Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Silicon Implementation of the Fly's Optomotor Control System
Neural Computation
Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes
Computer Vision and Image Understanding
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
Computer Vision and Image Understanding
An optical flow-based integrated navigation system inspired by insect vision
Biological Cybernetics
Research advances in intelligent collision avoidance and adaptive cruise control
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks
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In this paper, we studied the postsynaptic organisations of directional selective visual neurons for collision detection. Directional selective neurons can extract different directional visual motion cues fast and reliably by allowing inhibition spreads to further layers in specific directions with one or several time steps delay. Whether these directional selective neurons can be easily organised for other specific visual tasks is not known. Taking collision detection as the primary visual task, we investigated the postsynaptic organisations of these directional selective neurons through evolutionary processes. The evolved postsynaptic organisations demonstrated robust properties in detecting imminent collisions in complex visual environments with many of which achieved 94% success rate after evolution suggesting active roles in collision detection directional selective neurons and its postsynaptic organisations can play.