Robot navigation in complex environments necessitates controllers that are adaptive and safe. 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 lacks formal safety guarantees. 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 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.
View on arXiv@article{jäger2025_2503.10559,
title={ Towards Safe Path Tracking Using the Simplex Architecture },
author={ Georg Jäger and Nils-Jonathan Friedrich and Hauke Petersen and Benjamin Noack },
journal={arXiv preprint arXiv:2503.10559},
year={ 2025 }
}