Abstract

This thesis presents two algorithmic implementations of multi-robot formation control for the area coverage problem. It uses a space exploration scenario, with a marsupial robot society, for tasks like mapping, habitat construction, etc. The solutions are though applicable to a wider range of applications, since area coverage is seen as one of the canonical problems in multi-robot application. Starting with an overview of multi-robot systems in space applications, both currently in use and planned for the near future, it then presents the two algorithms and their implementation in C++: (i) a vector force based implementation and (ii) a machine learning approach. The second is based on an organizational-learning oriented classifier system (OCS) introduced by Takadama an evolution of Holland’s learning classifier system (LCS). To ease the development, testing and evaluation of the control algorithms a simulator, named SMRTCTRL, was implemented during a 3 months research stay at the University of Tokyo. The vector-based control approach was tested using a multi-robot society of LEGO Mindstorms Robots and a comparison of the two algorithm was done with the help of the simulator.