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IEEE International Conference on Robotics and Automation (ICRA)

作者： B. Bhanu P. Leang C. Cowden Y. Lin M. Patterson Center for Research in Intelligent Systems University of California Riverside CA USA

ISBN: (纸本)0780365763

This paper presents the design, implementation and testing of a real-time system using computer vision and machine learning techniques to demonstrate learning behavior in a miniature mobile robot. The miniature robot, through environmental sensing, learns to navigate a maze choosing the optimum route. Several reinforcement learning based algorithms, such as the Q-learning, Q(/spl lambda/)-learning, fast online Q(/spl lambda/)-learning and DYNA structure, are considered. Experimental results based on simulation and an integrated real-time system are presented for varying density of obstacles in a 15/spl times/15 maze.

关键词： Robot vision systems Robot sensing systems Real time systems Machine learning Cameras Orbital robotics Robot kinematics intelligent robots Learning systems Navigation

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Simultaneous On-line Discovery and Improvement of Robotic Skill Options

Simultaneous On-line Discovery and Improvement of Robotic Sk...

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IEEE/RSJ International Conference on intelligent robots and Systems

作者： Freek Stulp Laura Herlant Antoine Hoarau Gennaro Raiola Robotics and Computer Vision Ecole Nationale Superieure de Techniques Avancees (ENSTA-ParisTech) France

ISBN: (纸本)9781479969357

The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm "Simultaneous On-line Discovery and Improvement of Robotic Skills" (SODIRS) that is able to autonomously discover and optimize skill options for such task variations. We formalize the problem in a reinforcement learning context, and use the PI~(BB) algorithm [2] to continually optimize skills with respect to a cost function. SODIRS discovers new subskills, or "skill options", by clustering the costs of trials, and determining whether perceptual features are able to predict which cluster a trial will belong to. This enables SODIRS to build a decision tree, in which the leaves contain skill options for task variations. We demonstrate SODIRS' performance in simulation, as well as on a Meka humanoid robot performing the ball-in-cup task.

关键词： Cost function Covariance matrices robots Visualization Vectors Clustering algorithms

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Autonomous oil spill and pollution detection for large-scale conservation in marine eco-cyber-physical systems

Autonomous oil spill and pollution detection for large-scale...

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International Conference of Signal Processing and intelligent Systems (ICSPIS)

作者： Asma Bahrani Babak Majidi Mohammad Eshghi Khatam University Tehran Iran Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781665409391

In recent years, advancement of the industry and increased human activities created significant pollution in the marine environment and coastal regions of the Persian Gulf. These pollutions cause various diseases and serious damages to the human health and animal species. Early identification of various pollutions helps the coastal management to organize their resources and rapidly respond to the problems. Due to the large scale of the coastal regions, manual investigation of the pollutions is a very time-consuming task. Unmanned robots can be used as autonomous agents for rapid large-scale detection and classification of pollutions in the coastal regions. In this paper, an artificial intelligence-based vision system for autonomous marine pollution detection is proposed. A combination of computer vision and machine learning methods are used for autonomous detection of various pollutions in the coastal and marine environment. In this study, 3000 images of Persian Gulf coastal pollutions is collected and used for training an artificial intelligence system for coastal conservation. The experimental results shows that the proposed framework has a 98% accuracy for identifying and classifying coastal and marine pollutions. The proposed system can be used as the vision system of an autonomous coastal conservation robot and increase the speed of coastal conservation and management significantly.

关键词： Pollution Machine learning algorithms Marine pollution Environmentally friendly manufacturing techniques Machine vision Sea measurements Signal processing algorithms

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Simultaneous object recognition and position tracking for robotic applications

Simultaneous object recognition and position tracking for ro...

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International Conference on Mechatronics (ICM)

作者： Dimitris Aristos Spyros Tzafestas Intelligent Robotics and Automation Laboratory School of Electrical and Computer Engineering National and Technical University of Athens Athens Greece

Object recognition techniques are well known in the field of machine vision, and aim at the classification of certain observed rigid objects based on the information acquired by a specific sensor. These techniques can either be performed in the 2D image space by simply applying suitable image processing algorithms, or in the real world 3D space by performing surface reconstruction of the object's surface and comparing it with a database of known CAD data. This paper presents a combination of techniques which not only recognize and categorize the observed rigid objects but also compute their 3D pose (position and orientation) with respect an industrial robot's workspace frame. This task is very useful in the field of robotics as it allows an industrial robot to simultaneously recognize and follow the displacements of a specific rigid object, among other objects that may co-exist in the same environment.

