Fast Recognition and Control of Walking Mode for Humanoid Robot Based on Pressure Sensors and Nearest Neighbor Search
2018 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)(2018)
摘要
In this paper, we propose a nearest-neighbor multi-reference learning system for control of humanoid-robot movements, using real-time data from pressure sensors embedded in the robot feet, which is processed with parallelized pipeline architecture for high-speed recognition of actual surface conditions. A first nearest-neighbor (1-NN) classifier is used to recognize the most similar reference pattern in terms of the smallest Euclidean distance. Our proposed architecture achieves a classification time of about 2.4μ s with a total power consumption of 8.53mW at 100 MHz operating frequency when implemented on a low-cost FPGA (Cyclone-V GX-Series). The analysis results are further useful for a next-generation-ASIC-based AI-chip design for a robust real-time robot-learning system.
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关键词
Nearest neighbor searching (NNS),artificial neural network (ANN),pressure sensor,searching speed,power consumption,neuron (or perceptron)
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