A deep reinforcement learning-based multi-agent area coverage control for smart agriculture

Document Type

Article

Source of Publication

Computers & Electrical Engineering

Publication Date

7-1-2022

Abstract

Precision agriculture (PA) is a collage of strategies and technologies to optimize operations and decisions in farms by using spatial and temporal variabilities in yield, crops, and soil within an agricultural plot. It is a data-driven technique, therefore, selective treatment of crops and soil, and managing variabilities using robots and smart sensors is the next improvement in PA. In this paper, it is modeled as a multi-agent patrolling problem, where robots visit subregions that required immediate attention in the agricultural field. Furthermore, for area coverage / patrolling task in the agricultural plot, a centralized Convolutional Neural Network (CNN) based Dual Deep Q-learning (DDQN) is proposed. A customized reward function is designed, which rewards worth-visiting idle regions, and punishes undesirable actions. A proposed algorithm has been compared with various algorithms including individual Q-learning (IRL), uniform coverage (UC), and Behavior-Based Robotics coverage (BBR) for different scenarios in the agricultural plots.

ISSN

0045-7906

Publisher

Elsevier BV

Volume

101

First Page

108089

Last Page

108089

Disciplines

Computer Sciences

Keywords

Area coverage, Smart sensors, Deep reinforcement learning, Smart agriculture, Precision agriculture, Multi-robotics systems, Internet of agricultural things (IoAT)

Scopus ID

85131117051

Indexed in Scopus

yes

Open Access

no

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