Autonomous Navigation Rescue Vehicles: Measuring and Analyzing Trajectories with an Experimental Robot

The efficiency of autonomous vehicles largely depends on precise trajectory planning and execution, which has significant implications for humanitarian applications such as disaster response, medical supply delivery, and environmental monitoring. This study presents an experimental analysis of trajectory optimization using a small-scale robotic vehicle based on the JetRacer platform. The experiment evaluates the impact of key parameters such as speed, steering restrictions, and starting position on the ability of the vehicle to navigate curves with minimal deviation and optimal travel time. A vision based control system processes real time data, guiding the vehicle navigation through a trained neural network model. A total of 36 experimental iterations were conducted, capturing and analyzing trajectory data using video tracking software. The results provide insights into the influence of control parameters on navigation performance, offering a foundation for refining trajectory planning strategies in autonomous vehicles. These findings contribute to the development of more efficient autonomous systems that can improve mobility solutions in humanitarian efforts, such as emergency logistics, rescue operations, and sustainable transport in underserved communities.