Relevant bibliographies by topics / Robotic system identification

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Academic literature on the topic 'Robotic system identification'

Author: Grafiati
Published: 3 May 2025

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Journal articles on the topic "Robotic system identification"

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Ghiță, Alexandra Ștefania, and Adina Magda Florea. "Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach." Sensors 22, no. 15 (August 5, 2022): 5856. http://dx.doi.org/10.3390/s22155856.

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Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.
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Swamy, Puchala Jaswanth Phaneendra, B. YeswanthKumar Reddy, and K. Chandrasekhar. "Intelligent Surveillance Using Robot Eye and Sensor System." Applied Mechanics and Materials 917 (October 13, 2023): 27–38. http://dx.doi.org/10.4028/p-4yneed.

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Majority of the advancements, which have been made in the field of robotics are employed in household applications. Surveillance systems are used in guarding and security applications of household and workplaces. This paper presents an advanced surveillance system which integrates sensor system, robot technology and intelligent programming. Robotic surveillance system can be used to replace human guards in dangerous surveillance conditions. Several robots have been developed to assist in these operations. But the full scale employment of robots in crucial fields such as surveillance, security in defence industries and military bases is very limited. The system's overall structure is described, along with descriptions of each component. The robotic system consists of robot eyes, which are capable of sensing an object through IR sensor capability. This robotic system helps in the identification of human presence and alerting respective owners. These operations, performed by the robotic system are achieved using sensors, several number of servo motors, Arduino microcontroller. These components are placed in their respective locations, within a body which is fabricated using the 3D printing technology. The sensors in the robotic system capture the movements of objects. The robotic eyes are fitted with cameras which capture the movements. The robotic system is programmed in such a way that the robotic eyes, move in the direction of object which has been sensed by the sensors and focus on it. If there are multiple moving objects, then one robotic eye focuses on one object, while the other robotic eye focuses on another object. The captured surveillance footage can be viewed through a monitor connected to the robotic system.
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Prado da Fonseca, Vinicius. "Tactile Sensor Analysis during Early Stages of Manipulation for Single Grasp Identification of Daily Objects." Engineering Proceedings 6, no. 1 (May 17, 2021): 56. http://dx.doi.org/10.3390/i3s2021dresden-10091.

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Dexterous robotic manipulation in unstructured environments is still challenging, despite the increasing number of robots entering human settings each day. Even though robotic manipulation provides complete solutions in factories and industries, it still lacks essential techniques, displaying clumsy or limited operation in unstructured environments. Daily objects typically aim at the human hand, and the human somatosensory system is responsible for solving all the complex calculations required for dexterous manipulations in unstructured settings. Borrowing concepts of the human visuotactile system can improve dexterous manipulation and increase robotics usage in unstructured environments. In humans, required finger and wrist joint adjustments occur after fast identification of the object in the initial stages of manipulation. Fast object identification during those phases may increase robotic dexterous manipulation performance. The present paper explores human-inspired concepts such as haptic glance to develop robotic single-grasp object identification. This concept can assist early phases of robotic manipulation, helping automated decision-making, such as type of grasp and joint position, during manipulation tasks. The main stages developed here are detecting sensor activation and sample collection using signal-to-noise and z-score filtering on tactile data. This procedure automates touch detection and reduces the sensor space for classification. Experiments on a daily objects dataset presented compelling results that will assist in the later stages of the early phases of robotic grasping.
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Angel, L., J. Viola, and C. Hernández. "Parametric uncertain identification of a robotic system." IOP Conference Series: Materials Science and Engineering 138 (July 2016): 012008. http://dx.doi.org/10.1088/1757-899x/138/1/012008.

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Shafie, Amir Akramin, Azhar Bin Mohd Ibrahim, and Muhammad Mahbubur Rashid. "Smart Objects Identification System for Robotic Surveillance." International Journal of Automation and Computing 11, no. 1 (February 2014): 59–71. http://dx.doi.org/10.1007/s11633-014-0766-9.

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Ojha, Varsha. "Robotics In Gynecology- A Review." Obstetrics Gynecology and Reproductive Sciences 8, no. 5 (July 26, 2024): 01–07. http://dx.doi.org/10.31579/2578-8965/224.

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This review article focuses on the development of robotics-based operations in the medical field especially in gynecology. The goal of the surgical robot is to improve surgical skills and compensate for human limitations. The robot's performance in performing duties correctly and consistently has been the key to its success. Tubal anastomosis was the first gynecologic surgery performed with Robotic assistance in 2000 followed by first Robotic Hysterectomy in 2002. In 2003 Da Vinci surgical system was developed which was FDA approved for gynecological surgery in 2005. It has gained popularity fast and is already playing a big part in many of the places where it is available. It consists of a high-resolution three-dimensional (3D) vision system adjacent to the patient on a cart with robotic branches. It provides unique technical advancement, with tremor filtration, improved ergonomics and lower muscular load as compared to both laparoscopic and open surgeries. Autonomous camera and energy instrument control, wrist articulation with 7 degree freedom, telestration and dual- console capabilities, overcoming the limitations of conventional laparoscopy, such as counterintuitive hand movements, 2 dimensional visualization, limited degree of motion and tremor amplification. Compared to laparoscopy, robotic assistance has a lower conversion rate to open procedures. Endo wrist movement of an automated machine during myomectomy surgery can perform proper and better suturing than traditional laparoscopy. The automated program is a noticeable improvement over laparoscopic surgery and, if price issues are resolved, can gain popularity among gynecological surgeons around the globe. Not unique to robotic system, the integration of indigo cyanine green fluorescence with the Da Vinci robotic system allows identification of lesions, and assessment of perfusion of bowel and ureter during deep infiltrating endometriosis resection.
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Karras, George C., Panos Marantos, Charalampos P. Bechlioulis, and Kostas J. Kyriakopoulos. "Unsupervised Online System Identification for Underwater Robotic Vehicles." IEEE Journal of Oceanic Engineering 44, no. 3 (July 2019): 642–63. http://dx.doi.org/10.1109/joe.2018.2827678.

