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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Khan, Asif Siddiqui, Mohammad Atif Integral University Integral Robotics Lab Department of Computer Application Lucknow India Integral University Integral Robotics Lab Department of Electrical Engineering Lucknow India

ISBN: (纸本)9798350376425

The research study on the "Design and Implementation of an AI-Enhanced Mental Health Tool for Academic Stress"addresses the growing mental health crisis among students, particularly concerning academic pressures. The study highlights alarming statistics, such as over 13,000 student suicides in India in 2021 - a 4.5% increase from the previous year and a 70% rise since 2011 - indicating an urgent need for targeted interventions. The academic environment, characterized by high expectations, intense pressure, and fierce competition, places considerable strain on students' mental well-being. Recognizing that one-size-fits-all solutions are inadequate, this research advocates for a personalized approach to address these unique challenges. Artificial Intelligence (AI) is positioned as a key solution in this effort. The proposed AI-enhanced mental health tool aims to provide students with real-time, personalized support. Through proactive interventions and individualized assistance, it seeks to revolutionize mental health care in academic settings. This study underscores the importance of innovative approaches to tackling the student mental health crisis. By fostering resilience and well-being, the AI tool aims to effectively address academic stress and improve mental health outcomes. © 2024 IEEE.

关键词： Emotional intelligence

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A Novel Technology for Semiautomatic and Automatic Stem–Stake Coupling of Seedlings and Plants

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 254-262页

作者： Shirzi, Moteaal Asadi Kermani, Mehrdad R. Western University Advanced Robotics and Mechatronic Systems Laboratory Electrical and Computer Engineering Department LondonN6A 5B9 Canada Western University Department of Electrical and Computer Engineering LondonN6A 3K7 Canada Advanced Robotics and Mechatronic Systems Laboratory LondonN6A 5B9 Canada

This article introduces a novel mechatronic system for coupling the stems of seedlings and plants to wooden stakes or ropes, a crucial process for supporting them during growth, transportation, and fruiting in plant propagation facilities and greenhouses. The stem–stake coupling device utilizes interconnected mechanisms and an impedance control method to adjust motor torque and speed, shaping metallic wire into clips of various shapes and dimensions, effectively securing plant stems to stakes or ropes. In a robotic system, a claw-shaped arm mechanism, a stereo camera, and real-time vision techniques are integrated into the stem–stake coupling device to identify the optimal coupling point and automate the coupling task. This innovation addresses the labor-intensive task of manual coupling, offering a scalable solution for growers through handheld devices or fully automated robotic systems. In the context of increasing labor shortages and rising costs, the technology offers a sustainable and efficient alternative with significant potential to enhance operational efficiency in greenhouses and propagation facilities. © 2023 IEEE.

关键词： Seed

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BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization 32

BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detecti...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Saucedo, Mario A.V. Stathoulopoulos, Nikolaos Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George Luleå University of Technology Robotics & AI Group Department of Computer Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350395440

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes BOX3D, a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. BOX3D is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments. © 2024 IEEE.

关键词： Object detection

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Decentralized Fusion-Based Ego Velocity Estimation Using Multiple FMCW Radars 21

Decentralized Fusion-Based Ego Velocity Estimation Using Mul...

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21st International Conference on Advanced robotics, ICAR 2023

作者： Mukherjee, Moumita Banerjee, Avijit Koval, Anton Nikolakopoulos, George Robotics and AI Luleå University of Technology Department of Computer Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342291

This article presents a decentralized fusion-based ego velocity estimation framework utilizing multiple Frequency-Modulated Continuous-Wave (FMCW) radar sensors and an Inertial Measurement Unit (IMU). This method relies on the collaborative exchange of information among radar sensors, employing a decentralized estimation approach. This approach enhances system robustness and incorporates a unique and effective noise reduction technique to diagnose and eliminate undesirable measurements. Traditional robotics missions, relying on visual data, often face challenges, particularly in scenarios where Global Navigation Satellite System (GNSS) signals are either not accessible or unreliable and when visibility is hindered by factors such as darkness, direct sunlight, fog, or smoke. The proposed framework holds immense potential for enhancing the precision of navigation systems in autonomous vehicles and advanced driver assistance systems, particularly in challenging conditions like adverse weather and complex urban environments. Comparative analysis against traditional centralized fusion methods in experimental studies reveals sub-stantial enhancements in accuracy and reliability, highlighting the potential of decentralized fusion. © 2023 IEEE.

