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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

In many robotic exploration missions, robots have to learn specific policies that allow them to: (i) select high level goals (e.g., identify specific points of interest (POIs)), (ii) navigate (reach those POIs), (iii) and adapt to their environment (e.g., modify their behavior based on changing environmental conditions). Furthermore, those policies must be robust, scalable, and account for the physical limitations of the robots (e.g., limited battery power and computational power). In this paper we evaluate reactive and learning navigation algorithms for exploration robots that must avoid obstacles or reach specific points of interest (e.g., heat sources). Our results show that neuro-evolutionary algorithms with well designed evaluation functions can produce up to 50% better behavior than reactive algorithms in complex domains where the robot goals are to select paths that lead to seek specific POIs, while avoiding obstacles.

Abstract
1 Introduction
2 Navigation Algorithms
3 Problem Definition
4 Experiments
5 Conclusion
References
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