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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

Region-Based Planning for 3D Within-Hand-Manipulation via Variable Friction Robot Fingers and Extrinsic Contacts

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Region-Based Planning for 3D Within-Hand-Manipulation via Va...

IEEE International Conference on robotics and Automation (ICRA)

作者： Alp Sahin Adam J. Spiers Berk Calli Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA Imperial College London London United Kingdom

Attempts to achieve robotic Within-Hand-Manipulation (WIHM) generally utilize either high-DOF robotic hands with elaborate sensing apparatus or multi-arm robotic systems. In prior work we presented a simple robot hand with variable friction robot fingers, which allow a low-complexity approach to within-hand object translation and rotation, though this manipulation was limited to planar actions. In this work we extend the capabilities of this system to 3D manipulation with a novel region-based WIHM planning algorithm and utilizing extrinsic contacts. The ability to modulate finger friction enhances extrinsic dexterity for three-dimensional WIHM, and allows us to operate in the quasi-static level. The region-based planner automatically generates 3D manipulation sequences with a modified A* formulation that navigates the contact regions between the fingers and the object surface to reach desired regions. Central to this method is a set of object-motion primitives (i.e. within-hand sliding, rotation and pivoting), which can easily be achieved via changing contact friction. A wide range of goal regions can be achieved via this approach, which is demonstrated via real robot experiments following a standardized in-hand manipulation benchmarking protocol.

关键词： Three-dimensional displays Protocols Friction Benchmark testing Robot sensing systems Search problems Planning

Importance of considering microscopic structures in modeling brain stimulation

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Brain Stimulation 2025年第4期18卷 1150-1152页

作者： Zhen Qi Gregory M. Noetscher Alton Miles Konstantin Weise Thomas R. Knösche Cameron R. Cadman Alina R. Potashinsky Kelu Liu William A. Wartman Guillermo Nunez Ponasso Marom Bikson Hanbing Lu Zhi-De Deng Aapo Nummenmaa Sergey N. Makaroff Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Leipzig University of Applied Sciences (HTWK) Faculty of Engineering Leipzig Germany Department of Biomedical Engineering The City College of New York New York NY USA Neuroimaging Research Branch NIDA Intramural Research Program National Institutes of Health Baltimore MD USA Computational Neurostimulation Research Program Noninvasive Neuromodulation Unit Experimental Therapeutics and Pathophysiology Branch NIMH National Institutes of Health Baltimore MD USA Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester MA USA

On the Optimization of Benefit to Cost Ratios for Public Sector Decision Making

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arXiv 2022年

作者： Miller, Frederick Kaya, Yaren Bilge Dimas, Geri L. Konrad, Renata Maass, Kayse Lee Trapp, Andrew C. Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States Department of Mechanical and Industrial Engineering Northeastern University BostonMA United States Data Science Program Worcester Polytechnic Institute WorcesterMA United States School of Business Worcester Polytechnic Institute WorcesterMA United States

Decision making in the public sector centers on delivering resources and services for the common good, emphasizing an expansive set of objectives such as equity and efficiency that accompany short term returns to reflect the broader cares of society and public beneficiaries. These contexts often feature multiple stakeholders and objectives operating in highly complex, real-world environments, where significant changes to practical operations must be balanced against the degree of deployment and change management. Cost-benefit analysis (CBA) is a prevailing decision-making paradigm in the public sector used to evaluate next best steps beyond the status quo ("do nothing") alternative. The benefit to cost ratio (BCR) is a common tool used in CBA to assess the potential of actions beyond maintaining the status quo, yet necessitates the ex ante identification and selection of a limited number of alternatives, which can be laborious and may lead to missed opportunities in the wider solution landscape. We introduce a new framework for public sector decision making that simultaneously considers a potentially wide range of alternatives, and identifies one that maximizes the BCR. Our framework implicitly generates and evaluates a large number of alternatives (actions), subject to original problem context conditions and augmented with action constraints that ensure movement beyond the status quo. We represent this problem as a mixed-integer linear fractional program (MILFP) and employ a version of Newton’s Method that converts the MILFP to a sequence of linearized mixed-integer programs, which retains tractability for fairly large problem instances. Our framework is applicable to a broad set of decision contexts that involve maximizing the BCR for marginal activity beyond the status quo. We demonstrate the applicability of our framework on a compelling case study for the New York City (NYC) runaway and homeless youth (RHY) shelter syste – an area of high societal need. Our opt

关键词： Decision making

Detecting Olives with Synthetic or Real Data? Olive the Above

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arXiv 2023年

作者： Karabatis, Yianni Lin, Xiaomin Sanket, Nitin J. Lagoudakis, Michail G. Aloimonos, Yiannis Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Perception and Autonomous Robotics Group Robotics Engineering Worcester Polytechnic Institute MA01609 United States School of ECE Technical University of Crete Greece

