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Self-Organization, Computational Maps, and Motor Control, Volume 119 (Advances in Psychology) 1

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丛书名： Advances in psychology

1997年

作者： P.G. Morasso, V. Sanguineti

ISBN: (数字)9780080540917

ISBN: (纸本)0444823239

In the study of the computational structure of biological/robotic sensorimotor systems, distributed models have gained center stage in recent years, with a range of issues including self-organization, non-linear dynamics, field computing etc. This multidisciplinary research area is addressed here by a multidisciplinary team of contributors, who provide a balanced set of articulated presentations which include reviews, computational models, simulation studies, psychophysical, and neurophysiological *** book is divided into three parts, each characterized by a slightly different focus: in part I, the major theme concerns computational maps which typically model cortical areas, according to a view of the sensorimotor cortex as 'geometric engine' and the site of 'internal models' of external spaces. Part II also addresses problems of self-organization and field computing, but in a simpler computational architecture which, although lacking a specialized cortical machinery, can still behave in a very adaptive and surprising way by exploiting the interaction with the real world. Finally part III is focused on the motor control issues related to the physical properties of muscular actuators and the dynamic interactions with the *** reader will find different approaches on controversial issues, such as the role and nature of force fields, the need for internal representations, the nature of invariant commands, the vexing question about coordinate transformations, the distinction between hierachiacal and bi-directional modelling, and the influence of muscle stiffness.

关键词： robotics. Self-organizing systems. Locomotion Sensorimotor cortex. Computer simulation.

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Fuzzy, neural network, and genetic algorithm based control system

Fuzzy, neural network, and genetic algorithm based control s...

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International Conference on Industrial Electronics, Control and Instrumentation

作者： T. Fukuda K. Shimojima T. Shibata Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan Bio-Robotics Division Robotics Department Mechanical Engineering Laboratory M.I.T.I. Tsukuba Japan

This paper introduces a hierarchical control scheme based on a fuzzy, neural network, and a genetic algorithm for intelligent robotics. The scheme has three levels: learning level, skill level and adaptation level. The learning level manipulates symbols to reason logically for control strategies. The skill level produces control references along with the control strategies and sensory information on environments. The adaptation level controls robots and machines while adapting to their environments which include uncertainties. For these levels and to connect them, artificial intelligence, neural networks, fuzzy logic, and genetic algorithms are applied to the hierarchical control system while integrating and synthesizing themselves. To be intelligent, the hierarchical control system learns various experiences both in top-down manner and bottom-up manner. The hierarchical control scheme is effective for intelligent robotics and mechatronics.< >

关键词： Fuzzy control Fuzzy neural networks Fuzzy systems Neural networks Genetic algorithms Control systems Intelligent robots Artificial intelligence Intelligent sensors Learning systems

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Automatic generation of hierarchical structure of fuzzy inference by genetic algorithm

Automatic generation of hierarchical structure of fuzzy infe...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Ishigami Y. Hasegawa T. Fukuda T. Shibata Shin-Nagoya Thermal Power Station Cyubu Electric Power Company Inc. Nagoya Japan Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan Bio-Robotics Division Robotics Department MITI Mechanical Engineering Laboratory Tsukuba Japan

This paper deals with an automatic generation algorithm of a hierarchical fuzzy inference using genetic algorithms and the delta rule. The fuzzy inference can be applied to various problems. However, the determination of the membership functions is a difficult problem because the determination depends on human experts. The auto-tuning methods of the fuzzy model have been proposed to develop the time-consuming operation used by human experts. The problem is that a number of the fuzzy rules in the multiple input and multiple output is needed in the case of simple fuzzy inference. Therefore, the authors propose an auto-tuning method of hierarchical fuzzy inference. The proposed method enables the fuzzy model to construct the optimal and the minimal structures. In this paper, the authors show the effectiveness of the proposed method by simulation of water tanks level control. This general system can be applied to robotic motion control, sensing and recognition problems.< >

关键词： Genetic algorithms Humans Fuzzy systems Control systems Power generation Robot motion Motion control Robot control Fuzzy neural networks Inference algorithms

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Fuzzy Critic for intelligent planning by genetic algorithm 2

Fuzzy Critic for intelligent planning by genetic algorithm

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2nd IEEE International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993

