Motion Planning of the Citrus-Picking Manipulator Based on the TO-RRT Algorithm

Lv, Cheng; Feng, Qingchun; Tang, Zuoliang; Wang, Xiangyu; Geng, Jinping; Xu, Lijia

doi:10.3390/agriculture12050581

Cited by 20 publications

(16 citation statements)

References 41 publications

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“…To further verify the effectiveness of the gripper harvesting, the tests for the three cases of rigid fingers and soft fingers with or without slip detection under the gripper structure of this study were carried out, as shown in When the slip detection is turned off, we define the visible slippage as the fruit that is about to slide to the fingertips of the gripper or that has already broken from the gripper (the same as below). 2 Because the fruit damage due to slippage in the gripper and due to grasping are quite different in character, we can distinguish them more easily (the same as below). 1 With slip detection on, the second slippage of the fruit means that the picking has failed.…”

Section: Of 26mentioning

confidence: 99%

“…The high labor cost in the harvesting stage limits the fruit industry's development. With this backdrop, fruit-picking robots have become a hotspot for study in related fields [1,2]. Researchers have completed several projects and made significant progress in important technologies such as robot perception and positioning [3,4], system integration [5], and efficient harvesting end effector design.…”

Section: Introductionmentioning

confidence: 99%

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A Soft Gripper Design for Apple Harvesting with Force Feedback and Fruit Slip Detection

Chen

Yan

et al. 2022

Agriculture

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This research presents a soft gripper for apple harvesting to provide constant-pressure clamping and avoid fruit damage during slippage, to reduce the potential danger of damage to the apple pericarp during robotic harvesting. First, a three-finger gripper based on the Fin Ray structure is developed, and the influence of varied structure parameters during gripping is discussed accordingly. Second, we develop a mechanical model of the suggested servo-driven soft gripper based on the mappings of gripping force, pulling force, and servo torque. Third, a real-time control strategy for the servo is proposed, to monitor the relative position relationship between the gripper and the fruit by an ultrasonic sensor to avoid damage from the slip between the fruit and fingers. The experimental results show that the proposed soft gripper can non-destructively grasp and separate apples. In outdoor orchard experiments, the damage rate for the grasping experiments of the gripper with the force feedback system turned on was 0%; while the force feedback system was turned off, the damage rate was 20%, averaged for slight and severe damage. The three cases of rigid fingers and soft fingers with or without slip detection under the gripper structure of this study were tested by picking 25 apple samples for each set of experiments. The picking success rate for the rigid fingers was 100% but with a damage rate of 16%; the picking success rate for soft fingers with slip detection was 80%, with no fruit skin damage; in contrast, the picking success rate for soft fingers with slip detection off increased to 96%, and the damage rate was up to 8%. The experimental results demonstrated the effectiveness of the proposed control method.

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Section: Of 26mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Soft Gripper Design for Apple Harvesting with Force Feedback and Fruit Slip Detection

Chen

Yan

et al. 2022

Agriculture

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“…Meta-heuristic algorithms include the ant colony algorithm [ 4 ], particle swarm algorithm [ 5 ], and gray wolf algorithm [ 6 ]. From the aspect of traditional algorithms, Liu et al [ 7 ] proposed a time-optimal rapidly-exploring random tree (TO-RRT) algorithm for the path planning of citrus picking robots. The method controls the target offset probability of the random tree through the potential field.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Zhang

Wang

et al. 2023

PLoS ONE

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Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.

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“…Currently, citrus color approximation and prediction are based on the experience of investigators, which lacks accuracy and is constrained to those with such capability. Advances in machine vision techniques have attracted a lot of attention because of their nondestructive and objective nature, and they have been successfully deployed for a variety of precision agriculture tasks including the characterization of plant varieties [ 7 – 11 ], robotic harvesting [ 12 , 13 ], and diseases diagnosis [ 14 , 15 ]. Jiménez-Cuesta et al.…”

Section: Introductionmentioning

confidence: 99%

Predicting and Visualizing Citrus Color Transformation Using a Deep Mask-Guided Generative Network

Bao

Chen

et al. 2023

Plant Phenomics

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Citrus rind color is a good indicator of fruit development, and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest schedules. This work presents the complete workflow to predict and visualize citrus color transformation in the orchard featuring high accuracy and fidelity. A total of 107 sample Navel oranges were observed during the color transformation period, resulting in a dataset containing 7,535 citrus images. A framework is proposed that integrates visual saliency into deep learning, and it consists of a segmentation network, a deep mask-guided generative network, and a loss network with manually designed loss functions. Moreover, the fusion of image features and temporal information enables one single model to predict the rind color at different time intervals, thus effectively shrinking the number of model parameters. The semantic segmentation network of the framework achieves the mean intersection over a union score of 0.9694, and the generative network obtains a peak signal-to-noise ratio of 30.01 and a mean local style loss score of 2.710, which indicate both high quality and similarity of the generated images and are also consistent with human perception. To ease the applications in the real world, the model is ported to an Android-based application for mobile devices. The methods can be readily expanded to other fruit crops with a color transformation period. The dataset and the source code are publicly available at GitHub.

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Motion Planning of the Citrus-Picking Manipulator Based on the TO-RRT Algorithm

Cited by 20 publications

References 41 publications

A Soft Gripper Design for Apple Harvesting with Force Feedback and Fruit Slip Detection

A Soft Gripper Design for Apple Harvesting with Force Feedback and Fruit Slip Detection

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Predicting and Visualizing Citrus Color Transformation Using a Deep Mask-Guided Generative Network

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