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Condés, Ignacio, Jesús Fernández-Conde, Eduardo Perdices, and José M. Cañas. "Robust Person Identification and Following in a Mobile Robot Based on Deep Learning and Optical Tracking." Electronics 12, no. 21 (October 27, 2023): 4424. http://dx.doi.org/10.3390/electronics12214424.

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There is an exciting synergy between deep learning and robotics, combining the perception skills a deep learning system can achieve with the wide variety of physical responses a robot can perform. This article describes an embedded system integrated into a mobile robot capable of identifying and following a specific person reliably based on a convolutional neural network pipeline. In addition, the design incorporates an optical tracking system for supporting the inferences of the neural networks, allowing the determination of the position of a person using an RGB depth camera. The system runs on an NVIDIA Jetson TX2 board, an embedded System-on-Module capable of performing computationally demanding tasks onboard and handling the complexity needed to run a solid tracking and following algorithm. A robotic mobile base with the Jetson TX2 board attached receives velocity orders to move the system toward the target. The proposed approach has been validated on a mobile robotic platform that successfully follows a determined person, relying on the robustness of the combination of deep learning with optical tracking for working efficiently in a real environment.
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Rupali S. Kokate, Saniya Ansari, S. M. Khairnar, Ravindra R. Patil,. "AN ASSESSMENT - WATER QUALITY MONITORING PRACTICES AND SEWER ROBOTIC SYSTEMS." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (February 28, 2021): 140–48. http://dx.doi.org/10.17762/itii.v9i1.113.

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Water pollution has become a global issue and creating a severe problems due to contamination of various water sources such as agricultural waste, industrial waste, and sewage waste etc. So, the available sensory systems, nodes, and techniques plays a crucial role in identification of harmful pollutants in distinct water resources. The water pollutants monitoring techniques with prospered tools and sensors have been unfolded to find gap for future research maneuver. The need of real-time newer sensory system has been put forwarded for identification of mostly occurring harmful pollutants such as BOD, COD, TSS, and Hydrogen sulfide in examination of water quality. In this modern advancement, robotic systems have taken an intrinsic stand in diverse fields of humankind. An underground infrastructure is a base of modern society. In this paper, the existing methodologies and developed sewer robotic systems have been discussed and concluded on their applications, limitations and impact on realistic scenarios. Also, the cardinal point is revealed that previous art work focused only on sewer defect identification but not standardized work on sewer blockages detection and removal. So, the sewer robotic system with features of cost effectiveness and standardized accuracy matrix should be developed to resolve sewer blockage issues and followed by human scavenging. The survey outcomes extend a province of sewer robotics to resolve blockages issues of buried sewers of distinct diameters in real-time with substantial methodology.
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García-Luna, F., and A. Morales-Díaz. "Towards an artificial vision-robotic system for tomato identification." IFAC-PapersOnLine 49, no. 16 (2016): 365–70. http://dx.doi.org/10.1016/j.ifacol.2016.10.067.

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Dissertations / Theses on the topic "Robotic system identification"

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Dang, Kim Son Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. "Design and control of autonomous crop tracking robotic weeder : GreenWeeder." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2009. http://handle.unsw.edu.au/1959.4/44418.

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This thesis reports the design and control of the ??GreenWeeder??, a non-herbicidal autonomous weeding robot, in order to autonomously track crop rows for weeding through electrocution in the inter-row space. The four wheel mobile robot platform was designed and built with a motorised Ackerman steering system allowing the robot to steer up to 30 degree left and right. It was also equipped with an electronically geared rear wheel drive, a pair of stereo cameras, a SICK LMS-291 laser range finder to localize itself with respect to the crop rows, a GPS system for obtaining the robot position in the field and a long-range communication system for tele-supervision by operators. The first prototype of the robot electrocution system was also designed and constructed to ignite 22kV electrical arcs to destroy weeds. Its operation was tested in the research field of the University of Sydney and the results of this experiment were analysed to improve the efficiency of this first prototype. An improved prototype of the electrocution system was then constructed and attached to a cradle extending out at the back of the mobile robot platform. The testing of this improved prototype was conducted at Lansdowne farm, a research field of the University of Sydney. After the construction of the robot platform, the robot control was considered with the demands for robot localization with respect to crop rows, an autonomously tracking control system and a manual control mode in order to take the robot to transportation vehicles. Firstly, the robot localization was accomplished by utilizing SICK LMS-291 laser range finder sensor for the sensing and perception of the robot. Secondly, the robot control system was developed with a PID controller, a second order model of the robot system and a first order filter. The PID controller is in the standard form with the filtered derivative and the PI part being in automatic reset configuration. The second order model was identified using Matlab System Identification toolbox based on the robot kinematic analysis. The first order filter is utilized for filtering out the lateral deviations of the robot with respect to the crop rows received from the SICK laser sensor. A Simulink simulation model of the robot control system was also built in order to fine-tune PID and filter parameters. Thirdly, the manual control mode of the robot was produced. In this mode, a joystick can be attached to a notebook to wirelessly drive the robot around or it can be plugged into a USB port at the back of the robot to drive it without the notebook. After the robot control was implemented and simulated, some experiments were conducted with the robot autonomously tracking a strip of reflective tape mimicking a crop row stuck into the ground of a laboratory. Depending on distances from the row assigned to the controller, the robot tried to keep those distances away from the row. The results showed the lateral errors of the robot with respect to the row were approximately 4.5 cm which were sufficient for our current agricultural application.
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Giantomassi, Andrea. "Modeling, estimation and identification of complex system dynamics: issues and solutions." Doctoral thesis, Università Politecnica delle Marche, 2012. http://hdl.handle.net/11566/242023.

