用安全架构实现机器人路径跟踪，兼顾稳定性与自适应性。
Towards Safe Path Tracking Using the Simplex Architecture
- 结合强化学习与高保障控制器，实现动态适应与安全约束
- 仿真与实地测试表明安全性提升，性能接近先进方法
- 适合需安全验证的路径规划算法开发与评估
复杂环境中的机器人导航需要在保证安全的同时具备高性能和适应性。传统控制器如受控纯追逐、动态窗口法和模型预测路径积分虽可靠，但在动态条件下适应性差。强化学习虽具适应性，但通常缺乏状态级安全保证。为此，本文提出基于Simplex架构的路径跟踪控制器，融合强化学习控制器以提升适应性与性能，同时引入高保障控制器确保安全与稳定。主要目标是为路径规划算法（包括基于机器学习的规划）提供一个安全的测试平台。贡献有二：一是讨论了使用Simplex架构设计控制器时的一般稳定性和安全性考量；二是提出一种基于Simplex的路径跟踪控制器。仿真结果及初步实地测试表明，该控制器在保持安全性的前提下，性能可媲美当前最优方法。
原文摘要 · Abstract (English)
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.
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