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检索条件"机构=Robotics Engineering Worcester Polytechnic Institute"

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Leveraging Dexterous Picking Skills for Complex Multi-Object Scenes 23

Leveraging Dexterous Picking Skills for Complex Multi-Object...

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

作者： Dangle, Anagha Rajendra Deshmukh, Mihir Pradeep Boby, Denny Calli, Berk Polytechnic Institute Robotics Engineering Department of Worcester United States

ISBN: (纸本)9798350373578

This work focuses on the problem of robotic picking in challenging multi-object scenarios. These scenarios include difficult-to-pick objects (e.g., too small, too flat objects) and challenging conditions (e.g., objects obstructed by other objects and/or the environment). To solve these challenges, we leverage four dexterous picking skills inspired by human manipulation techniques and propose methods based on deep neural networks that predict when and how to apply the skills based on the shape of the objects, their relative locations to each other, and the environmental factors. We utilize a compliant, under-actuated hand to reliably apply the identified skills in an open-loop manner. The capabilities of the proposed system are evaluated through a series of real-world experiments, comprising 45 trials with 150+ grasps, to assess its reliability and robustness, particularly in cluttered settings. The videos of all experiments are provided at https://***/. This research helps bridge the gap between human and robotic grasping, showcasing promising results in various practical scenarios. © 2024 IEEE.

关键词： robotics

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Optimizing Automated Manufacturing Processes Using Axiomatic Design Methods 15th

Optimizing Automated Manufacturing Processes Using Axiomatic...

引用

15th International Conference on Axiomatic Design, ICAD 2023

作者： Browne, Michael J. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609-2280 United States

ISBN: (纸本)9783031499197

Automating industrial manufacturing processes is a task that is often easier said than done. Due to emergent behaviors in both the end item being assembled, and in the robotic assemblers themselves, it is not uncommon for a change in one aspect of the design of the combined system (product + robot assembling the product) to have unintended impacts in seemingly unrelated areas of the overall system. These emergent behaviors are usually the result of poor or incomplete mapping of all the interactions between all the characteristics of the system. However, the axiomatic method provides the tools necessary to not only begin mapping these interactions, but to also confirm that all the requirements of the system have been met and are organized in an optimal way. This paper aims to objectively analyze a hypothetical automated manufacturing environment, and all the aspects of its design that will be necessary for it to succeed in its mission of generating profit for the company that operates it. Currently, factories are often designed after-the-fact, after a product has been developed, and all manufacturing processes are tailored to suit it. Any defects or inefficiencies in a process are dealt with reactively, after they have already had a financial impact on the company. Instead, this paper proposes designing product and manufacturing process concurrently by utilizing the axiomatic design method, and by doing so, it becomes possible for interactions to be fully mapped and understood before anything—product or manufacturing tools—is built. By doing things this way, this paper shows that it then becomes possible to better utilize available robotic manufacturing tools & processes. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： robotics

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Project-Based Learning for Robot Control Theory: A Robot Operating System (ROS)-Based Approach

Project-Based Learning for Robot Control Theory: A Robot Ope...

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2023 ASEE Annual Conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Farzan, Siavash Robotics Engineering Department Worcester Polytechnic Institute United States

Control theory is an important cornerstone of the robotics field and is considered a fundamental subject in an undergraduate and postgraduate robotics curriculum. Furthermore, project-based learning has shown significant benefits in engineering domains, specifically in interdisciplinary fields such as robotics which require hands-on experience to master the discipline adequately. However, designing a project-based learning experience to teach control theory in a hands-on setting can be challenging, due to the rigor of mathematical concepts involved in the subject. Moreover, access to reliable hardware required for a robotics control lab, including the robots, sensors, interfaces, and measurement instruments, may not be feasible in developing countries and even many academic institutions in the US. The current paper presents a set of six project-based assignments for an advanced postgraduate Robot Control course. The sequence of assignments naturally builds on each other, and by the end of the course, students will be able to develop an advanced control framework in a real-world robotic simulation setting. The assignments leverage the Robot Operating System (ROS), an open-source set of tools, libraries, and software, which is a de facto standard for the development of robotics applications. The use of ROS, along with its physics engine simulation framework, Gazebo, provides a hands-on robotics experience equivalent to working with real hardware. The topics incorporated in the assignments include a set of linear and nonlinear control concepts, including dynamics modeling and analysis of multibody robotic systems, Jacobian linearization and characterization of equilibria in nonlinear state-space models, stabilization via state-feedback control, state estimation and control via observer design, linear quadratic regulator (LQR) control, trajectory tracking using inverse dynamics and feedback linearization, Lyapunov-based robust control design, and the formulation of adap

