Efficient Parallel Self-Reconfiguration Algorithm for MEMS Microrobots

In this paper we propose a distributed and efficient parallel self-reconfiguration algorithm for MEMS microrobots. MEMS microrobots perform various missions and tasks in a wide range of applications including odor localization, firefighting, medical service, surveillance and security, and search and rescue. To achieve these tasks the self-reconfiguration for MEMS microrobots is required. The self-reconfiguration with shared map does not scale. Because with the map (predefined positions of the target shape) each node should store all predefined positions of the target shape, therefore this is not always possible as MEMS nodes have a low-memory capacity. In this paper, we present an efficient self-reconfiguration algorithm without predefined positions of the target shape, which reduces the memory usage to a constant complexity. This algorithm improves the energy consumption by minimizing the amount of displacement and the number of messages.