Self-Organizing Formation Algorithm for Active Elements
SRDS '02 Proceedings of the 21st IEEE Symposium on Reliable Distributed Systems
Heterogeneous Swarm Formation Control Using Bivariate Normal Functions to Generate Potential Fields
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
International Journal of Systems, Control and Communications
Queues and Artificial Potential Trenches for Multirobot Formations
IEEE Transactions on Robotics
Swarm aggregations using artificial potentials and sliding-mode control
IEEE Transactions on Robotics
Multirobot Formations Based on the Queue-Formation Scheme With Limited Communication
IEEE Transactions on Robotics
Effective robot team control methodologies for battlefield applications
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Practical Formation Control of Multiple Unicycle-Type Mobile Robots with Limited Sensing Ranges
Journal of Intelligent and Robotic Systems
Stable swarm formation control using onboard sensor information
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Active leading through obstacles using ant-colony algorithm
Neurocomputing
An Approach for Optimal Goal Position Assignment in Vehicle Formations
Journal of Intelligent and Robotic Systems
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In this paper, we present a strategy for organizing swarms of unmanned vehicles into a formation by utilizing artificial potential fields that were generated from normal and sigmoid functions. These functions construct the surface on which swarm members travel, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables that force the swarm to behave according to set constraints, formation, and member spacing. The artificial potential functions and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation and userdefined constraints. This approach is computationally efficient and scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of 10 and 40 robots that follow circle, ellipse, and wedge formations. Experimental results are included to demonstrate the applicability of the approach on a swarm of four custom-built unmanned ground vehicles (UGVs).