Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

High-precision grasping and placing for mobile robots

This work presents a manipulation system for multiple labware in life science laboratories using the H20 mobile robots. The H20 robot is equipped with the Kinect V2 sensor to identify and estimate the position of the required labware on the workbench. The local features recognition based on SURF algorithm is used. The recognition process is performed for the labware to be grasped and for the workbench holder. Different grippers and labware containers are designed to manipulate different weights of labware and to realize a safe transportation

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system

Goal-driven developmental learning on a mobile robot

Seit den frühen 1980er Jahren wurden mobile Roboter entwickelt, die Objekten ausweichen und durch verschiedenste Umgebungen navigieren können. Hierfür wurde Vorwissen über das Zusammenspiel zwischen Sensoren und Motoren des Roboters verwendet. Als man komplexere Anforderungen an Robotik-Systeme stellte, scheiterten traditionelle Architekturen daran, diesen gerecht zu werden. Einen interessanten Ansatz für dieses Problem bietet der Forschungsbereich Developmental Robotics, der größtenteils von der kognitionswissenschaftlichen Community beeinflusst wurde und von den gemeinsamen Interessen der Entwicklungspsychologie und der Robotik profitiert. Der Forschungsbereich beschäftigt sich unter anderem damit, wie ein autonomer Roboter lernen kann komplexe Aufgaben zu bewältigen indem er seine Umgebung erforscht. Das Komplexitätsproblem der Robotik soll mit diesem erfahrungsbasierten Ansatz gelöst werden. In dieser Arbeit wird eine Lernarchitektur auf einem mobilen Roboter implementiert, welche auf den Kernprinzipien der Developmental Robotics basiert. Die Architektur ermöglicht die autonome Konstruktion eines sensomotorischen Modells, ohne Vorwissen über die Sensorenanordnung. Der Roboter verwendet die Vorhersagen dieses Modells um bestimmte Sensorenwerte zu erreichen. Dies resultiert in zielgerichtetem Verhalten. Die Ergebnisse zeigen, dass die Architektur es dem Roboter ermöglicht, die Sensorenwerte als Folgen seines Handelns vorherzusagen. Der Roboter war in der Lage, mit Hilfe seiner Ultraschall-Abstandssensoren bestimmte Entfernungen zu Objekten zu halten, ohne spezielle Vorprogrammierung. Wir schlussfolgern, dass erfahrungsbasiertes Lernen ein realisierbarer Ansatz in der Robotik ist, und es ist unsere Überzeugung, dass Forschung aus der Entwicklungspsychologie einen wichtigen Einfluss in diesem Gebiet darstellen wird.Since the early 1980s, mobile robots have been programmed to avoid objects and navigate environments. Prior knowledge about the robot's sensors and actuators was used to solve this problem. But as robotics systems were expected to address more complex tasks, traditional robot architectures failed to scale up to them. One interesting approach to this problem can be seen in the field of developmental robotics, which was influenced in large parts by the cognitive science community and benefits from the mutual interests of developmental psychology and robotics. The main research question in this field is how an embodied agent can learn complex tasks by exploring its environment. This experience-based learning approach aims to solve the scaling problem of robotics. In this thesis, a developmental learning architecture is implemented on a mobile robot. It allows the robot to autonomously construct a sensomotoric model, without prior knowledge about its sensor configuration. This model can then be used to predict the outcome of the robot's actions. In the proposed architecture, the agent uses these predictions to reach specific sensor states, resulting in goal-driven behavior. The results show that the robot was able to keep specific distances to objects using its ultrasonic distance sensors, without being preprogrammed with an algorithm that describes the necessary actions. We conclude that experience-based learning is a feasible approach in robotics, and it is our belief that research from developmental psychology will become an important influence in this area

Mobile robot transportation in laboratory automation

In this dissertation a new mobile robot transportation system is developed for the modern laboratory automation to connect the distributed automated systems and workbenches. In the system, a series of scientific and technical robot indoor issues are presented and solved, including the multiple robot control strategy, the indoor transportation path planning, the hybrid robot indoor localization, the recharging optimization, the robot-automated door interface, the robot blind arm grasping & placing, etc. The experiments show the proposed system and methods are effective and efficient

Asservissement d'un bras robotique d'assistance à l'aide d'un système de stéréo vision artificielle et d'un suiveur de regard

