(Pakistan Institute of Engineering and Applied Science, Pakistan)(2) * Ameer Tamoor Khan
(Hong Kong Polytechnic University, Hong Kong)(3) Masood Salik
(Pakistan Institute of Engineering and Applied Sciences, Pakistan)(4) Sunila Bakhsh
(Balochistan University of Information Technology, Engineering and Management Sciences, Pakistan)*corresponding author
AbstractIn this paper, we presented an autonomous control framework for the wall following robot using an optimally configured Gated Recurrent Unit (GRU) model with the hyperband algorithm. GRU is popularly known for the time-series or sequence data, and it overcomes the vanishing gradient problem of RNN. GRU also consumes less memory and is computationally more efficient than LSTMs. The selection of hyper-parameters of the GRU model is a complex optimization problem with local minima. Usually, hyper-parameters are selected through hit and trial, which does not guarantee an optimal solution. To come around this problem, we used a hyperband algorithm for the selection of optimal parameters. It is an iterative method, which searches for the optimal configuration by discarding the least performing configurations on each iteration. The proposed HP-GRU model is used on a dataset of SCITOS G5 robots with 24 sensors mounted. The results show that HP-GRU has a mean accuracy of 0.9857 and a mean loss of 0.0810, and it is comparable with other deep learning algorithms. KeywordsWall Following Robot; GRU; Hyperband; Hyper-parameters; RNN; Robotics; Control
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DOIhttps://doi.org/10.31763/ijrcs.v1i1.281 |
Article metrics10.31763/ijrcs.v1i1.281 Abstract views : 10010 | PDF views : 1182 |
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International Journal of Robotics and Control Systems
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