Advancement and field evaluation of a honeysuckle harvesting robot with integrated clamping and air-suction mechanisms

Vietnam is among the fastest-growing economies in the world. A significant amount of capital has been allocated by the Vietnamese government for infrastructure projects, including the construction of road tunnels. Mining companies also excavate tens of thousands of meters of tunnels annually to sustain production. Most transportation and mining tunnels are supported with concrete, reinforced concrete, or steel arch support. Although tunnel excavation is largely mechanized using equipment such as AM-50Z, AM-45 drills, and Sandvik drilling machines, the installation of steel arch supports remains predominantly manual with limited mechanical assistance. This study proposes a supporting tool that can be integrated with Sandvik drilling machines, designed to lift and assist in the installation of steel arch support systems during tunnel construction. The design process was carried out using SolidWorks software, a lifting and clamping mechanism was modeled and optimized to handle heavy steel arches. Field evaluation indicates that the mechanism reduces reliance on manual labor while improving accuracy, efficiency, and safety.

ABSTRACTStrawberry harvesting is a labor‐intensive and time‐consuming task, and the shortage of labor resources and high production costs have made strawberry harvesting robots a focus of attention in both academia and industry. This paper presents a dual‐arm strawberry harvesting robot suitable for ridge cultivation in polytunnels. The coordinated operation of the robot's software and hardware systems enables it to automatically harvest strawberries along the ridge. To ensure the integrity of the fragile fruit, a nondestructive end‐effector was designed that can directly separate and grip the stem, avoiding contact with the fruit itself. Additionally, a method based on composite models was introduced to accurately determine the location and tilt direction of the picking points, to address the randomness in the growth direction of the stems. Furthermore, the modular five degrees‐of‐freedom robotic arm is better suited to the harvesting task requirements. Field evaluation results show that the robot achieves harvesting success rates between 98.4% and 57.4%, and destructive harvesting success rates vary between 98.4% and 75.0% across four scenarios of different complexities. Overall, the success rates for nondestructive and destructive harvesting reach 78.8% and 87.2%, respectively. The average harvesting cycle in the dual‐arm working mode is 4.5 s per berry. Through an in‐depth analysis of the results obtained, this paper also discusses the existing limitations and future research directions.

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

<abstract> <b>Abstract.</b> Apple harvesting is not only labor intensive but also a time critical task requiring the rightly skilled workforce at the right time. The lack of mechanized or automated harvesting systems threatens the future of fresh market apple production because of the decreasing availability of farm labor. Over the last several decades, researchers have evaluated various types of mechanized and automated apple harvesting systems with limited successes. No commercially viable harvesting systems are available yet, primarily because of the challenges posed by the highly unstructured and biologically driven farming environment. This paper presents the novel approaches investigated at Washington State University to overcome these challenges in automated or robotic apple harvesting. First, a machine vision system capable of identifying apples in a naturally clustered and occluded conditions was developed using an over–the–row platform. The platform with artificial lighting provided a controlled imaging environment that minimized variability in lighting conditions and also provided capability for night time operation. Then, hand picking dynamics were studied to understand optimal picking patterns and forces required to detach apples from branches. Based on this study, a grasping end–effector was designed to meet requirements for robotic harvesting. The global vision system, robotic arm, and end–effector were then integrated and evaluated in a lab environment as a proof–of–concept followed by field evaluation in a commercial orchard in Prosser, WA. Results showed a huge potential for in–field automated robotic harvesting system capable of accurately identifying, localizing, and picking fruit at relative high speed. However, significant challenges for commercial implementation still remain. Future work to address these challenges are also discussed in this paper.

This paper presents an autonomous robot capable of picking strawberries continuously in polytunnels. Robotic harvesting in cluttered and unstructured environment remains a challenge. A novel obstacle‐separation algorithm was proposed to enable the harvesting system to pick strawberries that are located in clusters. The algorithm uses the gripper to push aside surrounding leaves, strawberries, and other obstacles. We present the theoretical method to generate pushing paths based on the surrounding obstacles. In addition to manipulation, an improved vision system is more resilient to lighting variations, which was developed based on the modeling of color against light intensity. Further, a low‐cost dual‐arm system was developed with an optimized harvesting sequence that increases its efficiency and minimizes the risk of collision. Improvements were also made to the existing gripper to enable the robot to pick directly into a market punnet, thereby eliminating the need for repacking. During tests on a strawberry farm, the robots first‐attempt success rate for picking partially surrounded or isolated strawberries ranged from 50% to 97.1%, depending on the growth situations. Upon an additional attempt, the pick success rate increased to a range of 75–100%. In the field tests, the system was not able to pick a target that was entirely surrounded by obstacles. This failure was attributed to limitations in the vision system as well as insufficient dexterity in the grippers. However, the picking speed improved upon previous systems, taking just 6.1 s for manipulation operation in the one‐arm mode and 4.6 s in the two‐arm mode.

Citrus harvesting is a labor‐intensive and time‐intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus‐harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus‐harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus‐harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high‐precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end‐effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus‐harvesting robots.

