As we know, the dynamic visual-tracking control, which is based on the depth-independent interaction matrix, has been proposed to cope with the general 3-D motion of robot manipulators. To deal with the unknown camera...
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As we know, the dynamic visual-tracking control, which is based on the depth-independent interaction matrix, has been proposed to cope with the general 3-D motion of robot manipulators. To deal with the unknown camera parameters, an adaptive law has been designed. It is noted, however, that the designed adaptive law uses a vector signal defined by the true depths of feature points, which are unavailable when the camera parameters are unknown. Nevertheless, such an adaptive law can still be implemented by replacing the exact depths with the estimated depths, since the estimated depths can be readily calculated by using the estimated values of the camera parameters. In this case, however, we cannot definitely say whether or not the robot system can be guaranteed to achieve asymptotical stability or even stable behavior. To overcome this problem, in this paper, we redefine the mentioned vector signal using the estimated depths and show that the design based on the new vector signal can make the robot system asymptotically stable. Additionally, the existing tracking control design based on the concept of depth-independent interactionmatrix is initial-state dependent. Thus, in this paper, we also modify the existing design to obtain an initial-state-independent result. To show the performance of the proposed designs, simulation results based on a two-link planar manipulator are presented. In addition, preliminary experimental results using an industrial manipulator are also given.
This paper proposes a Cartesian control scheme applied to a robotic assistant for laparoscopic surgery. This system's main characteristic is that it emulates the movements of a human assistant, guiding the laparos...
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ISBN:
(纸本)0780395050
This paper proposes a Cartesian control scheme applied to a robotic assistant for laparoscopic surgery. This system's main characteristic is that it emulates the movements of a human assistant, guiding the laparoscopic camera with precision to focus on the area in question inside the patient. Furthermore this control scheme requires adjustment of certain parameters in order to prevent saturation of the manipulator's actuators, and therefore the robot has been studied in terms of manipulability. The proposed movement control scheme has been implanted in the ERM robot used to carry out trials on thirty two patients.
This paper presents an adaptive trajectory planning method concerning to the robotic assistant ERM (Endoscopic robotic Manipulator), designed and developed by the authors for handling the camera in laparoscopic surger...
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ISBN:
(纸本)0780382323
This paper presents an adaptive trajectory planning method concerning to the robotic assistant ERM (Endoscopic robotic Manipulator), designed and developed by the authors for handling the camera in laparoscopic surgery. In order to emulate the human assistant, camera movements must be defined relative to the fulcrum point, where the optic passes through the patient skin and enters inside the abdominal cavity. Since the robot has a passive wrist, and it is not fixed to the operating table, the relative position between the robot camera holder and the insertion point is unknown. In this way, the proposed approach keeps the camera orientation according to the motion references in spite of this uncertainty, and compensates other unexpected disturbances about the relative robot-patient position. This motion planner is based on a schema of a cartesian motion controller with inner joint position-velocity loop, and has been tested by means of experimentation with alive animals.
In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter pr...
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In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter propagation Network-based Fuzzy controller (CPN-FC) which is able to self-organize and correct on-line its rule base. The self-tuning capability of the fuzzy logic controller is attained by taking advantage of the structural equivalence between the fuzzy logic controller and a counterpropagation network. The second control structure is a more familiar neural adaptivecontroller based on a feedforward (MLP) network. The neural controller learns the inverse dynamics of the robot joints, and gradually eliminates the model uncertainties and disturbances. Both schemes cooperate with the computed torque control algorithm, and in that way the reduction of their complexity is achieved. The ability of adaptive fuzzy systems to compete with neural networks in difficult control problems is demonstrated. A sufficient set of numerical results is included.
Free-floating space robot is different from the robot with inertially fixed base in that its Jacobian matrix is dependent not only on geometric parameters, but also on inertia parameters nonlinearly. Since the inertia...
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ISBN:
(纸本)0780332148
Free-floating space robot is different from the robot with inertially fixed base in that its Jacobian matrix is dependent not only on geometric parameters, but also on inertia parameters nonlinearly. Since the inertia parameters are usually not known accurately, large end-effector tracking error will be induced if the inaccurate Jacobian is used to calculate joint trajectory given the desired end-effector trajectory. In this paper, adaptive schemes are developed to generate joint rate and joint acceleration trajectories, the proposed schemes are proved to be stable and ensure convergence of end-effector tracking error despite of the uncertainties of inertia parameters. Simulation of planar two-link free-floating space robot system is implemented to verify the proposed adaptive schemes.
Neural network based adaptivecontrollers have been shown to achieve much improved accuracy compared with traditional adaptivecontrollers when applied to trajectory tracking in robot manipulators. This paper describe...
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Neural network based adaptivecontrollers have been shown to achieve much improved accuracy compared with traditional adaptivecontrollers when applied to trajectory tracking in robot manipulators. This paper describes a new Recursive Prediction Error technique for estimating network parameters which is more computationally efficient. Results show that this neural controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.
This paper presents the design and development of a real-time eye-in-hand stereo-vision system to aid robot guidance in a manufacturing environment. The stereo vision head comprises a novel camera arrangement with ser...
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This paper presents the design and development of a real-time eye-in-hand stereo-vision system to aid robot guidance in a manufacturing environment. The stereo vision head comprises a novel camera arrangement with servo-vergence, focus, and aperture that continuously provides high-quality images to a dedicated image processing system and parallel processing array. The stereo head has four degrees of freedom but it relies on the robot end-effector for all remaining movement. This provides the robot with exploratory sensing abilities allowing it to undertake a wider variety of less constrained tasks. Unlike other stereo vision research heads, the overriding factor in the Surrey head has been a truly integrated engineering approach in an attempt to solve an extremely complex problem. The head is low cost, low weight, employs state-of-the-art motor technology, is highly controllable and occupies a small-sized envelope. Its intended applications include high-accuracy metrology, 3-D path following, object recognition and tracking, parts manipulation and component inspection for the manufacturing industry.
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