Abstract
The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported.
Highlights
IntroductionUsages of unmanned aerial vehicles (UAVs) have been increasingly prominent for various applications such as surveillance, logistics, and rescue missions
In the recent years, usages of unmanned aerial vehicles (UAVs) have been increasingly prominent for various applications such as surveillance, logistics, and rescue missions.UAVs are very useful for monitoring activities which are tedious and dangerous for human intervention [33]
This paper proposes a methodology which includes an earliest available time (EAT) algorithm incorporated with particle swarm optimization (PSO) algorithm with an objective of minimizing the makespan
Summary
Usages of unmanned aerial vehicles (UAVs) have been increasingly prominent for various applications such as surveillance, logistics, and rescue missions. Tasks and other (on-demand) actions (i.e. recharge, hover, and wait-on-ground) are assigned to the UAVs at different execution times. This assignment is done in regard to the constraints exposed by the 3D positioning system, where precise position coordinates and position occupation management over time are crucial. This is the essential gap between the problem faced in this study and the state of the art of UAV applications in indoor environment. Developed a formal description of the problem in the form of a mathematical model
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