Behavior tree for autonomous drones in smart manufacturing

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Unmanned aerial vehicles (UAV) have been successfully applied to many outdoor tasks: surveillance, security, transportation, entertainment, etc. for a long time commercially and scientifically. Recently, the focus of UAVs operating inside warehouses has increased in both academia and industry. With the coming smart automation trend, drones will play the same role as autonomous mobile platforms, manipulators in improving operational productivity in factories. UAVs utilize the aerial space to perform the necessary tasks, such as monitoring, inventory management, inspection and maintenance, to maximize the plant's productivity.There are 3 main research challenges for development and deployment of UAVs in smart factories: (1) The autonomous operation of a a multiple UAV system in an information-rich network environment; (2) The safeness of the UAVs system with respect to people and incidents; (3) The optimization of the efficiency of the UAVs system. The drones should be able to change the operating mode according to real situations and handle them quickly. When operating in a smart factory, the amount of information exchanged between systems is rapidly enhanced. Taking advantage of this makes multi-UAV systems suitable for solving these problems. To accelerate the development of an autonomous multi-drone system in smart factory environments maximising productivity, this research aims to develop a planning tool for autonomous systems of unmanned aerial vehicles based on the behavior tree framework. The proposed research will contribute to the topic of drone system control in a smart factory environment. In this environment, the amount of information is exchanged quickly. This creates certain advantages as well as disadvantages that need to be taken into account when putting the drone system into operation. The study focuses on solving 3 specific problems and will have the following expected scientific contributions: (1) A planning framework for a system of agents (drones and servers) to perform tasks in an autonomous manner; (2) Extending and applying safety elements to the planning tool for a multi-agents system and (3) Developing safety and autonomy-based reinforcement learning for the planning framework.This project is strongly aligned with Warwick's Global Research Priorities on Innovative Manufacturing and Future Materials (especially Industry 4.0) and the sustainable development strategies in WMG, including Digital Technologies, Materials and Manufacturing, and Energy. This fits well within the research themes led by the supervisors, consequently, will boost their leadership in the fields.