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La modellazione dei sistemi è di fondamentale importanza in tutte le discipline, sono utili per l’analisi, la previsione o la simulazione dei sistemi. Esistono due pratiche per definire modelli: modellazione e di identificazione. La modellazione è basata su leggi note. L’identificazione consiste nella selezione di un modello sulla base delle osservazioni effettuate sul sistema. In questo lavoro si è dato un contributo all’identificazione e stima di dinamiche complesse di sistemi. Con attenzione ai sistemi reali, sono proposte tre soluzioni. Il primo argomento riguarda un inceneritore per rifiuti solidi urbani, dove i modelli matematici sono troppo complessi per essere utilizzati. La soluzione data è in grado di stimare e predire, la produzione di vapore di un inceneritore RSU. L’algoritmo di apprendimento si basa su reti di funzioni a base radiale e combina la tecnica Minimal Resource Allocating Network con un filtro di Kalman esteso adattativo per aggiornare i parametri della rete. Il secondo problema riguarda la compensazione degli errori di controllo per un manipolatore industriale. Se un contro è ben progettato l’errore di controllo non può essere compensato. Tuttavia nel controllo Sliding Mode discreto, l’errore di controllo presenta dinamiche residue. Si propongono sue approcci per compensare l’incertezza, l’obiettivo è sviluppare un SMC discreto più robusto con due soluzioni, una basata sullo stimatore di incertezza del modello, e un predittore autosintonizzante. La diagnosi guasti ha ricevuto un crescente interesse degli ultimi anni. L’ultimo argomento riguarda una procedura di rilevamento guasti e isolamento per la rilevazione e l’analisi di difetti di motori elettrici a fine linea di un impianto di produzione di cappe. L’obiettivo consiste nel rilevare e identificare i motori difettosi per l’analisi di qualità. Un approccio diagnostico basato sull’analisi dei segnali è preferibile per le caratteristiche dei segnali acquisiti e per la soluzione di implementazione.
Models of real systems are of fundamental importance in all disciplines, and they are useful for system analysis, prediction or simulation of a real system. Two practices exist to define models: modeling by physical laws and by identification. Physical modeling is based on known laws. Identification consists in the selection of a model in a specified class on the basis of observations performed on the system to be described. A contribution to complex system dynamics identification and estimation is given. With particular attention to real systems, three solutions are discussed. The first issue deals with a Municipal Solid Waste incinerator, where first principles mathematical models are too complex to be implemented. The procedure proposed is able to estimate and predict, the steam production of a MSW incinerator. The learning algorithm is based on radial basis function networks and combines the Minimal Resource Allocating Network technique with an adaptive extended Kalman filter to update the network parameters. The second issue regard the control error compensation for an industrial manipulator. If a controller is well designed the control error cannot be compensated. However in the discrete Sliding Mode Controller, control errors carry information about residual dynamics. Two approaches are proposed for uncertainties compensations, the objective is to develop a more robust and accurate discrete SMC using two solutions, a model based uncertainty estimator, and an auto-tuning predictor. Fault Detection and Diagnosis has received an increasing interest in years. The last issue regard a Fault Detection and Isolation procedure that is applied for the defects detection and analysis of electrical motors at the end of the production line in a hoods production plant. The objective consists of detect and identify defective motors for the quality analysis. A signal based FDI approach is preferred for the characteristics of acquired signals and for the implementation solution.
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Mahé, Antoine. "Identification de systèmes par réseaux de neurones pour la commande prédictive." Electronic Thesis or Diss., CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0010.

