基于大数据聚类的智能探测机器人运动控制系统设计

为了减小智能探测机器人运动轨迹误差,实现精准控制,提高智能探测机器人运动控制效率,设计基于大数据聚类的智能探测机器人运动控制系统;采用TMS320LF2407A主控芯片,集成650 V功率管,在电感电流断续模式下工作,提供系统驱动能量,设置光电耦合器,处理控制信号发射,调整控制电路内部电流关系;选用6ES7214-1AG40-0XB0控制器以及信号和通信模块扩展,控制机器人运动轨迹,结合内部驱动装置,整合运动数据信息进行存储,实现运动控制系统硬件结构设计;通过调节程序开始数据,结合内部脉冲数据,构建软件平台管理模块,获取机器人运动轨迹数据;采用大数据聚类技术,建立控制系统大数据分布结构模型,模拟非线性时变LFM控制信号,提取特征并聚类运动轨迹数据,获取精准运动轨迹数据,减少运动轨迹偏差程度,完成运动控制系统软件设计;实验结果表明,基于大数据聚类的运动控制系统的运动轨迹误差较小,能够有效实现精准控制,提高运动控制效率.

Design of motion control system of intelligent detection robot based on big data clustering

Aiming at the problem that the current robot motion control system can not achieve accurate control due to the small adjustment of motion data, which leads to large trajectory error and low efficiency of motion control, an intelligent detection robot motion control system based on big data clustering is designed. TMS320LF2407A main control chip and 650V power transistor are used to work in intermittent mode of inductor current to provide system driving energy, set optocoupler, process control signal emission and adjust internal current relationship of control circuit. The 6ES7214-1AG40-0XB0 controller and the signal and communication module are used to control the trajectory of the robot. Combined with the internal driving device, the motion data information is integrated for storage, and the hardware structure of the motion control system is designed. By adjusting the program start data, combined with the internal pulse data, the software platform management module is constructed to obtain the robot trajectory data. Using big data clustering technology, the control system big data distribution structure model is established to simulate nonlinear time-varying LFM control signal, feature extraction and clustering motion trajectory data to obtain accurate motion trajectory data, reduce the degree of motion trajectory deviation, and complete the motion control system software design. The experimental results show that the motion trajectory error of the motion control system based on big data clustering is small, which can effectively achieve accurate control and improve the efficiency of motion control.