Target detection, localization and safe trajectory planning of autonomous ground and aerial vehicles for coordinated search and rescue

This thesis explores distributed control strategies for autonomous vehicles, focusing on trajectory tracking and safety. The scenario considered involves both ground and aerial vehicles that must cooperate to reach a common target. A trajectory tracking controller is developed for differential-drive robots, enabling precise adherence to a reference path through model-based and optimization techniques, with validation in simulations and practical tests. A predictive safety filter is then introduced to enhance collision avoidance, modifying unsafe control inputs in real-time to ensure safe operation even in multi-vehicle settings. The study also incorporates a drone platform with object detection capabilities to identify emergencies in a controlled environment, guiding ground vehicles toward the target via optimized path planning. The proposed methods are validated in dynamic, real-time scenarios, establishing a versatile framework for autonomous navigation and safety.