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Le développement de la robotique mobile permet la réalisation de tâches de plus enplus variées de façon automatisée. Le projet GRoNe dans lequel s’inscrit cette thèse, a pour objectif de développer les connaissances et les expérimentations sur ce sujet. Dans ce cadre, l’automatisation est un élément clé.Le développement d’algorithmes de contrôle efficaces est une étape importante en ce sens. Les contrôleurs à commandes prédictives ont démontré de nombreux avantages en robotique mobile.La réalisation de ces algorithmes requiert la conception de modèles précis des systèmes afin de prédire leur évolution. Le problème de la modélisation en robotique est traité par l’identification de système. L’apprentissage automatique est fréquemment utilisé dans ce contexte. Afin de modéliser des systèmes robotiques des corpus de données des robots en fonctionement ontété collectés en simulation et sur le terrain. Différentes architectures de réseaux de neurones ont été comparées. Cependant les échantillons collectés ne correspondent pas toujours à la situation dans laquelle les modèles doivent être utilisés. Ce problème est traité en priorisant certains éléments. Pour cela, deux méthodes sont comparées. La réalisation de modèles n’est pas une fin en soit, il s’agit d’une composante de l’algorithme de contrôle. Il importe d’étudier les différents modèles au sein du contrôleur. L’application de ce contrôleur à un drone et un bateau, tant en simulation qu’en cas réel, permet d’étudier les avantages de son utilisation. Finalement un modèle priorisé est utilisé au sein d’un algorithme à commande prédictive sur le bateau réel afin de réaliser le suivi de la berge d'un lac artificiel
Developing mobile robotic allow to address ever more complex task autonomously.This thesis is part of the GRoNe project which aim at improving knowledge and experimentation on this topic. In this context automation is a key element. Developing efficient control algorithm is a step in that direction. Model predictive control has shown good result and interesting advantages in mobile robotic. Implementing this algorithm require precise system modelling in order to predict their evolution. In robotic modelling is usually solved by system identification. In this context machine learning is often a powerful tool. In order to model robotic system, data collection of their behaviour both in simulation and on the real platform have been collected. Several neural network architecture have been compared. Collected sample may not correspond to the condition of target task making part of the training irrelevant. A solution to that problem is to use prioritization during the training. Two prioritization scheme are compared. Modelling is only a step toward control. Thus it is important to test the obtained model as part of the whole control algorithm. The application of this controller to a drone and a boat, in simulation as well as on the real platform, allow to study its advantages. In the end a model train with prioritization is used in a model predictive controller on the real boat to perform shore following in an artificial lac
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Tuna, Eser Erdem. "PERCEPTION AND CONTROL OF AN MRI-GUIDED ROBOTIC CATHETER IN DEFORMABLE ENVIRONMENTS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1619795928790909.

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Åkesson, Ulrik. "Design of a multi-camera system for object identification, localisation, and visual servoing." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44082.

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In this thesis, the development of a stereo camera system for an intelligent tool is presented. The task of the system is to identify and localise objects so that the tool can guide a robot. Different approaches to object detection have been implemented and evaluated and the systems ability to localise objects has been tested. The results show that the system can achieve a localisation accuracy below 5 mm.
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Tout, Bilal. "Identification of human-robot systems in physical interaction : application to muscle activity detection." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. https://ged.uphf.fr/nuxeo/site/esupversions/36d9eab3-c170-4e40-abb6-e6b4e27aeee2.

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Ces dernières années, l’interaction physique humain-robot est devenue un sujet de recherche important, par exemple pour des applications de rééducation. Cette thèse vise à améliorer ces interactions, dans le cadre du développement de contrôleurs basés modèles, par des approches d’identification paramétrique des modèles des systèmes en interaction. Le but est de développer des méthodes d’identification en tenant compte de la variabilité et de la complexité du corps humain, et en n'utilisant que les capteurs du système robotique pour éviter l'ajout de capteurs externes. Les différentes approches présentées dans cette thèse sont testées expérimentalement avec un système robotique à un degré de liberté (1-DDL) permettant d'interagir avec la main d'une personne.Après un 1er chapitre présentant l'état de l'art, le 2e chapitre aborde les méthodes d'identification développées en robotique ainsi que la problématique du filtrage, analysée en simulation et expérimentalement. La question du réglage du filtre passe-bas est adressée, et en particulier le choix de la fréquence de coupure qui reste délicate pour un système non-linéaire. Pour surmonter ces difficultés, une technique de filtrage utilisant un filtre de Kalman étendu (EKF) est développée à partir du modèle dynamique du robot. La formulation EKF proposée permet un réglage en fonction des propriétés connues du capteur et de la confiance dans l’estimation initiale des paramètres. Cette méthode est comparée en simulation puis expérimentalement avec différentes méthodes existantes en analysant la sensibilité à l’initialisation et au réglage du filtre. Les résultats montrent que la méthode proposée est prometteuse si l’EKF est correctement réglé.Le 3e chapitre porte sur l'identification en continu des paramètres du modèle dynamique d'un système passif en interaction avec un système robotique, en combinant des méthodes d’identification de la charge utile avec des algorithmes en ligne, sans capteurs externes. Ces méthodes sont validées en simulation et expérimentalement en utilisant le système à 1-DDL dont la poignée est attachée à des bandes élastiques pour imiter une articulation humaine passive. L’analyse de l’effet de l’ajustement des méthodes en ligne met en évidence qu’un compromis est nécessaire entre la vitesse de convergence et la précision des estimations des paramètres. Enfin, la comparaison des méthodes d’identification de la charge utile montre que les méthodes identifiant séparément les paramètres du système robotique et de l’humain passif donnent une meilleure précision et une plus faible complexité de calcul.Le 4e chapitre porte sur l'identification durant l'interaction humain-système robotique. Un modèle à raideur quadratique est proposé afin de mieux représenter le comportement de l’articulation humaine passive qu’un modèle linéaire. Par la suite, ce modèle est utilisé avec une méthode d’identification itérative basée sur le rejet d’outliers, pour détecter l’activité musculaire de l’humain sans capteurs externes. Cette méthode est comparée expérimentalement à une méthode non-itérative utilisant des signaux d’électromyographie (EMG), en adaptant le système à 1-DDL pour interagir avec le poignet et permettre d’évaluer l’activité des muscles fléchisseurs et extenseurs de deux sujets. La méthode itérative proposée sans signaux EMG donne des résultats proches de ceux obtenus avec la méthode utilisant les signaux EMG lorsqu’un modèle représentant bien le comportement de l’articulation humaine passive est choisi. Les résultats de détection de l’activité musculaire obtenus avec ces deux méthodes montrent un niveau de similarité satisfaisant avec ceux obtenus directement à partir des signaux EMG
Over the last years, physical human-robot interaction has become an important research subject, for example for rehabilitation applications. This PhD aims at improving these interactions, as part of model-based controllers development, using parametric identification approaches to identify models of the systems in interaction. The goal is to develop identification methods taking into account the variability and complexity of the human body, and only using the sensor of the robotic system to avoid adding external sensors. The different approaches presented in this thesis are tested experimentally on a one degree of freedom (1-DOF) system allowing the interaction with a person’s hand.After a 1st chapter presenting the state-of-the-art, the 2nd chapter tackles the identification methods developed in robotics as well as the issue of data filtering, analyzed both in simulation and experimentally. The question of the low-pass filter tuning is addressed, and in particular the choice of the cut-off frequency which remains delicate for a nonlinear system. To overcome these difficulties, a filtering technique using an extended Kalman filter (EKF) is developed from the robot dynamic model. The proposed EKF formulation allows a filter tuning depending on the known properties of the sensor and on the confidence on the initial parameters estimations. This method is compared in simulation and experimentally to different existing methods by analyzing its sensitivity to initialization and filter tuning. Results show that the proposed method is promising if the EKF is correctly tuned.The 3rd chapter concerns the continuous identification of the parameters of the model of a passive system interacting with a robotic system, by combining payload identification methods with online identification algorithms, without external sensors. These methods are validated in simulation and experimentally with the 1-DOF system whose handle is attached to elastic rubber bands to emulate a passive human joint. The analysis of the effects of the online methods tuning highlights a necessary trade-off between the convergence speed and the accuracy of the parameters estimates. Finally, the comparison of the payload identification methods shows that methods identifying separately the robotic system and the passive human parameters give better accuracy and a lower computation complexity.The 4th chapter deals with the identification during the human-robot interaction. A quadratic stiffness model is proposed to better fit the passive human joint behavior than a linear stiffness model. Then, this model is used with an iterative identification method based on outlier rejection technique, to detect the human user muscle activity without external sensors. This method is compared experimentally to a non-iterative method that uses electromyography (EMG), by adapting the 1-DOF system to interact with the wrist and to allow the detection of the flexor and extensor muscle activity of two human users. The proposed iterative identification method not using EMG signals achieves results close to those obtained with the non-iterative method using EMG signals when a model that correctly represents the passive human joint behavior is selected. The muscle activity detection results obtained with both methods show a satisfactory level of similarity compared to those obtained directly from EMG signals
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Wang, Zeya. "Robotisation de la fabrication additive par procédé arc-fil : Identification et amélioration de la commande." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0068.

