Intelligent control of the Intelledex 605T robot manipulator

Sobajic,; Lu, Jiwen; Pao,

doi:10.1109/icnn.1988.23981

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…Their model was created so that they could control a walking biped robot as if it were an inverted pendulum. Sobajic et al (1988) explored a controller which uses the current joint angles and the target in polar coordinates to produce the control signal. This controller must have a built-in model of the inverse kinematics, and thus is affected by the results presented here.…”

Section: Inverse Kinematics (Including Inverse Jacobian)mentioning

confidence: 99%

Neural networks and the inverse kinematics problem

et al. 1993

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Inverse kinematics is a fundamental problem in robotics. Past solutions for this problem have been realized through the use of various algebraic or algorithmic procedures. In this paper the use of feedforward neural networks to solve the inverse kinematics problem is examined for three different cases. A closed kinematic linkage is used for mapping input joint angles to output joint angles. A three-degree-of-freedom manipulator in 3D space is used to test mappings from both cartesian and spherical coordinates to manipulator joint coordinates. A majority of the results have average errors which fall below 1% of the robot workspace. The accuracy indicates that neural networks are an alternate method for performing the inverse kinematics estimation, thus introducing the fault-tolerant and high-speed advantages of neural networks to the inverse kinematics problem.This paper also shows the use of a new technique which reduces neural network mapping errors with the use of error compensation networks. The results of the work are put in perspective with a survey of current applications of neural networks in robotics.

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Section: Inverse Kinematics (Including Inverse Jacobian)mentioning

confidence: 99%

Neural networks and the inverse kinematics problem

et al. 1993

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“…These applications include process control (Rehbein et al 1992), robotics (Kawato et al 1988, Sobajic et al 1988, industrial manufacturing (Kooi andKhorasani 1991, Bozich andMacKay 1991), and aerospace meade et al 1991), etc.…”

Section: Patten Recognition and Classificationmentioning

confidence: 98%

“…Sobajic et al (1988) study the use of neurocontrollers in robot arm movement with two degrees of freedom and with a desired final end effector position. The results achieved demonstrate the unusual efficiency and potential of neurocontrollers for use in robot kinematics control systems.…”

Section: Roboticsmentioning

confidence: 99%

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Neural networks in manufacturing: A survey

Huang

Zhang

Proceedings of 15th IEEE/CHMT International Electronic Manufacturing Technology Symposium

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Artificial Intelhgence (AI) has been claimed to yield revolutionary advances in manufacturing. AI technology is applicable to the entire range of manufacturing activities. As an AI technique, expert systems have been wildly used in manufacturing for over two decades. Recently, another AI technique, namely, neural networks, are gaining more and more visibility and have been successfully applied in manufacturing practices. While most of the survey papers about AI in manufacturing have been focused on expert systems application, fewer attentions have been paid to neural networks. However, expert systems are less effective in the ever-changing , complex, and open system environment of today's manufacturing systems. On the other hand, neural networks are able to learn, adapt to changes, and can mimic human thought processes with little human interventions. They could be of great help for the present Computer Integrated Manufacturing (CIM) and the future Intelligent Manufacturing Systems (IMS). Therefore, it is necessary to present the state-of-the-art of neural networks in manufacturing. The objective of this paper is to update information about neural networks in manufacturing, which will provide some guidelines and references 'for the research and implementation. In this paper, the basic concepts of neural networks are briefly introduced, Then, a survey of neural network applications in manufacturing is provided. Finally, the projection of future trends is given to help make decisions concerning neural networks implementation in manufacturing today and to aid in guiding research for tomorrow.

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Neural Learning Algorithms and Their Applications in Robotics

Genís¹

1991

Self-Organization, Emerging Properties, and Learning

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Intelligent control of the Intelledex 605T robot manipulator

Cited by 16 publications

References 8 publications

Neural networks and the inverse kinematics problem

Neural networks and the inverse kinematics problem

Neural networks in manufacturing: A survey

Neural Learning Algorithms and Their Applications in Robotics

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