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Hierarchical neural network for online robot swarm learning

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Murillo Rehder Batista
Total Authors: 1
Document type: Master's Dissertation
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
Defense date:
Examining board members:
Roseli Aparecida Francelin Romero; Heloisa de Arruda Camargo; Denis Fernando Wolf
Advisor: Roseli Aparecida Francelin Romero

A growing trend among Mobile Robotics researchers is developing robot swarms, in which a decentralized robot team solves tasks by combining simple behaviors. It is convenient to have mechanisms to increase a robot systems robustness. In this work, a neural network inspired in behavioral analysis is used to make robots from a swarm to learn how to act propoerly. This network combines two innate behaviors and, according to its experience, learns with the robots mistakes how to make this combination. Each robot has access to its own independent neural network, and can share its knowledge with neighboring robots. It is expected that such architecture learns by itself when to stimulate or supress each behaviors influence as it interacts with the environment. The task chosen to evaluate the proposed system is the escorting of a mobile agent. Two behaviors are balanced to achieve an escorting behavior: maintenance of a minimum distance between a robot and the escort target and an area coverage method based on Centroidal Voronoi Tessellations. Tests were meade using the Player/Stage simulator, and they show that the robots not only are capable of adapting themselves but also are able to use the stored knowledge to improve their effectiveness in doing the desired task (AU)

FAPESP's process: 12/14820-7 - Reinforcement Learning on Robot Swarms
Grantee:Murillo Rehder Batista
Support Opportunities: Scholarships in Brazil - Master