|Support type:||Scholarships in Brazil - Master|
|Effective date (Start):||March 01, 2013|
|Effective date (End):||March 31, 2014|
|Field of knowledge:||Physical Sciences and Mathematics - Computer Science|
|Principal researcher:||Roseli Aparecida Francelin Romero|
|Grantee:||Murillo Rehder Batista|
|Home Institution:||Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil|
In this project, the use of a reinforcement learning through an hierarchical neural network is proposed to manage behaviors of a robot swarm in order to enhance their collective actions to execute tasks. The chosen reinforcement model is able to balance the influence of previously implemented behaviors through interaction with the environment. Each robot has its own neural network, acquiring its knowledge individually through this interaction and also by sharing information with neighboring robots. In order do evaluate effectiveness, an escorting task is given to be done balancing two behaviors: area coverage through Centroidal Voronoi Tesselations and mainentance of a distance between a robot and a given target. To test the proposed approach, escort scenarios will be simulated in the Player/Stage environment.