Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

Rajendran, Vishnu; Debnath, Bappaditya; Mghames, Sariah; Willow, Mandil,; Parsa, Soran; Parsons, Simon; Esfahani, Amir Masoud Ghalamzan

doi:10.1002/rob.22230

Cited by 11 publications

(2 citation statements)

References 136 publications

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“…Supervised learning is pivotal for tasks like object recognition, where CNNs excel [117]. CNNs automatically learn hierarchical features from labeled images, enabling robots to detect and classify objects with remarkable accuracy [118][119][120]. Transfer learning, a technique within supervised learning, enables models trained on large datasets (e.g., ImageNet) to be fine-tuned for specific robotic tasks with limited labeled data [121][122][123].…”

Section: Enhancing Robotic Perceptionmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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This paper provides a comprehensive review of the integration of advanced power management systems and learning techniques in the field of robotics. It identifies the critical roles these areas play in reshaping the capabilities of robotic systems across diverse applications. To begin, it highlights the significance of efficient power usage in modern robotics. The paper explains how advanced power converters effectively control voltage, manage current and shape waveforms, thereby optimizing energy utilization. These converters ensure that robotic components receive the precise voltage levels they require, leading to improved motor performance and enabling precise control over motor behavior. Consequently, this results in extended operational times and increased design flexibility. Furthermore, the review explores the integration of learning approaches, emphasizing their substantial impact on robotic perception, decision-making and autonomy. It discusses the application of techniques such as reinforcement learning, supervised learning and unsupervised learning, showcasing their applications in areas like object recognition, semantic segmentation, sensor fusion and anomaly detection. By utilizing these learning methods, robots become more intelligent, adaptable and capable of autonomous operation across various domains. By examining the interaction between advanced power management and learning integration, this review anticipates a future where robots operate with increased efficiency, adapt to various tasks and drive technological innovation across a wide range of industries.

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Section: Enhancing Robotic Perceptionmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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“…The apple industry has become the third largest agricultural industry in China after grain and vegetables, with a total planted area of 12,000 hectares and a total output of more than 45 million tons. Because of the constraints of early cultivation and management costs, most apple planting models in China are in the shape of a spindle, and there are few structured V-shaped or Y-shaped “fruit wall” planting models suitable for automatic picking (Rajendran et al , 2023). At the same time, more than 90% of apples in China are mainly used for fresh consumption, and less than 10% are used for deep processing.…”

Section: Introductionmentioning

confidence: 99%

Research on system integration and control methods of an apple-picking robot in unstructured environment

Zhou,

Gao,

et al. 2024

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Purpose The purpose of this article is to solve the issue that apple-picking robots are easily interfered by branches or other apples near the target apple in an unstructured environment, leading to grasping failure and apple damage. Design/methodology/approach This study introduces the system units of the apple-picking robot prototype, proposes a method to determine the apple-picking direction via 3D point cloud data and optimizes the path planning method according to the calculated picking direction. Findings After the field experiments, the average deviation of the calculated picking direction from the desired angle was 11.81°, the apple picking success rate was 82% and the picking cycle was 11.1 s. Originality/value This paper describes a picking control method for an apple-picking robot that can improve the success and reliability of picking in an unstructured environment and provides a basis for automated and mechanized picking in the future.

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Modular autonomous strawberry picking robotic system

Parsa

Debnath

Khan

et al. 2023

Journal of Field Robotics

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Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the selective 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., high‐yielding and/or disease‐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 2.5‐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.

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Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

Cited by 11 publications

References 136 publications

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Research on system integration and control methods of an apple-picking robot in unstructured environment

Modular autonomous strawberry picking robotic system

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