Handling of a constrained flexible object by a robot

科研費報告書収録論文(課題番号:07455416・基盤研究(B)(2)・H7～H9/研究代表者:内山, 勝/フレキシブル双腕ロボットの協調制御に関する研究

Cooperative control of a vibrating flexible object by a rigid dual-arm robot

科研費報告書収録論文(課題番号:07455416・基盤研究(B)(2)・H7～H9/研究代表者:内山, 勝/フレキシブル双腕ロボットの協調制御に関する研究

Fuzzy logic combined with neural algorithm to control industrial robot

The problem of finding the optimal path for the robot arm is one of the most important problems of industrial robot. Problem consists when robot looking for specific routes that require the lowest power consumption. Path that established between any two end points, can follow many paths. All these paths require different amounts of energy depending on the distance, velocity and acceleration. Path planning for robotic arms have a several degrees of freedom. This problem is solved by using neuro-fuzzy techniques. Using analytical and numerical techniques is very difficult to find a good solution. Mathematically is more difficulty to move a robotic arm in the presence of obstacles, but child instinctively moving his hand in the presence of obstacles. A way that allows us to progress is a neuro-fuzzy fusion systems. Neural networks make the ability to learn, while Fuzzy logic is based on the emulation of thinking of an expert. In addition, as hardware technology advances, more and more value will be placed on solutions that can be used in parallel processing, such as neural networks and fuzzy logic with neural networks

Fuzzy logic combined with neural algorithm to control industrial robot

The problem of finding the optimal path for the robot arm is one of the most important problems of industrial robot. Problem consists when robot looking for specific routes that require the lowest power consumption. Path that established between any two end points, can follow many paths. All these paths require different amounts of energy depending on the distance, velocity and acceleration. Path planning for robotic arms have a several degrees of freedom. This problem is solved by using neuro-fuzzy techniques. Using analytical and numerical techniques is very difficult to find a good solution. Mathematically is more difficulty to move a robotic arm in the presence of obstacles, but child instinctively moving his hand in the presence of obstacles. A way that allows us to progress is a neuro-fuzzy fusion systems. Neural networks make the ability to learn, while Fuzzy logic is based on the emulation of thinking of an expert. In addition, as hardware technology advances, more and more value will be placed on solutions that can be used in parallel processing, such as neural networks and fuzzy logic with neural networks

Artificial neural network control of a nonminimum phase, single-flexible-link

©1996 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 1996 IEEE International Conference on Robotics and Automation (ICRA), April 22-28, 1996, Minneapolis, MN.DOI: 10.1109/ROBOT.1996.506994A single-link flexible manipulator with a rotary actuator at one end and a mass at the other is modeled using the Lagrangian method coupled with an assumed modes vibration model. A SIMO state space model is developed by linearizing the equations of motion and simplified by neglecting natural damping. Laplace domain pole-zero plots between torque input and tip position show nonmzmmum phase behavior. Nonminimum phase behavior causes difficulty for both conventional and artificial neural network (ANN) inversemodel control. The most promising ANN method for the control of flexible manipulators does not appear to converge to a solution when the system is lightly damped. To overcome this limitation, a modified cost junction is proposed. Simulations show that the ANN is able to converge to a solution even in the case of no damping. The modified approach fails, however, for beams exceeding some critical length measure. Identification of the critical length and proposals for extending the result are discussed