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