关键词： Object recognition Service robots Orbital robotics Surface reconstruction Robot sensing systems Machine vision Image processing Image reconstruction Image databases Object oriented databases

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Application of Keypoint Recognition for Industrial Human-Robot Safe Collaboration Scenarios

Application of Keypoint Recognition for Industrial Human-Rob...

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International Symposium on Autonomous Systems (ISAS)

作者： Zhixiang Ma Wenhua Jiao Lijuan Li Shiping Yang XiaoWei Xu College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

With the advent of Industry 5.0 and the rise of human-centered intelligent manufacturing, people have paid increasing attention to the issue of security in human-machine collaboration. Developing safe human-robot cooperation in constrained environments has emerged as the primary area of research interest. vision systems with deep learning have gradually supplanted more conventional approaches, such electronic wearables, electronic fences, and lidar techniques, to ensure safe collaboration. Object identification and posture estimation are two techniques that are currently in use to forecast distances in space more accurately. These techniques can track the approximate locations of humans and robots in real-time, significantly lowering the likelihood of safety incidents. Still, more accurate evaluation of the relative positions of humans and robots is needed for effective collaboration. This paper suggests SCC-HRNet, an efficient key point recognition technique. SCC-HRNet is able to find the important feature points of both humans and robots more precisely in dual-camera human-robot safe collaboration scenarios. Using our human-robot collaboration dataset, SCC-HRNet outperforms other algorithms with an average precision gain of 1.6%, correctly identifying key points.

关键词： Accuracy Service robots Collaboration Predictive models Rendering (computer graphics) Real-time systems Safety

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Guest editorial emerging circuits and systems techniques for ultra-low power body sensor networks

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IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2012年第1期2卷 1-3页

作者： Calhoun, Benton Chandrakasan, Anantha P. Otis, Brian Verma, Naveen Yoo, Hoi-Jun Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 United States Department of Electrical Engineering University of Washington Seattle WA 98195 United States Department of Electrical Engineering Princeton University Princeton NJ 08540 United States Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 08540 Korea Republic of

This special issue brings together 11 papers on approaches spanning circuits, architectures, algorithms, and system design that demonstrates promising solutions to emerging critical problems in body sensor networks.

关键词： Special issues and sections Low-power electronics Body sensor networks

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A Delay-tolerant, Potential field-based, Network Implementation of an Integrated Navigation System

A Delay-tolerant, Potential field-based, Network Implementat...

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2006 IEEE/RSJ International Conference on intelligent robots and Systems

作者： Rachana Ashok Gupta Ahmad A Masoud Mo-yuen Chow Electrical and Computer Engineering Department North Carolina State University Raleigh NC USA Lectrical Engineering Department KFUPM Dhahran Saudi Arabia Electrical Engineering Department KFUPM Dhahran Saudi Arabia

intelligent space (iSpace) is a large scale mechatronics system. It is a multidisciplinary effort whose aim is to produce a network structure and components that are capable of integrating sensors, actuators, DSP, communication, and control algorithms in a manner that suits time-sensitive applications including real-time navigation and/or obstacle avoidance. There are many challenges that must be overcome in order to put such a distributed, heterogeneous system together. This paper deals with one of these issues, i.e. the adverse effect of network and processing delays on the system. Here a delay-resistant sensory motor module for navigating a differential drive unmanned ground vehicle (UGV) in a cluttered environment is suggested. The module consists of an early vision edge detection stage, a harmonic potential field (HPF) planner, a network based quadratic curve fitting controller and gain schedule middleware, (GSM). Though the different techniques used to implement the navigation system have been well-studied as independent modules, the contribution in this paper is the way all these different modules are integrated together for the first time to create an efficient structure for a network based integrated navigation system. The structure of this module and its components are described. Thorough experimental results along with performance assessment comparing the suggested structure to a previous implementation of iSpace are also provided

关键词： Navigation intelligent sensors Delay systems intelligent systems intelligent actuators Large-scale systems Mechatronics Digital signal processing Communication system control Delay effects

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Augmented virtuality based immersive telepresence for control of mining robots

Augmented virtuality based immersive telepresence for contro...

