To achieve more accurate simulation and control in the use of the manipulator, it is necessary to establish an accurate dynamic model of the redundant manipulator. The research of this article focuses on the dynamic parameter identification method of the redundant manipulator. In the study, the spinor theory is applied to the Newton–Euler dynamic equation, the Coulomb + viscous friction model is adopted, and the minimum parameter set is obtained by linearization derivation. The parameter identification of the manipulator is realized using the method of offline identification of the measured current, and the coefficient of the excitation trajectory is optimized using the nonlinear optimization function. Finally, the parameter set with high accuracy is obtained, and the motion trajectory of each joint can be obtained. The scheme has high accuracy and can meet the needs of practical application. To verify the accuracy and reliability of this method, we have carried out experiments on a service robot “Walker” and obtained the desired results.

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This paper describes the development of unmanned surface vessel (USV) collision avoidance technology, introduces the basic principles and key technologies of image recognition technology, analyzes the shortcomings of traditional collision avoidance technology, and explains the research status of the image recognition technology in the collision avoidance of unmanned surface vessel. The design ideas of the existing research and development results are summarized. Advantages and disadvantages are analyzed and compared. This paper focuses on the techniques of stereo vision, deep learning, multi-sensor fusion, and the method of unmanned surface vessel collision avoidance based on image recognition technology. The future development trend of this field is also discussed.

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