Industrial robots have been extensively used in industry, however, geometric errors mainly caused by connecting rod parameter error and non-geometric errors caused by deflection and friction, etc., limit its application in high-accuracy machining. Aiming at addressing these two types of errors, parametric methods for error compensation based on the kinematic model and non-parametric methods of directly establishing the mapping relationship between the actual and target poses of the robot end-effector are investigated and proposed. Currently both types of methods are mainly offline and will be no longer applicable when the pose of the end-effector in the workspace changes dramatically or the working performance of the robot degrades. Thus, to compensate the positioning error of an industrial robot during long-term operation, this research proposes an adaptive hierarchical compensation method based on fixed-length memory window incremental learning and incremental model reconstruction. Firstly, the correlation between positioning errors and robot poses is studied, a calibration sample library is created, and thus the actively evaluating mechanism of the pose mapping model is established to overcome the problem of the robot’ workspace having a differential distribution of error levels. Then, an incremental learning algorithm with fixed-length memory window and an incremental model reconstruction algorithm are designed to optimize the pose mapping model in terms of its parameters and architecture and overcome the problem that the performance degradation of the robot exacerbates the positioning error and affects the applicability of the pose mapping model, ensuring that the pose mapping model runs stably above the target accuracy level. Finally, the proposed method is applied to the long-term compensation case of a Stäubli industrial robot and a UR robot, and compared to state-of-art methods. Verification results show the proposed method reduces the position error of the Stäubli robot from 0.85mm to 0.13mm and orientation error from 0.68° to 0.07°, as well as reduces the position error of the UR robot from 2.11mm to 0.17mm, demonstrating that the proposed method works in real world scenarios and outperforms similar methods.

工业机器人在工业上得到广泛应用，但主要由连杆参数误差引起的几何误差和挠度、摩擦等引起的非几何误差限制了其在高精度加工中的应用。针对这两类误差，研究并提出了基于运动学模型的参数化误差补偿方法和直接建立机器人末端执行器实际位姿与目标位姿映射关系的非参数化方法。目前这两种方法主要是离线的，当末端执行器在工作空间中的位姿发生剧烈变化或机器人工作性能下降时将不再适用。因此，为了补偿工业机器人在长期运行过程中的定位误差，本研究提出了一种基于固定长度记忆窗口增量学习和增量模型重建的自适应分层补偿方法。首先研究定位误差与机器人位姿的相关性，建立标定样本库，建立位姿映射模型的主动评价机制，克服机器人工作空间误差水平分布差异的问题。然后，设计了固定长度内存窗口的增量学习算法和增量模型重构算法，从参数和架构两方面对位姿映射模型进行优化，克服了机器人性能退化加剧定位误差影响定位的问题。姿态映射模型的适用性，确保姿态映射模型在目标精度水平之上稳定运行。最后，将所提出的方法应用于 Stäubli 工业机器人和 UR 机器人的长期补偿案例，并与最先进的方法进行比较。验证结果表明，该方法将史陶比尔机器人的位置误差从 0.85mm 降低到 0.13mm，方向误差从 0.68°降低到 0.07°，并将 UR 机器人的位置误差从 2.11mm 降低到 0.17mm，表明所提出的方法适用于现实世界的场景，并且优于类似的方法。