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International Symposium on Computational Intelligence and intelligent Informatics, ISCIII

作者： Karthik Mahesh Varadarajan Markus Vincze Automation and Control Institute (ACIN) Technical University of Vienna (TU Wien) Vienna Austria

Vast mineral resources of precious metals such as gold remain trapped and unexploited due to the lack of economical and practical means of exploration. This requires the development of alternate exploitation techniques. Mining robots form a significant alternative to convention mining techniques. However, there are several practical limitations that make such systems difficult to implement in practice. The primary hurdle in realizing such systems is the difficulty in tele-operating the robot under high latency conditions, which is typical of mining of environments. This is further compounded by poor representation of the environment, resulting in reduced situational awareness. The latency in tele-operation can be caused by numerous factors - system latency, compression scheme, communication protocols, constraints on bandwidth, channel contention, poor line of sight and display overhead. This is typically countered by reduction of frame rate or display resolution or quality. This further affects remote navigation of the robot. Non-holistic scene displays further degrade situational perception. This is intricately tied to the effectiveness of the Operator Control Unit (OCU). Besides, improvements in these capabilities without any vehicle intelligence do little in reducing the operator task-load. In this paper, we present the design of a novel augmented virtuality based visualization and operator interface unit along supported by vehicular intelligence, which are targeted at overcoming the above issues. These design considerations and presented algorithms are expected to form the foundation of next generation mining robots.

关键词： Cameras Robot vision systems Rendering (computer graphics) Robot kinematics Vehicles

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Particle filter localization and real time obstacle avoidance control based on 3D perception for wheelchair mobile robot

Particle filter localization and real time obstacle avoidanc...

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International IEEE Conference on intelligent Systems, IS

作者： Emna Baklouti Nader Ben Amor Mohamed Jallouli Ecole Nationale d'Ingénieur de Sfax Université de Sfax Tunisie Computer & Embedded Systems Laboratory (CES)

Obstacle detection and localization behaviours have shown to work robustly in 2D perceived environment. With the progress of proximity sensors, precisely the emergence of 3D vision techniques, it would be interesting to examine these motion controllers whether for 3D perceived environment. 2D basic algorithms for obstacle avoidance or robot location, needs reformulation in order to process data from 3D perception devices. In this work, we introduce a 3D obstacle detection controller, combined with particle filter localization. The obstacle detection control described in this paper address 3D obstacles with different shapes and the particle filter uses 3D environment data to correct the wheelchair localization based on his kinematics model.

关键词： Three-dimensional displays Wheelchairs robots Two dimensional displays Kinematics Collision avoidance Sensors

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Hy-Tech Shopping Cart Using Object Detection and Human Following Technology

Hy-Tech Shopping Cart Using Object Detection and Human Follo...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Anita Joshi Tanishq Thuse Prashant Thorve Raj Thakur Anshul Thote Isha Thakur Department Of Engineering Science and Humanities Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Recent advancements in artificial intelligence and robotics have transformed the retail industry, leading to innovative solutions that enhance the shopping experience. Our paper presents the High-Tech Shopping Cart, an intelligent system that leverages object detection and human-following technology to provide a seamless, hands-free shopping experience. The cart utilizes state-of-the-art object detection algorithms to identify and track items selected by shoppers while employing a robust human-following mechanism to navigate autonomously through stores. Our system integrates computer vision techniques, sensor fusion, and advanced path-planning algorithms within a modular framework, featuring cameras and depth sensors for real-time processing. Extensive experiments demonstrate high accuracy in object detection and reliable navigation in complex environments. User satisfaction surveys indicate a positive reception, highlighting the cart's potential to significantly enhance the shopping experience.

关键词： Surveys Navigation Service robots Object detection Sensor fusion Robot sensing systems Robustness Real-time systems Object recognition intelligent systems

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