Advanced Control Subsystem for Mobile Robotic Systems in Precision Agriculture

Abstract

This concept paper presents Mobile Agricultural Robots (MARs) for the development of precision agriculture and implicitly the smart farms through knowledge, reason, technology, interaction, learning and validation. Finding new strategies and control algorithms for MARs has led to the design of an Autonomous Robotic Platform Weed Control (ARoPWeC). The paradigm of this concept is based on the integration of intelligent agricultural subsystems into mobile robotic platforms. For maintenance activities in case of hoeing crops (corn, potatoes, vegetables, vineyards), ARoPWeC benefits from the automatic guidance subsystem and spectral analysis subsystem for differentiation and classification of the weeds. The elimination of weeds and pests is done through the Drop-on-Demand spray subsystem with multi-objective control, and for increasing efficiency through the Deep Learning subsystem.

Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning

“…In Eq. ( 3), C eq (1) , C eq (2) , C eq (3) , and C eq(4) represent the four best particles identified thus far, with C eq(ave) denoting their average. The utilization of these top four particles contributes to enhanced exploration capabilities for the EO process, while the incorporation of their average values fosters exploitation.…”

“…With the advent of the AI era, the intelligence and automation level of Autonomous Mobile Robots (AMRs) has witnessed remarkable advancements. AMRs find widespread application in cutting-edge fields like smart homes, intelligent logistics, and self-driving cars [1]. Path planning constitutes a vital component of automated mobile robot systems, tasked with generating feasible, safe, and smooth routes from starting to destination points within known or unknown environments.…”

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

“…The control systems, navigation algorithm, and objection detection algorithms utilized in various agricultural and non-agricultural robots [5][6][7][8][9][10][11][12][13] can also be adapted for cotton harvesting robots. Several research articles have focused on distinct sub-components of a robotic cotton harvesting system, such as the development of a cotton boll detection model [14][15][16][17][18][19], navigation and path planning algorithms [20][21][22][23][24][25], and end-effector designs [26][27][28][29][30].…”