Videos underlying the publication: A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots

This dataset contains the videos of the trajectories of a robot and victims in a simulated search-and-rescue scenario, the videos of experiments performed with robots in real life, and the tables with the uncertainty values used in the simulations.<br>The videos of the trajectories of a robot and victims in a simulated search-and-rescue scenario consider five different approaches for comparison purposes: our <strong>tube-based Model Predictive Control</strong> (MPC) approach; a <strong>Farrohksiar tube-based MPC</strong> approach; an <strong>A*-MPC</strong> approach; <strong>randomized MPC</strong> approach; and a <strong>Boustrophedon-motion-A*</strong> approach. The scenario consisted on a disaster building in which the robot has to explore the environment to detect 3 victims and avoid 5 static obstacles, and finally go to the exit point, while the victims move accordingly to an established crowd evacuation model.<br>The videos of experiments of our <strong>tube-based Model Predictive Control</strong> (MPC) approach with robots in real life consist of three scenarios in a lab environment, with a TurtleBot 3 Burger robot behaving as the search-and-rescue robot, an iRobot Create 3 robot behaving as the victim, and 3 static obstacles.<br>The dataset also contains the values of the uncertainties, i.e., the non-smoothness map values used for x and y coordinates.