Autonomous Steering of Concentric Tube Robots via Nonlinear Model Predictive Control
Autonomous Steering of Concentric Tube Robots via Nonlinear Model Predictive Control
复制标题
通过非线性模型预测控制实现同心管机器人的自主转向
DOI：
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复制
发表时间：
2020
影响因子：
7.8
通讯作者：
C. Bergeles
中科院分区：
文献类型：
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作者：
Mohsen Khadem;J. O’Neill;Zisos Mitros;L. da Cruz;C. Bergeles
本文提出了一种模型预测控制器（MPC）开发的同心管机器人（CTR）的自主转向。最先进的CTR控制依赖于由CTR力学模型的局部线性化开发的差分运动学，并且不能显式地处理对机器人的关节限制或通常被称为捕捉点的不稳定配置的约束。所提出的非线性MPC明确地考虑了对机器人配置空间的约束（即，关节限制）和机器人的工作空间（即，机器人曲率上的混合边界条件）。此外，MPC通过优化未来机器人配置的基于模型的预测来计算控制决策。这样，它就避免了无法恢复的配置，即，关节限制、奇异配置和捕捉。所提出的控制器进行评估，通过模拟和实验研究与各种轨迹的日益复杂。仿真结果表明，MPC的能力，以避免奇异，同时满足机器人的机械约束。实验结果表明，我们的解决方案，使以下的轨迹无法达到国家的最先进的控制器与平均误差对应的机器人弧长为1\%$。
This article presents a model predictive controller (MPC) developed for the autonomous steering of concentric tube robots (CTRs). State-of-the-art CTR control relies on differential kinematics developed by local linearization of the CTRs mechanics model and cannot explicitly handle constraints on robot's joint limits or unstable configurations commonly known as snapping points. The proposed nonlinear MPC explicitly considers constraints on the robot configuration space (i.e., joint limits) and the robot's workspace (i.e., mixed boundary conditions on robot curvature). Additionally, the MPC calculates control decisions by optimizing the model-based predictions of future robot configurations. This way, it avoids configurations it cannot recover from, i.e., joint limits, singular configurations, and snapping. The proposed controller is evaluated via simulations and experimental studies with a variety of trajectories of increasing complexity. Simulation results demonstrate the capability of MPC to avoid singularities while satisfying robot mechanical constraints. Experimental results demonstrate that our solution enables following of trajectories unattainable by state-of-the-art controllers with mean error corresponding to $1\%$ of robot arclength.