作者： Shibata, T. Fukuda, T. Tanie, K. Bio-Roboucs Division Robotics Depamnent Mechanical Engineering Laboratory 1-2 Namiki Tsukuba305 Japan Department of Mechano-Informatics and Systems Nagoya University Furo-cho Chikusa-ku Nagoya464-01 Japan

ISBN: (纸本)0780309855

A new strategy for motion planning is proposed. The strategy applies a genetic algorithm (GA) to optimize the motion planning. To evaluate the planned motion, the strategy also applies fuzzy logic to a fitness function. The fitness function is referred to as Fuzzy Critic. The Fuzzy Critic evaluates plans as populations in the GA with respect to multiple factors. Depending on the goals of the tasks, human operators can easily determine inference rules in the Fuzzy Critic because of the fuzzy logic. The strategy determines a path for a mobile robot which moves from a starting point to a goal point, while avoiding obstacles in a work space and picking up loads on the way. Simulation illustrates the effectiveness of the proposed strategy. © 1993 IEEE.

关键词： Genetic algorithms

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Synthesis of fuzzy, artificial intelligence, neural networks, and genetic algorithm for hierarchical intelligent control-top-down and bottom-up hybrid method

Synthesis of fuzzy, artificial intelligence, neural networks...

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International Joint Conference on Neural Networks (IJCNN)

作者： T. Shibata T. Fukuda K. Tanie Bio-Robotics Division Robotics Department Mechanical Engineering Laboratory Tsukuba Japan Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan

Autonomous robots, which perform tasks without human operators, are required in many fields. They have to be intelligent to determine their own actions in unknown environments by themselves based on sensory information. In advance, human operators can give the robots knowledge and skill in top-down manner, but when the robots perform tasks in unknown environment, the knowledge may not be useful, In this case, the robots have to adapt to their environments and acquire new knowledge by themselves through learning. This process proceeds in bottom-up manner. This paper introduces a control scheme for autonomous robots, hierarchical intelligent control. It consists of three levels: adaptation level, skill level and learning level. To link the three levels, the scheme uses artificial intelligence (AI), fuzzy logic, neural networks (NN) and genetic algorithm (GA). Each technique has advantages and disadvantages. In order to overcome the disadvantages, this paper introduces synthesis techniques of them. Those are key techniques for intelligent control of robots. This paper describes advantages and disadvantages of each technique, and explains how to construct the hierarchical intelligent control.

关键词： Artificial intelligence Network synthesis Fuzzy control Fuzzy neural networks Artificial neural networks Genetic algorithms Intelligent networks Intelligent robots Robot sensing systems Intelligent control

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Learning scheme for recurrent neural network by genetic algorithm

Learning scheme for recurrent neural network by genetic algo...

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Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS '93)

作者： T. Fukuda T. Kohno T. Shibata Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan Bio Robotics Division Robotics Department Mechanical Engineering Laboratory Mechanical Engineering Laboratory Tsukuba Japan

Recurrent neural networks have dynamic characteristics and can express functions which depend on time. To apply these neural networks to the memory of robotic motions, i.e., trajectories of manipulators, it is necessary to determine appropriate network interconnection weights. A new learning scheme for recurrent neural networks using a genetic algorithm (GA) is presented and used to determine the interconnection weights. The GA approach is compared with backpropagation through time. Simulations illustrate the performance of the new approach.

关键词： Recurrent neural networks Genetic algorithms Neurons Neural networks Multi-layer neural network Cost function Electronic mail Feedforward neural networks International trade Mechanical engineering

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Coordinative behavior in evolutionary multi-agent-robot system

Coordinative behavior in evolutionary multi-agent-robot syst...

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IEEE International Workshop on Intelligent Robots and systems (IROS)

作者： T. Shibata T. Fukuda Bio Robotics Division Robotics Department Mechanical Engineering Laboratory Mechanical Engineering Laboratory Tsukuba Japan Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan

A new strategy for motion planning of multiple robots as a multi-agent system is proposed. The system has a decentralized configuration. All the robots cannot communicate globally, but some robots can communicate locally and coordinate to avoid conflicts for public resources. In such systems, it is difficult for each robot to plan its motion effectively while considering other robots, because the robots cannot predict the motion of other robots as an unknown environment. Therefore, each robot only determines its motion selfishly while considering a known environment. In the proposed approach, each robot plans its motion while considering the known environment and using empirical knowledge. The robot considers its unknown environment including the other robots in the empirical knowledge. A genetic algorithm is applied to optimize the planning of the motion of each robot. Through iterations, each robot acquires knowledge empirically using fuzzy logic For an illustration, this paper deals with path planning of multiple mobile robots and performs simulations.

