Research on automatic planning system of robotic arm motion for fine musical instrument maintenance
Musical instruments pose extremely high requirements for repair work due to their precise and complex structures and subtle and variable acoustic characteristics.Traditional manual repair methods are not only inefficient,but also may lead to unstable repair quality due to the difference in the skill level and experience of the repairers.Based on this background,this study first builds a musical instrument fault object recognition model by combining deep learning technology,and then improves the traditional path op-timization algorithm and builds an obstacle avoidance path optimization model for the musical instrument repair robotic arm.The re-search results show that the designed fault recognition model and path optimization model both have better performance.The highest recognition accuracy of the recognition model is up to 0.962,and the highest obstacle avoidance accuracy of the path optimization model is up to 0.97.In conclusion,the fault recognition model and path optimization model proposed in this study can be used in the musical instrument repair robotic arm system to effectively improve the effect of automatic repair of musical instruments.
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