Files
Abstract
In the past decade, the increased world's population and global demand for food have motivated scientists to employ new sensing technologies and unmanned vehicles in farms for automating agricultural tasks and facilitating farms' management. One of the main purposes of precision agriculture is exploring the field, collecting data and map reconstruction for further investigations and analysis.
To achieve this goal, a significant number of research studies are focusing on developing advanced coverage control algorithms aiming to optimally distribute multi-robot systems for different applications including in area exploration, search and rescue.
In this study, a distributed algorithm integrated with the reinforcement learning method with obstacle avoidance capability is presented to deploy a group of autonomous mobile robots equipped with different sensors to navigate in the field, monitor plant rows cooperatively, and provide information on important areas.
To achieve this goal, a significant number of research studies are focusing on developing advanced coverage control algorithms aiming to optimally distribute multi-robot systems for different applications including in area exploration, search and rescue.
In this study, a distributed algorithm integrated with the reinforcement learning method with obstacle avoidance capability is presented to deploy a group of autonomous mobile robots equipped with different sensors to navigate in the field, monitor plant rows cooperatively, and provide information on important areas.