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TTCDist: Fast Distance Estimation From an Active Monocular Camera Using Time-to-Contact

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TTCDist: Fast Distance Estimation From an Active Monocular C...

IEEE International Conference on robotics and Automation (ICRA)

作者： Levi Burner Nitin J. Sanket Cornelia Fermüller Yiannis Aloimonos Perception and Robotics Group Electrical and Computer Engineering University of Maryland College Park Robotics Engineering Worcester Polytechnic Institute Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College Park

Distance estimation from vision is fundamental for a myriad of robotic applications such as navigation, manipu-lation, and planning. Inspired by the mammal's visual system, which gazes at specific objects, we develop two novel constraints relating time-to-contact, acceleration, and distance that we call the $\tau$ -constraint and $\Phi$ -constraint. They allow an active (moving) camera to estimate depth efficiently and accurately while using only a small portion of the image. The constraints are applicable to range sensing, sensor fusion, and visual servoing. We successfully validate the proposed constraints with two experiments. The first applies both constraints in a trajectory estimation task with a monocular camera and an Inertial Measurement Unit (IMU). Our methods achieve 30-70% less average trajectory error while running $25\times$ and $6.2\times$ faster than the popular Visual-Inertial Odometry methods VINS-Mono and ROVIO respectively. The second experiment demonstrates that when the constraints are used for feedback with efference copies the resulting closed loop system's eigenvalues are invariant to scaling of the applied control signal. We believe these results indicate the $\tau$ and $\Phi$ constraint's potential as the basis of robust and efficient algorithms for a multitude of robotic applications.

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OysterNet: Enhanced Oyster Detection Using Simulation

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OysterNet: Enhanced Oyster Detection Using Simulation

IEEE International Conference on robotics and Automation (ICRA)

作者： Xiaomin Lin Nitin J. Sanket Nare Karapetyan Yiannis Aloimonos Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College Park MD USA Robotics Engineering Worcester Polytechnic Institute MA USA

Oysters play a pivotal role in the bay living ecosystem and are considered the living filters for the ocean. In recent years, oyster reefs have undergone major devastation caused by commercial over-harvesting, requiring preservation to maintain ecological balance. The foundation of this preservation is to estimate the oyster density which requires accurate oyster detection. However, systems for accurate oyster detection require large datasets obtaining which is an expensive and labor-intensive task in underwater environments. To this end, we present a novel method to mathematically model oysters and render images of oysters in simulation to boost the detection performance with minimal real data. Utilizing our synthetic data along with real data for oyster detection, we obtain up to 35.1 % boost in performance as compared to using only real data with our OysterNet network. We also improve the state-of-the-art by 12.7%. This shows that using underlying geometrical properties of objects can help to enhance recognition task accuracy on limited datasets successfully and we hope more researchers adopt such a strategy for hard-to-obtain datasets.

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Printable Flexible Robots for Remote Learning

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arXiv 2022年

作者： Kendre, Savita V. Teran, Gus T. Whiteside, Lauryn Looney, Tyler Wheelock, Ryley Ghai, Surya Nemitz, Markus P. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

The COVID-19 pandemic has revealed the importance of digital fabrication to enable online learning, which remains a challenge for robotics courses. We introduce a teaching methodology that allows students to participate remotely in a hands-on robotics course involving the design and fabrication of robots. Our methodology employs 3D printing techniques with flexible filaments to create innovative soft robots;robots are made from flexible, as opposed to rigid, materials. Students design flexible robotic components such as actuators, sensors, and controllers using CAD software, upload their designs to a remote 3D printing station, monitor the print with a web camera, and inspect the components with lab staff before being mailed for testing and assembly. At the end of the course, students will have iterated through several designs and created fluidically-driven soft robots. Our remote teaching methodology enables educators to utilize 3D printing resources to teach soft robotics and cultivate creativity among students to design novel and innovative robots. Our methodology seeks to democratize robotics engineering by decoupling hands-on learning experiences from expensive equipment in the learning environment. © 2022, CC BY.

关键词： 3D printers

Active Telepresence Assistance for Supervisory Control: A User Study with a Multi-Camera Tele-Nursing Robot

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Active Telepresence Assistance for Supervisory Control: A Us...

IEEE International Conference on robotics and Automation (ICRA)

作者： Alexandra Valiton Hannah Baez Naomi Harrison Justine Roy Zhi Li Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

Supervisory control of a humanoid robot in a manipulation task requires coordination of remote perception with robot action, which becomes more demanding with multiple moving cameras available for task supervision. We explore the use of autonomous camera control and selection to reduce operator workload and improve task performance in a supervisory control task. We design a novel approach to autonomous camera selection and control, and evaluate the approach in a user study which revealed that autonomous camera control does improve task performance and operator experience, but autonomous camera selection requires further investigation to benefit the operator’s confidence and maintain trust in the robot autonomy.

