8 research outputs found

    Modular autonomous strawberry-picking robotic system

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    Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the elective harvesting of strawberries is a complicated robotic task forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, for example, picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g., highā€yielding and/or diseaseā€resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, for example, dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5ā€degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localize strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new data sets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

    Intelligent planning for robotic disassembly

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    Environmental concerns and demands, new legislations and rules, and material resource limitations have put pressure on production and manufacturing bodies to seek new strategies and method to meet those criteria. Remanufacturing and reusing of the End-Of-Life (EOL) products has shown a wide potential to deal with production waste and inefficiency. Manual disassembly is not efficient economically and the robotic systems are not reliable in dealing with complex disassembly operations as they have high-level uncertainty. In this research, the geometry of product components and difficulty of disassembly operations were analysed and based on that new optimisation parameters were defined to evaluate the disassemblability of the components. These include Disassembly Handling Index (DHI), Disassembly Operation Index (DOI) and Disassembly Demand Index (DDI). Genetic algorithm optimisation method was modified to find a near-optimal solution. The results using real case study products based on the proposed method shows minimum 10% improvement in manual disassembly time compare with conventional sequence planning methods. In addition, a mathematical model for peg-out-hole disassembly operation in a static equilibrium was developed to determine the relation between pegā€™s depth, and peg and hole diameter mathematically. Then, force/torque sensor mounter on a TM robot arm was employed to produce force and torque maps for peg-out-hole operation experimentally. The maps were analysed using redundancy method to estimate the peg and hole relative position. The results show the average estimation error using proposed method is 6% which can reliable for peg-out-hole operation. Finally, a disassembly planning method based on human-robot collaboration is proposed. This method employs the flexibility and ability to deal with complex tasks of human, alongside the repeatability and accuracy of the robot. PROMETHEE II method was employed to evaluate disassembly priorities based on the cleanability, repairability, and economy to target the product components based on the remanufacturability criteria. Human-robot collaboration characteristics were defined, and an algorithm was proposed to classify the disassembly task and allocate them to either human or robot. The model encoded using AND/OR method mathematically, and the genetic algorithm was employed to find a near-optimal solution for human-robot collaboration sequence planning. The results compared with Particle Swarm algorithm which shows 8.8% improvement in fitness value. Thus, the developments reported in this thesis have contributed to promoting the efficiency and reliability of the disassembly process as the key step of dealing with EOL products

    Intelligent planning using genetic algorithm for automated disassembly

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    Peduncle Gripping and Cutting Force for Strawberry Harvesting Robotic end-effector Design

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    Robotic harvesting of strawberries has gained much interest in the recent past. Although there are many innovations, they havenā€™t yet reached a level that is comparable to an expert human picker. The end effector unit plays a major role in defining the efficiency of such a robotic harvesting system. Even though there are reports on various end effectors for strawberry harvesting, but there they lack a picture of certain parameters that the researchers can rely upon to develop new end effectors. These parameters include the limit of gripping force that can be applied on the peduncle for effective gripping, the force required to cut the strawberry peduncle, etc. These estimations would be helpful in the design cycle of the end effectors that target to grip and cut the strawberry peduncle during the harvesting action. This paper studies the estimation and analysis of these parameters experimentally. It has been estimated that the peduncle gripping force can be limited to 10 N. This enables an end effector to grip a strawberry of mass up to 50 grams with a manipulation acceleration of 50 m/s2 without squeezing the peduncle. The study on peduncle cutting force reveals that a force of 15 N is sufficient to cut strawberry peduncle using a blade with a wedge angle of 16.60 at 300 orientation

    Selective Harvesting Robots: A Review

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    Climate change and population growth have created significant challenges for global food production, and ensuring food security requires a resilient food-production system. One of the most labour-intensive tasks in agriculture and food production is selective harvesting, which is vulnerable to risks such as a shortage of adequate labour force. To address this challenge, there is a growing need for robots that can deliver precise and efficient harvesting operations. However, developing robots for selective harvesting presents several technological challenges and raises a range of intriguing scientific questions. This paper provides an overview of the available robotic technologies for the selective harvesting of high-value crops and discusses the latest advancements and challenges in the relevant technology domains, including robotic hardware, robot perception, robot planning, and robot control. Additionally, this paper presents several open research questions that can serve as a research focus for further development in this field

    The Impact of Motion Scaling and Haptic Guidance on Operatorsā€™ Workload and Performance in Teleoperation

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    The use of human operator managed robotics, especially for safety critical work, includes a shift from physically demanding to mentally challenging work, and new techniques for Human-Robot Interaction are being developed to make teleoperation easier and more accurate. This study evaluates the impact of combining two teleoperation support features (i) scaling the velocity mapping of leader-follower arms (motion scaling), and (ii) haptic-feedback guided shared control (haptic guidance). We used purposely difficult peg-in-the-hole tasks requiring high precision insertion and manipulation, and obstacle avoidance, and evaluated the impact of using individual and combined support features on a) task performance and b) operator workload. As expected, long distance tasks led to higher mental workload and lower performance than short distance tasks. Our results showed that motion scaling and haptic guidance impact workload and improve performance during more difficult tasks, and we discussed this in contrast to participants preference for using different teleoperation features
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