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arXiv 2024年

作者： Lhoste, Clément Küçüktabak, Emek Barış Vianello, Lorenzo Amato, Lorenzo Short, Matthew R. Lynch, Kevin M. Pons, Jose L. Legs and Walking Lab Shirley Ryan AbilityLab ChicagoIL United States Center for Robotics and Biosystems Department of Mechanical Engineering Northwestern University Shirley Ryan AbilityLab EvanstonIL United States Shirley Ryan AbilityLab The Biorobotics Institute Scuola Superiore Sant’Anna Pontedera56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56127 Italy Shirley Ryan AbilityLab Department of Biomedical Engineering Northwestern University EvanstonIL United States Center for Robotics and Biosystems Department of Mechanical Engineering Northwestern University United States Shirley Ryan AbilityLab Center for Robotics and Biosystems Department of Mechanical Engineering Department of Biomedical Engineering Department of Physical Medicine and Rehabilitation Northwestern University United States

In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton’s controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model’s ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with R2 = 0.9 and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking. © 2024, CC BY.

关键词： Exoskeleton (robotics)

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Design of Fuel Salt Irradiation for Fission Products Gamma Spectroscopy and Off-Gassing Study

Design of Fuel Salt Irradiation for Fission Products Gamma S...

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2022 Transactions of the American Nuclear Society Winter Meeting and Technology Expo, ANS 2022

作者： Van Zile, Matthew Gordon, Emily Kandlakunta, Praneeth Kauffman, Andrew Bisbee, Matthew Li, Shelly Simpson, Michael Shahbazi, Shayan Cao, Lei R. Nuclear Engineering Program Department of Mechanical and Aerospace Engineering The Ohio State University United States Chemical Engineering Program Department of Chemical and Biomolecular Engineering The Ohio State University United States Nuclear Reactor Laboratory College of Engineering The Ohio State University United States Nuclear PyroMetallurgy Laboratory University of Utah United States Argonne National Lab United States

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Distributed Safe Navigation of Multi-Agent Systems using Control Barrier Function-Based Optimal Controllers

arXiv

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arXiv 2024年

作者： Mestres, Pol Nieto-Granda, Carlos Cortés, Jorge The Contextual Robotics Institute The Department of Mechanical and Aerospace Engineering UC San Diego CA United States AdelphiMD United States

This paper proposes a distributed controller synthesis framework for safe navigation of multi-agent systems. We leverage control barrier functions to formulate collision avoidance with obstacles and teammates as constraints on the control input for a state-dependent network optimization problem that encodes team formation and the navigation task. Our algorithmic solution is valid under general assumptions for nonlinear dynamics and state-dependent network optimization problems with convex constraints and strongly convex objectives. The resulting controller is distributed, satisfies the safety constraints at all times, and asymptotically converges to the solution of the state-dependent network optimization problem. We illustrate its performance in a team of differential-drive robots in a variety of complex environments, both in simulation and in hardware. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Adaptive Model Predictive Control with Data-driven Error Model for Quadrupedal Locomotion

arXiv

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arXiv 2024年

作者： Zeng, Xuanqi Zhang, Hongbo Yue, Linzhu Song, Zhitao Zhang, Linwei Liu, Yun-Hui The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Hong Kong Centre for Logistics Robotics Hong Kong

Model Predictive Control (MPC) relies heavily on the robot model for its control law. However, a gap always exists between the reduced-order control model with uncertainties and the real robot, which degrades its performance. To address this issue, we propose the controller of integrating a data-driven error model into traditional MPC for quadruped robots. Our approach leverages real-world data from sensors to compensate for defects in the control model. Specifically, we employ the Autoregressive Moving Average Vector (ARMAV) model to construct the state error model of the quadruped robot using data. The predicted state errors are then used to adjust the predicted future robot states generated by MPC. By such an approach, our proposed controller can provide more accurate inputs to the system, enabling it to achieve desired states even in the presence of model parameter inaccuracies or disturbances. The proposed controller exhibits the capability to partially eliminate the disparity between the model and the real-world robot, thereby enhancing the locomotion performance of quadruped robots. We validate our proposed method through simulations and real-world experimental trials on a large-size quadruped robot that involves carrying a 20 kg un-modeled payload (84% of body weight). Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Homotopic Path Set Planning for Robot Manipulation and Navigation

arXiv

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arXiv 2024年

作者： Huang, Jing Tang, Yunxi Au, Kwok Wai Samuel Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Multi-Scale Medical Robotics Center Hong Kong

This paper addresses path set planning that yields important applications in robot manipulation and navigation such as path generation for deformable object keypoints and swarms. A path set refers to the collection of finite agent paths to represent the overall spatial path of a group of keypoints or a swarm, whose collective properties meet spatial and topological constraints. As opposed to planning a single path, simultaneously planning multiple paths with constraints poses nontrivial challenges in complex environments. This paper presents a systematic planning pipeline for homotopic path sets, a widely applicable path set class in robotics. An extended visibility check condition is first proposed to attain a sparse passage distribution amidst dense obstacles. Passage-aware optimal path planning compatible with sampling-based planners is then designed for single path planning with adjustable costs. Large accessible free space for path set accommodation can be achieved by the planned path while having a sufficiently short path length. After specifying the homotopic properties of path sets, path set generation based on deformable path transfer is proposed in an efficient centralized manner. The effectiveness of these methods is validated by extensive simulated and experimental results. Copyright © 2024, The Authors. All rights reserved.