关键词： MATLAB

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Closed-loop contrast source regulation through real-time spectroscopic photoacoustic imaging: phantom evaluation

Closed-loop contrast source regulation through real-time spe...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Murakami, Ryo Gao, Shang Zhang, Haichong K. Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States Biomedical Engineering Worcester Polytechnic Institute WorcesterMA United States Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781510658639

Photoacoutisc (PA) imaging can not only depict the geometrical features of a target like ultrasound but also allow to decompose of the contribution from different material sources based on their own specific optical absorption characteristics. With the potential of PA imaging, how to utilize the extracted information beyond diagnosis and monitoring is an open research question. Despite this interest in the field, the PA-based automation of regulating conditions of contrast sources such as position and concentration has not been actively studied. This regulation has the potential to be used for several applications including precise/long-term dose control of exogenous contrast agents in the context of drug delivery. In addition, material decomposition allows for the selective regulation of specific contrast agents. This would be advantageous in clinical situations where it is necessary to use multiple types of contrast agents simultaneously and independently. In this report, we propose a system architecture to realize closed-loop source regulation through real-time spectroscopic PA imaging and develop an experimental setup to demonstrate the feasibility of this approach. The system autonomously terminated the ICG flow within the target performance range which is defined considering the specifications of the system. © 2023 SPIE.

关键词： Inverse problems

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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Acoustic Reflector-Integrated Encapsulation for Needle-Aligned Ultrasound-Guided PCNL Access

Acoustic Reflector-Integrated Encapsulation for Needle-Align...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Palisetti, Abhinav King, Alexander Priestner, Cabot Yi, Haohao Tang, Yichuan Murakami, Ryo Zhang, Haichong K. Worcester Polytechnic Institute Biomedical Engineering Department WorcesterMA United States Worcester Polytechnic Institute Mechanical Engineering Department WorcesterMA United States Worcester Polytechnic Institute Robotics Engineering Department WorcesterMA United States

ISBN: (纸本)9798350346459

Percutaneous Nephrolithotomy (PCNL) is a procedure to remove kidney stones by needle insertion into the kidney followed by dilation of the needle path for surgical tools to enter and stones to be removed. Current techniques for needle insertion either do not visualize the needle entry point into tissue, or require careful hand-hand coordination to visualize the needle. This research aims to develop a stable system to visualize the entire needle insertion path during ultrasound-guided PCNL. A novel method, designed by Tang et al. (2022), implemented two parallel acoustic reflectors to reflect ultrasound into the patient, and a needle inserted through a gap between the reflectors. Results showed the ability to visualize the needle path at any needle pitch angle, and, most importantly, the ability to visualize the needle from its insertion point to the target. In this current research, a modular design was created that allowed any probe to fit into the system by altering just three components. Several structural modifications were made to make the system waterproof, allowing the fluid material that housed the reflectors to be replaced with a fluid with better sound transmission. An algorithm was created to allow better needle tip and needle path visualization by analyzing raw image data. Custom acoustic impedance-matched materials were used to allow better sound transmission and better image quality. Through multiple tests and a mathematical model, the team developed an optimal combination of materials that produced the best image quality, determined through Contrast to Noise Ratio (CNR), Signal to Noise Ratio (SNR), and Full-Width Half-Maximum (FWHM). © 2023 IEEE.

关键词： Signal to noise ratio

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Swarm Intelligence Path-Planning Pipeline and Algorithms for UAVs: Simulation, Analysis and Recommendation 26

Swarm Intelligence Path-Planning Pipeline and Algorithms for...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Harris, Wyatt Tseng, Sean Viso, Tabatha Weissman, Max Ngan, Chun-Kit Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States

ISBN: (纸本)9789897586927

This research work aims to support domain experts in the selection of proper path planning algorithms for UAVs to solve a domain business problem (i.e., the last mile delivery of goods). In-depth analysis, insight, and recommendations of three promising approaches, including reinforcement learning-based, bio-inspired-based, and physics-based are used to address the multi-agent UAV path planning problem. Specifically, the contributions of this work are fourfold: First, we develop a unified pipeline to implement each approach to conduct this analysis. Second, we build a 2D UAV path planning environment to simulate each approach. Third, using this 2D environment, we run the 450 simulations in three different group sizes of swarm UAV agents (i.e., 3, 5, and 10) within three environments of varying complexity (i.e., Easy, Intermediate, and Hard). We aggregate the simulation data and compare their performance in terms of success rate, run-time, and path length while using the classical A* Search as a baseline. Finally, based upon the performance of each approach and our analytical investigations, we provide informed recommendations for the optimal use case of each UAV path planning approach. The recommendations are presented using parameters for environmental complexity and urgency of goods delivery. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Motion planning

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PFAS Incineration: Known Unknowns, Reaction Simulation, and ML to Predict Product Properties

PFAS Incineration: Known Unknowns, Reaction Simulation, and ...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Soiffer, Duncan Pyorre, Tuomas Kenney, David Teixeira, Andrew Worcester Polytechnic Institute Department of Computer Science and Mathematical Sciences WorcesterMA United States Worcester Polytechnic Institute Department of Robotics Engineering WorcesterMA United States Worcester Polytechnic Institute Department of Chemical Engineering WorcesterMA United States

ISBN: (纸本)9798350308600

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis. © 2023 IEEE.

关键词： Machine learning

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting 19

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Akl, James Kodate, Shreedhar Calli, Berk Worcester Polytechnic Institute Robotics Engineering Department WorcesterMA01609 United States Worcester Polytechnic Institute Data Science Program WorcesterMA01609 United States

ISBN: (纸本)9798350320695

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our 'Oxy-fuel Cutting Task State' (OCTS) dataset is available at ***/10.5281/zenodo.7734951. © 2023 IEEE.

关键词： Risk assessment

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Hand Gesture Classification Based on Forearm Ultrasound Video Snippets Using 3D Convolutional Neural Networks

Hand Gesture Classification Based on Forearm Ultrasound Vide...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Keshav Bimbraw Ankit Talele Haichong Zhang Robotics Engineering Worcester Polytechnic Institute Worcester USA Robotics and Biomedical Engineering Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound based hand movement estimation is a crucial area of research with applications in human-machine interaction. Forearm ultrasound offers detailed information about muscle morphology changes during hand movement which can be used to estimate hand gestures. Previous work has focused on analyzing 2-Dimensional (2D) ultrasound image frames using techniques such as convolutional neural networks (CNNs). However, such 2D techniques do not capture temporal features from segments of ultrasound data corresponding to continuous hand movements. This study uses 3D CNN based techniques to capture spatio-temporal patterns within ultrasound video segments for gesture recognition. We compared the performance of a 2D convolution-based network with (2+1)D convolution-based, 3D convolution-based, and our proposed network. Our methodology enhanced the gesture classification accuracy to 98.8 ± 0.9%, from 96.5 ± 2.3% compared to a network trained with 2D convolution layers. These results demonstrate the advantages of using ultrasound video snippets for improving hand gesture classification performance.

关键词： Hands Human computer interaction Image segmentation Solid modeling Ultrasonic imaging Three-dimensional displays Accuracy Gesture recognition Spatiotemporal phenomena Convolutional neural networks

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