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Fault detection and isolation

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Proceedings Article•DOI A new fault tolerance method for field robotics through a self-adaptation architecture Yanzhe Cui, Joshua Lane, Richard M. Voyles, Akshay Krishnamoorthy - Show less +3 more 01 Oct 2014 6 Citations	A demonstrative application of ReFrESH illustrates its applicability through a task for visual servoing to a target deployed on a multi-robot system.
Open access•Proceedings Article•DOI Disturbance detection, identification, and recovery by gait transition in legged robots Aaron M. Johnson, G. Clark Haynes, Daniel E. Koditschek - Show less +2 more 03 Dec 2010 30 Citations	We believe that extensions of this framework will enable reactive behaviors allowing the robot to function with guarded autonomy under widely varying terrain and self-health conditions.
Proceedings Article•DOI ReFrESH: A self-adaptation framework to support fault tolerance in field mobile robots Yanzhe Cui, Richard M. Voyles, Joshua T. Lane, Mohammad H. Mahoor - Show less +3 more 06 Nov 2014 11 Citations	A demonstrative application of ReFrESH illustrates its applicability through a target tracking task deployed on a mobile robot system.
Proceedings Article•DOI A framework for area coverage and the visual search for victims in USAR with a mobile robot Choon Yue Wong, Gerald Seet, Siang Kok Sim, Wee Ching Pang - Show less +3 more 01 Nov 2010 9 Citations	We find that the framework is suitable for implementation on a mobile robot for its envisioned purpose.
Book Chapter•DOI Vision-Based Humanoid Robot Navigation in a Featureless Environment Julio Delgado-Galvan, Alberto Navarro-Ramirez, Jose I. Nunez-Varela, Cesar A. Puente-Montejano, Francisco E. Martinez-Perez - Show less +4 more 24 Jun 2015 8 Citations	We show how our approach allows the robot to reliably navigate in this kind of environment in real-time.
Open access•Journal Article•DOI Internet Controlled Robotic Arm Wan Muhamad Hanif Wan Kadir, Reza Ezuan Samin, Babul Salam Ksm Kader Ibrahim - Show less +2 more 01 Jan 2012 51 Citations	This user friendly robot is expected to bridge the gap between robot and household chores.
Open access•Journal Article•DOI Robot Fast Adaptation to Changes in Human Engagement During Simulated Dynamic Social Interaction With Active Exploration in Parameterized Reinforcement Learning Mehdi Khamassi, George Velentzas, Theodore Tsitsimis, Costas S. Tzafestas - Show less +3 more 05 Jul 2018-IEEE Transactions on Cognitive and Developmental Systems 32 Citations	Altogether, these results suggest a novel efficient and robust framework for robot learning during dynamic HRI scenarios.

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What are the current algorithms in robotics?

Current algorithms in robotics encompass a wide range of approaches, including traditional planning algorithms, machine learning techniques, reinforcement learning, and policy gradient methods. Traditional planning algorithms involve graph search, sampling-based methods, and interpolating curve algorithms, while machine learning algorithms include support vector machines, LSTM, Monte-Carlo tree search, and CNN. Reinforcement learning algorithms like Q learning, deep Q-learning network, and policy gradient methods such as actor-critic algorithms are also prominent in robotics research. These algorithms aim to enhance robot navigation, obstacle avoidance, trajectory planning, and autonomous decision-making, contributing to the development of more efficient and capable robotic systems.What cost function can measure information gained against distance travelled?

The cost function that can measure information gained against distance traveled is the rent-commuting cost function, as proposed in the research by Youngsun Kwon. This function represents the relationship between rent and total commuting cost, offering a superior alternative to the traditional rent-distance function. The rent-commuting cost function's gradient remains independent of the commuting cost function's specific form and decreases as household income increases, making it a more effective tool for depicting and quantifying key urban characteristics. This innovative approach provides a valuable means of analyzing the trade-off between information acquisition and travel distance in urban settings, enhancing our understanding of the dynamics between information gain and spatial exploration.How implement multi-task reinforcement learning on particle accelerator control ’?

To implement multi-task reinforcement learning on particle accelerator control, researchers have explored various approaches. One method involves utilizing Deep Reinforcement Learning (DRL) algorithms to learn optimal policies for control tasks. Additionally, continuous model-free reinforcement learning with up to 16 degrees of freedom has been successfully tested at CERN facilities for trajectory steering in particle accelerators. Moreover, a comparison between model-based and model-free reinforcement learning on intensity optimization at the FERMI FEL system showed that model-based methods offer higher sample efficiency and representational power, while model-free methods exhibit slightly superior asymptotic performance. These studies highlight the potential of reinforcement learning techniques in enhancing the efficiency and performance of particle accelerator control systems.Which AI tools can be used with children?

AI tools that can be effectively utilized with children include Voice User Interfaces (VUIs) for early childhood language learning, high-fidelity prototypes for revealing children's needs and behaviors with AI-powered systems, social robots as guides for children in AI art generation to ensure safety and creativity, AI-assisted tools for addressing learning challenges in children with neurodevelopmental disorders (NDDs) to improve social interaction and supportive education, and AI models and tools for diagnosing and treating Attention Deficit Hyperactive Disorder (ADHD) in children with greater accuracy and precision using neural networks and EEG signals. These AI tools cater to various aspects of children's development, ranging from language learning and creativity to addressing mental health challenges and neurodevelopmental disorders.What is the Social and Emotional Well-Being framework?

