Rule-compliant and Fault-Tolerant Motion Planning: With Application to Autonomous Surface Vehicles

This thesis focuses on enabling safe and reliable navigation for Autonomous Surface Vessels (ASVs) operating in complex and mixed-traffic maritime environments. It presents a suite of motion planning and control algorithms that ensure fault tolerance and compliance with maritime traffic rules, even under uncertainty and component failures. Key contributions include a Model Predictive Contouring Control (MPCC) method that formalizes COLREGs compliance, a model-based fault diagnosis framework using residual analysis, a robust Set-Membership Estimation (SME) approach for fault parameter identification, and a Robust Adaptive Model Predictive Control (RAMPC) scheme that integrates fault information into trajectory optimization. Validated through extensive simulations and implemented in ROS, the proposed framework demonstrates robust performance in dynamic and uncertain conditions, laying the groundwork for real-world deployment of autonomous maritime systems.