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25th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2025

作者： Ribeiro, Jorge Brilhante, Marina Matos, Diogo Miguel Silva, Carlos A. Sobreira, Heber Costa, Pedro INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre of Robotics in Industry and Intelligent Systems Porto4200-465 Portugal University of Porto Faculty of Engineering Porto4200-465 Portugal Flowbotic Mobile Systems S.A Viseu3530-259 Portugal

ISBN: (纸本)9798331538606

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A∗ (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA∗ through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm. © 2025 IEEE.

关键词： Multipurpose robots

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How language of interaction affects the user perception of a robot

arXiv

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arXiv 2023年

作者： Sienkiewicz, Barbara Sejnova, Gabriela Gajewski, Paul Vavrecka, Michal Indurkhya, Bipin Cognitive Science Department Jagiellonian University Krakow Poland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic Institute of Computer Science AGH University of Science and Technology Krakow Poland

Spoken language is the most natural way for a human to communicate with a robot. It may seem intuitive that a robot should communicate with users in their native language. However, it is not clear if a user's perception of a robot is affected by the language of interaction. We investigated this question by conducting a study with twenty-three native Czech participants who were also fluent in English. The participants were tasked with instructing the Pepper robot on where to place objects on a shelf. The robot was controlled remotely using the Wizard-of-Oz technique. We collected data through questionnaires, video recordings, and a post-experiment feedback session. The results of our experiment show that people perceive an English-speaking robot as more intelligent than a Czech-speaking robot (z = 18.00, p-value = 0.02). This finding highlights the influence of language on human-robot interaction. Furthermore, we discuss the feedback obtained from the participants via the post-experiment sessions and its implications for HRI design. © 2023, CC BY-NC-ND.

关键词： Feedback

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HPPS: A Hierarchical Progressive Perception System for Luggage Trolley Detection and Localization at Airports

arXiv

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arXiv 2024年

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys at airports. However, existing methods for detecting and locating these luggage trolleys often fail when they are not fully visible. To address this, we introduce the Hierarchical Progressive Perception System (HPPS), which enhances the detection and localization of luggage trolleys under partial occlusion. The HPPS processes the luggage trolley’s position (x, y) and orientation (θ) separately, which requires only RGB images for labeling and training, eliminating the need for 3D coordinates and alignment. The HPPS can accurately determine the position of the luggage trolley with just one well-detected keypoint and estimate the luggage trolley’s orientation when it is partially occluded. Once the luggage trolley’s initial pose is detected, HPPS updates this information continuously to refine its accuracy until the robot begins grasping. The experiments on detection and localization demonstrate that HPPS is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. A website about this work is available at HPPS. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

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

arXiv

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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

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Text-Guided Image Generation for Railway Intrusion Anomaly Detection

Text-Guided Image Generation for Railway Intrusion Anomaly D...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Jinghang Chen Chi Zhang Xiaodan Feng Yuehu Liu Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China

Railway intrusion are characterized by strong suddenness, high unpredictability and many disturbing factors, which are key factors affecting railway safety. Currently, the deep neural network-based intrusion object detection algorithm relies on the diversity of training data. However, the data of railway intrusion image is scarce and difficult to obtain. Existing image generation methods face the problem of insufficient realism in the generated images and difficulty in generating large amounts of data. We aim to explore a text-guided image data generation framework for specific visual tasks by using an existing large language model combined with a text-to-image diffusion model. In this paper, we construct a framework for railway intrusion image generation guided by textual prompts and build a dataset of railway intrusion images based on this framework. The dataset generated in this paper contains a wide range of intrusion images and includes a variety of environmental factors such as weather and time, which provides better diversity than existing datasets. The evaluation results show that the intrusion images generated in this paper have high quality and realism.

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Uncertainty-aware Condensation for OCT Cervical Dataset Combining Coreset Selection and Distillation

Uncertainty-aware Condensation for OCT Cervical Dataset Comb...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuxuan Xiong Yongchao Xu Yan Zhang Kai Gui Bo Du School of Computer Science Wuhan University Wuhan China Department of Obstetrics and Gynecology Renmin Hospital of Wuhan University Wuhan China Yijiahe Robotics Research Institute Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Optical Coherence Tomography (OCT) cervical images offer a sensitive and non-invasive solution for screening cervical cancer. Currently, utilizing deep learning techniques to interpret these images has emerged as a promising approach for computer-aided diagnosis. However, challenges in computational efficiency and privacy protection persist in this field. To address these issues, we propose an uncertainty-aware filtering before distillation (UFD) framework to compress the OCT cervical dataset in a trustworthy and efficient manner. Our approach involves pre-training an uncertainty proxy to evaluate the importance of each sample, allowing us to extract a valuable subset (i.e., coreset) from the original dataset. We then apply importance-weighted distribution matching for dataset distillation. The distribution matching loss is calculated as the distance between the class centers of the uncertainty-weighted coreset and the synthetic distilled dataset. Extensive experiments validate the feasibility and effectiveness of our proposed UFD framework. It is worth noting that after selecting the most representative samples from the original OCT dataset through coreset selection, the performance of the trained network is comparable to, or sometimes even slightly higher than, that of the network trained with all the original data. This indicates that automated dataset compression and cleaning techniques are crucial for improving image classification problems in medical imaging datasets, such as OCT and ultrasound, which are rich in noise and erroneous samples.

