Evolving mobile robots in simulated and real environments
Artificial Life
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Anytime learning and adaptation of structured fuzzy behaviors
Adaptive Behavior - Special issue on environment structure and behavior
Module-Based Reinforcement Learning: Experiments with a Real Robot
Machine Learning - Special issue on learning in autonomous robots
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Improving Simple Linguistic Fuzzy Models by Means of the Weighted COR Methodology
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A fuzzy temporal rule-based velocity controller for mobile robotics
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
A reinforcement learning adaptive fuzzy controller for robots
Fuzzy Sets and Systems - Theme: Modeling and control
International Journal of Intelligent Systems
Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary learning of a fuzzy controller for wall-following behavior in mobile robotics
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Design of a fuzzy controller in mobile robotics using genetic algorithms
Applied Soft Computing
International Journal of Intelligent Systems
Novel fuzzy logic control based on weighting of partially inconsistent rules using neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Handling of inconsistent rules with an extended model of fuzzy reasoning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Evolutionary behavior learning for action-based environment modeling by a mobile robot
Applied Soft Computing
An overview on soft computing in behavior based robotics
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Fuzzy temporal rules for mobile robot guidance in dynamicenvironments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
A two-stage evolutionary process for designing TSK fuzzy rule-basedsystems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Quick Design of Fuzzy Controllers With Good Interpretability in Mobile Robotics
IEEE Transactions on Fuzzy Systems
Hi-index | 12.05 |
Service robots will play an increasing and more important role in the society in the next years. One of the main challenges is to endow robots with enough autonomy to operate on real environments. To reach that goal, the design of controllers to solve simple tasks must be automatized. Engineers look for learning algorithms that are general, robust, require low expertise knowledge, and generate controllers that can run on the real robot without any tuning stage. In this paper, a framework to learn behaviors (controllers) in mobile robotics, fulfilling the previous requirements, has been used. The framework is based on two modules: dataset generation and a data-driven evolutionary-based learning algorithm to obtain fuzzy controllers. Nevertheless, the design of a fuzzy controller still requires the selection of the type of learning algorithm, and also to choose the value of some design parameters. In this paper we present an exhaustive study on a set of evolutionary-based data-driven learning algorithms, for learning fuzzy controllers in mobile robotics, that cover a wide range of the accuracy/interpretability trade-off. The study has also evaluated the influence of the values of all the design parameters over accuracy and interpretability. The objective is to analyze the performance of the different algorithms for the design of behaviors in mobile robotics, and to extract some general rules that can help in the process to design new behaviors. The analysis comprises two different behaviors (wall-following and moving object following) and more than 450 tests, both in simulation and on a Pioneer II AT robot. Results have shown very good performances in complex and realistic conditions for the different combinations of algorithms and parameters.