Researchers created a neuromorphic vision-to-control system for autonomous drone flights, utilizing spiking neural networks.
Researchers have developed a neuromorphic vision-to-control mechanism that makes autonomous drone flights possible.For the project, a Delft University of Technology team in the Netherlands developed a five-layer spiking neural network comprising 28,800 neurons to process raw, event-based camera data. This network maps incoming raw events to estimate the camera’s 3D motion within its environment.
However, current implementations are limited to basic tasks with low-dimensional sensory inputs and motor actions. According to researchers, this limitation arises due to the constrained network size of existing embedded neuromorphic processors and the challenges involved in training spiking neural networks.
This approach avoids the need to generate high-frequency, visually realistic images for event generation, which would otherwise result in excessively long training times in an end-to-end learning setup.The fully neuromorphic vision-to-control pipeline was implemented on Intel’s Loihi neuromorphic processor and used on afor vision-based navigation. This system autonomously followed ego-motion set points without external aids.
Neural Network Neuromorphic Neurons
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