用非线性模型预测控制提升绳驱动连续机器人的避障与精度
A Non-Linear Model Predictive Task-Space Controller Satisfying Shape Constraints for Tendon-Driven Continuum Robots
- 基于分段常曲率模型加速轨迹计算，结合反馈控制器应对误差
- 30Hz实时控制下跟踪误差更小，尤其在扰动和形状约束下表现优
- 适合需要高安全性的远程操作场景，如微创手术
绳驱动连续机器人（TDCRs）有望用于微创手术和工业检测等需进入狭小空间的场景。本文提出一种模型预测控制（MPC）方法，利用TDCRs的非线性运动学和冗余特性实现全身避障，并具备30Hz实时处理能力。关键在于采用名义分段常曲率（PCC）模型以高效计算可行轨迹，并集成局部反馈控制器应对建模不确定性和外部扰动。仿真结果显示，该MPC在位置跟踪性能上优于传统雅可比法控制器，尤其在存在扰动和用户定义的形状约束时；同时支持控制量限制。进一步在硬件原型上验证，展示了其在提升远程操作安全性方面的潜力。
原文摘要 · Abstract (English)
Tendon-Driven Continuum Robots (TDCRs) have the potential to be used in minimally invasive surgery and industrial inspection, where the robot must enter narrow and confined spaces. We propose a Model Predictive Control (MPC) approach to leverage the non-linear kinematics and redundancy of TDCRs for whole-body collision avoidance, with real-time capabilities for handling inputs at 30Hz. Key to our method's effectiveness is the integration of a nominal Piecewise Constant Curvature (PCC) model for efficient computation of feasible trajectories, with a local feedback controller to handle modeling uncertainty and disturbances. Our experiments in simulation show that our MPC outperforms conventional Jacobian-based controller in position tracking, particularly under disturbances and user-defined shape constraints, while also allowing the incorporation of control limits. We further validate our method on a hardware prototype, showcasing its potential for enhancing the safety of teleoperation tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。