In wide-area and multi-sites manufacturing scenarios, the mobile manipulator suffers from inadequate autonomous parking performance due to the harsh industrial environment. Instead of struggling to model various errors or calibrate multiple sensors, this paper resolves the above challenge by proposing an iterative-learning error compensation scheme that consists of offline pre-regulation and online compensation, which can improve the compensation efficiency and accommodate the error fluctuations caused by environmental fluctuations. Integrating an improved Monte-Carlo localization and eye-in-hand vision technique, an effective measurement system is firstly developed to accurately obtain the parking data without requiring superfluous facilities or cumbersome measurement. Then, after removing the data outliers utilizing the Grubbs test, offline pre-regulation is achieved to give a suitable initial value and increase the compensation convergence. To reduce the time-varying systematic errors and parking error fluctuations, online compensation is presented by offering an efficacious estimation of environmental fluctuations using fuzzy logic rules and providing an adaptive iterative-learning law. Finally, the feasibility and effectiveness of the presented compensation method are validated by extensive experiments implemented on a self-developed mobile manipulator.

在广域和多站点制造场景中，由于恶劣的工业环境，移动机械手的自动停车性能不足。为解决上述挑战，本文提出了一种由离线预调节和在线补偿组成的迭代学习误差补偿方案，从而提高了补偿效率并适应了因误差引起的波动，而不是为各种误差建模或校准多个传感器而努力。受环境波动的影响。结合改进的蒙特卡洛定位技术和手眼视觉技术，首先开发了一种有效的测量系统，可以准确获取停车数据，而无需多余的设施或繁琐的测量。然后，在使用Grubbs测试删除数据异常值之后，实现离线离线调节以给出合适的初始值并增加补偿收敛。为了减少时变的系统误差和停车误差波动，提出了在线补偿，方法是使用模糊逻辑规则对环境波动进行有效的估计，并提供自适应的迭代学习法则。最后，通过在自行开发的移动机械手上进行的大量实验验证了所提出的补偿方法的可行性和有效性。在线补偿是通过使用模糊逻辑规则提供有效的环境波动估计并提供自适应迭代学习法来提出的。最后，通过在自行开发的移动机械手上进行的大量实验验证了所提出的补偿方法的可行性和有效性。在线补偿是通过使用模糊逻辑规则提供有效的环境波动估计并提供自适应迭代学习法来提出的。最后，通过在自行开发的移动机械手上进行的大量实验验证了所提出的补偿方法的可行性和有效性。