To solve the problems of high labor intensity and high cost when picking mango manually, a mango picking robot system with dual robotic arms was developed to realize automatic mango picking. Firstly, the YOLOMS network was used to realize the 3D localization of picking points for single mangoes and mango clusters in unstructured environments. Secondly, a new “shearing and grasping integrated” end‐effector for non‐destructive harvesting of mangoes was designed. Then, a task division method for the workspace of the dual robotic arm harvesting robot was proposed to minimize the likelihood of collisions between dual arms. Additionally, a depth‐first picking strategy was introduced to reduce fruit damage and enhance the success rates of picking mangoes from layered canopies. Finally, a mango harvesting robotic system with dual arms was developed and integrated. The performance of the system was evaluated by field mango picking experiments. The results showed that the average recognition rate and planning success rate of the harvesting robot were 83.94% and 98.45%, respectively. In addition, the average harvesting success rate of the robot was 73.92%, and the average single‐fruit harvesting time was 8.93 s. Compared with the robot with single arm, the harvesting time was reduced by 48.38%, which indicated that the harvesting efficiency of the dual robotic arm harvesting robot was significantly improved. The average collision‐free harvesting rate with the addition of the depth‐first harvesting strategy was 91.68%, which verified the rationality and effectiveness of the dual robotic arm collaborative mango harvesting robotic system. The results provide technical support for automated mango harvesting.

An apple fruit localization system based on accurate and flexible hand-eye pose acquisition for robotic harvesting

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.

Abstract. Fresh market apple harvesting is a difficult task that relies entirely on manual labor. Much research has been done on the development of mechanical harvesting techniques. Several selective harvesting robots have been developed for research studies, but there are no commercially available robotic systems. This article discusses the design and development of a novel pneumatic 3D-printed soft-robotic end-effector to facilitate apple separation. The end-effector was integrated into a robotic system with five degrees of freedom that was designed to simplify the picking sequence and reduce costs compared to previous versions. Apples were successfully harvested using the low-cost robotic system in a commercial orchard during the fall 2017 harvest. A detachment success rate on attempted apples of 67% was achieved, with an average time of 7.3 s per fruit from separation to storage bin. By conducting this study in an orchard where problematic apples were not removed to increase the detachment success rate, current pruning and thinning practices were assessed to help lay the foundation for future studies and develop strategies for successfully harvesting apples that are difficult to detach. Keywords: Apple catching, Apples, Automated harvesting, Field experimentation, Harvesting robot, Soft-robotic gripper.

Development, integration, and field evaluation of an autonomous Agaricus bisporus picking robot

Development and field evaluation of a robotic harvesting system for plucking high-quality tea

A pineapple harvesting robot consisted of a manipulator arm, an end-actuator, a positioning identification device, a frame, a fruit box, a traveling mechanism and a control system was put forward. Four degrees of freedom, including X, Y, Z axis translations and a wrist rotation were required for pineapple picking. A cartesian-coordinate mechanism scheme for the manipulator arm was designed. A sliding-lever double-fulcrum structure was selected for the clamping mechanism of the end-actuator. The cutting device was designed. Pro/E software was applied for establishing 3-D parametric model of parts, assembling parts or components. By setting constraint relations among parts or components, general assembly model of the harvesting manipulator was built. Through several times of simulation with different motion parameters, repeated result analysis and dynamic interference check, optimized structure and working parameters of the pineapple harvesting manipulator was obtained.

Decreased availability and rising cost in labor poses a serious threat to the long‐term profitability and sustainability of the apple industry in the United States and many other countries. Harvest automation is thus urgently needed. In this paper, we present the unified system design and field evaluation of a new apple harvesting robot. The robot is mainly composed of a specially designed perception component, a four‐degree‐of‐freedom manipulator, an improved vacuum‐based soft end‐effector, and a dropping/catching component to receive and transport picked fruits. Software algorithms are developed to enable synergistic coordination of the hardware components for efficient, automated harvesting of apples in challenging orchard environments. Specifically, by integrating modified triangulation and image processing and analysis algorithms, a novel perception strategy is developed to achieve robust apple detection and precise localization. Improved planning and control algorithms are developed to guide the robot to the target positions. The performance of the robotic system was evaluated through field tests in two apple orchards with different tree architectures and foliage conditions. In the orchard where trees were young and well‐pruned, the robot achieved 82.4% successful harvesting rate. In a second, older orchard with dense, clustered branches and foliage, the robot had 65.2% successful rate. The average cycle time to harvest a fruit was approximately 6 s, which included software algorithm processing and hardware execution. Moreover, through an in‐depth analysis of the obtained results, limitations and planned future works are discussed.

Path planning is crucial for several applications, including those in industrial facilities, network traffic, computer games, and agriculture. Enabling automated path-planning methods in smart farms is essential to the future development of agricultural technology. Path planning is divided into global and local planners. Global planners are divided into different types and use well-known grid-based and sampling-based algorithms. In this paper, we propose an algorithm suitable for smart farms in combination with simultaneous localization and mapping (SLAM) technology. The characteristics of the grid-based Dijkstra algorithm, the grid-based A* algorithm, the sampling-based rapidly exploring random tree (RRT) algorithm, and the sampling-based RRT* algorithm are discussed, and an algorithm suitable for smart farms is investigated through field tests. We hypothesized path planning for an agricultural harvesting robot, a spraying robot, and an agricultural transport robot, and conducted experiments in environments with static and dynamic obstacles. In addition, the set parameters are validated experimentally. The Shapiro&#x2013;Wilk test is used to confirm the shape of the normal distribution, and the analysis of variance (ANOVA) and Kruskal&#x2013;Wallis test are performed to confirm the significance of the experimental results. Smart farms aim to minimize crop damage; thus, it is vital to reach the goal point accurately rather than quickly. Based on the results, we determined that the A* algorithm is suitable for smart farms. The results also open the possibility of reaching the correct destination in the shortest time when working in smart farms.