A multi-arm robot system for efficient apple harvesting: Perception, task plan and control

Li, Tao; Xie, Feng; Zhao, Zhuoqun; Zhao, Hui; Guo, Xuhong; Feng, Qingchun

doi:10.1016/j.compag.2023.107979

Cited by 20 publications

(8 citation statements)

References 30 publications

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“…The robot was able to locate the cherries and calculate the best option for the end effector without any obstacles Besides, in the case of watermelon, the "STORK" robot showed that the researchers explored the possibility of harvesting large fruits such as melons. The feasibility of increasing the cutting cycle time of the robot by simultaneously performing multiple hand cutting operations has been investigated in [18]. Zion et al [19] primarily addresses the problem of alcohol allocation for melons in the model of melon farms.…”

Section: Related Workmentioning

confidence: 99%

Field Performance of a Dual Arm Robotic System for Efficient Tomato Harvesting

Lem,

Mackey

2024

JRS

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The robot device that is being addressed in this research has two arms: one for picking the fruit and the other for chopping it. The arms find and locate pods with the help of a complex vision system that employs cameras. In this human-robot workflow, the operator chooses the tomatoes they want picked, and then the robot does the actual picking. The robot management and communication system use the EtherCAT bus to create a link with the graphical user interface (GUI), enabling human administration and control. The objective of this project is to create and assess a robotic system for harvesting tomatoes, equipped with dual arms. This system incorporates a mobile model equipped with two robotic arms and an end effector to enhance the efficiency of tomato harvesting. The system uses a GUI to enhance interaction between the robot and the human operator. Additionally, it employs a vision model to streamline the process of fruit detection. Findings from this study demonstrate that HMI may significantly improve the accuracy of tomato harvesting robots. Finally, there were some difficulties in developing 3D models because this study included outdoor experiments.

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Section: Related Workmentioning

confidence: 99%

Field Performance of a Dual Arm Robotic System for Efficient Tomato Harvesting

Lem,

Mackey

2024

JRS

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“…Instead, they focused solely on whether there was a harvestable fruit present to make the decision. Dualarm robots required more efficient parking nodes planning and collaborative strategies compared to traditional single-arm robots and the aforementioned stopping methods [9]. There were two primary reasons for this.…”

Section: Introductionmentioning

confidence: 99%

“…Parking node planning for multi-arm robots can be viewed as a decision-making problem, akin to the multiple Traveling Salesman Problem (mTSP) or Vehicle Routing Problem (VRP). This issue has necessitated description and resolution through applications of operations research and mathematical programming techniques [9]. Barnett et al [7] devised a task distribution strategy for multi-arm kiwifruit harvesting robots, considering both task partitioning and reachability.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, some scholars have developed parking node planning algorithms using techniques such as grid maps [11], spiral-spanning tree coverage (STC) [12], area division [13], etc. These algorithms have the potential to significantly improve the working efficiency of harvesting robots [9]. However, vehicle parking node planning for multi-arm robots harvesting in multiple directions have presented challenges due to additional constraints such as task allocation, workspace, stop frequency, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Deep Reinforcement Learning (DRL), a type of machine learning algorithm where agents learn optimal behaviors through trial-and-error interactions with their environment, have demonstrated exceptional performance in robotics [9,14], path planning [2,15], and combinatorial optimization problems [16,17]. Kyaw et al [18] employed DRL based on grid maps to solve the Traveling Salesman Problem (TSP), achieving path planning for robots in large, complex environments.…”

Section: Introductionmentioning

confidence: 99%

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Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

Li,

Feng,

Zhang

et al. 2024

Biomimetics

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Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets. The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner.

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A selective harvesting robot for cherry tomatoes: Design, development, field evaluation analysis

Rong,

Hu,

Zhou

et al. 2024

Journal of Field Robotics

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With the aging population and increasing labor costs, traditional manual harvesting methods have become less economically efficient. Consequently, research into fully automated harvesting using selective harvesting robots for cherry tomatoes has become a hot topic. However, most of the current research is focused on individual harvesting of large tomatoes, and there is less research on the development of complete systems for harvesting cherry tomatoes in clusters. The purpose of this study is to develop a harvesting robot system capable of picking tomato clusters by cutting their fruit‐bearing pedicels and to evaluate the robot prototype in real greenhouse environments. First, to enhance the grasping stability, a novel end‐effector was designed. This end‐effector utilizes a cam mechanism to achieve asynchronous actions of cutting and grasping with only one power source. Subsequently, a visual perception system was developed to locate the cutting points of the pedicels. This system is divided into two parts: rough positioning of the fruits in the far‐range view and accurate positioning of the cutting points of the pedicels in the close‐range view. Furthermore, it possesses the capability to adaptively infer the approaching pose of the end‐effector based on point cloud features extracted from fruit‐bearing pedicels and stems. Finally, a prototype of the tomato‐harvesting robot was assembled for field trials. The test results demonstrate that in tomato clusters with unobstructed pedicels, the localization success rates for the cutting points were 88.5% and 83.7% in the two greenhouses, respectively, while the harvesting success rates reached 57.7% and 55.4%, respectively. The average cycle time to harvest a tomato cluster was 24 s. The experimental results prove the potential for commercial application of the developed tomato‐harvesting robot and through the analysis of failure cases, discuss directions for future work.

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A multi-arm robot system for efficient apple harvesting: Perception, task plan and control

Cited by 20 publications

References 30 publications

Field Performance of a Dual Arm Robotic System for Efficient Tomato Harvesting

Field Performance of a Dual Arm Robotic System for Efficient Tomato Harvesting

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

A selective harvesting robot for cherry tomatoes: Design, development, field evaluation analysis

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