UAV Route Planning to Anticipate the COVID-19 Crowd Clusters with Dynamic Programming

Crowds are considered trivial by the community because they feel they have implemented health protocols by wearing masks. Crowds must be minimized so that the spread of the Covid-19 virus does not get higher. This paper aims to plan a UAV shuttle route so that it can approach as many locations as possible with potential crowding while simultaneously leading to the destination of the flight route without having to go around first. The method used is a comparison of Greedy algorithms and Dynamic Programs in determining the most effective route. The flight simulation was carried out using Software in The Loop (SITL) and ArduPilot Mission Planner. The results obtained are that the Dynamic Program can visit 14 locations out of 18 existing location choices, whereas with the Greedy algorithm approach, UAV can only visit 8 locations out of 18 existing location choices. The conclusion is that the Dynamic Program is able to maximize routes so that more locations are visited by UAVs and certainly better than the Greedy Algorithm.