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

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

A review of computer vision-based approaches for physical rehabilitation and assessment

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered

Strawberry picking point localization ripeness and weight estimation

Labour shortage, difficulties in labour management, the digitalization of fruit production pipeline to reduce the fruit production costs have made robotic systems for selective harvesting of strawberries an important industry and academic research. One of the important components of such technologies yet to be developed is fruit picking perception. For picking strawberries, a robot needs to infer the location of picking points from the images of strawberries. Moreover, the size and weight of strawberries to be picked can help the robot to place the picked strawberries in proper punnets directly to be delivered to customers in supermarkets. This can save significant time and packing costs in packhouses. Geometry-based approaches are the most common approach to determine the picking point but they suffer from inaccuracies due to noise, occlusion, and varying shape and orientation of the berries. In contrast, we present two novel datasets of strawberries annotated with picking points, key-points (such as the shoulder points, the contact point between the calyx and flesh, and the point on the flesh farthest from the calyx), and the weight and size of the berries. We performed experiments with Detectron-2, which is an extended version of Mask-RCNN with key-points detection capability. The results show that the key-points detection approach works well for picking and grasping point localization. The second dataset also presents the dimensions and weight of strawberries. Our novel baseline model for weight estimation outperforms many state-of-the-art deep networks. The datasets and annotations are available at https://github.com/imanlab/strawberry-pp-w-r-dataset

Modular autonomous strawberry-picking robotic system

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., 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