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La fabrication additive de pièces métalliques a fait l'objet d'un vif intérêt ces dernières années comme une solution technologique importante pour la réalisation de pièces complexes. Parmi les différents procédés de la fabrication additive métallique, la fabrication additive arc-fil (FAAF) utilisant le soudage CMT (Cold metal transfer) est retenue pour notre étude grâce à son taux de dépôt important, faible coût des équipements et peu de perte de matière par projections lors de la fabrication. Dans la littérature, il est constaté que l'un des problèmes les plus importants qui empêchent l'application industrielle du procédé FAAF est la mauvaise précision géométrique des pièces fabriquées à cause de l'instabilité du procédé et du manque de contrôle-commande fiable pour traiter les irrégularités pendant le dépôt. L'objectif de ce travail est d'améliorer la stabilité et la performance géométrique du procédé. Dans ce travail, un système expérimental est mis en œuvre pour robotiser le procédé et contrôler la géométrie des pièces déposées. Le procédé est modélisé par les réseaux de neurones artificiels et un système contrôle-commande est développé permettant de commander la géométrie du dépôt et de réduire les erreurs de fabrication. De plus, une stratégie d'amélioration est appliquée afin de réduire les instabilités géométriques aux deux extrémités du cordon ; une méthode de contrôle in situ est également développée pour détecter les défauts internes des pièces déposées
Additive manufacturing of metallic parts has gained significant popularity in recent years as an important technological solution for the production of complex parts. Among the different processes of metal additive manufacturing, the wire-arc additive manufacturing (WAAM) using CMT (Cold metal transfer) welding is taken for our study because of its high deposition rate, low cost of equipment and little loss of material (low spatter) during manufacturing. In the literature review, it can be noted that one of the most important problems that prevent the industrial application of the WAAM is the poor geometric accuracy of the manufactured parts due to the instability of the process and the lack of reliable control system to deal with irregularities during deposition. The focus of this work is to improve the stability and geometric performance of the process. In this work, an experimental system is implemented to robotize the process and to monitor the geometry of the deposited parts. The process is modeled by artificial neural networks and a control system is developed to regulate the geometry of the deposit and to reduce manufacturing errors. Furthermore, an improvement strategy is applied in order to reduce the geometric instabilities at the ends of the bead; an in-situ monitoring method is also developed to detect the internal defects of deposited parts
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Cetin, Murat. "Performance identification and multi-criteria redundancy resolution for robotic systems /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Leijonhufvud, Peder, and Emil Bråkenhielm. "Image Processing for Improved Bacteria Classification." Thesis, Linköpings universitet, Programvara och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167416.

