The DOI displayed 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/c82c6bd7-f3d3-49e2-80d4-81025769f85b

Tsolakis, Anastasios (2025): Data underlying the project: Cooperation of Autonomous Vehicles in Mixed Traffic (SCoop). Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/c82c6bd7-f3d3-49e2-80d4-81025769f85b.v1

Categories

• Mechanical Engineering
• Water Transport
• Maritime Engineering
• Engineering
• Transport
• Technology

Keywords

autonomous surface vehicles autonomous vehicles fault diagnosis model predictive control motion planning

Time coverage

01/12/2020 - 01/12/2024

Licence

CC BY 4.0

Export as...

RefWorks BibTeX Reference Manager Endnote DataCite NLM DC CFF

by Anastasios Tsolakis

This data includes technical notes and results from my work at TU Delft.

I have attached a zipped folder "projects" which includes all the different projects/publications I worked on:

COLREGs-aware Trajectory Optimization for Autonomous Surface Vessels

Model Predictive Trajectory Optimization for Autonomous Vessels Considering Traffic Rules

Active Thruster Fault Diagnosis for an Overactuated Autonomous Surface Vessel

Set-Membership Estimation for Fault Diagnosis of Nonlinear Systems

For each project/publication, the folder includes the content of the work (text, notes, figures, plots, etc.). These publications align with the goal of our project (SCoop): The goal of the SCoop project is to design a cooperation framework to allow autonomous vehicles to safely navigate in mixed traffic even in the presence of faults. The results are generated from simulation experiments using ROS (Robot Operating System) and were collected in .rosbag files (to be further processed and generate plots).

The code that generated the data is available on Github: https://github.com/ttsolakis

History

• 2025-01-30 first online, published, posted

Publisher

4TU.ResearchData

Format

documents/.pdf, figures/.svg, text/.txt, videos/.mp4

References

• https://github.com/ttsolakis

Organizations

TU Delft, Faculty of Mechanical Engineering, Department of Cognitive Robotics, Learning & Autonomous Control