The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/22270498

Baglioni, Mirko (2024): Videos underlying the publication: A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/22270498.v1

Dataset

• Other Engineering
• Engineering

Coverage path planning, Disaster robotics, Model predictive control, Robots in search and rescue operations, Robust control, Tube-based model predictive control

CC BY-NC-ND 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Mirko Baglioni

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.

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 tube-based Model Predictive Control (MPC) approach; a Farrohksiar tube-based MPC approach; an A*-MPC approach; randomized MPC approach; and a Boustrophedon-motion-A* 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.

The videos of experiments of our tube-based Model Predictive Control (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.

The dataset also contains the values of the uncertainties, i.e., the non-smoothness map values used for x and y coordinates.

• 2024-09-27 first online, published, posted

4TU.ResearchData

AVI, MP4, CSV

A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots

• This research has been supported jointly by the TU Delft AI Labs program - as a part of the AI*MAN lab research - and by the NWO Talent Program Veni project "Autonomous drones flocking for search-and-rescue" (18120), which has been financed by the Netherlands Organisation for Scientific Research (NWO).

TU Delft, Faculty of Aerospace Engineering, Department of Control and Operations