Agricultural Field Coverage with a Group of Mobile Robots Considering a Soil Compaction Risk and Energy Efficiency

Abstract

This article considers a dual problem of optimizing field coverage while minimizing a soil compaction and managing energy constraints of agricultural robots. The soil compaction in precision agriculture is a major challenge, as mobile robots are becoming increasingly common in field operations. A proposed optimization combines a soil compaction risk assessment with energy-efficient trajectory planning for a fleet of mobile agricultural robots. The algorithm uses a grid representation of a field, where each cell is assigned a compaction risk value using a function, which allows to cluster cells into zones with similar characteristics of the soil compaction risk. Within these zones, maximum permissible velocities of agricultural robots are determined. The Boustrophedon algorithm generates optimal coverage paths for each zone to minimize turns and ensure complete coverage. A fitness function balances multiple objectives, including soil impact, a path length, and energy constraints. To eliminate energy constraints, a genetic algorithm is used that simultaneously optimizes a placement of static charging stations and a distribution of cover paths among a tractors’ fleet. The system balances soil conservation and requirements by adapting a robot velocity to each zone. The computational experiments for various types and sizes of agricultural fields demonstrated effectiveness of the proposed approach.