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IEEE International Conference on robotics and Automation (ICRA)

作者： Juan Antonio Barragan Jintan Zhang Haoying Zhou Adnan Munawar Peter Kazanzides Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks; yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications.

关键词： Training Visualization Medical robotics Three-dimensional displays Annotations Instruments Pose estimation

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See Spot Guide: Accessible Interfaces for an Assistive Quadruped Robot

arXiv

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arXiv 2024年

作者： Hata, Rayna Trikasemsak, Narit Giudice, Andrea Doore, Stacy A. The Robotics Institute Carnegie Mellon University PittsburghPA United States The Department of Computer Science Colby College WatervilleME United States

While there is no replacement for the learned expertise, devotion, and social benefits of a guide dog, there are cases in which a robot navigation assistant could be helpful for individuals with blindness or low vision (BLV). This study investigated the potential for an industrial agile robot to perform guided navigation tasks. We developed two interface prototypes that allowed for spatial information between a human-robot pair: a voice-based app and a flexible, responsive handle. The participants (n=21) completed simple navigation tasks and a post-study survey about the prototype functionality and their trust in the robot. All participants successfully completed the navigation tasks and demonstrated the interface prototypes were able to pass spatial information between the human and the robot. Future work will include expanding the voice-based app to allow the robot to communicate obstacles to the handler and adding haptic signals to the handle design. © 2024, CC BY.

关键词： Robots

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Research on hybrid flow-shop scheduling based on improved genetic algorithm 18

Research on hybrid flow-shop scheduling based on improved ge...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Chen, Haopeng Fang, Zaojun Zhong, Caiming Ningbo University Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo China Chinese & Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials & Technology Engineering Ningbo China Ningbo University College of Science & Technology Ningbo China

ISBN: (纸本)9798350312201

Aiming at the scheduling problem in the production process of hybrid flow-shop, a hybrid flow-shop scheduling model with the objective of minimizing the makespan is established, and a neighborhood search adaptive genetic algorithm (NSAGA) based on heuristic decoding strategy is proposed. An encoding strategy based on the first stage processing sequence and a decoding strategy based on a heuristic rule are used to guide the algorithm to find the optimal solution quickly. In order to speed up the convergence of the algorithm, the elitism strategy is introduced into the genetic operation, and adaptive crossover and mutation strategy based on sigmoid function is designed. In order to enhance the local search capability of the algorithm, a neighborhood search strategy is designed. Finally, the effectiveness and superiority of NSAGA for solving the hybrid flow-shop scheduling problem (HFSP) with different scales are verified by simulation experiments. © 2023 IEEE.

关键词： Genetic algorithms

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Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation

arXiv

引用

arXiv 2023年

作者： Wu, Qi Zhang, Yuyao Elbatel, Marawan The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Institute University of Girona Spain

Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability. One such model, the Segment Anything Model (SAM), a prompt-driven segmentation model, has shown remarkable performance improvements, surpassing state-of-the-art approaches in medical image segmentation. However, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. In this paper, we propose a novel perspective on self-prompting in medical vision applications. Specifically, we harness the embedding space of SAM to prompt itself through a simple yet effective linear pixel-wise classifier. By preserving the encoding capabilities of the large model, the contextual information from its decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15% compared to fine-tuning the mask decoder using a few images). Our code is available at https://***/PeterYYZhang/few-shot-self-prompt-SAM Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Position control of humanoid thorax based on fuzzy PID controller 18

Position control of humanoid thorax based on fuzzy PID contr...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Fang, Siyuan Fang, Zaojun Peng, Chengbin Zhang, Chi Yang, Guiling Ningbo University Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo China Chinese & Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials & Technology Engineering Ningbo China Ningbo University College of Science & Technology Ningbo China

ISBN: (纸本)9798350312201

Respiration is the most notable vital sign of humans so that it is usually employed to diagnose disease in medicine. Recently, a solution is proposed to decrease the cost of healthcare using flexible stretchable sensors to monitor respiration. However, the sensor is difficult to test millions of cycles on a real human body, which is essential to ensure accuracy and repeatability in practical applications. In this paper, a new humanoid thoracic cavity that mimics rib expansion/constriction is exhibited. Then, a position control based on Fuzzy PID method is presented. Focused on the scenario, the performance between the common PID method and the Fuzzy PID method is investigated. Firstly, the position control based on the Fuzzy PID method is constructed by utilizing position output error and its rate. Then, three gain parameters of PID are adjusted online according to the established fuzzy rules. Finally, the above two methods are compared by simulation and experiments. In a simulation, the fuzzy PID control algorithm improves the response speed, shortens the adjustment time, and meets the requirements of the system for high response and high accuracy. Besides, an experiment is carried out on a humanoid chest prototype. the results show that the control period of the presented method is shortened by about 50% v.s. the traditional PID under the step signal input. Tracking marked points using a laser tracker, the maximum tracking error is 0.0523 mm, the minimum error is 0.0450 mm, and the average error is 0.0473mm. Therefore, the presented method is validated to improve the performance of the humanoid chest. © 2023 IEEE.

