Source-seeking simulations for robot swarms

This repository contains a collection of Python simulations we use to numerically validate and test our source-seeking algorithms for swarms before moving to our robotic platforms.

• source_seeking_2D: Resilient source-seeking algorithm for robot swarms in 2D.
• source_seeking_3D: PD controller in SO(3) to align robot swarms with the ascending direction given by our resilient source-seeking algorithm.
• source_seeking_distr: A distributed consensus algorithm for robot swarms to estimate the ascending direction given by our source-seeking algorithm.

Research Journal Paper

ABSTRACT:

@misc{jbautista2025distributedss,
  title={Fully distributed and resilient source seeking for robot swarms}, 
  author={Jesus Bautista and Antonio Acuaviva and Jose Hinojosa and Weijia Yao and Juan Jimenez and Hector Garcia de Marina},
  year={2025},
  url={},
}

Research Conference Paper

ABSTRACT: We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from field strength measurements at the robot locations and their relative positions concerning the swarm centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the algorithm’s robustness by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots with a generic formation always calculate an ascending direction compensates for the potential loss of individuals.

@misc{acuaviva2024resilientsourceseekingrobot,
  title={Resilient source seeking with robot swarms}, 
  author={Antonio Acuaviva and Jesus Bautista and Weijia Yao and Juan Jimenez and Hector Garcia de Marina},
  year={2024},
  url={https://arxiv.org/abs/2309.02937},
}

This paper was presented in IEEE Conference on Decision and Control (CDC) 2024.

Technical Note

This technical note technical note aims to introduce geometric controllers to roboticist for aligning 3D robots with non-constant 3D vector fields. This alignment entails the control of the robot’s 3D attitude. We derive with excessive detail all the calculations needed for the analysis and implementation of the controllers.

@misc{bautista2024so3attitudecontrollersalignment,
  title={SO(3) attitude controllers and the alignment of robots with non-constant 3D vector fields}, 
  author={Jesus Bautista and Hector Garcia de Marina},
  year={2024},
  url={https://arxiv.org/abs/2406.14998}, 
}

We strongly recommend referring to this technical note for a comprehensive understanding of the mathematical theory underlying the code in source_seeking_3D.