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Real-Time Dynamic Gesture Recognition for Human-Robot Collaboration in Rescue Operations

Real-Time Dynamic Gesture Recognition for Human-Robot Collab...

引用

ieee international Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Shefali Dewangan Vamsi Krishna Origanti Frank Kirchner German Research Center for Artificial Intelligence (DFKI GmbH) Robotics Innovation Center Bremen Germany Department of Mathematics and Informatics University of Bremen Bremen Germany

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

This paper provides a methodology for dynamic gesture recognition for enhanced Human-Robot Collaboration in search and rescue missions. This research was conducted at DFKI in collaboration with THW, Germany, where an autonomous robot is built for the assistance of emergency workers during disaster scenarios for transporting the materials. This assistance robot will be highly functional in risk-prone areas where the safety of workers is in danger. A set of standard gestures were predefined and those will be used as commands from humans to the robot. Due to the criticality of the area of application of this assistance robot, the identification and interpretation of the gestures must be in real-time and extremely accurate. In this paper, we compare different methods and train different pipelines capable of performing dynamic gesture recognition. The final model pipeline can understand the difference between static as well as dynamic gestures and recognize both of them accurately. The key challenge to attaining real-time gesture recognition is the models's capability to give rapid and highly accurate responses.

关键词： Accuracy Heuristic algorithms computational modeling Pipelines Dynamics Human-robot interaction Gesture recognition Real-time systems Robustness Ensemble learning

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An efficient matching algorithm by a hybrid Hopfield network for object recognition

An efficient matching algorithm by a hybrid Hopfield network...

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ieee international symposium on Circuits and Systems (ISCAS)

作者： J.H. Kim S.H. Yoon Y.H. Kim E.H. Park C. Ntuen K.H. Sohn W.E. Alexander Department of Electrical Engineering North Carolina Agriculture and Technical State University Greensboro NC USA Department of Industrial Engineering North Carolina Agriculture and Technical State University Greensboro NC USA Department of ECE North Carolina State University Raleigh NC USA

Hopfield proposed two types of neural networks, the discrete Hopfield network (DHN) and the continuous Hopfield network (CHN). A new method for two-dimensional object recognition using a Hopfield neural network is proposed. A hybrid Hopfield network (HHN), which combines the merit of both the continuous Hopfield network and the discrete Hopfield network, is proposed and some of the advantages such as reliability and speed are discussed. Stable states of neurons are analyzed and predicted based upon the theory of the CHN after convergence in the DHN. The experiments showed that once solutions close to a global minimum were obtained in the DHN, the HHN can find the desired output by adjusting the states of neuron outputs. HHN is a robust approach to solve two-dimensional occlusion problems.< >

关键词： Object recognition Hopfield neural networks Neural networks Neurons Shape Traveling salesman problems computational modeling Differential equations robotics and automation Stochastic processes

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A Real-Time Feedback Approach Based on Semantic Clustering for Poverty Alleviation Problem

A Real-Time Feedback Approach Based on Semantic Clustering f...

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ieee international symposium on Parallel and Distributed Processing with Applications and ieee international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zizhen Peng Guobei Peng Zhiyi Mo Guangyao Pang Zongyuan Zheng Xiang Wei WuZhou Vocational College Wuzhou China Guangxi University Nanning China Wuzhou University Wuzhou China

ISBN: (纸本)9781728152103

Poverty has always been one of the most acute social problems in the world. In order to eradicate poverty, the Chinese government has invested a lot of manpower and material resources, and promised to lead all poor areas and poor people into a well-off society by 2020. In this process, the county government as the main battlefield of poverty alleviation, the complex work of poverty alleviation has brought considerable pressure to grass-roots cadres and helping cadres. For the sake of improving the efficiency of support work, we propose a real-time feedback approach based on semantic clustering for poverty alleviation problem, through which we can build a bridge between grass-roots cadres and decision makers. In this method, we first use a fast label extraction method to quickly extract important feature words from the complicated help information. Secondly, we use unsupervised text clustering method to identify important poverty alleviation problems from these feature words, so as to provide a reference for the poverty alleviation workers to carry out their work in an orderly and targeted manner. The experimental results for different regions show that the poverty alleviation problem identified by our proposed method can reflect regional characteristics.

关键词： data mining feature extraction government data processing pattern clustering social aspects of automation social sciences computing text analysis social aspects of automation Poverty social sciences computing Government data processing text processing Cluster Analysis agricultural regions Feature extraction data mining

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The Influence Maximization Problem in the Network Under Node Personalized Characteristics

The Influence Maximization Problem in the Network Under Node...

