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Design and evaluation of a modular robotic plum harvesting system utilizing soft components

机译:利用软组件的模块化机器人梅花收割系统的设计与评价

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The human labor required for tree crop harvesting is a major cost component in fruit production and is increasing. To address this, many existing research works have sought to demonstrate commercially viable robotic harvesting for tree crops, though successful commercial products resulting from these have been few and far between. Systems developed for specific crops such as sweet peppers or apples have shown promise, but the vast majority of cultivar types remain unaddressed, and developing a specific system for each one is inefficient. In this study, an easily modifiable development platform for robotic fruit harvesting is presented, this can be used to test specific design choices on different fruit and growing conditions. The system is evaluated in a commercial plum orchard, with no crop modifications. Both a hard and soft gripper are trialed, along with three object detector approaches and two picking motions. Some existing techniques are found to be counterproductive for plums, while soft robotics and persistent target tracking significantly improve performance. The best harvest success rate of 42%, was observed when using the soft gripper with complex motion. This is lower than expected based on prior testing with apples and indicates the difficulty in moving to new fruit types. Unique challenges specific to the plum type and growing style are examined in the context of system module design choices.
机译:树木作物收获所需的人工劳动是果实生产中的主要成本分量,正在增加。为了解决这一问题,许多现有的研究作品试图证明为树木作物的商业上可行的机器人收获,尽管这些造成的成功商业产品已经很少又一次。为甜椒或苹果等特定作物开发的系统已经表明了承诺,但绝大多数品种类型仍然是不合适的,并且为每个品种类型仍然是效率低下的特定系统。在这项研究中,提出了一种易于修改的机器人水果收获的开发平台,可用于测试不同水果和生长条件的特定设计选择。该系统在商业李子果园中进行评估,没有作物修改。试验硬质和软夹具,以及三种物体检测器方法和两个采摘动作。发现一些现有的技术对李子进行了补充,而软机器人和持久的目标跟踪显着提高了性能。使用具有复杂运动的软夹具时,观察到最佳收获成功率为42%。基于与苹果的先前测试,这低于预期,并表明难以移动到新的果实类型。在系统模块设计选择的背景下,检查了针对梅式和生长风格的独特挑战。

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