The Swarm Systems Lab has developed a fully distributed algorithm that resembles the tornado schooling fish. 

The combination of a smooth guiding vector field, an adaptive control barrier function, and a scalable overtaking rule on the outside, provides formal guarantees to avoid collisions while the swarm of unicycle robots orbit a convex path with constant but different speeds.

J Bautista, HG de Marina. Behavioral-based circular formation control for robot swarms. In the proceedings IEEE International Conference on Robotics and Automation (ICRA) 2024.

In our Swarm Systems Lab we have addressed the source-seeking problem by a robot swarm. The distributed emergent behavior helps them navigate,  remaining resilient even with malfunctioning robots and noise in actuators and sensors. We rigorously prove the effectiveness of our algorithm even with teams of unicycles with constant speeds like fixed-wing aircraft.

A Acuaviva, J Bautista, W Yao, J Jimenez, HG de Marina. Resilient source seeking with robot swarms. Submitted to CDC 2024 (xD).

We provide formal guarantees about the consequences of imperfect sensing in a swarm of robots, and we show how to exploit the imperfections to achieve complex synchronized motions in a fully distributed way.

HG de Marina. Maneuvering and robustness issues in undirected displacement-consensus-based formation control. IEEE Transactions on Automatic Control, 2021.

HG de Marina. Distributed formation maneuver control by manipulating the complex Laplacian. Automatica, 2021.

We control formations of fixed-wing UAVs flying at different constant speeds. We provide theoretical guarantees and their experimental validations in the following papers.

Z Sun, HG de Marina, G Seyboth, BDO Anderson, C Yu. Circular formation control of multiple unicycle-type agents with non-identical constant speeds.  IEEE Transactions on Control Systems and Technology. 27 (1), 192-205. 2019.

Z Sun, HG de Marina, GS Seyboth, B Anderson, C Yu. Collaborative target-tracking control using multiple autonomous fixed-wing UAVs with constant speeds. AIAA Journal of Guidance, Control, and Dynamics, 2021.

We inject distance disagreements to create 3D motions in rigid formations. We exploit the stability and convergence properties from the theory to reject the tension forces from a heavy load.

HG de Marina, B Jayawardhana, M Cao. Taming mismatches in inter-agent distances for the formation-motion control of second-order agents. IEEE Transactions on Automatic Control.  63 (2), 449-462. 2018.

HG de Marina, E Smeur. Flexible collaborative transportation by a team of rotorcraft. In the proceedings IEEE International Conference on Robotics and Automation (ICRA) 2019.

We exploit the converge properties of a guidance vector field together with consensus algorithms to control the rendezvous of a team of aircraft filying with constant speeds.

HG de Marina, Z Sun, M Bronz, G Hattenberger. Circular formation control of fixed-wing UAVs with constant speeds. In proceedings of the International Conference on Intelligent Robots and Systems (IROS) 2017.

HG de Marina, YA Kapitanyuk, M Bronz, G Hattenberger, M Cao. Guidance algorithm for smooth trajectory tracking of a fixed wing UAV flying in wind flows. In proceedings IEEE International Conference on Robotics and Automation (ICRA) 2017.

Different imperfect sensors in robots have different perceptions of the surroundings. These disagreements have an impact on the stability of formations. We developed distributed adaptive controllers to fix this problem. Furthermore, we exploit the disagreements as control inputs to manoeuvrer the formation.

HG de Marina, M Cao, B Jayawardhana. Controlling Rigid Formations of Mobile Agents Under Inconsistent Measurements. IEEE Transactions on Robotics, 31 (1), 31-39. 2015.

HG de Marina, B Jayawardhana, M Cao. Distributed Rotational and Translational Maneuvering of Rigid Formations and Their Applications.  IEEE Transactions on Robotics, 32 (3), 684-696. 2016.