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21st Polish Control Conference, PCC 2023

作者： Pilat, Adam Krzysztof Milanowski, Hubert Bieszczad, Rafal Sikora, Bartiomiej Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH-University of Science and Technology Krakow Poland

ISBN: (纸本)9783031351693

This elaboration presents the synthesis of the Takagi-Sugeno type Fuzzy Logic controller realizing the programmable parameters of the state feedback controller together with the steady state current for the active magnetic levitation system. Closed-loop dynamics was calculated precisely with respect to the requested dynamics. Two cases were considered: fixed-and variable-closed-loop dynamics. The Fuzzy Logic Controller was considered under four scenarios with linear, nonlinear, and constant in-range output. The study was supported by simulations and experimental investigations. The position of the levitating sphere stands as an illustration of the system in operation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Dynamics

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Preprocessing Taste Data for Deep Neural Networks 14

Preprocessing Taste Data for Deep Neural Networks

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Hyunjong Jung, Han Hee Kwak, Jeongho Yea, Junwoo Choi, Jihwan P. Jang, Kyung-In Department of Electrical Engineering and Computer Science Daegu Korea Republic of Dgist Department of Robotics and Mechatronics Engineering Daegu Korea Republic of Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of Department of Aerospace Engineering Dajeon Korea Republic of

ISBN: (纸本)9798350313277

Analyzing wine using taste data is a promising field due to the explosive expansion of online commerce. However, because of the wide variety of wine types with different flavors and aromas, it is difficult for consumers to choose the wine that suits their taste, and also difficult for sellers to recommend appropriate wines to consumers. Therefore, it is necessary to numerically analyze and classify wine, and a deep learning algorithm which mimics the human brain is appropriate for analyzing the wine data [1]. In this paper, we introduce several studies of wine classification using deep learning architectures and propose preprocessing methods for applying the taste data of wine to deep learning networks. © 2023 IEEE.

关键词： data augmentation deep learning preprocessing taste classification taste sensor wine

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Experimental evaluation of autonomous map-based Spot navigation in confined environments

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Biomimetic Intelligence & robotics 2022年第1期2卷 50-58页

作者： Anton Koval Samuel Karlsson George Nikolakopoulos Robotics&AI Team Deparment of ComputerElectrical and Space Engineering.Luled University of TechunologyLuled SE-97187Sweden

In this article,we address the task of experimental evaluation of autonomous map-based navigation of a Boston Dynamics Spot quadruped robot in confined environments equipped with the developed autonomy package that incorporates a 3D lidar,an IMU,and an onboard *** that,we propose an integrated software and hardware system that is considered as an enabler for robot localization and risk-aware path planning,based on the known *** system design itself is modular and incorporates the perception capabilities required for autonomous *** Spot robot first is utilized to build the offline map of the environment by utilizing the Google Cartographer simultaneous localization and mapping(SLAM)*** the next step,the online environmental information from the autonomy package sensors and the offline map are provided to the onboard computer to localize the robot on the known map by utilizing means provided by ***,the occupancy information is provided to the online grid-based path planner that generates risk-aware *** extensive experimental evaluation of the proposed system is performed in corridors and SubT environments.

关键词： Spot SubT Map-based navigation 3D lidar Autonomy package

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A Survey on Machine Learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on Machine Learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining AI with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of AI and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP International Federation for Information Processing 2024.

关键词： Machine learning

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Jogeshwar, Bhaavin Kishore Bohannon, Joshua Sevil, Hakki Erhan Department of Intelligent Systems and Robotics PensacolaFL32514 United States Department of Electrical and Computer Engineering PensacolaFL32514 United States

ISBN: (纸本)9798331504847

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies. © 2025 IEEE.

关键词： Object detection

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From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection 38

From News to Forecast: Integrating Event Analysis in LLM-Bas...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xinlei Feng, Maike Qiu, Jing Gu, Jinjin Zhao, Junhua School of Electrical and Computer Engineering The University of Sydney Australia School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium, our method adaptively integrates social events into forecasting models, aligning news content with time series fluctuations to provide richer insights. Specifically, we utilize LLM-based agents to iteratively filter out irrelevant news and employ human-like reasoning to evaluate predictions. This enables the model to analyze complex events, such as unexpected incidents and shifts in social behavior, and continuously refine the selection logic of news and the robustness of the agent's output. By integrating selected news events with time series data, we fine-tune a pre-trained LLM to predict sequences of digits in time series. The results demonstrate significant improvements in forecasting accuracy, suggesting a potential paradigm shift in time series forecasting through the effective utilization of unstructured news data. © 2024 Neural information processing systems foundation. All rights reserved.