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Mastitis is a common disease among cows in dairy farms. Diagnosis of the infection is today done manually, by analyzing bacteria growth on agar plates. However, classifiers are being developed for automated diagnostics using images of agar plates. Input images need to be of reasonable quality and consistent in terms of scale, positioning, perspective, and rotation for accurate classification. Therefore, this thesis investigates if a combination of image processing techniques can be used to match each input image to a pre-defined reference model. A method was proposed to identify important key points needed to register the input image to the reference model. The key points were defined by identifying the agar plate, its compartments, and its rotation within the image. The results showed that image registration with the correct key points was sufficient enough to match images of agar plates to a reference model despite any varieties in scale, position, perspective, or rotation. However, the accuracy depended on the identification of the salient features of the agar plate. Ultimately, the work proposes an approach using image registration to transform images of agar plates based on a pre-defined reference model, rather than a reference image.
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Larsson, Joel, and Rasmus Hedberg. "Development of machine learning models for object identification of parasite eggs using microscopy." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414386.

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Over one billion people in developing countries are afflicted by parasitic infections caused by soil-transmitted helminths. These infections are treatable with cheap and safe medicine that is widely available. However, diagnosis of these infections has proven to be a bottleneck by the fact that it is time-consuming, requires expensive equipment and trained personnel to be consistent and accurate. This study aimed to investigate the viability and performance of five machine learning models and a 'modular neural network' approach to localize and classify the following parasite eggs in microscopic images: Ascaris lumbricoides, Trichuris trichuria, Hookworm and Schistosoma mansoni. These models were implemented and evaluated on the Nvidia Jetson AGX Xavier to establish that they fulfilled the specifications of 95\% specificity and sensitivity, but also a speed requirement of 40000 images per 24 hours. The results show that R-FCN ResNet101 was the best model produced in this study, which performed the best on average. However, it did not fulfill the specifications entirely but is still considered a success due to being an improvement to the current implementation at Etteplan. Evaluation of the modular neural network approach would require further investigation to verify the performance of the system, but the results indicate it could be a possible improvement to the off-the-shelf machine learning models. To conclude, the study showed that the data and data infrastructure provided by Etteplan has proven to be a very powerful tool in training machine learning models to classify and localize parasite eggs in stool samples. However, expansion of the data to reduce the imbalance between the representations of the classes but also include more patient information could improve the training and evaluation process of the models.
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Books on the topic "Robotic system identification"

1

Khalil, W. Modeling, identification & control of robots. London: HPS, 2002.

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E, Dombre, ed. Modeling identification & control of robots. New York, NY: Taylor & Francis, 2002.

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E, Dombre, ed. Modeling, identification & control of robots. London: Kogan Page Science, 2004.

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M, Milanese, ed. Bounding approaches to system identification. New York: Plenum Press, 1996.

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International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (1996 Venice, Italy). Proceedings: International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, Venice, Italy, August 21-23, 1996. Los Alamitos, CA: IEEE Computer Society Press, 1996.

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Liu, Chengjun. Cross Disciplinary Biometric Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Snow, Edward Ramsey. Advances in grasping and vehicle contact identification: Analysis, design and testing of robust methods for underwater robot manipulation. Cambridge, Mass: Massachusetts Institute of Technology, 1999.

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H, Connell Jonathan, Pankanti Sharath, Ratha Nalini K, and Senior Andrew W, eds. Guide to biometrics. New York, NY: Springer, 2004.

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Chrisina, Jayne, and SpringerLink (Online service), eds. Engineering Applications of Neural Networks: 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Corfu, Greece, September 15-18, 2011, Proceedings Part I. Berlin, Heidelberg: IFIP International Federation for Information Processing, 2011.

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Travieso-González, Carlos M. Advances in Nonlinear Speech Processing: 5th International Conference on Nonlinear Speech Processing, NOLISP 2011, Las Palmas de Gran Canaria, Spain, November 7-9, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Book chapters on the topic "Robotic system identification"

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Numerical Experiments and Real World Cases." In Regularized System Identification, 343–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_9.

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AbstractThis chapter collects some numerical experiments to test the performance of kernel-based approaches for discrete-time linear system identification. Using Monte Carlo simulations, we will compare the performance of kernel-based methods with the classical PEM approaches described in Chap. 10.1007/978-3-030-95860-2_2. Simulated and real data are included, concerning a robotic arm, a hairdryer and a problem of temperature prediction. We conclude the chapter by introducing the so-called multi-task learning where several functions (tasks) are simultaneously estimated. This problem is significant if the tasks are related to each other so that measurements taken on a function are informative with respect to the other ones. A problem involving real pharmacokinetics data, related to the so-called population approaches, is then illustrated. Results will be often illustrated by using MATLAB boxplots. As already mentioned in Sect. 7.2, when commenting Fig. 7.8, the median is given by the central mark while the box edges are the 25th and 75th percentiles. The whiskers extend to the most extreme fits not seen as outliers. Then, the outliers are plotted individually.
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Vasudevan, Ram. "Hybrid System Identification via Switched System Optimal Control for Bipedal Robotic Walking." In Springer Tracts in Advanced Robotics, 635–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29363-9_36.

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Liu, Minglei, Hongbo Zhou, and Aiping Pang. "Research on Motion Control System of 6-DOF Robotic Arm." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019), 53–61. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_6.

