Control and learning in robotic systems
Robotics began as a science fiction creation which has become quite real, first in assembly line operations such as automobile manufacturing, aeroplane construction etc. They have now reached such areas as the internet, ever-multiplying-medical uses and sophisticated military applications. Control of today's robots is often remote which requires even more advanced computer vision capabilities as well as sensors and interface techniques. Learning has become crucial for modern robotic systems as well. This new book deals with control and learning in robotic systems.
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Modeling for Reactive Control and Decision Making in
Evolutionary Acquisition of Fuzzy Control Based Performance
Uncertain Variables and Their Applications in Intelligent
8 other sections not shown
action adaptive schemes AHGA1 AHGA2 AHGA3 allocation applications approach automata automaton backward giant circle battery pack behavior Bubnicki certainty distribution condition considered constraints decision maker decision problem denote described discrete distance environment equation estimate evaluation Figure fuzzy control fuzzy control rules genetic algorithm given global optimum handstand heuristic IEEE important attributes influence diagram input instances Internet iterative learning linear machine learning metaparameters method mobile robot Model MQBP motion nearest neighbors nodes nominal Nova Science Publishers number of examples obtained operators optimal optimum output packet parameters performance permutation planning production proposed queue reactive decision Reinforcement learning Relief algorithms rings gymnastic robot position robot system Robotics and Automation scheduling search technique selected sensors sequence simulation solution step subgroup switching task topo-geometric map topological trajectory trinomial protocol uncertain variables underactuated unexpected events vector Voronoi Diagram