In recent years, robotic machining has become one of the most important paradigms for the machining of large and complex parts due to the advantages of large workspaces and flexible configurations. However, different configurations will correspond to very different system performances, influenced by the position-dependent properties. Therefore, the configuration optimization of robotic machining system is the key to ensure the quality of robotic operation. In response to the fact that little attention has been paid in current research to the effect of mapping model distribution differences on the optimization results, a sparse knowledge embedded configuration optimization method for robotic machining systems toward improving machining quality is proposed. The knowledge of theoretical model-based optimization in terms of stage, density and redundancy is embedded into high-fidelity data by three steps sparse and real measurement. Pre-training and domain adaptation fine-tuning strategies are used to reconstruct the real mapping model accurately. The reconstructed mapping model is re-optimized to obtain a more accurate system configuration. The effectiveness of the proposed method is verified by machining experiments on space segment parts. The proposed method reduces the absolute position error and machining error by 48.67 % and 28.73 %, respectively, compared to the current common theoretical model-based optimization. This is significant for more accurate and reliable robot system optimization. Furthermore, this work confirms the influence of mapping model distribution differences on the optimization effect, providing a new and effective perspective for subsequent research on the optimization of robotic machining system configurations.

中文翻译：

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一种提高加工质量的机器人加工系统稀疏知识嵌入配置优化方法


近年来，机器人加工由于工作空间大、配置灵活等优点，已成为大型复杂零件加工最重要的范例之一。然而，不同的配置将对应于非常不同的系统性能，受位置相关属性的影响。因此，机器人加工系统的配置优化是保证机器人作业质量的关键。针对目前研究很少关注映射模型分布差异对优化结果影响的问题，提出一种面向提高加工质量的机器人加工系统稀疏知识嵌入配置优化方法。通过稀疏和真实测量三步将基于理论模型的阶段、密度和冗余优化知识嵌入到高保真数据中。使用预训练和领域自适应微调策略来准确地重建真实的映射模型。对重建的映射模型进行重新优化，以获得更准确的系统配置。通过空间段零件的加工实验验证了该方法的有效性。与目前常见的基于理论模型的优化相比，该方法的绝对位置误差和加工误差分别降低了48.67%和28.73%。这对于更准确、更可靠的机器人系统优化具有重要意义。此外，本工作证实了映射模型分布差异对优化效果的影响，为后续机器人加工系统配置优化研究提供了新的有效视角。