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On this research, controlling the omni directional mobile robot is divided by two controls, high level control which covers positioning and path tracking, also the low-level control which covers omnidirectional kinematic, speed control, heading lock and communication. This also covers thread management, priority fixation and stack memory fixation. Program is divided into several main threads, i.e., IMU, speed controller, communication and motion thread (kinematic). Stack memory configuration for the above threads can be allotted as IMU thread with 1024 bytes of stack memory, speed control with 512 bytes, motion management with 512 bytes, communication with 128 bytes. IMU execution time lapses for 5.6uS, speed control elapses for 19.7uS, whereas motion elapses for 11uS and communication elapses for 1.2mS. Robot’s low-level control which includes robot movement kinematic (e.g., heading lock, speed control etc) is required to support the predefined system and enhance the robot sensor’s response in navigation.

Glaucoma is an eye disease that can result in permanent blindness if not detected and treated in the early stages of the disease. The worst part of Glaucoma is that it does not come up with a lot of visible symptoms, instead, it can go from the preliminary stage to a serious issue quickly. A Deep Learning (DL) model is capable of detecting the presence of Glaucoma by analyzing the image of the retina which is uploaded by the user. In this research, two DL algorithms were used to detect the presence of Glaucoma in the uploaded image. The DL algorithms include the convolutional neural network or the CNN and the transfer learning algorithm. The transfer learning algorithm is implemented by the VGG-19 model. Once two DL models were developed using the above-mentioned algorithms, the models were trained and tested using the images of the retina. The trained models are tested to find the better model. The efficiency of the model is measured based on some metrics. These metrics include the True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). Using these metrics, the true positive rate, the true negative rate, the false-positive rate, and the false-negative rate are calculated. From the above values, the DL algorithm, which is more efficient than the other one in identifying Glaucoma, can be found.

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