在未知系统中实现带输入限制的安全导航，实时保障路径安全。
Input-Constrained Spatiotemporal Tubes for Safe Navigation of Unknown Euler-Lagrange Systems in Dynamic Environments
- 将输入约束融入时空管控制器设计，确保控制可行
- 离线验证条件可关联控制能力与不确定性边界
- 无需近似，计算高效，适合机器人实时应用
在动态环境中进行安全导航时，若系统动力学未知且执行器输入受限，传统方法或依赖精确模型、或需在线优化、或未显式考虑输入限制。本文提出一种针对未知欧拉-拉格朗日系统的实时控制框架，可在满足执行器约束条件下，保证有限时间到达-规避-驻留（FT-RAS）规范。通过将输入约束引入时空管（STT）框架的控制器设计，并推导出可离线验证的可行性条件，该条件将可用控制能力与管状结构设计及不确定性上界相联系。所提方法无需近似，计算效率高，适用于实时部署。通过移动机器人、四旋翼和航天器的仿真以及移动机器人的硬件实验，验证了该方法在满足执行器约束下实现安全导航的有效性。
原文摘要 · Abstract (English)
Safe navigation in dynamic environments is challenging when system dynamics are unknown and actuator inputs are limited. Existing methods either rely on accurate models, require online optimization, or do not explicitly account for input constraints. This paper presents a real-time control framework for unknown Euler-Lagrange systems that guarantees finite-time reach-avoid-stay (FT-RAS) specifications while respecting actuator limits. We extend the spatiotemporal tube (STT) framework by incorporating input constraints into the controller design and derive offline-verifiable feasibility conditions that relate the available control authority to the tube design and uncertainty bounds. The resulting framework is approximation-free and computationally efficient, making it suitable for real-time implementation. The proposed approach is validated through simulations on a mobile robot, a quadrotor, and a spacecraft, together with hardware experiments on a mobile robot, demonstrating safe navigation while satisfying actuator constraints.
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