融合视觉伺服与深度学习，提升绳驱动连续机器人的控制精度与鲁棒性。
Hybrid Visual Servoing of Tendon-driven Continuum Robots
- 结合图像式与深度学习视觉伺服，实现平稳切换与协同优化。
- 相比纯深度学习方法，迭代时间更短、收敛更快、误差更低。
- 适合复杂动态环境，对遮挡、光照变化等干扰有强适应性。
本文提出一种新型混合视觉伺服（HVS）方法，用于控制绳驱动连续机器人（TDCRs）。HVS系统将基于图像的视觉伺服（IBVS）与基于深度学习的视觉伺服（DLBVS）相结合，克服各自局限，提升整体性能。在特征丰富的环境中，IBVS具有更高精度和更快收敛速度；而DLBVS则增强对扰动的鲁棒性并拓展工作空间。通过实现IBVS与DLBVS之间的平滑过渡，所提HVS在动态、非结构化环境中仍能有效控制。仿真与实验证明，相较于单独使用DLBVS，HVS在迭代时间、收敛速度、最终误差及运行平滑性方面均有显著改善，同时保持了在遮挡、光照变化、执行器噪声及物理冲击等挑战条件下的鲁棒性。
原文摘要 · Abstract (English)
This paper introduces a novel Hybrid Visual Servoing (HVS) approach for controlling tendon-driven continuum robots (TDCRs). The HVS system combines Image-Based Visual Servoing (IBVS) with Deep Learning-Based Visual Servoing (DLBVS) to overcome the limitations of each method and improve overall performance. IBVS offers higher accuracy and faster convergence in feature-rich environments, while DLBVS enhances robustness against disturbances and offers a larger workspace. By enabling smooth transitions between IBVS and DLBVS, the proposed HVS ensures effective control in dynamic, unstructured environments. The effectiveness of this approach is validated through simulations and real-world experiments, demonstrating that HVS achieves reduced iteration time, faster convergence, lower final error, and smoother performance compared to DLBVS alone, while maintaining DLBVS's robustness in challenging conditions such as occlusions, lighting changes, actuator noise, and physical impacts.
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