Robot navigation in complex environments necessitates controllers that prioritize safety while remaining performant and adaptable. Traditional controllers like Regulated Pure Pursuit, Dynamic Window Approach, and Model-Predictive Path Integral, while reliable, struggle to adapt to dynamic conditions. Reinforcement Learning offers adaptability but state-wise safety guarantees remain challenging and often absent in practice. To address this, we propose a path tracking controller leveraging the Simplex architecture. It combines a Reinforcement Learning controller for adaptiveness and performance with a high-assurance controller providing safety and stability. Our main goal is to provide a safe testbed for the design and evaluation of path-planning algorithms, including machine-learning-based planners. Our contribution is twofold. We firstly discuss general stability and safety considerations for designing controllers using the Simplex architecture. Secondly, we present a Simplex-based path tracking controller. Our simulation results, supported by preliminary in-field tests, demonstrate the controller's effectiveness in maintaining safety while achieving comparable performance to state-of-the-art methods.

翻译：在复杂环境中进行机器人导航，需要控制器在保持高性能与适应性的同时优先确保安全性。传统控制器如Regulated Pure Pursuit、Dynamic Window Approach和Model-Predictive Path Integral虽然可靠，但难以适应动态环境变化。强化学习虽具备适应能力，但在实践中往往难以提供严格的状态级安全保证。为此，我们提出一种基于Simplex架构的路径跟踪控制器。该架构将具备适应性与高性能的强化学习控制器与提供安全性和稳定性的高可信保障控制器相结合。本研究的主要目标是为路径规划算法（包括基于机器学习的规划器）的设计与评估提供安全测试平台。我们的贡献体现在两个方面：首先，系统阐述了基于Simplex架构设计控制器时需考虑的稳定性与安全性原则；其次，提出了一种基于Simplex的路径跟踪控制器。仿真结果及初步现场测试表明，该控制器在保持安全性的同时，其性能可与现有先进方法相媲美。