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ieee international symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Weimin Li Jun Mo Yue Liu Nobuyasu Ito Yohsuke Murase Jianwei Liu School of Computer Engineering and Technology Shanghai University Shanghai China RIKEN Center for Computational Science Kobe Hyogo Japan Department of Automation China University of Petroleum Beijing Changping China

ISBN: (纸本)9781538675199

Considering the personalization of nodes in complex networks, we propose an algorithm that maximizes the influence of similarity based on overlapping nodes and nodes. In the proposed algorithm, we investigate the overlapping of communities in the network and use overlapped nodes as the initial propagation seed set. Then, we discuss the personalized characteristics of nodes and introduce the concept of sparse attributes. An improved independent cascaded model is built to integrate the similarity of node attributes. Finally, experiments are performed on real data sets. The results show that the proposed algorithm is better than others.

关键词： Greedy algorithms Heuristic algorithms Social network services Integrated circuit modeling Scientific computing computational modeling

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A mechanical intelligence in assisting the navigation by a force feedback steering wheel for a snake rescue robot

A mechanical intelligence in assisting the navigation by a f...

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ieee international Workshop on Robot and Human Communication (ROMAN)

作者： Z. Yang K. Ito K. Hirotsune K. Saijo A. Gofuku F. Matsuno Okayama University Okayama Okayama Japan University of Electro-communications Chofu Tokyo Japan

We developed a snake rescue robot basing on the proposed mechanical intelligence. The mechanical intelligence is designed to avoid obstacles and to realize desired motions when the robot is navigated by a remote force feedback steering wheel interface. We use free joints to connect modules of the snake robot. Modules can freely turn according to their neighbors. An obstacle-avoiding wheel is mounted on the head of the snake robot. When the head encounters an obstacle, the wheel touches it first to transfer the sliding friction between the wheel and the obstacle into rolling friction, so that the head avoid the obstacle easily. A metal wire is used to link gears mounted on both sides of each module. When any part of the snake robot's body encounters an obstacle, the wire length of each side varies automatically to change the robot's body shape, so that the snake robot avoids the obstacle. The wire length of each side can also be adjusted by a motor. By adjusting the wire length of each side, the snake robot can move in the desired direction. The mechanical intelligence based snake rescue robot has light body, low cost and low computation cost. Experiment results show that the designed mechanical intelligence is effective in realizing desired robot motions together with the force feedback steering wheel interface.

关键词： Intelligent robots Navigation Force feedback Wheels Mobile robots Wire robotics and automation Friction computational efficiency Gears

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Deducing human emotions by robots: Computing basic non-verbal expressions of performed actions during a work task

Deducing human emotions by robots: Computing basic non-verba...

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ieee international symposium on Intelligent Control (ISIC)

作者： Martina Truschzinski Helge Ü. Dinkelbach Nicholas Müller Peter Ohler Fred Hamker Peter Protzel Chair of Automation Technology Technische Universität Chemnitz Chemnitz Germany Professorship of Artificial Intelligence Technische Universität Chemnitz Chemnitz Germany Institute of Media Research Technische Universität Chemnitz Chemnitz Germany Technische Universitat Chemnitz Chemnitz Sachsen DE

We have established an emotional model to enhance a virtual worker simulation, which could be also used to support robots in a joined human-robot work-task inside an industrial setting. The robot is able to understand people's individual and specific knowledge as well as capabilities, which are ultimately linked to an emotional consequence. As a result, the emotional model outputs the emotional valence calculated as positive or negative values, respective to reward and punishment. This output is applied as value function for a reinforcement learning agent. There we use an actor critic algorithm extended by eligibility traces and task specific conditions to learn the optimal action sequences. We show the influence of emotional reward leads to differences in the learned action sequences in comparison to a simple task performance evaluation reward. Therefore the robot is able to calculate emotional feelings of a human during a given working task, is able to decide if there is a better, more emotional stable path to doing this working task and moreover the robot is able to decide when the human is needed help or even not.

关键词： computational modeling Robots Sensitivity Learning (artificial intelligence) Ergonomics Physiology Standards

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Direct Inverse Hysteresis Compensation of a Pneumatic Artificial Muscles Actuated Delta Mechanism

Direct Inverse Hysteresis Compensation of a Pneumatic Artifi...

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ieee international Conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Pengxiu Geng Yanding Qin Liangjian Zhong Feng Qu Dongyang Bie Jianda Han Institute of Robotics and Automatic Information System College of Artificial Intelligence and Tianj in Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9781728190105

ISBN: (纸本)9781728190112

Pneumatic artificial muscle (PAM) is a popular bionic actuator featuring flexible actuation. However, the inherent hysteresis of PAM reduces its tracking accuracy. This paper proposed a feedforward-feedback composite control method to compensate for the hysteresis nonlinearity of the PAM in a 2 degrees-of-freedom (DOF) Delta mechanism. Direct inverse modeling approach was utilized to obtain the inverse hysteresis model of the PAM directly from the measurement without inversion calculation. Subsequently, the inverse hysteresis model was cascaded in the feedforward loop to attenuate the PAM's hysteresis. Further, PID feedback control was also integrated to account for the unmodeled dynamics and the modeling uncertainties of the inverse hysteresis model. Experimental results showed that most of the angular tracking error could be reduced to the range of ± 0.5 °, which indicated that the proposed feedforward-feedback composite control method could effectively compensate the hysteresis of the PAM.