关键词： Robot programming

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High-Precision 3-DOF Compliant Positioning Stage for Large Range Micro-Manipulation Applications

High-Precision 3-DOF Compliant Positioning Stage for Large R...

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IEEE International Conference of Intelligent Robotic and Control engineering (IRCE)

作者： Ashenafi Abrham Kassa Bijan Shirinzadeh Julian Smith Department of Mechanical and Aerospace Engineering Robotics and Mechatronics Research Laboratory (RMRL) Monash University Melbourne Australia Department of Surgery Monash University Melbourne Australia

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

This paper introduces a novel high-precision compliant mechanism-based 3-degree-of-freedom (3-DOF) positioning stage with a large range and planar layout. The design incorporates a cartwheel revolute flexure and compound basic parallelogram (CBP) prismatic flexure modules arranged in series to establish a PPR-kinematic chain. Three such kinematic chains are symmetrically arranged at 120° to each other around the output platform. This design ensures large deformation and high dynamic performance while minimizing cross-coupling and parasitic motion. The compliance matrix method (CMM) is utilized to establish the stiffness model of the designed mechanism. Computational analysis is performed to validate the developed analytical model. Moreover, the reachable workspace, inverse kinematics, and dynamic performance of the proposed stage are investigated. A workspace of 4.6 mm × 5.1 mm × 83 mrad is achieved while maintaining a high natural frequency of 58 Hz. From the analysis, the proposed stage can be adopted in micro-manipulation applications that require high-precision motion over an extended range.

关键词： Transmission line matrix methods Symmetric matrices 3-DOF Dynamics Layout Kinematics Fasteners Manufacturing Motion control Robots

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Experimental performance evaluation on piezo-hydraulic pump using flexurally amplified piezoelectric actuators

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Materials Today: Proceedings 2023年 92卷 84-91页

作者： Nayak S. Muralidhara R. Department of Mechanical Engineering NMAM Institute of Technology (Visvesvaraya Technological University Belagavi) Nitte 574110 Karkala Taluk Karnataka Udupi District India Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Department of Robotics and Artificial Intelligence Nitte 574110 Karkala Taluk Karnataka Udupi District India

This paper presents an experimental study on a piezo-hydraulic pump using two different flexurally amplified piezoelectric (FAP) actuators. FAP actuators produce amplified displacement using piezoelectric stack actuators as prime mover. FAP376 and FAP640, having different characteristics are used as pumping actuator to pump the fluid from the piezo-hydraulic pump. The pumping chamber is made of stainless-steel metallic bellows and unidirectional valves are connected to the inlet and outlet port of the piezo-hydraulic pump. Mathematical model is developed for theoretical calculations on the flowrate of piezo-hydraulic pump. Also, pump flowrate is determined experimentally using flowmeter for two different FAP actuators and compared with the respective theoretical values. Further, performance evaluation is done by comparing the flowrate developed by the piezo-hydraulic pump at different actuation frequencies using two different FAP actuators. © 2023

关键词： Flowmeter Flowrate Piezo-hydraulic pump Piezoelectric actuators

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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Optimal Control for a Shape Memory Alloy Actuated Soft Digit using Iterative Learning Control

Optimal Control for a Shape Memory Alloy Actuated Soft Digit...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Richard Desatnik Mikhail Khrenov Zachary Manchester Philip LeDuc Carmel Majidi Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Modeling and control of soft robotic systems remains a challenging and active field of research, especially for robots actuated with shape-memory-alloys (SMA). Difficulties in controlling SMA-actuated robots arise in modeling the nonlinearities of the SMA dynamics as well as accounting for their hysteresis behavior. This article helps address these challenges by using sensorization of the end effector along with SMA actuators combined with a simplified two-axis pendulum model to implement an optimal control scheme on a soft digit. The control stack presented uses a direct collocation method (DIRCOL) for initial trajectory optimization, with the actual state of the soft digit then captured and used to refine the system inputs using Iterative Learning Control (ILC) to increase trajectory tracking accuracy. The system is demonstrated on hardware, achieving very close trajectory tracking performance for two-dimensional motion tasks.

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Selective Privacy Control on Video Feed Using Pre-Trained DNN

Selective Privacy Control on Video Feed Using Pre-Trained DN...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Anupam Ghosh Sayan Mukherjee Shamir Khan Swarit Anand Singh Riby Abraham Boby Department of Mechanical Engineering Inter-disciplinary Research Platform - Robotics and Mobility Systems Indian Institute of Technology Jodhpur Jodhpur Rajasthan INDIA

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This article introduces an approach to provide privacy control on live video feeds using a pretrained D NN model. Due to privacy being a major concern in today's world, this approach would ensure the blurring of the faces in live feeds on a real-time basis to protect the privacy of participants. Users are also provided with the liberty to selectively blur/unblur the face of any specific participants as required, thereby providing enhanced flexibility and control over privacy settings during live video conferences or communications. The proposed approach also lets users upload a picture of someone. This ensures that the person's identity stays unobfuscated in the video stream. This feature comes in handy when some people are required to be seen, for example, in work meetings and online conferences. The developed solution can blur faces in real time and let users unblur or keep certain faces clear. This gives users a complete set of tools to handle privacy concerns. It tackles the growing concerns about facial privacy in our digital world. This solution enhances privacy during live video calls by utilizing advanced computer vision to selectively blur unwanted faces while maintaining clear visibility of intended participants.

关键词： Privacy Streaming media Web conferencing Signal processing Control systems Real-time systems Feeds Protection Faces Videoconferences

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