论文标题
在动态环境中，通过轻松的CBF通过NMPC增强移动机器人导航的安全性和效率
Enhancing Mobile Robot Navigation Safety and Efficiency through NMPC with Relaxed CBF in Dynamic Environments
论文作者
论文摘要
在本文中，开发了针对非独立机器人的安全关键控制策略，以生成控制信号，从而在动态环境中产生最佳，无障碍的路径。我们将控制综合问题作为最佳控制问题（OCP），该问题使用控制屏障函数（CBF）来控制系统稳定性以及安全关键限制，并具有屏障功能的放松衰减率。非线性模型预测控制（NMPC）与CLF和CBF集成在一起，以确保系统安全并促进短暂预测范围内的最佳性能，从而减少了实时实现的计算负担。此外，我们将基于欧几里得规范的障碍限制纳入NMPC框架中，展示了CBF方法在解决移动机器人系统的点稳定和轨迹跟踪挑战方面的优势。通过大量的模拟，提出的控制器在各种情况下都表明了静态和动态障碍的避免效果。使用Husky A200机器人与模拟结果保持一致的实验验证，从而增强了我们在现实世界中提出的方法的适用性，特别是提高了实际移动机器人应用程序的计算效率和安全性。
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.
