Neural network for robotic control
Neural network for robotic control
An Behavior-based Robotics
Optimal Selection of Uncertain Actions by Maximizing Expected Utility
Autonomous Robots
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Sensor-based fuzzy reactive navigation of a mobile robot throughlocal target switching
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
Integration of reactive behaviors and enhanced topological map for robust mobile robot navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Journal of Intelligent and Robotic Systems
Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
The stable and precise motion control for multiple mobile robots
Applied Soft Computing
Robotic path planning using multi neuron heuristic search
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
A reactive architecture for autonomous agent navigation using fuzzy logic
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Design and implementation of a navigation system for autonomous mobile robots
International Journal of Ad Hoc and Ubiquitous Computing
Mobile robot navigation: neural Q-learning
International Journal of Computer Applications in Technology
A target-reaching controller for mobile robots using spiking neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
FPGA-Based architecture for extended associative memories and its application in image recognition
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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A hybrid control architecture combining behavior based reactive navigation and model based environment classification has been developed. It is also hybrid in the sense that both competitive coordination and cooperative coordination are used for the BBC (Behavior Based Control) part. The contributions are as follows. First, a Neural Network (NN) in charge of environment classification has been developed based on 16 prototypes of topological maps roughly describing various local navigation environments. This environment classification NN not only enables the navigator to avoid local minimum points but also eliminates the requirement for prior detailed modeling of the environment since it needs to memorize only “rough” information on local environments encountered along the way that might be sufficient for navigation. Next, an NN based reactive behavior controller will be trained to learn human steering commands for each of the 16 prototype local environments. Third, the modified potential field (MPF) method obtained by adding the free space vector as the third component is used to select a particular reactive behavior in conjunction with the classification NN. Finally, a hybrid control architecture integrating all three of these concepts was developed. It avoids local minimum traps as well as solves the problems of poor obstacle clearance or oscillation. It is robust against sensor noise and adaptive to dynamic environments. This hybrid architecture is also amenable to easy addition of new behaviors due to the modularity of the BBC architecture. The effectiveness of the proposed architecture has been verified through both computer simulation and an actual robot called MORIS (MObile Robot as an Intelligent System).