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

Probabilistic planning with preferences over temporal goals

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arXiv 2021年

作者： Fu, Jie The Department of Robotics Engineering Worcester Polytechnic Institute Worcester01520 United States

We present a formal language for specifying qualitative preferences over temporal goals and a preference-based planning method in stochastic systems. Using automata-theoretic modeling, the proposed specification allows us to express preferences over different sets of outcomes, where each outcome describes a set of temporal sequences of subgoals. We define the value of preference satisfaction given a stochastic process over possible outcomes and develop an algorithm for time-constrained probabilistic planning in labeled Markov decision processes where an agent aims to maximally satisfy its preference formula within a pre-defined finite time duration. We present experimental results using a stochastic gridworld example and discuss possible extensions of the proposed preference *** Codes 90C40, 03B44, 68T27 © 2021, CC BY-NC-SA.

关键词： Stochastic systems

Actuated reflector-based three-dimensional ultrasound imaging with adaptive-delay synthetic aperture focusing

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arXiv 2021年

作者： Tang, Yichuan Tsumura, Ryosuke Kaminski, Jakub T. Zhang, Haichong K. The Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Biomedical Engineering Computer Science and Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Three-dimensional (3D) ultrasound (US) imaging addresses the limitation in field-of-view (FOV) in conventional two-dimensional (2D) US imaging by providing 3D viewing of the anatomy. 3D US imaging has been extensively adapted for diagnosis and image-guided surgical intervention. However, conventional approaches to implement 3D US imaging require either expensive and sophisticated 2D array transducers, or external actuation mechanisms to move a one-dimensional array mechanically. Here, we propose a 3D US imaging mechanism using actuated acoustic reflector instead of the sensor elements for volume acquisition with significantly extended 3D FOV, which can be implemented with simple hardware and compact size. To improve image quality on the elevation plane, we introduce an adaptive-delay synthetic aperture focusing (AD-SAF) method for elevation beamforming. We first evaluated the proposed imaging mechanism and AD-SAF with simulated point targets and cysts targets. Results of point targets suggested improved image quality on the elevation plane, and results of cysts targets demonstrated a potential to improve 3D visualization of human anatomy. We built a prototype imaging system that has a 3D FOV of 38 mm (lateral) by 38 mm (elevation) by 50 mm (axial) and collected data in imaging experiments with phantoms. Experimental data showed consistency with simulation results. The AD-SAF method enhanced quantifying the cyst volume size in the breast mimicking phantom compared to without elevation beamforming. These results suggested that the proposed 3D US imaging mechanism could potentially be applied in clinical scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Phantoms

Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape

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arXiv 2024年

作者： Huang, Chao Zang, Wenshuo Pinciroli, Carlo Li, Zhi Jane Banerjee, Taposh Su, Lili Liu, Rui College of eronautics and Engineering Kent State University KentOH44240 United States Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Industrial Engineering University of Pittsburgh 4200 Fifth Ave PittsburghPA15260 United States Electrical and Computer Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States

Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to uncertain and various obstacles (e.g., building clusters and trees). With a limited understanding of environmental uncertainty on performance, an MRS cannot flexibly adjust its behaviors (e.g., teaming, load sharing, trajectory planning) to ensure both environment adaptation and task accomplishments. In this work, a novel joint preference landscape learning and behavior adjusting framework (PLBA) is designed. PLBA efficiently integrates real-time human guidance to MRS coordination and utilizes Sparse Variational Gaussian Processes with Varying Output Noise to quickly assess human preferences by leveraging spatial correlations between environment characteristics. An optimization-based behavior-adjusting method then safely adapts MRS behaviors to environments. To validate PLBA's effectiveness in MRS behavior adaption, a flood disaster search and rescue task was designed. 20 human users provided 1764 feedback based on human preferences obtained from MRS behaviors related to {"task quality", "task progress", "robot safety"}. The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption. © 2024, CC BY-NC-ND.

关键词： Multipurpose robots

Predictive optimal control with data-based disturbance scenario tree approximation

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arXiv 2021年

作者： Jing, Ran Zeng, Xiangrui The Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Efficiently computing the optimal control policy concerning a complicated future with stochastic disturbance has always been a challenge. The predicted stochastic future disturbance can be represented by a scenario tree, but solving the optimal control problem with a scenario tree is usually computationally demanding. In this paper, we propose a data-based clustering approximation method for the scenario tree representation. Differently from the popular Markov chain approximation, the proposed method can retain information from previous steps while keeping the state space size small. Then the predictive optimal control problem can be approximately solved with reduced computational load using dynamic programming. The proposed method is evaluated in numerical examples and compared with the method which considers the disturbance as a non-stationary Markov chain. The results show that the proposed method can achieve better control performance than the Markov chain method. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision trees