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Yang, Chunxia, Ming Zhan, and Ziying Yao. "Multi-agent Simulation-Based Urban Waterfront Public Space Quality Comprehensive Measurement Indexes." In Computational Design and Robotic Fabrication, 536–48. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3433-0_47.

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Abstract From the perspective of behavior, the study combines multi-agent simulation and measurement index system for the waterfront area. Connecting with the waterfront space characteristics, the study combines spatial, behavioral and correlation measurement indexes to form the measurement dimensions of access-efficiency, stay-comfort and water-friendliness. Based on literature research and behavior simulation output characteristics, the study initially constructs an index system for measuring the quality of public space in urban waterfront, with a total of 18 measuring indexes. 6 typical waterfront sections along the Huangpu River are selected as samples and the indexes of each period are output through the behavior simulation model based on the Anylogic. Through the extraction of the mean value, maximum value and minimum value, an urban waterfront public space measurement index system suitable for the Huangpu River in Shanghai is finally formed. By measuring different samples and normalizing the results, it is found that stay-comfort is the most important index, while access-efficiency and water-friendliness are of less importance. The study proposes a comprehensive measurement index system from behavioral needs, which forms a more accurate and intelligent index for analyzing waterfront areas in three dimensions. In addition, based on user's recreational behavior, a dynamic measurement method is developed to provide a more efficient quantitative reference for the identification and optimization of weakness in urban waterfront.
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Balabantaray, Bunil Kumar, Bandita Das, and Bibhuti Bhusan Biswal. "Comparison of Edge Detection Algorithm for Part Identification in a Vision Guided Robotic Assembly System." In Soft Computing Techniques in Engineering Applications, 183–206. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04693-8_12.

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Robinett, Rush D., Clark R. Dohrmann, G. Richard Eisler, John T. Feddema, Gordon G. Parker, David G. Wilson, and Dennis Stokes. "System Identification." In Flexible Robot Dynamics and Controls, 133–59. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0539-6_4.

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Zhou, Jian-jun, and Finn Conrad. "Identification and Evaluation of Hydraulic Actuator Models for a Two-Link Robot Manipulator." In Robotic Systems, 577–84. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2526-0_66.

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Renders, Jean-Michel, José del R. Millan, and Marc Becquet. "Non-Geometrical Parameters Identification for Robot Kinematic Calibration by use of Neural Network Techniques." In Robotic Systems, 37–44. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2526-0_5.

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Brent, Sarah, Chengzhi Yuan, and Paolo Stegagno. "Swarm Localization Through Cooperative Landmark Identification." In Distributed Autonomous Robotic Systems, 429–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92790-5_33.

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Nehmzow, Ulrich. "Accurate Simulation Through System Identification." In Robot Behaviour, 1–17. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-397-2_8.

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Conference papers on the topic "Robotic system identification"

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Coleman, David, and Moble Benedict. "System Identification of a Hover-Capable Robotic Hummingbird." In Vertical Flight Society 72nd Annual Forum & Technology Display, 1–13. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11359.

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This paper presents the first ever linear system identification of the flight dynamics of a hover-capable robotic hummingbird which utilizes only two wings for flying as well as for all its control and stabilization. The vehicle was developed in-house, using state-of-the-art materials, electronics, and innovative design/fabrication techniques, and a description of its development is provided. Systematic experimental studies were conducted to develop flexible, aeroelastically tailored wings, along with novel wing kinematic modulation mechanisms for controlling roll, pitch and yaw. Additionally, a custom, lightweight, autopilot implementing PID control was developed, and after a series of rigorous flight testing, the trim and feedback gains were determined which allowed stable, hovering flight. Once this was achieved, a motion capture camera system was used to track the position and attitude of the vehicle during flight tests which involved providing a series of inputs to excite the vehicle modes and measuring the response. A linearized six degree of freedom state-space model for hovering flight was then extracted from this data using time domain system identification. An analysis of the eigenstate of the model reveals four modes present: an unstable mode that excites all the vehicle states; a stable mode that excites yaw and translational motion; and two oscillatory modes, one stable and one marginally unstable, both responsible for translational and rotational excitation about all axes. This finally demonstrates experimentally for the first time the unstable nature of two-winged, hover-capable flapping flight. These four modes, which show a strong coupling between the longitudinal and lateral dynamics, suggest that assumptions which decouple longitudinal and lateral degrees of freedom may not be valid for this type of flight. Additionally, this unstable, coupled dynamics demonstrates definitively the superior agility inherent in this type of aerial locomotion.
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Tian, Huanyu, Martin Huber, Christopher E. Mower, Zhe Han, Changsheng Li, Xingguang Duan, and Christos Bergeles. "Excitation Trajectory Optimization for Dynamic Parameter Identification Using Virtual Constraints in Hands-on Robotic System." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 11605–11. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610950.

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Brisacier-Porchon, Lorraine, and Omar Hammami. "Identification and listing of operation research problems in the framework of heterogeneous robotic swarms in System-of-Systems: a path for consistent research targets." In 2024 19th Annual System of Systems Engineering Conference (SoSE), 314–20. IEEE, 2024. http://dx.doi.org/10.1109/sose62659.2024.10620935.

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Mishra, Dhananjay, and Jyoti Ohri. "Review on Controlling Flexible Robotic Arm - System Identification of Intelligent Evolutionary Algorithm and Dynamic Modelling of Mechanical and Hydraulic Manipulator." In 2023 Second IEEE International Conference on Measurement, Instrumentation, Control and Automation (ICMICA), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icmica61068.2024.10732548.

