基于RBF网络的果蔬采摘机器人运动轨迹控制研究

为了提高果蔬采摘机器人机械手运动的精确性,提高机器人移动的效率,提出了一种基于遗传算法和RBF网络的机器人运动轨迹控制方法,并对果蔬机器人机械手的活动和整体的移动轨迹进行优化,有效地提高了果蔬采摘机器人的工作精度和作业效率。为了验证设计的采摘机器人的可靠性,在大棚内对机器人的采摘性能进行了测试,包括机器人移动路径规划和机械手路径规划。通过测试发现:使用RBF神经网络算法可以有效地控制机械手在三维空间内的运动;在遗传算法控制下,机器人可以通过较少的计算次数利用神经网络算法搜索得到最优路径,计算精度达到了99%以上。其计算精度及效率高,为高效果蔬采摘机器人的设计提供了较有价值的参考。

Research on Motion Trajectory Control of Fruit and Vegetable Picking Robot Based on RBF Network

In order to improve the accuracy of robot manipulator movement and improve the efficiency of robot movement, a methodis proposed based on genetic algorithm and RBF neural network .The robot manipulator's movement and the whole trajectory are optimized.In order to verify the design of the picking robot reliability, in the experimental green-house on the robot's picking performance were tested, test items include robot path planning of mobile and manipulator path planning.Through the test, we found that using the RBF neural network algorithm can effectively control of manipu-lator motion in the three-dimensional space, in under the control of the genetic algorithm, the robot can with less amount of calculation using neural network algorithm search to get the optimal path, and the calculation precision is above 99%, for its high accuracy, which provides a valuable reference for the fast computational efficiency and effect of high vegetable production picking robot design.