关键词： Robot kinematics Humans Motion planning Intelligent control Multiagent systems Mechanical engineering Laboratories International trade Strategic planning Fuzzy logic

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Intelligent motion planning by genetic algorithm with fuzzy critic

Intelligent motion planning by genetic algorithm with fuzzy ...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： T. Shibata T. Fukuda Bio Robotics Division Robotics Department Mechanical Engineering Laboratory Tsukuba Ibaraki Japan Department of Mechano Informatics and Systems University of Nagoya Nagoya Aichi Japan

A strategy for motion planning in robotics is proposed. The proposed strategy applies a genetic algorithm (GA) to optimize the motion planning. To evaluate the planned motion, the strategy also applies fuzzy logic to a fitness function. The fitness function is referred to as fuzzy critic. The fuzzy critic evaluates plans as populations in the GA with respect to multiple factors. Depending on the goals of the tasks, human operators can easily determine inference rules in the fuzzy critic because of the fuzzy logic. The strategy determines a path for a mobile robot which moves from a starting point to a goal point while avoiding obstacles in a work space and picking up loads on the way. Simulations illustrate the effectiveness of the proposed strategy.< >

关键词： Genetic algorithms Motion planning Strategic planning Fuzzy logic Humans Intelligent robots Orbital robotics Robot kinematics Mobile robots Algorithm design and analysis

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Fuzzy critic for robotic motion planning by genetic algorithm in hierarchical intelligent control

Fuzzy critic for robotic motion planning by genetic algorith...

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International Joint Conference on Neural Networks (IJCNN)

作者： T. Shibata T. Fukuda K. Tanie Bio-Robotics Division Robotics Department Mechanical Engineering Laboratory M.I.T.I. Tsukuba Japan Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan

In this paper, a new strategy for motion planning in robotics is proposed. When robots performs some tasks, they work along with the motion plans. The plane should be effective. The proposed strategy applies a genetic algorithm (GA) to optimize the plans. To evaluate the planned motion, the strategy applies fuzzy logic as a fitness function. The fitness function is referred to as fuzzy critic. The fuzzy critic evaluates populations in the GA with respect to multiple factors. Depending on the goals of the tasks, human operators can easily transfer inference rules in the fuzzy critic because of the fuzzy logic. In this paper, the strategy determines a path for a mobile robot which moves from a starting point to a goal point while avoiding obstacles in a work space and picking up loads on the way. Simulations illustrate the effectiveness of the proposed strategy.

关键词： Fuzzy control Robot motion Motion planning Genetic algorithms Intelligent control Robot kinematics Humans Fuzzy logic Mobile robots Strategic planning

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Nonlinear backlash compensation using recurrent neural network. Unsupervised learning by genetic algorithm

Nonlinear backlash compensation using recurrent neural netwo...

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International Joint Conference on Neural Networks (IJCNN)

作者： T. Shibata T. Fukuda K. Tanie Bio-Robotics Division Robotics Department Mechanical Engineering Laboratmy M.I.T.I. Tsukuba Japan Department of Mechano-Informatics and Systems University of Nagoya Nagoya Japan

This paper presents a new method to compensate for nonlinearity in machine control. The method uses a recurrent neural network as a servo controller with a feedback loop. The recurrent neural network has dynamic characteristics and can express functions which depend on time. It is necessary to determine appropriate interconnection weights of the network. The approach proposed applies the genetic algorithm to determine the interconnection weights of the recurrent neural networks. This approach does not need the teaching signals. The proposed method is applied to compensate for nonlinear backlash in machine control. Simulations illustrate the performance of the proposed approach.

关键词： Recurrent neural networks Unsupervised learning Gears Education Genetic algorithms Machine control Neural networks Manipulator dynamics Robots Mechanical engineering

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