Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electromagnetic Navigation Systems (eMNSs) are believed to have a superior actuation bandwidth, facilitating trajectory tracking and disturbance rejection. This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions. To showcase the dynamic capabilities of eMNSs, we successfully stabilize a (non-magnetic) inverted pendulum on the tip of a magnetically driven arm. Our approach employs a model-based framework that leverages Lagrangian mechanics to capture the interaction between the mechanical dynamics and the magnetic field. Using system identification, we estimate unknown parameters, the actuation bandwidth, and characterize the system's nonlinearity. To explore the limits of electromagnetic navigation and evaluate its scalability, we characterize the electrical system dynamics and perform reference measurements on a clinical-scale eMNS, affirming that the proposed dynamic control methodologies effectively translate to larger coil configurations. A state-feedback controller stabilizes the inherently unstable pendulum, and an iterative learning control scheme enables accurate tracking of non-equilibrium trajectories. Furthermore, to understand structural limitations of our control strategy, we analyze the influence of magnetic field gradients on the motion of the system. To our knowledge, this is the first demonstration to stabilize a 3D inverted pendulum through electromagnetic navigation.
Expanding the Workspace of Electromagnetic Navigation Systems Using Dynamic Feedback for Single- and Multi-agent Control
Multi-Scale Robotics Lab, ETH Zurich（多尺度机器人实验室，苏黎世联邦理工学院）；Learning and Dynamical Systems Group, Max Planck Institute for Intelligent Systems（学习与动态系统组，马克斯·普朗克智能系统研究所）
Electromagnetic Micro-Guidewire Control in Large Workspaces
Multi-Scale Robotics Lab, ETH Zurich（苏黎世联邦理工学院多尺度机器人实验室）；Medical Robotics Lab, ETH Zurich（苏黎世联邦理工学院医疗机器人实验室）；Learning and Dynamical Systems Group, Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所学习与动力系统组）
CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics
University of Texas at Austin（德克萨斯大学奥斯汀分校）；NOVA School of Science and Technology (NOVA-FCT)（新里斯本科学技术学院）；Center of Technology and Systems (UNINOVA-CTS)（技术与系统中心）；Institute for Systems and Robotics (ISR-Lisbon), Instituto Superior Técnico (IST)（系统与机器人研究所）
Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China（电子工程系，香港中文大学（深圳）沙田分校，香港，中国）；Department of Mechanical Engineering, Stanford University（机械工程系，斯坦福大学）
Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
稳定性和安全性是成功部署自动控制系统的关键属性。作为一个激励性的例子，考虑在复杂环境中的自主移动机器人导航。推广到不同操作条件的控制设计需要系统动态模型、对建模误差的鲁棒性以及满足安全性\NEWZL{constraints}，例如避免碰撞。本文提出了一种神经元常微分方程网络，从弹道数据中学习哈密顿系统的动力学。学习的哈密顿模型用于合成基于能量成形无源性的控制器，并分析其\emph{robustness}对学习的模型中的不确定性以及其\emph{safety}相对于环境施加的约束。给定一个所需的参考路径的系统，我们扩展我们的设计使用虚拟参考总督实现跟踪控制。调节器状态作为一个调节点，沿着参考路径自适应地移动，平衡系统能量水平，模型不确定性界限，以及距离安全违规，以保证鲁棒性和安全性。我们的哈密顿动力学学习和跟踪控制技术证明在\Revised{simulated hexarotor and quadrotor robots}在混乱的3D环境中导航。
Institute of Advanced Technology University of Science and Technology of China（科学技术大学先进技术学院）；School of Computer Science and Technology University of Science and Technology of China（科学技术大学计算机科学与技术学院）