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Movement-Based Interaction: An Approach to Visual and Auditory Stimulus and Memory 6

Movement-Based Interaction: An Approach to Visual and Audito...

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6th Experiment at International Conference, exp.at 2023

作者： Nogueira, Maria Rita Menezes, Paulo Institute of Systems and Robotics University of Coimbra Coimbra Portugal University of Coimbra Dept. of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350314403

Movement-based interaction design is an innovative field that leverages the body's natural movements to create intuitive and engaging interfaces. This approach takes a sensorial approach to visual and auditory memory, seeking to enhance user engagement and create more meaningful experiences. The use of movement-based interfaces has significant implications for senior citizens, who may have mobility limitations or cognitive impairments. Using movement-based interfaces, seniors can engage in physical activity and maintain cognitive function, essential aspects for healthy aging. Movement-based interfaces can also help children develop physical coordination, cognitive skills, and social interaction. Furthermore, movement-based interaction design can benefit everyone's physical well-being by promoting physical activity and reducing sedentary behavior. Studies have demonstrated that movement-based interfaces can enhance posture, physical activity levels, and general health. By combining movement with sensory experiences, movement-based interaction design can create a more engaging and enjoyable experience for users, which can motivate them to maintain physical and cognitive health. In this article, we present two case studies that research the effectiveness of movement-based interaction design in enhancing visual and auditory memory. Overall, the results of these case studies suggest that movement-based interaction design is an effective approach to enhancing visual and auditory memory. The introduction of movement-based interfaces can give participants a more enjoyable and memorable experience, improving user engagement and information retention. © 2023 IEEE.

关键词： Machine learning

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Reactive Multi-agent Coordination using Auction-based Task Allocation and Behavior Trees

Reactive Multi-agent Coordination using Auction-based Task A...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Dahlquist, Niklas Lindqvist, Bjorn Saradagi, Akshit Nikolakopoulos, George Luleå University of Technology Robotics and Ai Group Department of Computer Science Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350335446

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as 'pick and place', whose arrival times are not known a priori. In such a scenario, a coordinating architecture is expected to be reactive to newly arrived tasks and the resulting rerouting of agents should be dependent on the stage of completion of their current multi-stage tasks. In the novel architecture proposed in this article, a central auctioning system gathers bids (cost-estimates for completing currently available tasks) from all agents, and solves a combinatorial problem to optimally assign tasks to agents. For every agent, it's participation in the auctioning system and execution of an assigned multi-stage task is managed using behavior trees, which switch among several well-defined behaviors in response to changing scenarios. The auctioning system is run at a fixed rate, allowing for newly added tasks to be incorporated into the auctioning system, which makes the solution reactive and allows for rerouting of some agents (subject to the states of the behavior trees). We demonstrate that the proposed architecture is especially well-suited for multistage tasks, where high costs are incurred when rerouting agents who have completed one or more stages of their current tasks. The proposed framework is experimentally validated in multiple scenarios in a lab environment. A video of a demonstration can be found at: https://***/ZdEkoOOlB2g. © 2023 IEEE.

关键词： Cost estimating

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Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems 49

Efficient Real-time Smoke Filtration with 3D LiDAR for Searc...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Kyuroson, Alexander Koval, Anton Nikolakopoulos, George Luleå University of Technology Robotics and Artificial Intelligence Group Department of Computer Electrical and Space Engineering LuleåE-97187 Sweden

ISBN: (纸本)9798350331820

Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots. © 2023 IEEE.

关键词： Smoke

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Contact-Rich Task Learning on an Articulated Soft Robot Arm Through Simulation 25th

Contact-Rich Task Learning on an Articulated Soft Robot Ar...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Elstner, Laurenz Kyrkjebø, Erik Stoelen, Martin F. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway Fieldwork Robotics Ltd. Cambridge United Kingdom

ISBN: (纸本)9783031720611

Traditional rigid robots face limitations in exploration during learning processes in dynamic and unknown environments where contact and impacts are common. Soft robots that leverage compliant materials, for example as part of variable stiffness actuators (VSAs), offer promising avenues for enhanced adaptability and safety. Deep Reinforcement Learning (RL), while proven to be a powerful tool, requires a lot of training data that is time-consuming to gather. This paper introduces a novel approach to simulating robots driven by antagonistic VSAs, as well as full-task learning with a soft robot arm in three environments with different contact types: path following (no contact), door opening (kinematic constraint), and surface wiping (continuous contact). We show that the tendon-driven robot can learn all three tasks, even if learning in a very low-level actuation space. The stretchable tendons enables it to perform relatively well on the contact-rich tasks. Further, using the proposed variable stiffness controller the robot is able to develop a stable behaviour in terms of forces applied to the environment. The results are promising for extending the approach to the physical robot. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tendons

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