Target Capture Strategy of Robotic Arm Based on Dynamic Trajectory Predictive Control
Capturing moving objects with a robotic arm is a challenging task since the trajectory prediction of the target,the calculation of the landing point,and the motion control of the robotic arm need to be completed within milli-seconds.To this end,this paper proposes a target capture strategy for robotic arms based on motion trajectory predic-tion control.First,an improved motion trajectory prediction model was designed by combining the long short-term memory neural network and the Kalman filter algorithm to realize the calculation of the dynamic target's landing point.Second,the position-based visual servo control based on the model prediction method was constructed to correct the posture of the robotic arm in real-time,so as to realize the capture of moving objects.Finally,a simulation and experi-mental environment based on the UR5 manipulator was established to verify the effectiveness of the proposed scheme.
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