关键词： Smoke

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Effect of EKF Localization on Path Tracking in Autonomous Vehicle Navigation Systems

Effect of EKF Localization on Path Tracking in Autonomous Ve...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Cho, Eun Ho Yang, Seung Geon Kwon, Girim Lim, Seung-Chan School of ICT Robotics and Mechanical Engineering Hankyong National University Anseong Korea Republic of School of Electrical and Computer Engineering University of Seoul Seoul Korea Republic of

ISBN: (纸本)9798331510756

This paper presents an integrated approach to evalu-ating the interplay between localization and path-tracking performances in autonomous vehicle navigation systems. Considering stochastic uncertainties in motion and measurement models, we design an extended Kalman filter (EKF) for localization and employ the pure pursuit method for steering control. We analyze how localization accuracy impacts path-tracking performance through extensive simulations, varying the number of landmarks and noise covariance settings. Our results highlight the critical role of accurate localization in improving path tracking, offering valuable insights for designing robust autonomous navigation systems. © 2025 IEEE.

关键词： Stochastic systems

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Autonomous Drifting Based on Maximal Safety Probability Learning 27

Autonomous Drifting Based on Maximal Safety Probability Lear...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Hoshino, Hikaru Li, Jiaxing Menon, Arnav Dolan, John M. Nakahira, Yorie University of Hyoto Department of Electrical Materials and Engineering Japan Carnegie Mellon University Department of Electrical and Computer Engineering United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9798331505929

This paper proposes a novel learning-based framework for autonomous driving based on the concept of maximal safety probability. Efficient learning requires rewards that are informative of desirable/undesirable states, but such rewards are challenging to design manually due to the difficulty of differentiating better states among many safe states. On the other hand, learning policies that maximize safety probability does not require laborious reward shaping but is numerically challenging because the algorithms must optimize policies based on binary rewards sparse in time. Here, we show that physics-informed reinforcement learning can efficiently learn this form of maximally safe policy. Unlike existing drift control methods, our approach does not require a specific reference trajectory or complex reward shaping, and can learn safe behaviors only from sparse binary rewards. This is enabled by the use of the physics loss that plays an analogous role to reward shaping. The effectiveness of the proposed approach is demonstrated through lane keeping in a normal cornering scenario and safe drifting in a high-speed racing scenario. © 2024 IEEE.

关键词： Contrastive Learning

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ELECTROMECHANICAL DESIGN AND VEHICLE DRIVING CAPABILITY UPGRADE OF A FULL-SIZED HUMANOID, HART

ELECTROMECHANICAL DESIGN AND VEHICLE DRIVING CAPABILITY UPGR...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Bickford, Harry Yang, Jaesung Esteves, Andrew Shen, Xin Sohn, Kiwon Electrical and Computer Engineering University of Hartford West HartfordCT United States Robotics Engineering University of Hartford West HartfordCT United States

ISBN: (纸本)9780791888636

In this paper, the applicability of various gear designs and layouts that can increase manipulators' joint torque outputs of a full-sized humanoid is studied first. The investigation encompasses an exploration of different configurations involving varied gearing, array arrangements, and magnet placements to optimize the resultant magnetic field of the magnetic gear. Then, a waypoint navigation control utilizing a monocular lane and intersection detection algorithm developed based on Region of Interest (RoI) Hough Transform and landmark detection built based on the Convolutional Neural Network (CNN) model is investigated to enable the humanoid to drive its assigned vehicle in an autonomous way. The RoI detection and CNN models are developed and trained using diverse intersections, enabling algorithms to determine the distance between the vehicle and the intersection. Lastly, testing and evaluation of the built gear mechanism and driving control system are implemented in real-world human-centered environments. Copyright © 2024 by ASME.

关键词： Hough transforms

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Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces 37

Balancing Memorization and Generalization in RNNs for High P...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Costello, Joseph T. Temmar, Hisham Cubillos, Luis H. Mender, Matthew J. Wallace, Dylan M. Willsey, Matthew S. Patil, Parag G. Chestek, Cynthia A. Department of Electrical and Computer Engineering Biomedical Engineering Robotics and Neurosurgery University of Michigan Ann ArborMI United States

ISBN: (纸本)9781713899921

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Mammals

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