关键词： Hysteresis Mathematical model Inverse problems Analytical models Feedforward systems Creep computational modeling

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A Mega Programmable Interactive Robotic Surface (AMPIRS)

A Mega Programmable Interactive Robotic Surface (AMPIRS)

引用

international Conference on Industrial and Information Systems, ICIIS

作者： Saeid Nahavandi Centre for Intelligent Systems Research Deakin University VIC Australia

The idea of this research is creation of a mega programmable interactive robotic surface, dynamically responsive three dimensional surface that configures in real-time according to input from a variety of input sources (movements sensors, sound signals, video signals, maths data generated within the controller etc). As such, it can be thought of as a three-dimensional surface or screen, but one that can be attuned to its surrounding environment, creating the potential for a physically responsive surface which has also attracted the attention of innovative architects for its use in various architectural structures. It may also be used to display text or images, responding rapidly enough to be able to map the movements and sounds of people. Therefore, it can function alternately as a tactile mirror, an information surface or a multi cavity mould. This talk will focus on the system design and novel control architecture that enables large numbers of pneumatic cylinders to be controlled in a highly coordinated manner to create complex 3D shapes. This system is referred to as a mega programmable interactive robotic surface (AMPIRS) similar to hundreds or thousands of Stuart platforms working together to create a single large complex surface.

关键词： Intelligent robots Robot kinematics Competitive intelligence Intelligent systems Control systems Shape control robotics and automation computational intelligence Intelligent sensors Robot sensing systems

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Characterization of Human Trust in Robot through Multimodal Physical and Physiological Biometrics in Human-Robot Partnerships

Characterization of Human Trust in Robot through Multimodal ...

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ieee international Conference on automation Science and Engineering (CASE)

作者： Jesse Parron Rui Li Weitian Wang Mengchu Zhou School of Computing Montclair State University Montclair NJ USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Trust is an attribute that many people use daily, whether consciously thinking of it or not. Although commonly designated as a firm belief in reliability, trust is more complex than many think. It is not just physical, but rather an emotion, feeling, or choice that has many layers, and can be influenced in a variety of ways. As robotics and artificial intelligence grow, humans have to deliberate whether they trust working with these technical counterparts or not. In this work, we build computational models to quantitatively characterize and analyze humans' trust in robots using multimodal physical and physiological biometric data based on the TrustBase we have created through user studies in human-robot collaborative tasks. During human-robot collaborative processes, we have collected physical and physiological attribute data of human subjects as well as the users' trust levels for each interaction. This data is used to develop a database known as TrustBase. With the data from TrustBase, computational and analytical approaches are used to investigate the correlation between robot performance factors and humans' trust levels and to characterize humans' trust in robots during human-robot collaboration. Results and their analysis suggest the effectiveness of the developed models, providing new findings to the human factors and cognitive ergonomics in human-robot interaction. Future research directions are also discussed.

关键词： Biometrics Analytical models computational modeling Biological system modeling Human-robot interaction Collaboration Human factors Reliability engineering Physiology Robots

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Recognition Method of Tomato Ripeness Based on YOLOv7 and YOLOv8

Recognition Method of Tomato Ripeness Based on YOLOv7 and YO...

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robotics, automation and Artificial intelligence (RAAI), ieee international Conference on

作者： Leong Yoong Hung Jamaludin Jalani Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor MALAYSIA

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

The increasing demand for efficient and accurate agricultural practices necessitates advanced technological solutions in crop management. One critical aspect is the accurate classification of tomato ripeness, essential for optimizing harvesting processes and ensuring product quality. Traditional methods are labor-intensive and prone to errors, emphasizing the need for automated systems. This research addresses this issue by exploring the application of advanced computer vision models, specifically YOLOv7 and YOLOv8, for tomato ripeness classification. The primary objectives were to develop and train YOLOv7 and YOLOv8 models, compare their performance, and evaluate YOLOv8's robustness under various simulated real-world conditions. Trained on a dataset of unripe, ripe, and rotten tomatoes, YOLOv8 demonstrated superior performance compared to YOLOv7, with higher precision, recall, and F1-scores across all categories of tomatoes. Further analysis included assessing YOLOv8's resilience to vary brightness, blurring, and noise addition. Results indicated that YOLOv8 maintained robust performance under these conditions, though slight declines in accuracy were observed. In conclusion, YOLOv8 proved to be a more effective and robust model for tomato ripeness classification, achieving higher true positive rates and overall better performance metrics than YOLOv7. These findings suggest that YOLOv8 is well-suited for practical deployment in agricultural settings, offering significant improvements in the accuracy and reliability of tomato classification.

关键词： Measurement Computer vision Accuracy computational modeling Noise Brightness Robustness Product design Quality assessment Resilience

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