MONODA: A Neural Modular Architecture for Obstacle Avoidance Without Knowledge of the Environment



A new technique is proposed to detect and avoid obstacles for a mobile robot in an unknown environment. The usual problem of having too much sensorial information is dealt with by using several neural networks that cooperate in the guidance of the robot. Several unknown obstacle configurations were presented to the modular networks, proving that the MONODA architecture is very effective for obstacle avoidance when there is neither a priori nor a posteriori map of the environment.


Neural networks, modularity, mobile robotics


IJCNN 2000, July 2000

Cited by

Year 2009 : 1 citations

 Machine Learning for Automated Robot Navigation in Rough
Terrain Radoslaw Sobolewski, Computing Laboratory
University of Oxford, M.Sc. in Computer Science
September 2009

Year 2006 : 1 citations

 Zou, A.-M., Hou, Z.-G., Fu, S.-Y., Tan, M.
Neural networks for mobile robot navigation: A survey
2006 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3972 LNCS, pp. 1218-1226