基于IPSO-BPNN-PID控制的食品并联机器人抓取技术

目的:解决并联机器人在食品分拣中存在的效率低、精度差等问题。方法:在食品分拣系统结构的基础上,提出了一种改进BP神经网络与PID控制相结合的Delta机器人运动目标抓取策略。通过改进的粒子群优化算法优化BP神经网络初始权值,并利用优化的BP神经网络对PID控制参数进行实时调整。通过试验分析该方法的性能验证其优越性。结果:相比于传统控制方法,所提方法能够较为准确、高效地实现动态目标捕获,动态抓取成功率达到98%以上,能够满足食品分拣的需要。结论:通过对动目标抓取策略的优化可以有效地提高Delta机器人的抓取效率和精度。

Research on food parallel robot grasping technology based on IPSO-BPNN-PID control

Objective: In order to solve the problems of low efficiency and poor precision of parallel robot in food sorting. Methods: Based on the structure of the food sorting system, a moving target grasping strategy of delta robot based on Improved BP neural network and PID control is proposed. The improved particle swarm optimization algorithm is used to optimize the initial weight of BP neural network, and the optimized BP neural network is used to adjust the PID control parameters in real time. The performance of this method is analyzed by experiments, and its superiority is verified. Results: Compared with traditional control methods, the proposed method can achieve dynamic target capture more accurately and efficiently, and the success rate of dynamic capture is more than 98%, which can meet the needs of food sorting. Conclusion: The grasping efficiency and accuracy of delta robot can be effectively improved by optimizing the grasping strategy of moving target.