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A Framework and Environment Design of Human-Machine Cooperations in Junior science Education 8

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A Framework and Environment Design of Human-Machine Cooperat...

8th International Symposium on System Security, Safety, and Reliability, ISSSR 2022

作者： Meng, Sa Luo, Liang Liu, Jun Dai, Yuanshun School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

ISBN: (数字)9781665453974

ISBN: (纸本)9781665453974

Human-Machine-Environment (HME) Tri-Co (Coexisting, Cooperative, Cognitive) Education is an indispensable prerequisite and main content of Artificial Intelligence empowering junior science education. Moreover, it is also a critical requirement for improving the quality of education and promoting the high-end development of education information. Building a unique learning environment with scene perception and the intelligent cognitive process is a tremendous challenge for diverse needs in junior scientific learning scenarios. Therefore, it is necessary to design advanced human-computer interaction scenarios and functions. This paper aims to tackle the critical scientific problem of this field for HME Tri-Coeducation, i.e., HME multimodal real-time scene perception and Tri-Co collaborative analysis via Multi-source data, fundamental theories, application method, and verification system, and to build HME Tri-Coeducation environment in 'Perception-Analysis-Interaction' architecture. For the requirements of an education environment empowered by artificial intelligence, this paper will also study critical technologies of human-machine cooperative and education environment building driven by big data. It is significant to promote the cross-integration of multi-disciplines such as pedagogy, intelligent technology, and cognitive science. © 2022 IEEE.

关键词： Human computer interaction

Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems

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arXiv 2025年

作者： Diao, Yufeng Zhang, Yichi De Martini, Daniele Zhao, Philip Guodong Li, Emma Liying School of Computing Science University of Glasgow United Kingdom Department of Computer Science University of Manchester United Kingdom Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom

This paper proposes a task-oriented co-design framework that integrates communication, computing, and control to address the key challenges of bandwidth limitations, noise interference, and latency in mission-critical industrial Cyber-Physical Systems (CPS). To improve communication efficiency and robustness, we design a task-oriented Joint Source-Channel Coding (JSCC) using Information Bottleneck (IB) to enhance data transmission efficiency by prioritizing task-specific information. To mitigate the perceived End-to-End (E2E) delays, we develop a Delay-Aware Trajectory-Guided Control Prediction (DTCP) strategy that integrates trajectory planning with control prediction, predicting commands based on E2E delay. Moreover, the DTCP is co-designed with task-oriented JSCC, focusing on transmitting task-specific information for timely and reliable autonomous driving. Experimental results in the CARLA simulator demonstrate that, under an E2E delay of 1 second (20 time slots), the proposed framework achieves a driving score of 48.12, which is 31.59 points higher than using Better Portable Graphics (BPG) while reducing bandwidth usage by 99.19%. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

Blockchain Technology and Artificial Intelligence's Future Applications in Cyber Security 3

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Blockchain Technology and Artificial Intelligence's Future A...

3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, AI can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and AI are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

Complexity based investigation in collaborative assembly scenarios via non intrusive techniques 4

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Complexity based investigation in collaborative assembly sce...

4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Panagou, Sotirios Sileo, Monica Papoutsakis, Konstantinos Fruggiero, Fabio Qammaz, Ammar Argyros, Antonis School of Engineering University of Basilicata Potenza85100 Italy Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion70013 Greece

Human and robot collaboration in assembly tasks is an integral part in modern manufactories. Robots provide advantages in both process and productivity with their repeatability and usability in different tasks, while human operators provide flexibility and can act as safeguards. However, process complexity increases which can lower the overall quality. Increased complexity can negatively influence decision making due to cognitive load on human operators, which can lead to lower quality, be it product, process or human work. Moreover, it can lead to safety risks, human-system error and accidents. In this work, we present the preliminary results on an experiment performed with student-participants, based on an assembly task. The experiment was set up to emulate an industrial assembly, and data collection was performed through qualitative and non-intrusive quantitative methods. Questionnaires were used to assess perceptual task complexity and cognitive load, while a stereo camera provided recordings for after-task analysis on process errors and human work quality based on a 3D skeleton-based human pose estimation and tracking method. The aim of the study is to investigate causes of errors and implications on quality. Future direction of the work is discussed. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Decision making

Temporally Robust Multi-Agent STL Motion Planning in Continuous Time

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Temporally Robust Multi-Agent STL Motion Planning in Continu...

American Control Conference (ACC)

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Signal Temporal Logic (STL) is a formal language over continuous-time signals (such as trajectories of a multiagent system) that allows for the specification of complex spatial and temporal system requirements (such as staying sufficiently close to each other within certain time intervals). To promote robustness in multi-agent motion planning with such complex requirements, we consider motion planning with the goal of maximizing the temporal robustness of their joint STL specification, i.e. maximizing the permissible time shifts of each agent's trajectory while still satisfying the STL specification. Previous methods presented temporally robust motion planning and control in a discrete-time optimization scheme. In contrast, we parameterize the trajectory by continuous Bézier curves, where the curvature and the time-traversal of the trajectory are parameterized individually. We show an algorithm generating continuous-time temporally robust trajectories and prove soundness of our approach. Moreover, we empirically show that our parametrization realizes this with a considerable speed-up compared to state-of-the-art methods based on constant interval time discretization.

