Titlebook: Design and Control of Intelligent Robotic Systems; Dikai Liu,Lingfeng Wang,Kay Chen Tan Book 2009 Springer-Verlag Berlin Heidelberg 2009 A

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Punctuated Anytime Learning to Evolve Robot Control for Area Coverage,lation is not completely accurate and adapt the control program for changes in the robot’s capabilities. The method’s viability is demonstrated by its application to learning the control program for an actual hexapod robot’s performing area coverage.

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Learning of Hierarchical Perception-Action Cycles,at can adapt its abilities to changes of the environment..This chapter presents the overview of a robotic system that is programmed according to the above outlined principles. Its task is to learn to solve a shape-sorter puzzle which contains blocks of different shapes and colors and a plate with ap

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Design, Modeling and Control of an Ankle Rehabilitation Robot,urate estimation of state variables, a kinematic self calibration routine has also been developed for the parallel robot using redundant sensing. To allow for further development of the controller, the recursive least squares algorithm has been applied to estimate the ankle stiffness and damping par

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Markovito: A Flexible and General Service Robot,oal, the states are represented by a set of variables affected by the general modules, and the actions correspond to the execution of the different modules. In order to create a new application the user only needs to define a new MDP whose solution provides an optimal policy that coordinates the dif

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Book 2009interest from researchers. This edited book entitled “Design and Control of Intelligent Robotic Systems” in the book series of “Studies in Computational Intelligence” is a collection of some advanced research on design and control of intelligent robots. The works presented range in scope from design

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Strukturieren, Formalisieren, Axiomatisierenapter, a general view of robot planning as well as the role of emotions in robots are exposed first. Moreover, an example of an emotion inspired planner applied to a topological navigation system is shown, as well as the results obtained with a real robot.

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https://doi.org/10.1007/978-3-322-85621-0 parallel swarm-robotic learning of obstacle avoidance behavior using both Genetic Algorithms and Particle Swarm Optimization. We also observe the diversity of robotic controllers throughout the learning process using two different metrics in an attempt to better understand the evolutionary process.

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