Journal of Intelligent and Robotic Systems
A Neural Approach for Detection of Road Direction in Autonomous Navigation
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
Genetic programming for robot vision
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
An approach to beacons detection for a mobile robot using a neural network model
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Neural Network Mapping of Magnet Based Position Sensing System for Autonomous Robotic Vehicle
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Finding multiple lanes in urban road networks with vision and lidar
Autonomous Robots
An approach to beacons detection for a mobile robot using a neural network model
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Lamarckian neuroevolution for visual control in the quake II environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Backpropagation without human supervision for visual control in quake II
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
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This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks