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FIRE-3DV: Framework-Independent Rendering Engine for 3D Graphics using Vulkan

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arXiv 2024年

作者： Allison, Christopher John Zhou, Haoying Munawar, Adnan Kazanzides, Peter Barragan, Juan Antonio Khoury College of Computer Sciences Northeastern University BostonMA02115 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01608 United States

Interactive dynamic simulators are an accelerator for developing novel robotic control algorithms and complex systems involving humans and robots. In user training and synthetic data generation applications, high-fidelity visualizations from the simulation are essential. Yet, robotic simulators often limit their rendering algorithms to preserve real-time interaction with the simulation. Advancements in Graphics Processing Units (GPU) enable improved visualization without compromising performance. However, these advancements cannot be fully leveraged in simulation frameworks that use legacy graphics application programming interfaces (API) to interface with the GPU. This paper presents a performance-focused and lightweight rendering engine supporting the modern Vulkan graphics API that can be easily integrated with other simulation frameworks to enhance visualizations. To illustrate the proposed method, our engine is used to modernize the legacy rendering pipeline of the Asynchronous Multi-Body Framework (AMBF), a dynamic simulation framework used extensively for interactive robotics simulation development. This new rendering engine implements graphical features such as physically based rendering (PBR), anti-aliasing, and ray-traced shadows, significantly improving the image fidelity of AMBF. Computational experiments show that the engine can render a simulated scene with over seven million triangles while maintaining GPU computation times within two milliseconds. Copyright © 2024, The Authors. All rights reserved.

关键词： Anti-aliasing

Optimizing Transcutaneous Oxygen Measurement Sites on Humans

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Optimizing Transcutaneous Oxygen Measurement Sites on Humans

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Abigail Leonardi Ciara Murphy Sydney Hobson Vanshika Rohera Vladimir Vakhter Burak Kahraman Guixue Bu Foroohar Foroozan Lawrence Rhein Ulkuhan Guler Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA Analog Devices Inc Wilmington MA USA Department of Pediatrics UMass Chan Medical School University of Massachusetts Worcester MA USA

This study utilizes an optical method of transcutaneous oxygen sensing that has the potential to revolutionize at-home care. This technique is based on quenching the luminescence of a platinum porphyrin film. Since oxygen quenches luminescence, its lifetime is further measured to assess the partial pressure of transcutaneous oxygen diffusing through the skin. Unlike conventional transcutaneous oxygen monitors that use electrochemical sensors, the luminescence-based sensor allows the use of dry electrodes that do not require heating and reduce the risk of accidental skin irritations or burns. These properties not only improve patient safety but also allow the creation of miniature wearable transcutaneous oxygen sensors for continuous and accurate remote respiratory monitoring. To this end, it is critical to assess the efficiency of the wearable sensor by determining the optimal location for its placement on the body. Depending on the location on the body, physiological factors such as blood flow rate and skin thickness affect dermal perfusion of transcutaneous oxygen. In this work, four healthy volunteers participated in subject testing. We assessed each participant at the following locations: thumb, top of the wrist, forearm, thigh, and shin. All locations consistently reported accurate and reliable data. Among them, the thumb demonstrated shorter settling times and the most uniform luminescence lifetime values.

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Sound Tagging in Infant-centric Home Soundscapes

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arXiv 2024年

作者： Khan, Mohammad Nur Hossain Li, Jialu McElwain, Nancy L. Hasegawa-Johnson, Mark Islam, Bashima Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States Department of Electrical and Computer Engineering University of Illinois United States Department of Human Development and Family Studies University of Illinois United States Beckman Institute for Advanced Science and Technology University of Illinois United States

Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or from the perspective of adults. Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant’s position or vice versa. Thus, despite the recent success of large pretrained models for noise event detection, the performance of these models on infant-centric noise soundscapes in the home is yet to be explored. To bridge this gap, we have collected and labeled noises in home soundscapes from 22 families in an unobtrusive manner, where the data are collected through an infant-worn recording device. In this paper, we explore the performance of a large pre-trained model (Audio Spectrogram Transformer [AST]) on our noise-conditioned infant-centric environmental data as well as publicly available home environmental datasets. Utilizing different training strategies such as resampling, utilizing public datasets, mixing public and infant-centric training sets, and data augmentation using noise and masking, we evaluate the performance of a large pre-trained model on sparse and imbalanced infant-centric data. Our results show that fine-tuning the large pre-trained model by combining our collected dataset with public datasets increases the F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84 (combined datasets) and Cohen’s Kappa from 0.013 (public datasets) and 0.77 (collected datasets) to 0.83 (combined datasets) compared to only training with public or collected datasets, respectively. © 2024, CC BY.

关键词： Large datasets

Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study

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arXiv 2024年

作者： Zhou, Haoying Jiang, Yiwei Gao, Shang Wang, Shiyue Kazanzides, Peter Fischer, Gregory S. Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future. © 2024, CC BY.

关键词： Demonstrations

Efficient Sum of Squares-Based Verification and Construction of Control Barrier Functions by Sampling on Algebraic Varieties

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Efficient Sum of Squares-Based Verification and Construction...