关键词： Position control

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Making a Complete Mess and Getting Away with it: Traveling Salesperson Problems with Circle Placement Variants

arXiv

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arXiv 2024年

作者： Woller, David Mansouri, Masoumeh Kulich, Miroslav Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic School of Computer Science University of Birmingham United Kingdom

This paper explores a variation of the Traveling Salesperson Problem, where the agent places a circular obstacle next to each node once it visits it. Referred to as the Traveling Salesperson Problem with Circle Placement (TSP-CP), the aim is to maximize the obstacle radius for which a valid closed tour exists and then minimize the tour cost. The TSP-CP finds relevance in various real-world applications, such as harvesting, quarrying, and open-pit mining. We propose several novel solvers to address the TSP-CP, its variant tailored for Dubins vehicles, and a crucial subproblem known as the Traveling Salesperson Problem on self-deleting graphs (TSP-SD). Our extensive experimental results show that the proposed solvers outperform the current state-of-the-art on related problems in solution quality. © 2024, CC BY.

关键词： Open pit mining

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

引用

2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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A Clustering-Based Support Vector Classifier for Dynamic Time-Linkage Optimization

A Clustering-Based Support Vector Classifier for Dynamic Tim...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Gao, Meng Liu, Xiao-Fang Zhan, Zhi-Hui Zhang, Jun Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

ISBN: (纸本)9781665430654

Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time. © 2023 IEEE.

关键词： dynamic optimization evolutionary computation support vector machine time-linkage

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Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation

arXiv

引用

arXiv 2023年

作者： Gervet, Theophile Xian, Zhou Gkanatsios, Nikolaos Fragkiadaki, Katerina Machine Learning Department School of Computer Science Carnegie Mellon University United States Robotics Institute School of Computer Science Carnegie Mellon University United States

3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to process. As a result, most manipulation policies operate directly in 2D, foregoing 3D inductive biases. In this paper, we introduce Act3D, a manipulation policy transformer that represents the robot’s workspace using a 3D feature field with adaptive resolutions dependent on the task at hand. The model lifts 2D pre-trained features to 3D using sensed depth, and attends to them to compute features for sampled 3D points. It samples 3D point grids in a coarse to fine manner, featurizes them using relative-position attention, and selects where to focus the next round of point sampling. In this way, it efficiently computes 3D action maps of high spatial resolution. Act3D sets a new state-of-the-art in RLBench, an established manipulation benchmark, where it achieves 10% absolute improvement over the previous SOTA 2D multi-view policy on 74 RLBench tasks and 22% absolute improvement with 3x less compute over the previous SOTA 3D policy. We quantify the importance of relative spatial attention, large-scale vision-language pre-trained 2D backbones, and weight tying across coarse-to-fine attentions in ablative experiments. Code and videos are available at our project website: https://***/. © 2023, CC BY.

关键词： Robots

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SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection

arXiv

引用

arXiv 2024年

作者： Leng, Kai Zhang, Zhijie Liu, Jie Sui, Wei Boukhers, Zeyd Yang, Cong Li, Zhijun School of Computer Science and Technology Harbin Institute of Technology China School of Future Science and Engineering Soochow Universit China Fraunhofer Institute for Applied Information Technolog Sankt Augustin Germany Horizon Robotics

Edge detection is a fundamental technique in various computer vision tasks. Edges are indeed effectively delineated by pixel discontinuity and can offer reliable structural information even in texture-less areas. State-of-the-art heavily relies on pixel-wise annotations, which are labor-intensive and subject to inconsistencies when acquired manually. In this work, we propose a novel self-supervised approach for edge detection that employs a multilevel, multi-homography technique to transfer annotations from synthetic to real-world datasets. To fully leverage the generated edge annotations, we developed SuperEdge, a streamlined yet efficient model capable of concurrently extracting edges at pixel-level and object-level granularity. Thanks to self-supervised training, our method eliminates the dependency on manual annotated edge labels, thereby enhancing its generalizability across diverse datasets. Comparative evaluations reveal that SuperEdge advances edge detection, demonstrating improvements of 4.9% in ODS and 3.3% in OIS over the existing STEdge method on BIPEDv2. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge detection

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