Imagine you felt an unpleasant smell on the way home. You want to handle the problem but your motivation to determine its source and the hazard level is undermined by a self-preservation instinct. This is a suitable situation to exploit UAV (Unmanned Aerial Vehicle) services to take care of the occasion. To know more about how these services may be organised and operate, please visit a Simlab's blog on blockchain framework for drone market
In the described case, a drone or a drone fleet responsible for air measuring would receive data on the area where the smell is distinct. The area, or a polygon, may have holes. For example, drones cannot access buildings or people might feel extremely uncomfortable noticing drones circling above playgrounds. This polygon is the input for a CPP algorithm. The output is a route flying through which the drone would reach each point of the polygon with the attached equipment.
There is a bunch of reasons why it is unacceptable to make some
route that allows reaching each point of a polygon. First, drone's charge is too limited to waste it on long suboptimal routes. Second, the definition of 'long route' is a bit tricky regarding drones. While for ground vehicles it is mostly the matter of distance, a remarkably power-consuming element of routes for drones is turns. To make drone missions cheap, fast and effective, it is crucial to optimise the distance and the number of turns while path planning.