Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Machine vision
Computer Vision and Image Understanding
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Vision
Computer Vision
Using an Individual Evolution Strategy for Stereovision
Genetic Programming and Evolvable Machines
Dynamic flies: a new pattern recognition tool applied to stereo sequence processing
Pattern Recognition Letters
Dynamic Flies: Using Real-Time Parisian Evolution in Robotics
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Adaptation on the Evolutionary Time Scale: A Working Hypothesis and Basic Experiments
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
From Hough to Darwin: An Invidual Evolutionary Strategy Applied to Artificial Vision
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Integrated Surface, Curve and Junction Inference from Sparse 3-D Data Sets
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Embedded harmonic control for dynamic trajectory planning on FPGA
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
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The "Fly algorithm" is a fast artificial evolution-based image processing technique. Previous work has shown how to process stereo image sequences and use the evolving population of "flies" as a continuously updated representation of the scene for obstacle avoidance in a mobile robot. In this paper, we show that it is possible to use several sensors providing independent information sources on the surrounding scene and the robot's position, and fuse them through the introduction of corresponding additional terms into the fitness function. This sensor fusion technique keeps the main properties of the fly algorithm: asynchronous processing. no low-level image pre-processing or costly image segmentation, fast reaction to new events in the scene. Simulation test results are presented.