Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
The Advantages of Evolving Perceptual Cues
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Robot Odor Localization: A Taxonomy and Survey
International Journal of Robotics Research
Robotics and Autonomous Systems
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Theoretical analysis of three bio-inspired plume tracking algorithms
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Active categorical perception of object shapes in a simulated anthropomorphic robotic arm
IEEE Transactions on Evolutionary Computation
Comparing Insect-Inspired Chemical Plume Tracking Algorithms Using a Mobile Robot
IEEE Transactions on Robotics
Chemical Plume Source Localization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.01 |
An Evolutionary Robotics (ER) approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated turbulent odor plumes. In the experiments, the simulated robot is equipped with a single chemical sensor and a wind direction sensor. Three main contributions are made. First, it is shown that the ER approach can be successfully applied to odor source localization in both low-turbulent and high-turbulent conditions. Second, it is demonstrated that a small neural network is able to successfully perform all three sub-tasks of odor source localization: (i) finding the odor plume, (ii) moving toward the odor source, and (iii) identifying the odor source. Third, the analysis of the evolved behaviors reveals two novel odor source localization strategies. These strategies are successfully re-implemented as finite state machines, validating the insights from the analysis of the neural controllers.