On the Intelligent Control Design of an Agricultural Mobile Robot for Cotton Crop Monitoring

Oliveira, Adalberto I. S.; Carvalho, Thiago; Martins, Felipe F.; Leite, Antonio C.; Figueiredo, Karla; Vellasco, Marley; Caarls, Wouter

doi:10.1109/dese.2019.00108

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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].…”

Section: Introductionmentioning

confidence: 99%

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

Thapa,

Rains,

Porter

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

Several studies on robotic cotton harvesters have designed their end-effectors and harvesting algorithms based on the approach of harvesting a single cotton boll at a time. These robotic cotton harvesting systems often have slow harvesting times per boll due to limited computational speed and the extended time taken by actuators to approach and retract for picking individual cotton bolls. This study modified the design of the previous version of the end-effector with the aim of improving the picking ratio and picking time per boll. This study designed and fabricated a pullback reel to pull the cotton plants backward while the rover harvested and moved down the row. Additionally, a YOLOv4 cotton detection model and hierarchical agglomerative clustering algorithm were implemented to detect cotton bolls and cluster them. A harvesting algorithm was then developed to harvest the cotton bolls in clusters. The modified end-effector, pullback reel, vacuum conveying system, cotton detection model, clustering algorithm, and straight-line path planning algorithm were integrated into a small red rover, and both lab and field tests were conducted. In lab tests, the robot achieved a picking ratio of 57.1% with an average picking time of 2.5 s per boll. In field tests, picking ratio was 56.0%, and it took an average of 3.0 s per boll. Although there was no improvement in the lab setting over the previous design, the robot’s field performance was significantly better, with a 16% higher picking ratio and a 46% reduction in picking time per boll compared to the previous end-effector version tested in 2022.

show abstract

Section: Introductionmentioning

confidence: 99%

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

Thapa,

Rains,

Porter

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

show abstract

“…Research interest in the field of robotics has increased as a result of the challenge of route tracking and control of wheeled mobile robots (Gupta et al., 2014; Vans et al., 2014). Path‐following problems are often associated with mobile robots in industrial and manufacturing environments and in agricultural applications (Oliveira et al., 2019). In order to realize robot path tracking based on environmental factors, driving path tracking and robot control algorithms are the keys.…”

Section: Introductionmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

View full text Add to dashboard Cite

Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross‐entropy‐based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT‐UMbot) for road cracks. Specifically, the CEAFC method uses cross‐entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT‐UMbot based on a four‐wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state‐of‐the‐art performance with high efficiency.

show abstract

“…Assim, este trabalho propõe uma nova abordagem para a navegação autônoma de um robô para aplicações de monitoramento de safras. Usando o robô desenvolvido para a área agrícola, chamado Soybot (Oliveira et al, 2019), este trabalho une esforços especificamente para automatizar o monitoramento de cultivos de soja e algodão. A estratégia de navegação utiliza técnicas de visão computacional para percorrer uma fileira da plantação e um controle ponto-aponto para efetuar a manobra de troca de fileira.…”

Section: Introdu ç ãOunclassified

Sistema de navegação autônoma para o robô agrícola Soybot

Martins

Carvalho

Ramos

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

The autonomy of agricultural machines has been an important research field for Precision Agriculture. With the use of global positioning systems (GPS), the farmer is able to maximize the yield of his land through precise sowing. For other machines to perform actions after planting, the farmer often does not have data on the exact location of each row of the plantation; therefore, just being guided by the global position is not enough. The present work proposes an approach consisting of two steps for the control of the Soybot agricultural robot: a computer vision technique for the robot to travel through the interior of a grown plantation, without damaging it; and a path planning technique so that, at the end of the plantation row, it enters in the next row. Resumo: A autonomia de máquinas agrícolas tem sido uma área de pesquisa importante para a Agricultura de Precisão. Com a utilização de sistemas de posição global (GPS), o fazendeiro consegue maximizar o rendimento de seu terreno por meio de um semeio preciso. Para que outras máquinas realizem ações após o plantio, muitas vezes o fazendeiro não possui dados da localização exata de cada fileira da plantação; logo, se guiar apenas pela posição global não é suficiente. O presente trabalho propõe uma abordagem composta por dois passos para o controle do robô agrícola Soybot: uma técnica por visão computacional para que o robô percorra o interior de uma plantação crescida, sem danificá-la; e uma técnica de planejamento de caminho para que este, ao final de uma fileira percorrida na plantação, manobre e entre na próxima fileira.

show abstract

On the Intelligent Control Design of an Agricultural Mobile Robot for Cotton Crop Monitoring

Cited by 5 publications

References 15 publications

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Sistema de navegação autônoma para o robô agrícola Soybot

Contact Info

Product

Resources

About