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arXiv 2022年

作者： Schrage, Matthijs Medany, Mahmoud Ahmed, Daniel Acoustic Robotics Systems Lab Institute of Robotics and Intelligent Systems Department of Mechanical and Process Engineering ETH Zurich8092 Switzerland

Powered by acoustics, existing therapeutic and diagnostic procedures will become less invasive and new methods will become available that have never been available before. Acoustically driven microrobot navigation based on microbubbles is a promising approach for targeted drug delivery. Previous studies have used acoustic techniques to manipulate microbubbles in vitro and in vivo for the delivery of drugs using minimally invasive procedures. Even though many advanced capabilities and sophisticated control have been achieved for acoustically powered microrobots, there remain many challenges that remain to be solved. In order to develop the next generation of intelligent micro/nanorobots, it is highly desirable to conduct accurate identification of the micro-nanorobots and to control their dynamic motion autonomously. Here we use reinforcement learning control strategies to learn the microrobot dynamics and manipulate them through acoustic forces. The result demonstrated for the first time autonomous acoustic navigation of microbubbles in a microfluidic environment. Taking advantage of the benefit of the second radiation force, microbubbles swarm to form a large swarm, which is then driven along the desired trajectory. More than 100 thousand images were used for the training to study the unexpected dynamics of microbubbles. As a result of this work, the microrobots are validated to be controlled, illustrating a good level of robustness and providing computational intelligence to the microrobots, which enables them to navigate independently in an unstructured environment without requiring outside assistance. © 2022, CC BY.

关键词： Ultrasonics

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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 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

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arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

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Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning

Evolutionary Morphology Towards Overconstrained Locomotion v...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Yenan Chen Chuye Zhang Pengxi Gu Jianuo Qiu Jiayi Yin Nuofan Qiu Guojing Huang Bangchao Huang Zishang Zhang Hui Deng Wei Zhang Fang Wan Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech School of Design Department of System Design and Intelligent Manufacturing SUSTech. Shenzhen Key Lab of Intelligent Robotics & Flexible Manufacturing Systems SUSTech. SUSTech. SUSTech Institute of Robotics

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of ‘intelligent design under constraints’–hereafter referred to as constraint-driven design intelligence–in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4- bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.

关键词： Couplings Legged locomotion Technological innovation Education Noise Morphology Stairs

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Robustness of Log-Linear Learning in Network Coordination Games with Stubborn Players

Robustness of Log-Linear Learning in Network Coordination Ga...

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IEEE Conference on Decision and Control

作者： Aqsa Shahzadi Akber Hassan Jaleel Department of Electrical Engineering Intelligent Machines & Sociotechnical Systems (iMaSS) Lab Syed Babar Ali School of Science & Engineering at LUMS Lahore Pakistan NCRA National Agricultural Robotics Lab at LUMS

We analyze Log-Linear Learning (LLL) in a networked multi-agent system with stubborn players that can influence other players but do not update their actions. We are interested in the robustness of LLL against stubborn players in a coordination game setup in which the players have to decide between a status quo and an innovative practice that is inherently superior to the status quo. We investigate the impact of interaction network topology and the payoff gain offered by the innovation on the steady state behavior of the population in the presence of stubborn agent/s. We present conditions for the robustness of various networks, namely a class of 3-regular networks, $n$ × $n$ grid networks, and Erdős-Rényi (ER) random networks. For these networks, we derive the threshold values of the payoff gain for which the system behavior is robust to the presence of stubborn players under LLL.

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Hazard Analysis of Collaborative Automation systems: A Two-layer Approach based on Supervisory Control and Simulation

Hazard Analysis of Collaborative Automation Systems: A Two-l...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

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Robust Sliding Mode Based Finite-time Bilateral Shared Teleoperation System with Unsymmetrical Time-Varying Delay* 48