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Morales, Cecilia G., Dhruv Srikanth, Jack H. Good, Keith A. Dufendach, and Artur Dubrawski. "Bifurcation Identification for Ultrasound-driven Robotic Cannulation." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6990–96. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801749.

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Memar, Amirhossein H., and Ehsan T. Esfahani. "Modeling and Dynamic Parameter Identification of the SCHUNK Powerball Robotic Arm." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47703.

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This paper presents the modeling and dynamic parameter identification of the 6-DoF SCHUNK Powerball LWA 4P robotic arm. Precise positioning, zero backlash and compact design of the joints which integrate two perpendicular axes, make this robot ideal for service robotics applications and human-robot interaction. Due to the significant effect of the lubricant temperature on the behavior of viscous friction in the harmonic drives, a systematic procedure is developed to overcome this problem. A series of experiments have been conducted to model the friction at each joint, then the procedure of identification has been applied based on an inverse dynamic model and linear least-square techniques. Finally, a verification trajectory is executed by the robot to validate the estimated parameters of the system.
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D’Imperio, Mariapaola, Ferdinando Cannella, Luca Carbonari, Nahian Rahman, and Darwin G. Caldwell. "Dynamic Modelling and Analysis of an Articulated Robotic Leg." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47140.

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In robotics, both the analytical and numerical studies play a vital role to predict the dynamic behaviours of robotic systems. Nevertheless, dynamic modelling is essential to predict the system behaiours. In this paper, both the analytical and numerical approaches are worked out for a leg of a hydraulic quadruped robot. The research aims at the identification of system parameters like inertial and geometrical magnitudes of a complex assembled leg at various cases. Due to the articulated mechanism, the model owns a significant mathematical complexity which should be possibly reduced through a combined use of analytical and multibody tools as a preliminary step to experimental identification. Both simulations and experiments results have been carried out for the goal.
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Balabantaray, Bunil Kumar, and Bibhuti Bhusan Biswal. "Part identification in robotic assembly using vision system." In Sixth International Conference on Machine Vision (ICMV 13), edited by Branislav Vuksanovic, Jianhong Zhou, and Antanas Verikas. SPIE, 2013. http://dx.doi.org/10.1117/12.2051309.

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Cheng, Marvin, and Ezzat Bakhoum. "Tracking Control Design and Implementation of Multiaxial Controller for Social Robotic Devices." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70510.

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Abstract In the recent years, robotic devices have been widely used to interact with human beings in various scenarios, including healthcare, education, tourism, and manufacturing applications. These applications of robotic devices have also been expanded to many social activities. These social robots can take the form of a traditional mobile robot or a humanoid system that provide one-on-one interaction. Among different types of robotic devices, the bio-inspired humanoid robotics has received extensive attention in therapeutic settings by providing psychological and physiological benefits. With the social benefits, humanoid type of social robots can be an important tool to assist people in many different situations. To allow social robotic devices to better interact with human being, it is desired that these robotic systems can identify on-going human motions and respond to the motions by mimicking human movements. Thus, these systems need to acquire human motions and predict the types of these movements in real-time. Such a technique has been investigated by various research groups. Once the human motions have been identified, corresponding reactions of the robots can be determined accordingly, which usually requires the involved joints to move along specific trajectories. To synthesize such an interactive robotic system, a platform of a multi-axial robotic device, a motion identification model of human motions, a reference generator based on the identified motions, the sensors used for real-time motion measurements, and an adequate control strategy need to be integrated as a single system. The major bottleneck of such a system is that the processing and control units might not be efficient enough and can cause dramatic legacy. To validate the overall process, a simplified system was developed to investigate the feasibility of such an interactive robotic system. In this study, an experimental multi-axial robotic arm was adopted. A developed motion identification model was used to determine the on-going motions of the interacting person. Once the motion being identified, the responding motion of robotic device can be determined based on a pre-selected motion library. The trajectories of individual joints of the robotic arm can then also be generated accordingly. The robotic arm was then following the pre-selected trajectories for corresponding interactions. To compensate for the nonlinear factors caused by existing mechanical/electrical components and the cross-coupled dynamics among the mechanical components, a control strategy that integrates an adaptive robust control method and a linear controller for motion tracking was applied. With the proposed control scheme, an adequate controlled outcome can be achieved.
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Vakil, Mohammad, Reza Fotouhi, and Peter N. Nikiforuk. "Parameter Identification of a Friction Model for Robotic Joints." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29141.

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In this paper a new identification method to obtain the friction parameters in the joints of robotic manipulators is presented. These parameters are coulomb friction, static friction, Stribeck velocity constant and viscous damping coefficient. The available methods to find these parameters either require the design of a controller or the precise value of system parameters such as mass moment of inertia. In contrast, the new method proposed here finds these parameters by a nonlinear optimization approach which requires neither any knowledge of system’s parameters nor any controller design. The corresponding nonlinear optimization problem is solved using an efficient technique which does not require iteration or any initial estimate of optimization parameters. The new method proposed in this paper was experimentally verified on a robotic manipulator available in the robotics laboratory at the University of Saskatchewan.
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Reports on the topic "Robotic system identification"

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Moore, Christina J. Development of an Integrated Robotic Radioisotope Identification and Location System. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada548792.

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Bechar, Avital, Shimon Nof, and Yang Tao. Development of a robotic inspection system for early identification and locating of biotic and abiotic stresses in greenhouse crops. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600042.bard.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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