用混合方法控制柔性机器人形状，提升精度与稳定性。
Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach
- 结合物理模型与神经网络，捕捉各段间的空间耦合关系。
- 稳态误差降低75.5%，收敛速度提升20.5%，优于纯模型或纯学习方法。
- 适合需要高精度柔顺控制的复杂动态任务场景。
超冗余机器人具有高灵活性，适用于狭小非结构化环境。为扩展工作空间，我们构建了一种多段柔性齿轮驱动的平面型超冗余机器人。然而，柔性机构的变形导致系统不稳定，对外部与内部不确定性敏感。为此，提出一种混合式运动学引导与学习驱动的形状控制方法——SpatioCoupledNet。该神经网络采用分层设计，显式建模段间双向空间耦合，并刻画沿机器人本体的局部扰动。通过置信度门控机制融合先验运动学知识，实现模型与学习组件的自适应权衡，提升收敛性与轨迹保真度。在五段平面超冗余机器人上验证三种典型构型。实验表明，该方法持续优于解析模型与纯神经控制器：在复杂场景下，相比解析模型稳态误差降低75.5%，较数据驱动基线收敛加速20.5%。门控分析揭示状态依赖的权威融合策略——不稳定状态下偏向数据驱动预测，其余情况则依赖物理先验。最后，在动态避障任务中，机器人保持末端位置固定，实现均值误差10.47 mm的精确定位。
原文摘要 · Abstract (English)
Hyper-redundant robots offer high dexterity, making them good at operating in confined and unstructured environments. To extend the reachable workspace, we built a multi-segment flexible rack actuated planar robot. However, the compliance of the flexible mechanism introduces instability, rendering it sensitive to external and internal uncertainties. To address these limitations, we propose a hybrid kinematics-informed and learning-based shape control method, named SpatioCoupledNet. The neural network adopts a hierarchical design that explicitly captures bidirectional spatial coupling between segments while modeling local disturbance along the robot body. A confidence-gating mechanism integrates prior kinematic knowledge, allowing the controller to adaptively balance model-based and learned components for improved convergence and fidelity. The framework is validated on a five-segment planar hyper-redundant robot under three representative shape configurations. Experimental results demonstrate that the proposed method consistently outperforms both analytical and purely neural controllers. In complex scenarios, it reduces steady-state error by up to 75.5% against the analytical model, and accelerates convergence by up to 20.5% compared to the data-driven baseline. Furthermore, gating analysis reveals a state-dependent authority fusion, shifting toward data-driven predictions in unstable states, while relying on physical priors in the remaining cases. Finally, we demonstrate robust performance in a dynamic task where the robot maintains a fixed end-effector position while avoiding moving obstacles, achieving a precise tip-positioning accuracy with a mean error of 10.47 mm.
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