An Extensive Review of Mobile Agricultural Robotics for Field Operations: Focus on Cotton Harvesting

Abstract

In this review, we examine opportunities and challenges for 21st-century robotic agricultural cotton harvesting research and commercial development. The paper reviews opportunities present in the agricultural robotics industry, and a detailed analysis is conducted for the cotton harvesting robot industry. The review is divided into four sections: (1) general agricultural robotic operations, where we check the current robotic technologies in agriculture; (2) opportunities and advances in related robotic harvesting fields, which is focused on investigating robotic harvesting technologies; (3) status and progress in cotton harvesting robot research, which concentrates on the current research and technology development in cotton harvesting robots; and (4) challenges in commercial deployment of agricultural robots, where challenges to commercializing and using these robots are reviewed. Conclusions are drawn about cotton harvesting robot research and the potential of multipurpose robotic operations in general. The development of multipurpose robots that can do multiple operations on different crops to increase the value of the robots is discussed. In each of the sections except the conclusion, the analysis is divided into four robotic system categories; mobility and steering, sensing and localization, path planning, and robotic manipulation.

Internet of radar things for cognitive robotics

“…The robotics that performs mobile functions need to have the ability to react to changes such as in crop sizes, weather conditions, light availability, field patterns, etc. (Fue et al, 2020). Finally, we exploit enablers between the radar sensors, robotics, and IoT to shape the support of cognitive robotics (Fig.…”

Evaluation of a Stereo Vision System for Cotton Row Detection and Boll Location Estimation in Direct Sunlight

“…These massive machines are costly to maintain and expensive to own. The emergence of modern technologies in robotics provides an opportunity to explore alternative harvesting methods [1][2][3][4][5]. The introduction of rover that used image processing techniques to acquire features and perform modeling and matching, but a commercial product was not developed [28].…”

Autonomous Navigation of a Center-Articulated and Hydrostatic Transmission Rover using a Modified Pure Pursuit Algorithm in a Cotton Field

“…One alternative to current large-scale farm and machinery systems is to introduce small, multi-purpose, robotic rovers that can navigate and perform agricultural operations autonomously without the need for human–machine control. Fortunately, there is a booming industry in robotics and machine learning technologies, and robotics have been developed to solve many pertinent issues in agriculture [ 1 , 2 , 5 , 6 , 7 ].…”

“…The autonomous navigation of robotic systems depends upon four modules: sensors, vehicle mobility, perception, and control algorithms ( Figure 1 ). Sensors, such as RTK-GNSS, Red-Green-Blue (RGB) cameras, Stereo camera, Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR), Ultrasonic, Radio-frequency identification (RFID), Inertial Measurement Unit (IMU), Laser scanner, RAdio Detection And Ranging (RADAR), encoders, thermal imaging, hyperspectral, and infrared, have been used extensively to detect fruits and plants in agricultural fields ( Figure 1 ) [ 5 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Additionally, many vehicle mobility systems have been developed primarily for agricultural use, such as continuous tracks, the Ackermann four-wheel drive, center-articulated drives, legged-robots, swinging robots, omnidirectional drives, and sliding-on-the-rail robots ( Figure 1 ).…”

“…There are multiple control system methods used: fuzzy logic, nonlinear, proportional-integral-derivative (PID), adaptive, model-based, rear-wheel feedback, linear-quadratic regulator, model predictive control (MPC), and machine learning, such as neural networks and reinforcement learning. After assimilating a control system, several techniques must be incorporated to develop autonomous navigation in an unstructured environment, such as localization, mapping, obstacle avoidance, simultaneous localization, and Mapping (SLAM), row-following in row crops and path planning to perform complex farm operation movements [ 5 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Furthermore, algorithms that guide the robots to avoid obstacles should be incorporated so that the robot can work smoothly in the agricultural field while interacting with humans and other robots [ 28 , 29 , 30 ].…”