Trajectory accuracy, a crucial metric in assessing the dynamic performance of grinding robots, is influenced by the uncertain movement of the tool center point, directly impacting the surface quality of processed workpieces. This article introduces an innovative method for compensating trajectory errors. Initially, a strategy for error compensation is derived using differential kinematics theory. Subsequently, a robot kinematic calibration method utilizing ring particle swarm optimization (RPSO) is proposed to address static errors in the grinding robot. Simultaneously, a method for predicting robot joint variables based on a dual-channel feedforward neural network (DCFNN) is designed to mitigate dynamic errors. Finally, a simulation platform is developed to validate the proposed method. Simulation analysis using extensive data demonstrates an 89.3% improvement in absolute position accuracy and a 74.2% reduction in error fluctuation range, outperforming sparrow search algorithm (SSA), improved mayfly algorithm (IMA), multi-representation integrated predictive neural network (MRIPNN), etc. Algorithmic comparison reveals that kinematic calibration significantly reduces the average trajectory error, while joint variable prediction notably minimizes error fluctuation. Validation through trajectory straightness testing and a 3D printing propeller grinding experiment achieves a trajectory straightness of 0.2425 mm. Implementing this method enables achieving 86.1% surface machining allowance within tolerance, making it an optimal solution for grinding robots.

中文翻译：

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基于运动学标定和关节变量预测的磨削机器人轨迹误差补偿方法


轨迹精度是评估磨削机器人动态性能的关键指标，它受刀具中心点不确定运动的影响，直接影响加工工件的表面质量。本文介绍了一种补偿轨迹误差的创新方法。最初，使用差分运动学理论推导出误差补偿策略。随后，提出了一种利用环形粒子群优化 （RPSO） 的机器人运动学标定方法来解决磨削机器人中的静态误差。同时，设计了一种基于双通道前馈神经网络 （DCFNN） 的机器人关节变量预测方法，以减轻动态误差。最后，开发了仿真平台对所提方法进行了验证。使用大量数据的仿真分析表明，绝对位置精度提高了 89.3%，误差波动范围减少了 74.2%，优于麻雀搜索算法 （SSA）、改进的蜉蝣算法 （IMA）、多表示集成预测神经网络 （MRIPNN） 等。算法比较表明，运动学标定显著降低了平均轨迹误差，而联合变量预测显著减少了误差波动。通过轨迹直线度测试和 3D 打印螺旋桨研磨实验进行验证，实现了 0.2425 毫米的轨迹直线度。采用这种方法可以在公差范围内实现 86.1% 的表面加工余量，使其成为磨削机器人的最佳解决方案。