Neural Networks in Robotics by David DeMers, Kenneth Kreutz-Delgado (auth.), George A.
By David DeMers, Kenneth Kreutz-Delgado (auth.), George A. Bekey, Kenneth Y. Goldberg (eds.)
Neural Networks in Robotics is the 1st publication to provide an built-in view of either the applying of synthetic neural networks to robotic regulate and the neuromuscular types from which robots have been created. The habit of organic structures presents either the foundation and the problem for robotics. The target is to construct robots that may emulate the facility of dwelling organisms to combine perceptual inputs easily with motor responses, even within the presence of novel stimuli and adjustments within the setting. the facility of dwelling structures to benefit and to evolve presents the normal opposed to which robot platforms are judged. in an effort to emulate those skills, a couple of investigators have tried to create robotic controllers that are modelled on identified procedures within the mind and musculo-skeletal method. a number of of those types are defined during this ebook.
nonetheless, connectionist (artificial neural community) formulations are beautiful for the computation of inverse kinematics and dynamics of robots, simply because they are often knowledgeable for this function with out particular programming. a few of the computational merits and difficulties of this procedure also are provided.
For any critical scholar of robotics, Neural Networks in Robotics presents an imperative connection with the paintings of significant researchers within the box. equally, on account that robotics is an exceptional program quarter for man made neural networks, Neural Networks in Robotics is both vital to staff in connectionism and to scholars for sensormonitor keep watch over in residing structures.
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Additional resources for Neural Networks in Robotics
Neural Network Control and Learning NEURAL NETWORK CONTROL Recently, the field of neural network research has gained more attention from engineering disciplines and found various applications. One attractive feature of the neural networks is their learning capabilities. They can learn a complex nonlinear relationship, such as robot kinematics and dynamics through a training procedure and approximate the function with significantly less computations. Thus, neural networks offer an alternative to direct modelling of a complex system in any applications under real-time constraint.
A direct inverse control scheme was developed in  in which a neural net learns the inverse dynamics of a system so that the system can follow a desired trajectory . Recently, some researchers have been utilizing neural networks in the frame of traditional adaptive control theory and coined the term "Neural Adaptive Control" . The linear mapping used in the conventional adaptive control structure is replaced with neural nets. The advantage is the greater robustness and ability to handle nonlinearity.
That is, there exists a setting of parameter values that would cause the model in the control computer to match exactly the dynamics of the actual manipulator. In this case, the nonperfect linearization and decoupling of the plant by the model based computed torque control, are due solely to parameter estimate errors. 3, we will present a technique designed to learn the parameter estimate errors, such that compensation torques can be generated to compensate for the nonperfect model used in the computed torque controller.