other hand, struggle to establish such intuitions and form tight links between plans and observations. This can be attributed in part to the high cost of obtaining densely labelled data for textile manipulation, both in quality and quantity. The problem of data collection is a long standing issue in data-based approaches to garment manipulation. Currently, the generation of high quality and labelled garment manipulation data is mainly attempted through advanced data capture procedures that create simplified state estimations from real-world observations. In this work, however, we propose to generate real-world observations from given object states. To achieve this, we present GARField (Garment Attached Radiance Field) a differentiable rendering architecture allowing data generation from simulated states stored as triangle meshes. Code is available on the project website. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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Visual Tracking of Needle Tip in 2D Ultrasound based on Global Features in a Siamese Architecture

Visual Tracking of Needle Tip in 2D Ultrasound based on Glob...

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IEEE International Conference on robotics and automation (ICRA)

作者： Wanquan Yan Qingpeng Ding Jianghua Chen Kim Yan Raymond Shing-Yan Tang Shing Shin Cheng Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Medicine and Therapeutics and Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute Shun Hing Institute of Advanced Engineering Multi-Scale Medical Robotics Center and Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong

Ultrasound (US) is widely used in image-guided needle procedures. Correctly tracking the needle tip position in US images during the procedure plays an important role in improving the needle targeting accuracy and patient safety. This paper presents a leaning-based visual tracking network with a Siamese architecture, which makes full use of the attention mechanism to explore the potential of global features and takes advantage of an online target model prediction module to robustly track the needle tip in US images. Several self- and cross-attention modules are applied to learn global features from the whole US image. A discriminative target model is also learned as a complementary part to improve the discriminability of the proposed tracker. The template used during the tracking is updated frequently according to the tracking results to ensure that the tracker can always capture the latest characteristics of the appearance of the needle tip. Experimental results in both phantom and tissue showed that the proposed tracking network was more robust than other state-of-the-art visual trackers. The mean success rates of the proposed tracker are 7.1% and 9.2% higher than the second best performing visual tacker when the needle was inserted by motors and human hands in the tissue experiments.

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Self-configuring motion planner for automated vehicles based on human driving styles

Self-configuring motion planner for automated vehicles based...

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IEEE Symposium on Intelligent Vehicle

作者： Juan Medina-Lee Antonio Artuñedo Jorge Godoy Vinicius Trentin Jorge Villagra Computer Science and Engineering Department University of Puerto Rico Mayagüez PR USA Autopia Group Centre for Automation and Robotics (CSIC-UPM) Arganda del Rey Spain

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Automated vehicles are expected to enter the market in the coming years. To achieve this goal, it is essential to develop vehicles that prioritize both safety and reliability, but they should also ensure a comfortable user experience. As a result, autonomous driving functions should adapt to the individual preferences and requirements of drivers. This paper proposes an algorithm to dynamically update the parameters of a motion planner while driving to fit a driving style based on human-driver data. The motion planner generates multiple trajectory candidates and it can adjust the selection criteria online to prioritize the trajectory that matches better to a given behavior. The system was implemented in a real vehicle and compared to different human drivers, showing that it can reproduce their driving styles.

关键词： Heuristic algorithms User experience Trajectory Safety Reliability Autonomous vehicles

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Research on Path Planning of Mobile Robots Based on Snow Ablation Optimizer

Research on Path Planning of Mobile Robots Based on Snow Abl...

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第43届中国控制会议

作者： Wenlong Chen Zile Wang Yuhang Yang Siqi Wang Heng Wang Yasar Ayaz School of Automation and Electrical Engineering University of Science and Technology Beijing National Center of Artificial Intelligence (NCAI) Department of Robotics and Artificial Intelligence National University of Sciences and Technology (NUST)

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Path planning is one of the key technologies for mobile robots. Metaheuristic algorithms hold significant promise in generating an effective and reliable path for mobile robots. However, most metaheuristic algorithms do not balance exploration and exploitation in the solution space, which may lead to premature convergence and cannot guarantee to find a satisfying *** ablation optimizer(SAO) is a novel metaheuristic technique based on the sublimation and melting phenomenon of snow,which can overcome the shortcomings of most other metaheuristic algorithms. This paper proposes a path planning method for mobile robots based on the SAO algorithm and undertakes a comparative study with the planning method based on particle swarm optimization(PSO) using simulation experiments. The results show that the proposed path planner is superior to the PSO-based path planner in both planning speed and path quality.

