Vision-based motion estimation is a key technique in autonomously mobile robot. This paper presents an integrated method for motion estimation of the mobile robot based on floor plane extraction. The whole processes i...
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Evolvable hardware is able to offer considerably higher performance than general-purpose processors and significantly more flexibility than ASICs. In order to take the advantages of general-purpose processors and ASIC...
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Evolvable hardware is able to offer considerably higher performance than general-purpose processors and significantly more flexibility than ASICs. In order to take the advantages of general-purpose processors and ASICs, dividing a complex process into subprocesses is essential. In this paper, we propose a evolutionary method called context switching that splits a task into a set of subtasks whose complexity is manageable on the given hardware. The method is based on genetic programming. Due to its expressive power generic program can represent flexible strategies for decomposing complex tasks. The effectiveness of context switching is demonstrated on the design of adaptive controllers for a team of autonomous mobile robots.
作者：
Suh, Il HongKim, Tae WonIntelligent Contr. and Robotics Lab.
Dept. of Electronics Engineering Hanyang University Ansan-City Kyungki-Do 425-791 Sa 1-Dong 1271 South Korea W/B Team
Semiconductor System Division Samsung Aerospace Industries Ltd. Sungnam-City Kyungki-Do 462-121 145-3 Sangdaewon 1-Dong South Korea
A visual servoing algorithm is proposed for a robot with a camera in the hand to track a moving object in terms of image features and their variations, where fuzzy logics and fuzzy-neural networks are involved to lear...
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A visual servoing algorithm is proposed for a robot with a camera in the hand to track a moving object in terms of image features and their variations, where fuzzy logics and fuzzy-neural networks are involved to learn feature Jacobian-based kinematic control law. Specifically, novel image features are suggested by employing a viewing model of the perspective projection to estimate the relative pitching and yawing angles. Such perspective projection-based features would not interact with the relative distance between the object and the camera, and, desired feature trajectories for learning the visually guided line-of-sight robot motion are obtained by measuring features by the camera in the hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a commercially provided function of linear motion, and then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of the proposed algorithm, some experimental results are illustrated, where a four-axis SCARA robot with a BW CCD camera is used.
This paper presents the fuzzy inference-based reinforcement learning algorithm of dynamic recurrent neural network, similar to the psychological learning scheme of the higher animals. The proposed method follows the w...
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ISBN:
(纸本)0780342089
This paper presents the fuzzy inference-based reinforcement learning algorithm of dynamic recurrent neural network, similar to the psychological learning scheme of the higher animals. The proposed method follows the way linguistic and conceptional expressions have an effect on human's behavior by reasoning reinforcement based on fuzzy rules. The intervals of fuzzy membership functions are found optimally by genetic algorithms. By using the recurrent neural network composed of dynamic neurons as action-generation network, not only the current state but also the past state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.
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