Global ETD Search

31	Neurodynamische Module zur Bewegungssteuerung autonomer mobiler Roboter Hild, Manfred 07 January 2008 (has links) In der vorliegenden Arbeit werden rekurrente neuronale Netze im Hinblick auf ihre Eignung zur Bewegungssteuerung autonomer Roboter untersucht. Nacheinander werden Oszillatoren für Vierbeiner, homöostatische Ringmodule für segmentierte Roboter und monostabile Neuromodule für Roboter mit vielen Freiheitsgraden und komplexen Bewegungsabläufen besprochen. Neben dem mathematisch-theoretischen Hintergrund der Neuromodule steht in gleichberechtigter Weise deren praktische Implementierung auf realen Robotersystemen. Hierzu wird die funktionale Einbettung ins Gesamtsystem ebenso betrachtet, wie die konkreten Aspekte der zugrundeliegenden Hardware: Rechengenauigkeit, zeitliche Auflösung, Einfluss verwendeter Materialien und dergleichen mehr. Interessante elektronische Schaltungsprinzipien werden detailliert besprochen. Insgesamt enthält die vorliegende Arbeit alle notwendigen theoretischen und praktischen Informationen, um individuelle Robotersysteme mit einer angemessenen Bewegungssteuerung zu versehen. Ein weiteres Anliegen der Arbeit ist es, aus der Richtung der klassischen Ingenieurswissenschaften kommend, einen neuen Zugang zur Theorie rekurrenter neuronaler Netze zu schaffen. Gezielte Vergleiche der Neuromodule mit analogen elektronischen Schaltungen, physikalischen Modellen und Algorithmen aus der digitalen Signalverarbeitung können das Verständnis von Neurodynamiken erleichtern. / How recurrent neural networks can help to make autonomous robots move, will be investigated within this thesis. First, oscillators which are able to control four-legged robots will be dealt with, then homeostatic ring modules which control segmented robots, and finally monostable neural modules, which are able to drive complex motion sequences on robots with many degrees of freedom will be focused upon. The mathematical theory of neural modules will be addressed as well as their practical implementation on real robot platforms. This includes their embedding into a major framework and concrete aspects, like computational accuracy, timing and dependance on materials. Details on electronics will be given, so that individual robot systems can be built and equipped with an appropriate motion controller. It is another concern of this thesis, to shed a new light on the theory of recurrent neural networks, from the perspective of classical engineering science. Selective comparisons to analog electronic schematics, physical models, and digital signal processing algorithms can ease the understanding of neural dynamics. Robotik Rekurrente Neuronale Netze Neurodynamik Neuromodul Dynamische Systeme Lernregel Homöostase Autonome Mobile Roboter Humanoide Roboter Bewegungssteuerung Oszillator Ringoszillator Sensomotorische Schleife Recurrent Neural Networks Neurodynamics Neuromodule Dynamical Systems Learning Rule Homeostasis Autonomous Mobile Robots Humanoid Robots Robotics Motion Control Oscillator Ring Oscillator Sensorimotor Loop 510 Mathematik 27 Mathematik ddc:510
32	Empirical Data Based Predictive Warning System on an Automated Guided Vehicle / Empiriskt databaserat predikterande varningsystem för självkörande truckar Blåberg, Anton, Lindahl, Gustav January 2022 (has links) An Automated Guided Vehicle (AGV) must follow protective regulations to avoidcrashing into people when autonomously driving in industries. These safety norms require AGVs to enable protective fields, which perform hard braking when objects enter aspecific area in front of the vehicle. Warning fields, or warning systems, are similar fieldsthat decrease the speed of the AGV before objects enter the protective fields to enable asteadier driving. Today at Toyota Material Handling Manufacturing Sweden (TMHMS),warning systems have been implemented but the systems are too sensitive to objects outside of the AGVs path.The purpose of this thesis is to develop a predictive warning system based on empiricaldata from previous driving scenarios. By storing previous positions, the warning systemcould estimate a trajectory based on simple statistics and deploy speed limiting decisionsif objects appear in the upcoming predicted path.The predictive warning system was compared to the current warning system and adeactivated warning system setup in driving performance and driving dynamics. Performance was measured by measuring time to finish an industry-like test track and dynamicswas subjectively rated from a group of experienced AGV developers from TMHMS. Results showed that a predictive warning system drove the test track faster and with betterdynamics than the current warning system and the no warning system setup.Key findings are that a predictive warning system based on empirical data performedbetter in most cases but has some extra requirements to function. Firstly, the method require the AGV to mostly drive on previously driven paths to produce good results. Secondly the warning system requires a somewhat powerful on board computer to handlethe computations. Finally, the warning system requires spatial awareness of pose for thevehicle, as well as structure and shape for deployed protective fields. AGV Automated Guided Vehicles Autonomous Mobile Robots LiDAR Warning Field Warning System Collision Avoidance Braking System Robotics Autonomous Vehicles Path Predictive Position Forecasting Speed Regulation Decision Making Protective Field Protective System Industry 4.0 AI Självkörande truckar Varningsfält Varningssystem Säkerhetsfält Säkerhetssystem Industri 4.0 Automatiskt inbromsning Positionsprognos Beslutsfattande Embedded Systems Inbäddad systemteknik Robotics Robotteknik och automation Computer Systems Datorsystem Vehicle Engineering Farkostteknik

Search results

Neurodynamische Module zur Bewegungssteuerung autonomer mobiler Roboter

Empirical Data Based Predictive Warning System on an Automated Guided Vehicle / Empiriskt databaserat predikterande varningsystem för självkörande truckar