基于深度学习的多机械手轨迹规划系统设计

目前提出的多机械手轨迹规划系统路径规划精准度低，避障能力差。基于深度学习对多机械手的规划系统进行设计，通过研究传统系统中存在精确度、智能性不足的缺点，在设计的系统分别引入了相应的解决条件，在硬件结构的设计中本文应用ISL-320型号的伺服电机提升多机械手的动力功能，应用SKT64系列的芯片提升多机械手的路径精准度；在应用程序设计上应用拟合算法与叠加算法对规划路径中的节点精准运算，在提升系统整体精准度的同时提升了系统的智能程度。实验结果表明，基于深度学习的多机械手轨迹规划系统路径与标准路径十分接近，说明该方法的规划精准度较高，避障能力得到有效增强。

Design of multi-manipulator trajectory planning system based on deep learning

At present, the path planning accuracy of multi manipulator trajectory planning system is low and obstacle avoidance ability is poor. The planning system of multi manipulator is designed based on deep learning. Through the research of the shortcomings of the traditional system, such as the lack of accuracy and intelligence, the corresponding solution conditions are introduced in the designed system. In the design of hardware structure, this paper uses isl-320 servo motor to enhance the power function of multi manipulator, and skt64 series chip is used to improve the path of multi manipulator Accuracy: in the application design, the application of fitting algorithm and superposition algorithm on the nodes in the planning path can improve the overall accuracy of the system and enhance the intelligent degree of the system. The experimental results show that the path of the multi manipulator trajectory planning system based on deep learning is very close to the standard path, which shows that the planning accuracy of the method is high and the obstacle avoidance ability is effectively enhanced.