Picking Path Planning Method of Dual Rollers Type Safflower Picking Robot Based on Improved Ant Colony Algorithm

Zhang, He; Ge, Yun; Sun, Cheng; Hai-feng, Zhang; Liu, Na

doi:10.3390/pr10061213

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…During the flowering period, safflower capitula progressively blooms from the top of the main stem toward the tops of the peripheral branches. Consequently, optimizing the efficiency of safflower-harvesting robots becomes a critical endeavor [5,6].…”

Section: Introductionmentioning

confidence: 99%

Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm

Guo,

Qiu,

Gao

et al. 2024

Agriculture

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In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of safflower. Bezier curves were utilized to optimize the picking trajectory, mitigating the abrupt changes produced by the delta mechanism during operation. Furthermore, to overcome the slow convergence speed and the tendency of the ant colony algorithm to fall into local optima, a safflower harvesting trajectory planning method based on an ant colony genetic algorithm is proposed. This method includes enhancements through an adaptive adjustment mechanism, pheromone limitation, and the integration of optimized parameters from genetic algorithms. An optimization model with working time as the objective function was established in the MATLAB environment, and simulation experiments were conducted to optimize the trajectory using the designed ant colony genetic algorithm. The simulation results show that, compared to the basic ant colony algorithm, the path length with the ant colony genetic algorithm is reduced by 1.33% to 7.85%, and its convergence stability significantly surpasses that of the basic ant colony algorithm. Field tests demonstrate that, while maintaining an S-curve velocity, the ant colony genetic algorithm reduces the harvesting time by 28.25% to 35.18% compared to random harvesting and by 6.34% to 6.81% compared to the basic ant colony algorithm, significantly enhancing the picking efficiency of the safflower-harvesting robotic arm.

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Section: Introductionmentioning

confidence: 99%

Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm

Guo,

Qiu,

Gao

et al. 2024

Agriculture

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“…Safflower is a specialty cash crop globally. The cultivated area of safflower in the world is 8 × 10 5 hm 2 with a production of 6.5 × 10 5 tons [1]. Safflower filaments are the main product of safflower.…”

Section: Introductionmentioning

confidence: 99%

Improved Faster Region-Based Convolutional Neural Networks (R-CNN) Model Based on Split Attention for the Detection of Safflower Filaments in Natural Environments

Zhang,

Shi,

Xing

et al. 2023

Agronomy

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The accurate acquisition of safflower filament information is the prerequisite for robotic picking operations. To detect safflower filaments accurately in different illumination, branch and leaf occlusion, and weather conditions, an improved Faster R-CNN model for filaments was proposed. Due to the characteristics of safflower filaments being dense and small in the safflower images, the model selected ResNeSt-101 with residual network structure as the backbone feature extraction network to enhance the expressive power of extracted features. Then, using Region of Interest (ROI) Align improved ROI Pooling to reduce the feature errors caused by double quantization. In addition, employing the partitioning around medoids (PAM) clustering was chosen to optimize the scale and number of initial anchors of the network to improve the detection accuracy of small-sized safflower filaments. The test results showed that the mean Average Precision (mAP) of the improved Faster R-CNN reached 91.49%. Comparing with Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, and YOLOv6, the improved Faster R-CNN increased the mAP by 9.52%, 2.49%, 5.95%, 3.56%, and 1.47%, respectively. The mAP of safflower filaments detection was higher than 91% on a sunny, cloudy, and overcast day, in sunlight, backlight, branch and leaf occlusion, and dense occlusion. The improved Faster R-CNN can accurately realize the detection of safflower filaments in natural environments. It can provide technical support for the recognition of small-sized crops.

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“…This paper uses "cost" to indicate the distance, time, and other physical attributes of a network. It is applied in a variety of domains (both as a stand-alone model and as a subproblem in complex problems), such as transportation [1][2][3], biological networks [4], social networks [5], circuit board layout [6], and robotic search/navigation processes [7][8][9][10][11][12][13]. In recent years, the rapid development of sensing and Internet of Things (IoT) technology has allowed us to obtain environmental changes rapidly [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Improved Discrete Jaya Algorithm for Shortest Path Problems in Transportation-Related Processes

et al. 2023

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Shortest path problems are encountered in many engineering applications, e.g., intelligent transportation, robot path planning, and smart logistics. The environmental changes as sensed and transmitted via the Internet of Things make the shortest path change frequently, thus posing ever-increasing difficulty for traditional methods to meet the real-time requirements of many applications. Therefore, developing more efficient solutions has become particularly important. This paper presents an improved discrete Jaya algorithm (IDJaya) to solve the shortest path problem. A local search operation is applied to expand the scope of solution exploration and improve solution quality. The time complexity of IDJaya is analyzed. Experiments are carried out on seven real road networks and dense graphs in transportation-related processes. IDJaya is compared with the Dijkstra and ant colony optimization (ACO) algorithms. The results verify the superiority of the IDJaya over its peers. It can thus be well utilized to meet real-time application requirements.

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Picking Path Planning Method of Dual Rollers Type Safflower Picking Robot Based on Improved Ant Colony Algorithm

Cited by 10 publications

References 5 publications

Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm

Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm

Improved Faster Region-Based Convolutional Neural Networks (R-CNN) Model Based on Split Attention for the Detection of Safflower Filaments in Natural Environments

An Improved Discrete Jaya Algorithm for Shortest Path Problems in Transportation-Related Processes

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