Crash course learning: an automated approach to simulation-driven LiDAR-based training of neural networks for obstacle avoidance in mobile robotics
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This paper proposes and implements a self-supervised simulation-driven approach to data collection used for training of perception-based shallow neural networks for mobile robot obstacle avoidance. In the approach, a 2D LiDAR sensor was used as an information source for training neural networks. The paper analyzes neural network performance in terms of numbers of layers and neurons, as well as the amount of data needed for reliable robot operation.
Nội dung trích xuất từ tài liệu:
Crash course learning: an automated approach to simulation-driven LiDAR-based training of neural networks for obstacle avoidance in mobile robotics
Nội dung trích xuất từ tài liệu:
Crash course learning: an automated approach to simulation-driven LiDAR-based training of neural networks for obstacle avoidance in mobile robotics
Tìm kiếm theo từ khóa liên quan:
Turkish Journal of Electrical Engineering and Computer Sciences Electrical Engineering Computer Sciences Crash course learning Autonomous mobile robots Obstacle avoidanceGợi ý tài liệu liên quan:
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