IEEE Conference on Decision and Control

作者： Hongchao Zhang Zhouchi Li Hongkai Dai Andrew Clark Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA Toyota Research Institute Los Altos CA USA

Safety is a critical property of control systems in vital applications such as manufacturing, energy, and autonomous vehicles. Control barrier functions have been proposed for safe control, however, verifying the safety guarantees of a given CBF and constructing CBFs to satisfy safety constraints are computationally challenging. In this paper, we propose a new approach to addressing these challenges, in which the global safety properties of CBFs are characterized based on a finite set of sample points. Specifically, we propose new algorithms for verifying CBFs for polynomial systems by solving a system of linear equalities and sum-of-squares constraints at a set of points sampled on an algebraic variety induced by the CBF. We extend this approach to high-order CBFs as well as systems with actuation constraints. Turning to the problem of constructing CBFs, we propose an algorithm that first selects a finite set of samples, and then computes a CBF such that the samples lie on the boundary of the safe region by solving a mixed-integer convex program. We prove that, if the number of samples is sufficiently large and a CBF exists, then our approach returns a function that satisfies necessary conditions of a CBF. We evaluate our approach on a linear cruise control scenario and a nonlinear quadrotor UAV, and find that both the verification and synthesis algorithms significantly outperform another state-of-the-art SOS-based algorithm.

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Rearranging the Environment to Maximize Energy with a Robotic Circuit Drawing

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Rearranging the Environment to Maximize Energy with a Roboti...

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Tan, Xianglong Liu, Zhikang Yu, Chen Schwertfeger, Sören Rosendo, Andre Hamlyn Centre Imperial College London United Kingdom University of York Department of Computer Science United Kingdom School of Information Science and Technology ShanghaiTech University China Worcester Polytechnic Institute Robotics Engineering United States

ISBN: (纸本)9781665481090

The ability of acquiring energy form environments is greatly beneficial for robot to function in uncertain environments. In this work, we present a robot capable of drawing circuits with conductive ink while also rearranging the visual world to receive maximum energy from a power source. A range of circuit drawing tasks is designed to simulate real-world scenarios, including avoiding physical obstacles and regions that would discontinue drawn circuits. We adopt the state-of-the-art Transporter networks for pick-and-place manipulation from visual observation. We conduct experiments in both simulation and real-world settings, and our results show that, with a small number of demonstrations, the robot learns to rearrange the placement of objects (removing obstacles and bridging areas unsuitable for drawing) and to connect a power source with a minimum amount of conductive ink. As autonomous robots become more and more involved in our daily lives, our proposed approach brings a novel way for machines to keep themselves functional by rearranging their surroundings to create their own electric circuits. © 2022 IEEE.

关键词： Timing circuits

Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability

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arXiv 2023年

作者： Varde, Aparna S. Liang, Jianyu Department of Computer Science Clean Energy & Sustainability Analytics Center Montclair State University NJ United States Mechanical and Materials Engineering Worcester Polytechnic Institute WorcesterMA United States

It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties;deep learning via advances such as convolutional neural networks to explore grain size detection;and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes;creating domain-specific tools using machine learning for decision support in agile manufacturing;and assessing impacts on sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science. © 2023, CC BY.

关键词： Deep learning

The Impact of Motion Artifacts on Transcutaneous Oxygen Measurements

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The Impact of Motion Artifacts on Transcutaneous Oxygen Meas...

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Vladimir Vakhter Burak Kahraman Guixue Bu Foroohar Foroozan Beth A. Beidleman Ulkuhan Guler Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA Analog Devices Inc. Wilmington MA USA United States Army Research Institute of Environmental Medicine Natick MA USA

Transcutaneous oxygen monitoring is a noninvasive method of measuring the partial pressure of oxygen permeating through the skin (PtcO 2 ). Its medical significance lies in correlating changes in PtcO 2 with alterations in arterial oxygen levels. Luminescent lifetime-based oxygen sensing is suitable for measuring PtcO 2 with miniaturized systems like wearables, where a consistent and reliable signal is crucial for accurate measurements. This study presents the preliminary results of examining the impact of motion artifacts on PtcO 2 measurements based on the luminescent lifetime-based technique. To conduct these tests, a custom wearable PtcO 2 monitor was used, and its onboard accelerometer was calibrated. The study included two human subjects who executed basic forearm movements (lateral and medial rotations and flexion). The pilot tests showed that the luminescent lifetime-based technique is notably resilient against motion artifacts, as the change in lifetime values during motions did not exceed 4%. Importantly, we believe this study is the first in the literature to specifically investigate the impact of motion artifacts on the lifetime-based PtcO 2 measurements through human tests.

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Reinforcement Learning of CPG-regulated Locomotion Controller for a Soft Snake Robot

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arXiv 2022年

作者： Liu, Xuan Onal, Cagdas D. Fu, Jie The Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

Intelligent control of soft robots is challenging due to the nonlinear and difficult-to-model dynamics. One promising model-free approach for soft robot control is reinforcement learning (RL). However, model-free RL methods tend to be computationally expensive and data-inefficient and may not yield natural and smooth locomotion patterns for soft robots. In this work, we develop a bio-inspired design of a learning-based goal-tracking controller for a soft snake robot. The controller is composed of two modules: An RL module for learning goal-tracking behaviors given the unmodeled and stochastic dynamics of the robot, and a central pattern generator (CPG) with the Matsuoka oscillators for generating stable and diverse locomotion patterns. We theoretically investigate the maneuverability of Matsuoka CPG’s oscillation bias, frequency, and amplitude for steering control, velocity control, and sim-to-real adaptation of the soft snake robot. Based on this analysis, we proposed a composition of RL and CPG modules such that the RL module regulates the tonic inputs to the CPG system given state feedback from the robot, and the output of the CPG module is then transformed into pressure inputs to pneumatic actuators of the soft snake robot. This design allows the RL agent to naturally learn to entrain the desired locomotion patterns determined by the CPG maneuverability. We validated the optimality and robustness of the control design in both simulation and real experiments, and performed extensive comparisons with state-of-art RL methods to demonstrate the benefit of our bio-inspired control design. © 2022, CC BY-NC-SA.

关键词： Reinforcement learning