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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Nagothu Bala Srikanth S Jeba Priya Subathra M.S.P Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Robotics and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by tremors, stiffness, and difficulty with movement, impacting mil-lions worldwide. Electroencephalography (EEG) signals can help detect Parkinson's disease by revealing abnormal brain wave patterns associated with motor dysfunction. This abstract examines the use of EEG data for PD classification, giving attention to the analysis of frequency bands using machine learning (ML) and deep learning (DL) methods. This research suggests a novel ML and DL models based on Extended Empirical Mode Decomposition (EEMD) signal processing method to classify PD in its early stages. The aim of the proposed method is to divide patients into two groups: PD positive and negative. The developed model uses a Machine learning algorithms including Random Forest, Gradient Boost, SVM and deep learning algorithms including CNN, DNN developed for the detection of PD among patients. Random Forest and Gradient Boost excelin ensemble learning. Enriching the performance of the modela decomposition method - Extended Empirical Mode Decomposition (EEMD), is employed to analyze complex EEG signals. EEMD's application enhances feature extraction for ML and DL algorithms, facilitating accurate PD detection. The diagnostic accuracy is further improved using CNN and DNN models, which identify complex patterns in EEG data. This integrated approach showcases promise for early classification and continuous monitoring, contributing to advancements in understanding and managing PD. The proposed CNN model achieved a greater of 0.98 accuracy metric.

关键词： Deep learning Support vector machines Accuracy Machine learning algorithms Empirical mode decomposition Parkinson's disease Signal processing algorithms

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A Novel Aerial-Aquatic Locomotion Robot with Variable Stiffness Propulsion Module

arXiv

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arXiv 2024年

作者： Hu, Junzhe Chen, Pengyu Feng, Tianxiang Wen, Yuxuan Wu, Ke Dong, Janet The UC Center for Robotics Research College of Engineering and Applied Science University of Cincinnati CincinnatiOH United States The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Robotics department Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

In recent years, the development of robots capable of operating in both aerial and aquatic environments has gained significant attention. This study presents the design and fabrication of a novel aerial-aquatic locomotion robot (AALR). Inspired by the diving beetle, the AALR incorporates a biomimetic propulsion mechanism with power and recovery strokes. The variable stiffness propulsion module (VSPM) uses low melting point alloy (LMPA) and variable stiffness joints (VSJ) to achieve efficient aquatic locomotion while reduce harm to marine life. The AALR's innovative design integrates the VSPM into the arms of a traditional quadrotor, allowing for effective aerial-aquatic locomotion. The VSPM adjusts joint stiffness through temperature control, meeting locomotion requirements in both aerial and aquatic modes. A dynamic model for the VSPM was developed, with optimized dimensional parameters to increase propulsion force. Experiments focused on aquatic mode analysis and demonstrated the AALR's swimming capability, achieving a maximum swimming speed of 77 mm/s underwater. The results confirm the AALR's effective performance in water environment, highlighting its potential for versatile, eco-friendly operations. Copyright © 2024, The Authors. All rights reserved.

关键词： Aircraft propulsion

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Risk-aware Spatio-temporal Logic Planning in Gaussian Belief Spaces

Risk-aware Spatio-temporal Logic Planning in Gaussian Belief...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Matti Vahs Christian Pek Jana Tumova The authors are with the Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

In many real-world robotic scenarios, we cannot assume exact knowledge about a robot's state due to unmodeled dynamics or noisy sensors. Planning in belief space addresses this problem by tightly coupling perception and planning modules to obtain trajectories that take into account the environment's stochasticity. However, existing works are often limited to tasks such as the classic reach-avoid problem and do not provide risk awareness. We propose a risk-aware planning strategy in belief space that minimizes the risk of violating a given specification and enables a robot to actively gather information about its state. We use Risk Signal Temporal Logic (RiSTL) as a specification language in belief space to express complex spatio-temporal missions including predicates over Gaussian beliefs. We synthesize trajectories for challenging scenarios that cannot be expressed through classical reach-avoid properties and show that risk-aware objectives improve the uncertainty reduction in a robot's belief.

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Diver Interest via Pointing: Human-Directed Object Inspection for AUVs

Diver Interest via Pointing: Human-Directed Object Inspectio...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Chelsey Edge Junaed Sattar Department of Computer Science and Engineering (CSE) Minnesota Robotics Institute (MnRI) University of Minnesota Twin Cities Minneapolis MN USA

In this paper, we present the Diver Interest via Pointing (DIP) algorithm, a highly modular method for conveying a diver's area of interest to an autonomous underwater vehicle (AUV) using pointing gestures for underwater humanrobot collaborative tasks. DIP uses a single monocular camera and exploits human body pose, even with complete dive gear, to extract underwater human pointing gesture poses and their directions. By extracting 2D scene geometry based on the human body pose and density of salient feature points along the direction of pointing, using a low-level feature detector, the DIP algorithm is able to locate objects of interest as indicated by the diver. DIP makes it possible for scuba divers and swimmers to use directional cues, through pointing, to an AUV for inspection, surveillance, manipulation, and navigation. We examine the elements that make up our method, provide quantitative and qualitative evaluation, and demonstrate AUV actuation based on diver pointing gestures in closed-water human-robot collaborative experiments. Our evaluations demonstrate the high efficacy of the DIP algorithm in correctly identifying the direction of a pointing gesture and locating an object within that region of interest. We also show that the findings of the algorithm qualitatively conform with human assessment of pointing gestures, directions, and targets.

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

Suturing Tasks Automation Based on Skills Learned from Demon...

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International Symposium on Medical robotics (ISMR)

作者： Haoying Zhou Yiwei Jiang Shang Gao Shiyue Wang Peter Kazanzides Gregory S. Fischer Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

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.

