容器(类型理论)
计算机科学
任务(项目管理)
启发式
班级(哲学)
可靠性(半导体)
机器人
人工智能
范畴变量
序列(生物学)
运动(物理)
计算机视觉
模拟
实时计算
机器学习
工程类
功率(物理)
物理
系统工程
量子力学
生物
遗传学
机械工程
作者
Shuo Yang,Dayou Li,Chunting Zhao,Wei Peng,Yibin Li,Wei Zhang
标识
DOI:10.1109/rcar58764.2023.10249938
摘要
Container unloading is one of the most challenging tasks in depalletizing applications and has not been well addressed. Because it is non-trivial to accurately detect irregularly packed boxes and efficiently unload boxes in confined spaces, in this paper, we investigate both the detection step and the motion planning step in the container unloading task. First, we introduce the multi-class 4-DoF box detection into container unloading tasks and build a detection method based on YOLOv5. To generate the unloading sequence of boxes, a heuristic rule is proposed by using human prior knowledge. And to match the categorical attributes of different types of boxes and task setups, we design suitable motion trajectories. Based on the proposed methods, a complete robotic unloading system is developed. We evaluate our system via real-world experiments and the results indicate the efficiency and reliability of our robotic system. The video demonstration can be found at: https://youtu.be/nYvCbNoYi5Y.
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