Robust Sliding Mode Based Finite-time Bilateral Shared Teleo...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Islam, Shafiqul Sunda-Meya, Anderson Xavier University of Louisiana Dual Degree Engineering Program Intelligent Robotics Mechatronics & Autonomous Systems Lab New Orleans70125 United States Xavier University of Louisiana College of Arts & Sciences New Orleans70125 United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This paper presents sliding mode-based finite-time synchronization algorithms for bilateral shared teleoperation systems under unsymmetrical time-varying delay and uncertainty. The synchronization algorithms are designed by using Lyapunov and sliding mode control theory. First, the model-based finite-time synchronization algorithms are designed by using sliding mode control theory with the presence of time-varying delays and uncertainty. Then, robust adaptive finite-time synchronization algorithms are designed for bilateral shared teleoperation systems with the presence of time-varying delays. Adaptive learning algorithms learn and adapt with uncertainty associated with the unmodeled dynamics and external disturbances. Lyapunov analysis shows that the tracking errors between master and slave manipulators converge to the sliding surface in finite time. It also shows that the tracking errors asymptotically converge to zero on the sliding surface. The design provides finite-time convergence of the states to reach the sliding surface as opposed to asymptotic convergence-based bilateral shared teleoperation systems. The design and analysis do not use the exact bound of the uncertainty. In contrast with asymptotic design, finite-time convergence can ensure faster and more robust tracking in the presence of uncertainty and time-varying delays. Unlike reported designs, the proposed algorithms can ensure convergence with the presence of unsymmetrical time-varying delays. © 2022 IEEE.

关键词： Time delay

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China 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 National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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Convergence Analysis for Delay Dependent LFC Scheme for Power systems Networks Over Open Communication Networks with Disturbances

Convergence Analysis for Delay Dependent LFC Scheme for Powe...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Shafiqul Islam Dual-Degree Computer Science and Engineering Intelligent Robotics Mechatronics and Autonomous Systems Lab NCF 263 Xavier University of Louisiana New Orleans LA USA

This paper presents a delay-dependent convergence criterion for load frequency control (LFC) for smart power grid networks with the presence of disturbances and time-varying delays. The LFC scheme comprises delayed remote measurement and control signals with local state feedback signals. The convergence criteria are developed by using Lyapunov-Krasovskii functional for both symmetrical and asymmetrical time-varying delays associated with remote measurement and control channel. First, the analysis shows that all the power system states are bounded and asymptotically stable without the presence of disturbances. Then, the convergence analysis proves that all the power systems states are bounded and their bounds guarantee the convergence property of the $H_{\infty}$ control with the presence of bounded disturbances. These convergence properties are achieved by using well know Linear Matrix Inequalities (LMIs). Compared with the existing design, the proposed analysis uses both symmetrical and asymmetrical time-varying delays in the measurement channel from RTU to the control center and in the control channel from the control center to the power generation unit. In contrast with reported designs, the proposed design and analysis uses the disturbances associated with the closed loop systems appearing from modeling errors and other external load disturbances and faults.

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Automated Leaf-Level Inspection of Crops Combining UAV and UGV Robots 16th

Automated Leaf-Level Inspection of Crops Combining UAV and...

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16th European robotics Forum, ERF 2025

作者： Esser, Felix Marks, Elias Magistri, Federico Weyler, Jan Bultmann, Simon Zaenker, Tobias Ahmadi, Alireza Schreiber, Michael Kuhlmann, Heiner McCool, Chris Popović, Marija Stachniss, Cyrill Behnke, Sven Bennewitz, Maren Klingbeil, Lasse Institute of Geodesy and Geoinformation University of Bonn Bonn Germany Autonomous Intelligent Systems University of Bonn Bonn Germany Humanoid Robots Lab University of Bonn Bonn Germany Agricultural Robotics and Engineering University of Bonn Bonn Germany Center for Robotics University of Bonn Bonn Germany

ISBN: (纸本)9783031894701

Using areal and ground-based robotics systems is one possible component on the way to more sustainable crop production. In this work, we show, how a combination of a UAV and ground robot can be used to automate the process of single plant and even leaf-level inspection of crops in the field, as the farmer usually does manually by physically going to the plants. The work integrates UAV-based plant and weed mapping, UGV path planning and execution, viewpoint planning and control of a robot-mounted arm with a multi-camera head, as well as high-resolution 3D reconstruction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Crops

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