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I have researched some new features & published papers on those topics. This page shows those researches.
Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.
See PublicationThis research focuses on autonomous traversal of land vehicles through exploring undiscovered tracks and overcoming environmental barriers. Most of the existing systems can only operate and traverse in a distinctive mapped model especially in a known area. However, the proposed system which is trained by Deep Reinforcement Learning can learn by itself to operate autonomously in extreme conditions. The dynamic double deep Q-learning (DDQN) model enables the proposed system not to be confined only to known environments. The ambient environmental obstacles are identified through Faster R-CNN for smooth movement of the autonomous vehicle. The exploration and exploitation strategies of DDQN enables the autonomous agent to learn proper decisions for various dynamic environments and tracks. The proposed model is tested in a gaming environment. It shows the overall effectiveness in traversing of autonomous land vehicles in comparison to the existing models. The goal is to integrate Deep Reinforcement learning and Faster R-CNN to make the system effective to traverse through undiscovered paths by detecting obstacles.
See PublicationIn this project, we have tried to develop a system to monitor water quality data of some water bodies in Dhaka city using a small autonomous hovering boat. It is a matter of concern that the water we have access to in Bangladesh is polluted and lacks a proper monitoring system. To manage and protect water, reliable and dynamic water monitoring is a must. Water monitoring systems that exist today require human intervention and expensive methodologies. Moreover, it is difficult to gather, analyze and index water quality data using these conventional systems. This problem can be solved by automating the process and reducing the dependency on existing flawed systems. The proposed project uses a miniature boat shaped automated hovering platform for mobility in water which is guided by a GPS and compass. The device consists of several water quality sensors for on-board data collection. At the same time, a small pump is used to collect water samples for laboratory tests and further analysis. Further, the system is integrated in a communication system to autonomously store data on real time servers through internet connectivity. Being lightweight and rechargeable the portable device can be taken to remote places conveniently. Therefore, this autonomous GPS guided water monitoring system can have a positive impact on water quality monitoring as well as assessment of water quality parameters.
See PublicationIt is well known that level crossing gates are operated manually with the aid of a gate keeper. This human intervention can be prevented by means of automating the gates of railway level crossing. In situations where the train is late for some reason, the gates continue to be closed for longer periods causing dense traffic near the gates. This can be prevented as well with the usage of automation. The proposed device uses infra crimson sensors to come across the arrival and departure of trains on the railway crossing and Arduino to manipulate the opening or closing of gates. The device also uses two IR sensors to locate the advent of the train and a third IR sensor to discover the departure of the train. At the same time, when the appearance of the train is sensed, signs or warnings are provided to the passers-by indicating the arrival of the train at the track. While the second sensor detects the train, the sign turns crimson and the motor operates to shut the gate. The gate remains closed until the train absolutely moves away from the gate. As the departure of the train is detected by the usage of the IR sensor, the visitors sign turns green and the motor operates to open the gate. Therefore, automation of rail gate is a safe step towards proper transportation system.