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| Author(s): |
Alisson Gusatti Azzolini
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação |
| Defense date: | 2011-05-13 |
| Examining board members: |
Fernando José Von Zuben;
Mauricio Fernandes Figueiredo;
Wagner Caradori do Amaral
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| Advisor: | Fernando José Von Zuben |
| Abstract | |
There is an increasing demand for advanced solutions in autonomous navigation of mobile robots. A system is presented for the synthesis and learning of controllers for such purpose. A probabilistic controller is proposed, consisting of the coupling of a partially observable Markov decision process (POMDP) with a multinomial logistic classifier. The parametrization used for the POMDP draws on an earlier proposal of robot control based on artificial immune networks, that has shown to present flexibility and knowledge representation capability in the execution of challenging autonomous navigation tasks. Learning the logistic classifier parameters is accomplished through a reinforcement learning algorithm based on policy gradient, while the POMDP parameters are learned by a likelihood maximization algorithm. Three computational experiments are performed, two of them using only the logistic classifier, and the third one using the coupling of a POMDP with a logistic classifier. The results show some strong points and drawbacks of both approaches. The work also points torwards a potential reinterpretation of the immune network based controller in terms of a probabilistic model similar to the one proposed (AU) | |
| FAPESP's process: | 09/06767-6 - Antecipatory and Developemental Mechanisms in the Synthesis of Dynamic Cognitive Networks for Autonomous Navigation |
| Grantee: | Alisson Gusatti Azzolini |
| Support Opportunities: | Scholarships in Brazil - Master |