关键词： Path Planning Mobile Robots Snow Ablation Optimizer Particle Swarm Optimization

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A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-hand manipulation

A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-han...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zhou, Jianshu Li, Yunquan Yang, Yang Cao, Hanwen Huang, Junda Liu, Yunhui Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Center of Logistics Robotics Mechanical Engineering University of Hong Kong School of Automation Nanjing University of Information Science and Technology

ISBN: (纸本)9781665436786

Bio-inspired soft robots present distinctive superiorities in safety issues working in a human-centered environment. Soft robotic hands are of prominent popularity for soft robots to be applied in real applications. With continuous efforts being devoted, researchers have been developing various soft hands performing excellent grasping capability towards daily objects with salient safety insurance. However, the functional dexterity of soft robotic hands is still limited, especially because lacking in-hand object manipulation capabilities, which inevitably limits the application potential of soft robotic hands. This restriction is induced by the limited dexterity of soft actuators usually with a one-DOF per actuator providing a predefined motion trajectory. To tackle this challenge, a novel bio-inspired multi-DOF soft actuator based on V-joint is proposed, which achieves human finger-joint-like motion. By connecting the V-joint in a desired order, it is possible to build multi-DOF soft robots like traditional rigid counterparts. A 22-DOFs anthropomorphic soft hand (S-22) is fabricated out based on the V-joint, which is capable of realizing dexterous hand gestures and 6 kinds of in-hand manipulation. The presented soft robotic approach is a promising solution for dexterous soft robotic hand design and broden the potential application of soft hands. © 2021 IEEE.

关键词： Robotic arms

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

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Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings

arXiv

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arXiv 2024年

作者： Katyara, Sunny Sharma, Suchita Damacharla, Praveen Garcia, Carlos O’Farrell, Francis Long, Philip Department of Robotics & Automation Irish Manufacturing Research Company Ltd Ireland Software Engineering Division Shutterfly United States KineticAI INC. WoodlandsTX United States

Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concerning environment perception and robot control. In this research, we introduce a novel approach for collaborative environment monitoring and robot motion regulation to address this multifaceted problem. Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent quadrants, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover, we also leverage 3D visual information to track dynamic human articulations and extended intrusions. Drawing upon the fused sensory data from 2D and 3D perceptual spaces, our proposed hierarchical controller stably regulates robot velocity, validated using Lasalle in-variance principle. Empirical evaluations demonstrate that our approach significantly reduces task execution time and system response delay, resulting in improved efficiency and resilience within collaborative settings. © 2024, CC BY.

关键词： Ergonomics

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SIGMA: A Minimalistic 3D Hopping Robot with Parallel 3-RSR Mechanism for Safeguarding Humanitarian Demining

SIGMA: A Minimalistic 3D Hopping Robot with Parallel 3-RSR M...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： C. Y. Ivan Fan Xiangyu Chu F. Y. Fiat Wong Fan Shi K. W. Samuel Au Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR Department of Mechanical Engineering Johns Hopkins University USA Multiscale Medical Robotics Centre Hong Kong SAR National University of Singapore Singapore

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

Humanitarian demining is a pressing task to safeguard the lives of civilians living near minefields and free up land occupied by landmines for development. Conventional demining robots, while capable of demining, have a high cost and can be damaged during demining. In this work, we propose SIGMA, a SIngle-leG Mine-clearing Assistant Robot, which is a lightweight and low-cost robotic tool that can complement conventional manual and robotic demining efforts. It is a hopping monopod robot based on a parallel spatial leg mechanism with the capability of omnidirectional movement and force. During the demining process, SIGMA can be deployed ahead of human or robotic deminers to trigger unstable mines and chart a safe path for traversing, reducing risks to deminers. A prototype of SIGMA is designed and built. Its capability to traverse and activate mines is also verified experimentally in simulated scenarios.

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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Computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints

Dynamic Manipulation of Deformable Objects using Imitation L...

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

作者： Eric Hannus Tran Nguyen Le David Blanco-Mulero Ville Kyrki Intelligent Robotics Group at The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland CSIC-UPC Institut de Robòtica i Informàtica Industrial Barcelona Spain

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot’s physical limits. These limits are especially relevant in dynamic manipulation where high velocities and accelerations are typical. To address this problem, we propose a framework that first maps a dynamic demonstration into a motion that respects the robot’s constraints using a constrained Dynamic Movement Primitive. Second, the resulting object state is further optimized by quasi-static refinement motions to optimize task performance metrics. This allows both efficiently altering the object state by dynamic motions and stable small-scale refinements. We evaluate the framework in the challenging task of bag opening, designing the system BILBO: Bimanual dynamic manipulation using Imitation Learning for Bag Opening. Our results show that BILBO can successfully open a wide range of crumpled bags, using a demonstration with a single bag. See supplementary material at https://***/view/bilbo-bag.

关键词： Measurement Learning systems Translation Shape Imitation learning Dynamics Kinematics Hardware Manipulator dynamics Intelligent robots

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