The Social and Emotional Well-Being framework encompasses feelings, behavior, relationships, goals, and personal strengths, reflecting a state of constructive emotional management and positive social interactions. It is crucial to consider cultural variations, individual differences, and temperament when assessing well-being. Additionally, emotional well-being is viewed as a fluctuating continuum that impacts one's ability to engage in daily activities, practice self-care, and nurture relationships, with its absence linked to diminished relationship quality and capacity for love. The development of societal well-being frameworks aims to go beyond economic growth, integrating both objective and subjective measures of well-being from diverse disciplines to provide a holistic view of progress and social welfare.Which oversampling method performs the best?

The oversampling methods proposed in the research papers show promising results in addressing the class imbalance issue in various domains. Among these methods, the outlier detectable generative adversarial network (OD-GAN), the preprocessing method based on SeqGAN, the oversampling method OS-CCD, and the WASSKIL methodhave demonstrated effectiveness in improving classification performance on imbalanced datasets. Additionally, an oversampling method for string data has been proposed, showing better results than existing methods. Each method introduces unique approaches to tackle the imbalanced dataset problem, with experiments showcasing their superiority over traditional oversampling techniques. However, the specific performance comparison across these methods is not directly provided in the contexts, making it challenging to definitively determine which oversampling method performs the best.What is backtrack in mobile robot SLAM?

Backstepping in mobile robot SLAM refers to a control algorithm design technique used for trajectory tracking. This method focuses on developing robust output tracking controllers based on Lyapunov stability analysis to ensure accurate and stable tracking of trajectories in various conditions. Backstepping theory is relatively new in the field of mobile robot control, showing effectiveness in solving nonholonomic constraints and achieving satisfactory performance. The approach involves designing controllers that provide desired linear and angular velocities for the robot to follow a given trajectory, enhancing stability and accuracy in tracking errors. Overall, backstepping plays a crucial role in improving the trajectory tracking capabilities of mobile robots in SLAM applications.Issues with delivery robots

Delivery robots face several challenges highlighted in the research papers. One major issue is the difficulty in climbing curbs and stairs, especially for six-wheeled robots, due to inadequate mechanisms. Another challenge is ensuring secure last-mile delivery, which is addressed through AI-driven systems incorporating user authentication and identification modules for accurate and safe parcel delivery. Autonomous outdoor delivery robots encounter obstacles and environmental conditions, necessitating robust localization, safe navigation, and effective path planning strategies to navigate dynamic outdoor environments successfully. Moreover, energy supply during abnormal conditions like disasters is critical for uninterrupted robot operation, leading to the exploration of various power sources such as direct energy, solar energy, and flywheel storage energy for continuous delivery services.What are the main barriers to the adoption of robotics in warehouse logistics?

The main barriers to the adoption of robotics in warehouse logistics include the lack of top management support, legal and regulatory frameworks, infrastructure challenges, resistance to change, and difficulties in handling single products rather than boxes. These barriers hinder the successful implementation of disruptive technologies in the logistics sector. Additionally, traditional programs lacking flexibility and human auto-following techniques struggling with target identification and obstacle avoidance pose challenges in utilizing robots effectively for warehouse tasks. Furthermore, the gap in understanding the benefits and barriers of Industry 4.0 technologies in warehouse management highlights the need for adequate financial support and new skills for successful implementation.Does a serious games can be utilized even during leisure time for training?

Serious games have shown potential for training purposes beyond traditional therapy sessions, extending to leisure time activities. These games, designed for various rehabilitation contexts like myoelectric prosthesis training, stroke rehabilitation, and skill development in persons with developmental disabilities, offer benefits such as increased motivation, engagement, and skill enhancement. They incorporate elements like immediate feedback, repetition, and interactive experiences to make training enjoyable and effective. Moreover, serious games combined with cable-driven robots have been successful in bimanual rehabilitation, enhancing user acceptance and engagement. Therefore, utilizing serious games during leisure time can not only provide a fun and engaging way to train but also contribute to skill development and rehabilitation progress, making them a valuable resource for continuous improvement outside formal therapy sessions.What are the uses of evolutionary algorithms in robotics ?

Evolutionary algorithms play a crucial role in robotics, particularly in areas such as robot locomotion, route planning, and controller evolution. These algorithms, inspired by natural evolution and genetics, are utilized to optimize robot behaviors and designs. In robot locomotion, evolutionary algorithms aid in evolving controllers that adapt to variable conditions, ensuring robust performance. Additionally, these algorithms are instrumental in mobile robot route planning, helping robots navigate environments efficiently by finding optimal paths while considering various constraints and obstacles. Moreover, evolutionary algorithms enable the automated design of robots, optimizing their structures to exhibit desired behaviors rapidly and effectively. Overall, evolutionary algorithms enhance the capabilities of robots in diverse applications, ranging from search and rescue to transportation and beyond.