关键词： Training Medical robotics Automation Heuristic algorithms Instruments Phantoms Needles

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Covariance Based Terrain Mapping for Autonomous Mobile Robots

Covariance Based Terrain Mapping for Autonomous Mobile Robot...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Lennart Werner Pedro Proença Andreas Nüchter Roland Brockers Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Computer Science XVII – Robotics Julius-Maximilians-Universität Würzburg Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we present a local, robot-centric navigation map optimized for autonomous mobile robots operating in unknown environments, enhancing their onboard perception systems for collision-free operation with far look-ahead distances. Utilizing a novel converging covariance cell representation, our approach effectively analyzes hazards such as obstacles and hazardous slopes in both terrestrial and aerial navigation contexts. The new technique specifically targets mapping from stereo scenarios with ultra short baseline and highly oblique viewpoints close to the *** methodology surpasses traditional window-based hazard analysis by resolving sub-cell size obstacles and terrain gradients at the individual cell level, thereby avoiding the computational overhead typically associated with such analyses. It leverages a multi-resolution strategy adaptive to the range errors common in stereo vision systems, making it particularly suitable for embedded systems with computational *** includes constant-time queries for height, obstacle presence, and slope details, boasting improvements in run time, memory usage, precision, and resolvable obstacle size compared to existing grid-based mapping algorithms. We validate our approach through rigorous simulation and real-world testing. This technique will be used for the local mapping and collision avoidance on NASA’s CADRE lunar rovers.

关键词： Uncertainty Three-dimensional displays Navigation Moon Terrain mapping Hazards Mobile robots

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Computing optimal nash equilibria in multiplayer games 23

Computing optimal nash equilibria in multiplayer games

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Youzhi Zhang Bo An V. S. Subrahmanian Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences School of Computer Science and Engineering Nanyang Technological University Singnapore Department of Computer Science Northwestern University Evanston

Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. For example, when there is a team of players independently playing against an adversary in a game (e.g., several groups in a forest trying to interdict illegal loggers in green security games), these team members may need to find an NE minimizing the adversary's utility. Finding an optimal NE in multiplayer games can be formulated as a mixed-integer bilinear program by introducing auxiliary variables to represent bilinear terms, leading to a huge number of bilinear terms, making it hard to solve. To overcome this challenge, we first propose a general framework for this formulation based on a set of correlation plans. We then develop a novel algorithm called CRM based on this framework, which uses Correlation plans with their Relations to restrict the feasible solution space after the convex relaxation of bilinear terms while Minimizing the number of correlation plans to reduce the number of bilinear terms. We show that our techniques can significantly reduce the time complexity, and CRM can be several orders of magnitude faster than the state-of-the-art baseline.

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Robotic Detection and Estimation of Single Scuba Diver Respiration Rate from Underwater Video

arXiv

引用

arXiv 2023年

作者： Kutzke, Demetrious T. Sattar, Junaed The Department of Computer Science & Engineering The Minnesota Robotics Institute University of Minnesota– Twin Cities MinneapolisMN55455 United States

Human respiration rate (HRR) is an important physiological metric for diagnosing a variety of health conditions from stress levels to heart conditions. Estimation of HRR is well-studied in controlled terrestrial environments, yet robotic estimation of HRR as an indicator of diver stress in underwater for underwater human robot interaction (UHRI) scenarios is to our knowledge unexplored. We introduce a novel system for robotic estimation of HRR from underwater visual data by utilizing bubbles from exhalation cycles in scuba diving to time respiration rate. We introduce a fuzzy labeling system that utilizes audio information to label a diverse dataset of diver breathing data on which we compare four different methods for characterizing the presence of bubbles in images. Ultimately we show that our method is effective at estimating HRR by comparing the respiration rate output with human analysts. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination

Lighting the Smart Way: A Technological Odyssey in Urban Str...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： M S Vinu P Kanaga Priya P Thilakaveni J M Ashforn Hermina K Prashanthini M Misba Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Engineering PSG Polytechnic College Coimbatore India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Bethlahem Institute of Engineering Karungal Nagercoil India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure

关键词： Smart cities Lighting Data visualization Collaboration Sensor systems Real-time systems Energy efficiency Internet of Things Sustainable development Intelligent sensors

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A Transfer Entropy Based Approach for Fault Isolation in Industrial Robots

引用

ASME Letters in Dynamic Systems and Control 2022年第1期2卷 011009页

作者： Vallachira, Sathish Norrlof, Mikael Orkisz, Michal Butail, Sachit Robotics Service Intelligence Unit ABB Ability Innovation Center Karnataka Bangalore560048 India Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi New Delhi110020 India ABB Robotics Division Vasteras721 68 Sweden ABB Corporate Technology Center Krakow31-038 Poland Department of Mechanical Engineering Northern Illinois University DeKalbIL60115 United States

In this paper, we cast the problem of fault isolation in industrial robots as that of causal analysis within coupled dynamical processes and evaluate the related efficacy of the information-theoretic approach of transfer entropy. To create a realistic and exhaustive dataset, we simulate wear-induced failure by increasing friction coefficient on select axes within an in-house robotic simulation tool that incorporates an elastic gearbox model. The source axis of failure is identified as one which has the highest net transfer entropy across all pairs of axes. In an exhaustive simulation study, we vary the friction successively in each axis across three common industrial tasks: pick and place, spot welding, and arc welding. Our results show that transfer entropy-based approach is able to detect the axis of failure more than 80% of the time when the friction coefficient is 5% above the nominal value and always when friction coefficient is 10% above the nominal value. The transfer entropy approach is more than twice as accurate as cross-correlation, a classical time series analysis used to identify directional dependence among processes. Copyright © 2021 by ASME.

关键词： Industrial robots

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