关键词： Deep learning Training Uncertainty Image coding Ultrasonic imaging Filtering Redundancy Protection Image classification Biomedical imaging

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RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVED MULTI-OPTIMIZATION ALGORITHM AND SUPPORT VECTOR REGRESSION 14

RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVE...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Tao, Yulong Wang, Jun Wang, Kai School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Intelligent Technology for Chemical Process Industry Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Robotics and Artificial Intelligence Manufacturing Innovation Institute Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781837242672

The lithium-ion battery is increasingly critical in the fields of electric vehicles and sustainable energy. Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential to mitigate risks and minimize potential losses. This paper introduces a novel fusion algorithm, FPSOGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) with an enhanced Circle chaos strategy and a random adaptive weighting strategy. The FPSOGWO algorithm, integrated with Support Vector Regression (FPSOGWO-SVR), was utilized to predict the RUL of health factors in lithium batteries. Compared to other optimization algorithms, FPSOGWO exhibits superior optimization speed and value, effectively overcoming issues of local optimality. Specifically, when benchmarked against the Whale Optimization Algorithm-SVR (WOA-SVR), the R-Square values of FPSOGWO-SVR showed improvements of 15.62%, 1.28%, 5.67%, and 10.65% across four different types of batteries. Experimental results demonstrate that FPSOGWO-SVR provides more accurate RUL predictions for lithium batteries, highlighting its potential for broader application in battery health management systems. © The Institution of Engineering & Technology 2024.

关键词： Support vector regression

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DistB-VNET: Distributed Cluster-based Blockchain Vehicular Ad-Hoc Networks through SDN-NFV for Smart City

DistB-VNET: Distributed Cluster-based Blockchain Vehicular A...

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International Conference on computer and Information Technology (ICCIT)

作者： Anichur Rahman MD. Zunead Abedin Eidmum Dipanjali Kundu Mahir Hossain MD Tanjum An Tashrif Md Ahsan Karim Md. Jahidul Islam Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research (NITER) Constituent Institute of Dhaka University Savar Dhaka Dept. of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Computer Science and Engineering Green University of Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In the developing topic of smart cities, Vehicular Ad-Hoc Networks (VANETs) are crucial for providing successful interaction between vehicles and infrastructure. This research proposes a distributed Blockchain-based Vehicular Ad-hoc Network ("DistB-VNET") architecture that includes binary malicious traffic classification, Software Defined Networking (SDN), and Network Function Virtualization (NFV) to ensure safe, scalable, and reliable vehicular networks in smart cities. The suggested framework is the decentralized blockchain for safe data management, SDN-NFV for dynamic network management and resource efficiency, and a noble isolation forest algorithm that works as an IDS (Intrusion Detection System). Further, "DistB-VNET" offers a dual-layer blockchain system, where a distributed blockchain provides safe communication between vehicles, while a centralized blockchain in the cloud is in charge of data verification and storage. This improves security, scalability, and adaptability, ensuring better traffic management, data security, and privacy in VANETs. Furthermore, the unsupervised isolation forest model achieves a high accuracy of 99.23% for detecting malicious traffic. Additionally, reveals that our method greatly improves network performance, offering decreased latency, increased security, and reduced congestion, an effective alternative for existing smart city infrastructures.

关键词： Smart cities Scalability Heuristic algorithms Forestry Dynamic scheduling Blockchains Network function virtualization Resource management Vehicle dynamics Software defined networking

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

iMTSP: Solving Min-Max Multiple Traveling Salesman Problem w...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yifan Guo Zhongqiang Ren Chen Wang Flight Dynamics & Control/Hybrid Systems Lab Purdue University West Lafayette IN Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab University at Buffalo NY USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents).

关键词： Urban areas Estimation Reinforcement learning Traveling salesman problems Internet Resource management Optimization Intelligent robots Faces Convergence

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6D Object Pose Estimation from Approximate 3D Models for Orbital robotics

6D Object Pose Estimation from Approximate 3D Models for Orb...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Maximilian Ulmer Maximilian Durner Martin Sundermeyer Manuel Stoiber Rudolph Triebel Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Department of Computer Science Technical University of Munich (TUM) Garching Germany

We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model. To achieve this, we employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel. In addition to the 3D coordinates, our model also estimates the pixel-wise coordinate error to discard correspondences that are likely wrong. This allows us to generate multiple 6D pose hypotheses of the object, which we then refine iteratively using a highly efficient region-based approach. We also introduce a novel pixel-wise posterior formulation by which we can estimate the probability for each hypothesis and select the most likely one. As we show in experiments, our approach is capable of dealing with extreme visual conditions including overexposure, high contrast, or low signal-to-noise ratio. This makes it a powerful technique for the particularly challenging task of estimating the pose of tumbling satellites for in-orbit robotic applications. Our method achieves state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021 post-mortem competition. Code, trained models, and the used satellite model will be made publicly available.

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