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IEEE/RSJ International Conference on intelligent robots and Systems

作者： Giuseppe Loianno Vincenzo Lippiello Carlo Fischione Bruno Siciliano PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II via Claudio 21 80125 Naples Italy ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical model-based techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

关键词： Motion Estimation via Pareto-Optimization control robotic applications motion estimation Pareto optimisation Inertial Visualization Standing Orders Data fusion data integration Kalman filters computer vision complexity classes

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Feature detection and map building using ranging sensors

Feature detection and map building using ranging sensors

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International Conference on intelligent Transportation

作者： M. Kulich P. Stepan L. Preucil The Gerstner Laboratory for Intelligent Decision Making and Control Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

This paper describes two methods for feature extraction. The goal is to interpret the data obtained from sonar measurements of the robot's environment as geometric map primitives. The extracted primitives can be used to update the robot position and the global map. This is the process how the robot can explore an unknown environment. The first algorithm gathers sonar measurements into sensor-based map and then using computer-vision techniques and morphological operations extracts geometric description of detected obstacles. The second method is started whenever the border tracking algorithm has been activated. This method uses raw sonar data as the input while obstacle border is robustly detected by the border tracking process. The algorithms have been verified with an experimental mobile robot at the Czech Technical University.

关键词： computer vision Robot sensing systems Sonar measurements Mobile robots Sonar detection Sensor fusion intelligent sensors Data mining Robustness Laboratories

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Multiscale Design and Modeling of Nanorobots

Multiscale Design and Modeling of Nanorobots

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2007 IEEE/RSJ International Conference on intelligent robots and Systems

作者： Mustapha Hamdi Antoine Ferreira Laboratoire Vision et Robotique ENSI de Bourges Université d''Oran-Es-senia Bourges France

Nanorobotic structure design and modeling require large-scale computer simulation tools and numerical algorithms in order to better understand, control and accelerate development of multiscale nanosystems. Research on modeling and simulation of physical, chemical and biological systems at the nanoscale will include techniques such as quantum mechanics, multi-particle simulation, molecular dynamics simulation, grain and continuum based models. In this paper, we present a novel approach that makes use of multi-scale and multi- physics modeling coupled to virtual reality for nanorobotic prototyping systems. First, a CAD system that integrated principles for multiscale approach in nanorobotics structure design is presented. Then, we focus in the different design levels, more specifically, the optimization of geometry structure carried out by quantum mechanics, molecular dynamics and continuum mechanics methodologies. As illustration of the proposed multiscale modeling concepts, we tested by simulation the dynamics characteristics of a nanorobotic parallel platform with one degree-of-freedom (DOF) composed of proteins-based passive kinematic chains and actuated by a viral protein-based nanoactuator. Multiscale simulations proved the effectiveness and accuracy of the proposed design and modeling approaches.

关键词： Biological system modeling Nanobioscience Computational modeling Nanostructures Quantum mechanics Proteins Algorithm design and analysis Large-scale systems computer simulation Acceleration

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Real time self-localization of omni-vision robot by pattern match system

Real time self-localization of omni-vision robot by pattern ...

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International Conference on Advanced Robotics and intelligent Systems (ARIS)

作者： Shu-Yin Chiang Xingzhi Guo Hsien-Wen Hu Department of Information and Telecommunications Engineering Ming Chuan University Taoyuan Taiwan

In this study, we designed an autonomous mobile robot according to the rules of the Federation of International Robot-soccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The real time self-localization scheme of the mobile robot is based on the algorithms extracting white lines from the soccer field featured in the images from the omni-vision system. The proposed algorithm uses the white line distances from 360 degree direction between the robot and the rotation angle of the robot obtained from the gyroscope as a featured vector. The feature vector is compared with the pre-built in patterns of the database. The location of the robot is obtained from the location of the matched pattern with the minimum error of the featured vector. The results demonstrate that the proposed algorithm is accurate, exhibiting a 10 cm (distance) and 1 degree (rotation) position error in a soccer field measuring 750 cm × 550 cm within processing time about 5 milliseconds. The real time self-localization system can be practically implemented in 2014 FIRA Robot Soccer Competition to fulfill the contest requirement.

关键词： Pattern matching Real-time systems Vectors Mobile robots Heuristic algorithms Feature extraction

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A WSM-based Comparative Study of vision Tracking Methodologies

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INTERNATIONAL JOURNAL OF ADVANCED computer SCIENCE AND APPLICATIONS 2021年第8期12卷 87-98页

作者： Bouraya, Sara Belangour, Abdessamad Hassan II Univ Lab Informat Technol & Modeling Fac Sci Ben Msik Casablanca Morocco

vision tracking is a key component of a video sequence. It is the process of locating single or multiple moving objects over time using one or many cameras. The latter's function consists of detecting, categorizing, and tracking. The development of the trustworthy solution for video sequence analysis opens up new horizons for a variety of applications, including intelligent transportation systems, biomedical, agriculture, human-machine interaction, augmented reality, video surveillance, robots, and many crucial research areas. To make efficient models, there are challenges in video observation to deal with, such as problems with the environment, light variation, pose variation, motion blur, clutter, occlusion, and so on. In this paper, we present several techniques that addressed the issues of detecting and tracking multiple targets on video sequences. The proposed comparative study relied on different methodologies. This paper's purpose is to list various approaches, classify them, and compare them, using the Weighted Scoring Model (WSM) comparison method. This includes studying these algorithms, selecting relevant comparison criteria, assigning weights for each criterion, and lastly computing scores. The obtained results of this study will reveal the strong and weak points of each algorithm mentioned and discussed.

关键词： Multiple object tracking object detection WSM method computer vision video analysis

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Real-time robot learning

Real-time robot learning

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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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