关键词： Redundancy Formal languages Robustness Encoding Trajectory Planning Logic

GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

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arXiv 2023年

作者： Bakshi, Nikhil Angad Gupta, Tejus Ghods, Ramina Schneider, Jeff Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of ≈75,000 m2. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline. © 2023, CC BY-NC-ND.

关键词： Multipurpose robots

A Quality Diversity Method to Automatically Generate Multi-Agent Path Finding Benchmark Maps

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arXiv 2024年

作者： Qian, Cheng Zhang, Yulun Bhatt, Varun Fontaine, Matthew C. Nikolaidis, Stefanos Li, Jiaoyang Robotics Institute Carnegie Mellon University United States Thomas Lord Department of Computer Science University of Southern California United States

We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to generate benchmark maps for Multi-Agent Path Finding (MAPF) algorithms. Previously, MAPF algorithms are tested using fixed, human-designed benchmark maps. However, such fixed benchmark maps have several problems. First, these maps may not cover all the potential failure scenarios for the algorithms. Second, when comparing different algorithms, fixed benchmark maps may introduce bias leading to unfair comparisons between algorithms. Third, since researchers test new algorithms on a small set of fixed benchmark maps, the design of the algorithms may overfit to the small set of maps. In this work, we take advantage of the QD algorithm to (1) generate maps with patterns to comprehensively understand the performance of MAPF algorithms, (2) be able to make fair comparisons between two MAPF algorithms, providing further information on the selection between two algorithms and on the design of the algorithms. Empirically, we employ this technique to generate diverse benchmark maps to evaluate and compare the behavior of different types of MAPF algorithms, including search-based, priority-based, rule-based, and learning-based algorithms. Through both single-algorithm experiments and comparisons between algorithms, we identify patterns where each algorithm excels and detect disparities in runtime or success rates between different algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Cellular automata

Late-Fusion Multimodal Human Detection Based on RGB and Thermal Images for Robotic Perception

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Late-Fusion Multimodal Human Detection Based on RGB and Ther...

European Conference on Mobile Robots (ECMR)

作者： Elísio Sousa Kennedy O. S. Mota Iago P. Gomes Luís Garrote Denis F. Wolf Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Institute of Mathematics and Computer Science University of São Paulo Brazil

This paper addresses the problem of detecting humans in RGB and Thermal (long-wave IR) images taken by cameras mounted onboard a mobile robot. Human/Pedestrian detection is currently one of the most pertinent object detection problems, mainly due to safety concerns in autonomous vehicles. The majority of approaches apply deep-learning techniques based solely on RGB images. However, they have a few shortcomings, namely that during foggy weather, nighttime, and low-light scenarios, these images may not contain sufficient information. To address these issues, this work studies the use of thermal cameras as a complementary source of information for human detection in indoor and outdoor environments. The proposed approach uses YOLOv5 to detect pedestrians in both thermal and RGB images. Moreover, the different modalities are combined using early and late fusion techniques. Evaluation of the proposed approach is carried out in the FLIR Aligned dataset and in a new in-house dataset. Results indicate that the use of fusion techniques highlights a promising way to improve the overall performance in this application domain.

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The Michigan robotics Undergraduate Curriculum: Defining the Discipline of robotics for Equity and Excellence

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arXiv 2023年

作者： Jenkins, Odest Chadwicke Grizzle, Jessy Atkins, Ella Stirling, Leia Rouse, Elliott Guzdial, Mark Provost, Damen Mann, Kimberly Millunchick, Joanna Robotics and Electrical Engineering & Computer Science United States Undergraduate Education Robotics Institute United States Robotics Institute United States Graduate Programs Robotics Institute United States Robotics and Industrial Engineering United States Robotics and Mechanical Engineering United States Electrical Engineering & Computer Science United States Engineering Education Research Program United States Undergraduate Education College of Engineering United States University of Michigan United States

The robotics Major at the University of Michigan was successfully launched inthe 2022-23 academic year as an innovative step forward to better servestudents, our communities, and our society. Building on our guiding principleof "robotics with Respect" and our larger robotics Pathways model, the Michiganrobotics Major was designed to define robotics as a true academic disciplinewith both equity and excellence as our highest priorities. Understanding thattalent is equally distributed but opportunity is not, the Michigan roboticsMajor has embraced an adaptable curriculum that is accessible through adiversity of student pathways and enables successful and sustained career-longparticipation in robotics, AI, and automation professions. The results afterour planning efforts (2019-22) and first academic year (2022-23) have beenhighly encouraging: more than 100 students declared robotics as their major,completion of the robotics major by our first two graduates, soaringenrollments in our robotics classes, thriving partnerships with HistoricallyBlack Colleges and Universities. This document provides our original curricularproposal for the robotics Undergraduate Program at the University of Michigan,submitted to the Michigan Association of State Universities in April 2022 andapproved in June 2022. The dissemination of our program design is in the spiritof continued growth for higher education towards realizing equity andexcellence. The most recent version of this document is also available on Google Docsthrough this link: https://***/robotics_majorMSC Codes 97 © 2023, CC BY.

关键词： Curricula