Coverage Path Planning for Spraying Drones

TL;DR

This paper introduces a novel coverage path planning algorithm for spraying drones that models realistic spray dispersion and efficiently navigates bounded environments, demonstrated through simulations for disinfection and other applications.

Contribution

It presents a new sprayer model and an efficient planning algorithm that improves coverage and collision avoidance for spraying drones in bounded scenes.

Findings

The algorithm covers more area than existing methods.

Simulation results validate the effectiveness of the approach.

Applicable to various spraying tasks beyond disinfection.

Abstract

The pandemic by COVID-19 is causing a devastating effect on the health of global population. There are several efforts to prevent the spread of the virus. Among those efforts, cleaning and disinfecting public areas have become important tasks. In order to contribute in this direction, this paper proposes a coverage path planning algorithm for a spraying drone, a micro aerial vehicle that has mounted a sprayer/sprinkler system, to disinfect areas. In contrast with planners in the state-of-the-art, this proposal presents i) a new sprayer/sprinkler model that fits a more realistic coverage volume to the drop dispersion and ii) a planning algorithm that efficiently restricts the flight to the region of interest avoiding potential collisions in bounded scenes. The drone with the algorithm has been tested in several simulation scenes, showing that the algorithm is effective and covers more areas with respect to other approaches in the literature. Note that the proposal is not limited to disinfection applications, but can be applied to other ones, such as painting or precision agriculture.