研究目的
To design an online image-based visual servoing (IBVS) controller for a 6-degrees-of-freedom (DOF) robotic system based on the robust model predictive control (RMPC) method, considering the system's input and output constraints.
研究成果
The proposed online MPC-based IBVS controller effectively handles the system's input and output constraints, avoiding the inverse of the image Jacobian matrix and solving intractable problems for the classical IBVS controller. Real-time experiments demonstrate the controller's effectiveness in ensuring stability and convergence of the robot motion.
研究不足
The major drawback of MPC is the long computational time required to solve the optimization problem, which often exceeds the sampling interval in real-time situations.
1：Experimental Design and Method Selection:
The controller is designed using the robust model predictive control (RMPC) method, avoiding the inverse of the image Jacobian matrix.
2：Sample Selection and Data Sources:
Real-time experiments are conducted on a 6-DOF robot manipulator with eye-in-hand configuration.
3：List of Experimental Equipment and Materials:
A CCD camera mounted on the robot’s end-effector and a 6-DOF Denso robot are used.
4：Experimental Procedures and Operational Workflow:
The visual servoing task is completed when the image features match the desired features, with the controller ensuring constraints are respected.
5：Data Analysis Methods:
The effectiveness of the proposed algorithm is verified through real-time experimental results.
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