Learning for Microrobot Exploration: Model-based Locomotion, Sparse-robust Navigation, and Low-power Deep Classification
| Autor: | Brian Liao, Nathan Lambert, Farhan Toddywala, Kristofer S. J. Pister, Eric Zhu, Lydia Lee |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Scale (ratio) business.industry Computer science Simultaneous localization and mapping Thresholding Data modeling Upsampling Computer Science - Robotics Reinforcement learning Robot Computer vision Artificial intelligence Visual odometry business Robotics (cs.RO) |
| Popis: | Building intelligent autonomous systems at any scale is challenging. The sensing and computation constraints of a microrobot platform make the problems harder. We present improvements to learning-based methods for on-board learning of locomotion, classification, and navigation of microrobots. We show how simulated locomotion can be achieved with model-based reinforcement learning via on-board sensor data distilled into control. Next, we introduce a sparse, linear detector and a Dynamic Thresholding method to FAST Visual Odometry for improved navigation in the noisy regime of mm scale imagery. We end with a new image classifier capable of classification with fewer than one million multiply-and-accumulate (MAC) operations by combining fast downsampling, efficient layer structures and hard activation functions. These are promising steps toward using state-of-the-art algorithms in the power-limited world of edge-intelligence and microrobots. 6 pages; 2 pages appendices |
| Databáze: | OpenAIRE |
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