串行模块化软体机器人的模块数量自适应视觉形状控制
Module Number Adaptive Visual Shape Control for Serial Modular Soft Robots
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中文总结 AI 辅助
本文针对串行模块化软体机器人，提出仅用单模块数据训练的控制器，通过分解图像为模块块、结合几何数据增强与轻量掩码网络，实现了跨1-5模块及环境变化下的形状控制，无需特定构型数据。
中文摘要 AI 辅助
基于图像的形状控制为软体机器人的整体构型控制提供了一种简便方法，但现有数据驱动方法通常针对固定机器人结构开发，当模块数量变化时需要新的控制数据。本文提出一种针对串行模块化气动软体机器人的模块数量自适应视觉形状控制方法，仅基于单模块驱动形状数据训练的控制器，通过将全身相机图像分解为局部模块块，可复用至1至5个模块的机器人。单个通用模块分割器可在所有测试构型中定位单个模块，相同的局部控制器应用于每个提取的块；几何数据增强提升了向下游模块的可迁移性，轻量掩码重建网络可重建合成移除的执行器掩码通道。物理机器人实验表明，该方法可在模块数量变化、环境变化及负载条件下实现形状控制，结果显示单模块控制学习可实现可扩展的整体控制，无需收集特定构型的控制数据。
英文摘要
Image based shape control provides a simple means of controlling the whole body configuration of soft robots. However, existing data driven approaches are typically developed for fixed robot structures and require new control data when the number of modules changes. This paper presents a module number adaptive visual shape control method for serial modular soft pneumatic robots. A controller trained only on single module actuation shape data is reused for robots with one to five modules by decomposing whole body camera images into local module patches. A single common module segmenter localizes individual modules across all tested configurations, while the same local controller is applied to every extracted patch. Geometric data augmentation improves transferability to downstream modules, and a lightweight mask reconstruction network reconstructs a synthetically removed actuator mask channel. Experiments on physical robots demonstrate shape control across varying numbers of modules and under environmental changes and payload loading. The results show that single module control learning enables scalable whole body control without configuration specific control data collection.
发表机构
- Graduate School of Engineering Science, The University of Osaka（大阪大学工程科学研究生院）
- Faculty of Science and Technology, Sophia University（上智大学理工学部）
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