Algorithms for Particle Swarms Using Global Control: Aggregation, Mapping, Coverage, Foraging, and Shape Control



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Targeted drug delivery is a promising technique to reduce the side effects of drugs by delivering them in concentrated doses using large swarms (10^16) of controllable microbots only targeting bad or infected tissue. A promising way to control small steerable microbots is by using a global control field such as the magnetic gradient of an MRI machine. In this work we develop benchmark algorithms for performing aggregation of microbots using global control. Using our findings we develop algorithms for a novel approach of mapping tissue and vascular systems without the use of harmful contrast agents in an MRI. In our work we consider a swarm of particles in a 1D, 2D, and 3D grids that can be tracked and controlled by an external agent thus building a map. We present algorithms for controlling particles using global inputs to perform: (1) Mapping, i.e., building a representation of the free and obstacle regions of the workspace; (2) Foraging, i.e., ensuring that at least one particle reaches each target location;and (3) Coverage, i.e., ensuring that every free region on the map is visited by at least one particle. Finally we also demonstrate shape control of large swarms using global control by developing an algorithm for position control.



Global Control, Uniform Control, Control algorithms, Swarms, Microbot, Shape Control, Aggregation, Mapping, Robotics


Portions of this document appear in: Mahadev, Arun V., Dominik Krupke, Jan-Marc Reinhardt, Sándor P. Fekete, and Aaron T. Becker. "Collecting a swarm in a grid environment using shared, global inputs." In 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1231-1236. IEEE, 2016.