通过具有松弛控制屏障函数的非线性模型预测控制增强动态环境中移动机器人导航的安全性与效率
Enhancing Mobile Robot Navigation Safety and Efficiency through NMPC with Relaxed CBF in Dynamic Environments
AI总结：
本文提出一种结合松弛控制屏障函数与非线性模型预测控制的策略，用于非完整移动机器人在动态环境中实现安全高效的避障导航，并通过仿真和实验验证了其有效性。
摘要：
本文针对非完整机器人开发了一种安全关键控制策略，以生成在动态环境中实现最优且无碰撞路径的控制信号。我们将控制综合问题表述为一个最优控制问题（OCP），该问题施加控制李雅普诺夫函数（CLF）约束以确保系统稳定性，并使用具有屏障函数松弛衰减率的控制屏障函数（CBF）施加安全关键约束。非线性模型预测控制（NMPC）与CLF和CBF集成，以确保系统安全并在短预测时域内促进最优性能，从而降低实时实现中的计算负担。此外，我们将基于欧几里得范数的避障约束纳入NMPC框架，展示了CBF方法在解决移动机器人系统的点镇定和轨迹跟踪挑战方面的优越性。通过大量仿真，所提出的控制器在各种场景下展示了在静态和动态障碍物避障方面的熟练能力。使用Husky A200机器人进行的实验验证与仿真结果一致，增强了我们提出的方法在现实场景中的适用性，显著提高了实际移动机器人应用中的计算效率和安全性。
英文摘要：
In this paper, a safety-critical control strategy for a nonholonomic robot is developed to generate control signals that result in optimal, obstacle-free paths through dynamic environments. We formulate the control synthesis problem as an Optimal Control Problem (OCP) that enforces Control Lyapunov Function (CLF) constraints for system stability as well as safety-critical constraints using Control Barrier Function (CBF) with a relaxing decay rate of the barrier function. A Nonlinear Model Predictive Control (NMPC) integrates with CLF and CBF to ensure system safety and facilitate optimal performance within a short prediction horizon, reducing the computational burden in real-time implementation. Additionally, we incorporate an obstacle avoidance constraint based on the Euclidean norm into the NMPC framework, showcasing the CBF approach's superiority in addressing mobile robotic systems' point stabilisation and trajectory tracking challenges. Through extensive simulations, the proposed controller demonstrates proficiency in static and dynamic obstacle avoidance under various scenarios. Experimental validations conducted using the Husky A200 robot align with simulation results, reinforcing the applicability of our proposed approach in real-world scenarios, notably improving the computational efficiency and safety in practical mobile robot applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
