Embedded Artificial Intelligent (AI) To Navigate Cart Follower

Tang, Khai Luen (2018) Embedded Artificial Intelligent (AI) To Navigate Cart Follower. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)

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The concern of the societies in creating a quality life for everyone without laying aside of the right of disable person leads to research on designing and fabricating autonomous robot. Wheelchair user usually faces the problem of carrying luggage along during travel as they need both of their hands to navigate their wheelchair. One of the solution for the problem is to create an Artificial Intelligent (AI) cart follower. Therefore, this research is to create an AI system for the AI cart follower with a visual based sensor. The visual based sensor gathered the information of the width, height, angle, x and y coordination of the colour pattern board which situated behind the wheelchair and translate this information into relative position information which enable the cart to follow the wheelchair. This translation can be done in neural network. However, the data needs to be collected in such a way that the output distance is manipulated between 20cm to 69cm and the output angle is manipulated between -30 to 30 with its restriction for each case. The test MSE value is used to evaluate the performance of NN and validation MSE value is used to prevent overfitting. The weights and biases generated through the training process is depended on the training algorithm, initial weights and biases for training and the dataset used in the process. The training algorithm may also vary with different sets of parameters, number of neurons and activation function. The set of parameters used in traingd are lr, max_fail, min_grad, goal, time, and epochs. The final weights and biases generated with the minimum MSE performance after several run is used to train NN in the FPGA together with the structure of NN obtained in Simulink. The implementation of neural network on the FPGA can be done through software or hardware configuration. However, the floating-point operation circuit needs to be built to ensure the NN on FPGA is functioning.

Item Type: Monograph (Project Report)
Subjects: T Technology
T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Monograph
Depositing User: Mr Engku Shahidil Engku Ab Rahman
Date Deposited: 20 Jul 2022 08:00
Last Modified: 20 Jul 2022 08:00
URI: http://eprints.usm.my/id/eprint/53484

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