With the advancement in Industry 4.0, new technologies like parcelcopters, unmanned aerial vehicles (UAV) and drones are continuously being launched for different Internet of Things (IoT) applications. It is inferred that new application such as construction, fire and safety rescue missions will be developed in the future where drones will operate in dynamic environments, i.e., areas shared with other vehicles, machines, people, or moving items. New safety mechanisms will be essential in this novel situation to minimise dangers.
This PhD project will undergo the design of an enhanced obstacle avoidance system study that can forecast, lower or eliminate the risk of a UAV colliding with things or people that are in the path of the intended mission environment specifically indoor environments. The system then computes a change of the planned course to avoid conflict in the event of a collision prediction, optimising it in terms of departure from the planned path in terms of time and position. The key objectives of this PhD study are to minimize the time of flight and deviation from the original trajectory with reduced power and computation usage. Similarly, the presence of a camera on the UAVs can help avoid obstacles and can also assist in the search and rescue missions for the ground teams. Additionally, stability enhancement in such situations is mandatory that must be taken into consideration
The project will involve the implementation of optimal control theory, metaheuristic algorithms and machine learning techniques to design a robust obstacle avoidance mechanism. The ideal candidate will have a first class masters degree in mechatronics, electronic, electrical engineering, computer science, or related discipline. Excellent skills in programming such as Matlab and Python are essential. Strong knowledge of machine learning, good mathematical and analytical skills are prerequisite.
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