RÉSUMÉ L’utilisation récente de bras robotiques sériels dans le but d’assister des personnes ayant des problèmes de motricités sévères des membres supérieurs soulève une nouvelle problématique au niveau de l’interaction humain-machine (IHM). En effet, jusqu’à maintenant le « joystick » est utilisé pour contrôler un bras robotiques d’assistance (BRA). Pour les utilisateurs ayant des problèmes de motricité sévères des membres supérieurs, ce type de contrôle n’est pas une option adéquate. Ce mémoire présente une autre option afin de pallier cette problématique. La solution présentée est composée de deux composantes principales. La première est une caméra de stéréo vision utilisée afin d’informer le BRA des objets présents dans son espace de travail. Il est important qu’un BRA soit conscient de ce qui est présent dans son espace de travail puisqu’il doit être en mesure d’éviter les objets non voulus lorsqu’il parcourt un trajet afin d’atteindre l’objet d’intérêt pour l'utilisateur. La deuxième composante est l’IHM qui est dans ce travail représentée par un suiveur de regard à bas coût. Effectivement, le suiveur de regard a été choisi puisque, généralement, les yeux d’un patient ayant des problèmes sévères de motricités au niveau des membres supérieurs restent toujours fonctionnels. Le suiveur de regard est généralement utilisé avec un écran pour des applications en 2D ce qui n’est pas intuitif pour l’utilisateur puisque celui-ci doit constamment regarder une reproduction 2D de la scène sur un écran. En d’autres mots, il faut rendre le suiveur de regard viable dans un environnement 3D sans l’utilisation d’un écran, ce qui a été fait dans ce mémoire. Un système de stéréo vision, un suiveur de regard ainsi qu’un BRA sont les composantes principales du système présenté qui se nomme PoGARA qui est une abréviation pour Point of Gaze Assistive Robotic Arm. En utilisant PoGARA, l’utilisateur a été capable d’atteindre et de prendre un objet pour 80% des essais avec un temps moyen de 13.7 secondes sans obstacles, 15.3 secondes avec un obstacle et 16.3 secondes avec deux obstacles.----------ABSTRACT The recent increased interest in the use of serial robots to assist individuals with severe upper limb disability brought-up an important issue which is the design of the right human computer interaction (HCI). Indeed, so far, the control of assistive robotic arms (ARA) is often done using a joystick. For the users who have a severe upper limb disability, this type of control is not a suitable option. In this master’s thesis, a novel solution is presented to overcome this issue. The developed solution is composed of two main components. The first one is a stereo vision system which is used to inform the ARA of the content of its workspace. It is important for the ARA to be aware of what is present in its workspace since it needs to avoid the unwanted objects while it is on its way to grasp the object of interest. The second component is the actual HCI, where an eye tracker is used. Indeed, the eye tracker was chosen since the eyes, often, remain functional even for patients with severe upper limb disability. However, usually, low-cost, commercially available eye trackers are mainly designed for 2D applications with a screen which is not intuitive for the user since he needs to constantly watch a reproduction of the scene on a 2D screen instead of the 3D scene itself. In other words, the eye tracker needs to be made viable for usage in a 3D environment without the use of a screen. This was achieved in this master thesis work. A stereo vision system, an eye tracker as well as an ARA are the main components of the developed system named PoGARA which is short for Point of Gaze Assistive Robotic Arm. Using PoGARA, during the tests, the user was able to reach and grasp an object for 80% of the trials with an average time of 13.7 seconds without obstacles, 15.3 seconds with one obstacles and 16.3 seconds with two obstacles

Navigation of Automatic Vehicle using AI Techniques

In the field of mobile robot navigation have been studied as important task for the new generation of mobile robot i.e. Corobot. For this mobile robot navigation has been viewed for unknown environment. We consider the 4-wheeled vehicle (Corobot) for Path Planning, an autonomous robot and an obstacle and collision avoidance to be used in sensor based robot. We propose that the predefined distance from the robot to target and make the robot follow the target at this distance and improve the trajectory tracking characteristics. The robot will then navigate among these obstacles without hitting them and reach the specified goal point. For these goal achieving we use different techniques radial basis function and back-propagation algorithm under the study of neural network. In this Corobot a robotic arm are assembled and the kinematic analyses of Corobot arm and help of Phidget Control Panel a wheeled to be moved in both forward and reverse direction by 2-motor controller have to be done. Under kinematic analysis propose the relationships between the positions and orientation of the links of a manipulator. In these studies an artificial techniques and their control strategy are shown with potential applications in the fields of industry, security, defense, investigation, and others. Here finally, the simulation result using the webot neural network has been done and this result is compared with experimental data for different training pattern

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book