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Control of a legged rover for planetary exploration using embedded and evolved dynamical recurrent artificial neural networks

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5 Author(s)
A. Bursi ; Dept. of Aerosp. Eng., Politecnico di Milano, Milan ; M. Di Perna ; M. Massari ; G. Sangiovanni
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This paper presents a new method for realizing the control system of a legged rover for planetary exploration. The controller is realized using a class of dynamical recurrent artificial neural networks called CTRNN, and evolutionary algorithms. The proposed approach allows realizing the design of the controller in a modular way, decomposing the global problem into a collection of low-level tasks to be reached. The embodied dynamical neural network realized has been tested on a virtual legged hexapod called N.E.Me.Sys. The neural-controller has a high degree of robustness facing sensors noises and errors, tolerates a certain amount of degradation, but above all it allows the robot performing complex reactive behaviors, as overcoming hills and narrow valleys

Published in:

Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

Date of Conference:

24-28 July 2005