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• W2974278665 abstract "To guarantee safety in a shared work space between humans and robots, robust yet flexible robotic motion control is required. Algorithms for motion planning of complex robotic systems are too computationally expensive to enable a real-time solution on conventional hardware. We apply neuromorphic sensors and Spiking Neural Networks to create an obstacle memory of a robot's work space. We create a neuron population representing all objects of the robot's work cell except for the robot itself. Hereby, we use two sensor networks for proprioception and exteroception. Furthermore, we adapt the network to preserve older states while still reacting to new events, obtaining a correct obstacle memory at any given point in time. This is done by extending the network with further neurons and introducing a neighborhood-based structure. The system is evaluated with experiments with increasing complexity on simulated data. The results show that even though the issues with spatially not-separated objects and fast motions remain, this method of obtaining a neural memory works. Our network of spiking neurons represents a neural memory of obstacles and a robotic arm. The long-term goal of performing a reactive path planning algorithm on it makes it interesting in the context of Human-robot interaction." @default.
• W2974278665 created "2019-09-26" @default.
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• W2974278665 date "2019-08-01" @default.
• W2974278665 modified "2023-10-14" @default.
• W2974278665 title "Creating an Obstacle Memory Through Event-Based Stereo Vision and Robotic Proprioception" @default.
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• W2974278665 doi "https://doi.org/10.1109/coase.2019.8843238" @default.
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