Research On Space Motion Tracking System Of Robot Teaching Based On MEMS Sensor

With the continuous advancement of science and technology,robot technology has developed rapidly.Robot teaching technology is used in various fields,and the commonly used teaching methods still have many deficiencies,including complicated teaching processes,poor flexibility and low security.By collecting human motion information and optimizing data fusion,it can make up for the shortcomings of common teaching methods,improve the efficiency of the teaching process,and ensure safety.This thesis collects human arm motion information through MEMS sensors,and uses strapdown inertial navigation technology attitude solution method,proposes a multi-MEMS combined sensor information fusion attitude solution control strategy,respectively through static and dynamic simulation test improved attitude control strategy feasibility.The spatial position algorithm is used to calculate the position and movement trajectory of the end of the human arm in three-dimensional space,and the effect of the MEMS sensor on the collection of arm motion information is verified through experiments.The experimental results show that the use of multi-MEMS combined sensors can collect accurate arm motion information,and the cost is low,and this method can be applied to robot teaching.First,based on the physical characteristics of the human arm,the kinematic model of the arm is built,the motion of each joint is analyzed,and the degrees of freedom of each joint are obtained.The construction principle of the MEMS sensor and the working principle of each microsensor are analyzed.The main sources of error of the MEMS sensor are explained.Median filtering method is used to filter the raw data collected by the sensor,so as to improve the accuracy of the collected data.Then,the principles of the direction cosine method,euler angle method and quaternion method are analyzed,and the advantages and disadvantages of each method are compared.For the performance characteristics of each microsensor module,the complementary filtering principle is used for data fusion,and the kalman filter is used for data forecast update.The core idea of this thesis is to propose a multi-MEMS combined sensor information fusion attitude solution control strategy.Four groups of MEMS sensors are used for data collection on each axis of the carrier coordinate system,the attitude angle data is obtained through the spatial rotation matrix,the collected data and the quaternion estimated data are cross-multiplied,and the data is combined and adjusted by the fuzzy algorithm and pi algorithm deviation,complementary filtering for data fusion,suppression of gyroscope drift,aekf predicts and updates thedata,and uses the spatial position method and spatial position superposition method to solve the displacement trajectory of the arm end in three-dimensional space.Finally,through the experimental test of the continuous movement of the arm in space to obtain thefinal displacement trajectory data,through experimental analysis,a multi-MEMS combined sensor information fusion attitude solution control strategy proposed in this thesis can accurately obtain the arm end in space displacement trajectory can reduce the error drift of displacement trajectory and can be used in robot teaching.