关键词： Telepresence Automation Robot kinematics Conferences Robot vision systems Humanoid robots Supervisory control

WorldGen: A Large Scale Generative Simulator

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WorldGen: A Large Scale Generative Simulator

IEEE International Conference on robotics and Automation (ICRA)

作者： Chahat Deep Singh Riya Kumari Cornelia Fermüller Nitin J. Sanket Yiannis Aloimonos Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College Park MD USA Robotics Engineering Worcester Polytechnic Institute MA USA

In the era of deep learning, data is the critical determining factor in the performance of neural network models. Generating large datasets suffers from various challenges such as scalability, cost efficiency and photorealism. To avoid expensive and strenuous dataset collection and annotations, researchers have inclined towards computer-generated datasets. However, a lack of photorealism and a limited amount of computer-aided data has bounded the accuracy of network predictions. To this end, we present WorldGen - an open source framework to automatically generate countless structured and unstructured 3D photorealistic scenes such as city view, object collection, and object fragmentation along with its rich ground truth annotation data. WorldGen being a generative model gives the user full access and control to features such as texture, object structure, motion, camera and lens properties for better generalizability by diminishing the data bias in the network. We demonstrate the effectiveness of WorldGen by evaluating deep optical flow. We hope such a tool can open doors for future research in a myriad of domains related to robotics and computer vision by reducing manual labor and cost for acquiring rich and high-quality data.

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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

Real-Time Road Accident Scene Recognition Using Computer Vision Applied to Drone Imagery

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Real-Time Road Accident Scene Recognition Using Computer Vis...

IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Adama Coulibaly Ibrahima Ngom Jean Marie Dembélé Kevin Leahy Ibrahima Diagne Marc Momar Tall Ousmane Sadio Moustapha Ndiaye Applied Sciences and Technology University Gaston Berger Saint Louis Senegal Ecole Supérieure Polytechnique University Checkh Anta Diop Dakar Senegal Robotics Engineering Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798331542016

ISBN: (纸本)9798331542023

Rapid urbanization has led to a variety of problems in road traffic, such as recurrent congestion and traffic accidents. To mitigate these problems, it is necessary to monitor and analyze traffic volumes in order to anticipate or detect anomalies in road traffic. This work proposes a solution for traffic accident scene recognition using computer vision applied to drone imagery. A Mavic Air 2S drone collects geo-referenced aerial images of the portion of road to be monitored, and the data is annotated, trained and tested on Roboflow. The results of the experiment give an overall object detection rate of 95.3%, accuracy of 94.4% and recall of 91.3%. Geographical coordinates of images containing accident scenes are extracted and stored in a database for statistical and rescue purposes.

关键词： Computer vision Image recognition Databases Roads Urban areas Real-time systems Road traffic Monitoring Accidents Drones

Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives

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arXiv 2023年

作者： Li, Lening Rahmani, Hazhar Fu, Jie The Department of Robotics Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32605 United States

This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existing works reflect such preferences as a prioritized list of goals. This paper introduces a new specification language, termed prioritized qualitative choice linear temporal logic on finite traces, which augments linear temporal logic on finite traces with prioritized conjunction and ordered disjunction from prioritized qualitative choice logic. This language allows for succinctly specifying temporal objectives with corresponding preferences accomplishing each temporal task. The finite traces that describe the system's behaviors are ranked based on their dissatisfaction scores with respect to the formula. We propose a systematic translation from the new language to a weighted deterministic finite automaton. Utilizing this computational model, we formulate and solve a problem of computing an optimal policy that minimizes the expected score of dissatisfaction given user preferences. We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each. Copyright © 2023, The Authors. All rights reserved.

关键词： Specification languages

Mobile Augmented Reality Framework with Fusional Localization and Pose Estimation

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arXiv 2025年

作者： Hou, Songlin Lin, Fangzhou Huang, Yunmei Peng, Zhe Xiao, Bin Department of Computer Science Worcester Polytechnic Institute Worcester01609 United States Department of Robotics Engineering Worcester Polytechnic Institute Worcester01609 United States Unprecedented-scale Data Analytics Center Tohoku University Sendai980-8577 Japan Department of Forestry and Natural Resources Purdue University West Lafayette47907 United States Department of Computing The Hong Kong Polytechnic University 999-077 Hong Kong

As a novel way of presenting information, augmented reality (AR) enables people to interact with the physical world in a direct and intuitive way. While there are some mobile AR products implemented with specific hardware at a high cost, the software approaches of AR implementation on mobile platforms(such as smartphones, tablet PC, etc.) are still far from practical use. GPS-based mobile AR systems usually perform poorly due to the inaccurate positioning in the indoor environment. Previous vision-based pose estimation methods need to continuously track predefined markers within a short distance, which greatly degrade user experience. This paper first conducts a comprehensive study of the state-of-the-art AR and localization systems on mobile platforms. Then, we propose an effective indoor mobile AR framework. In the framework, a fusional localization method and a new pose estimation implementation are developed to increase the overall matching rate and thus improving AR display accuracy. Experiments show that our framework has higher performance than approaches purely based on images or Wi-Fi signals. We achieve low average error distances (0.61-0.81m) and accurate matching rates (77%-82%) when the average sampling grid length is set to 0.5m. © 2025, CC0.

关键词： Wi-Fi