Coverage Path Planning in Precision Agriculture: Algorithms, Applications, and Key Benefits

TL;DR

This paper reviews coverage path planning algorithms for precision agriculture, demonstrating methods for single and multiple robots to optimize field coverage, data collection, and farm management.

Contribution

It introduces a wavefront coverage algorithm for single robots and a two-step multi-robot division and path generation approach for efficient field coverage.

Findings

Single robot coverage path optimized with wavefront algorithm

Multi-robot division of field into convex polygons for efficient coverage

Proposed methods reduce coverage time and improve data collection

Abstract

Coverage path planning (CPP) is the task of computing an optimal path within a region to completely scan or survey an area of interest using one or multiple mobile robots. Robots equipped with sensors and cameras can collect vast amounts of data on crop health, soil conditions, and weather patterns. Advanced analytics can then be applied to this data to make informed decisions, improving overall farm management. In this paper, we will demonstrate one approach to find the optimal coverage path of an agricultural field using a single robot, and one using multiple robots. For the single robot, we used a wavefront coverage algorithm that generates a sequence of locations that the robot needs to follow. For the multi-robot approach, the proposed approach consists of two steps: dividing the agricultural field into convex polygonal areas to optimally distribute them among the robots, and generating an optimal coverage path to ensure minimum coverage time for each of the polygonal areas.