Under the background of 5G, Internet, artificial intelligence technol,ogy and robot technology, warehousing, and logistics robot technology has developed rapidly, and products have been widely used. A practical application is to help warehouse personnel pick up or deliver heavy goods at dispersed locations based on dynamic routes. However, programs that can only accept instructions or pre-set by the system do not have more flexibility, but existing human auto-following techniques either cannot accurately identify specific targets or require a combination of lasers and cameras that are cumbersome and do not accomplish obstacle avoidance well. This paper proposed an algorithm that combines DeepSort and a width-based tracking module to track targets and use artificial potential field local path planning to avoid obstacles. The evaluation is performed in a self-designed flat bounded test field and simulated in ROS. Our method achieves the SOTA results on following and successfully reaching the end-point without hitting obstacles.
翻译:在5G的背景下,互联网、人工智能技术、机器人技术、机器人技术、仓储和物流机器人技术迅速发展,产品被广泛使用,实际应用是帮助仓库人员在以动态路线为基础的分散地点取走或运送重型货物,然而,只能接受指示或系统预先设定的程序没有更大的灵活性,但现有的人类自动跟踪技术要么无法准确确定具体目标,要么需要混合激光和摄影机,这些激光和摄影机十分繁琐,无法很好地避免障碍。本文建议采用一种算法,将DeepSort和宽度跟踪模块结合起来,以跟踪目标,并利用人为潜在实地当地路径规划来避免障碍。评价是在一个自设计的固定封闭的测试场进行,并在ROS中模拟进行。我们的方法是在不设置障碍的情况下,在跟踪和成功到达终点后取得SOTA结果。