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In this paper, a machine learning based approach is introduced to estimatependubot angular position from its captured images. Initially, a baselinealgorithm is introduced to estimate the angle using conventional imageprocessing techniques. The baseline algorithm performs well for the cases thatthe pendubot is not moving fast. However, when moving quickly due to a freefall, the pendubot appears as a blurred object in the captured image in a waythat the baseline algorithm fails to estimate the angle. Consequently, a DeepNeural Network (DNN) based algorithm is introduced to cope with this challenge.The approach relies on the concept of transfer learning to allow the trainingof the DNN on a very small fine-tuning dataset. The base algorithm is used tocreate the ground truth labels of the fine-tuning dataset. Experimental resultson the held-out evaluation set show that the proposed approach achieves amedian absolute error of 0.02 and 0.06 degrees for the sharp and blurry imagesrespectively.

Transfer Learning for Estimation of Pendubot Angular Position Using Deep Neural Networks