arXiv:2409.03439cs.ROcs.AI2024-09被引 1

为工业机器人设计专用编程语言,让无编程经验的工人快速部署视觉引导操作。

KiloBot: A Programming Language for Deploying Perception-Guided Industrial Manipulators at Scale

  • 设计领域专用语言,简化视觉引导任务与运动规划问题构建。
  • 支持灵活控制流,适配不同产线的定制化需求。
  • 无需编程基础的操作员经数小时培训即可使用,适合大规模部署。

我们希望工业机器人能在非结构化环境中通过摄像头和感知系统自主作业。与传统机器人仅回放预设轨迹不同,这类应用需要在线行为规划。除了感知与规划算法外,部署感知引导机械臂还需大量集成工作。现有方法通常用Python等语言编写脚本,虽在少数机器人上可行,但当部署规模超过10,000个机器人工作站、覆盖2,000多个客户站点时,已难以维持。为此,我们提出一种面向感知引导操作的应用领域专用语言(DSL)。该语言可:1)提供易于访问的接口,用于构建和求解实际应用中关键的一类任务与运动规划(TAMP)问题;2)通过灵活的控制流机制实现模块集成,满足不同工业场景的定制需求。结合直观的图形化编程前端,该语言主要由无传统编程经验的机器操作员使用。经过数小时培训,操作员即可用其编排复杂的操作行为。大量实际部署验证了该方法的有效性。

原文摘要 · Abstract (English)

We would like industrial robots to handle unstructured environments with cameras and perception pipelines. In contrast to traditional industrial robots that replay offline-crafted trajectories, online behavior planning is required for these perception-guided industrial applications. Aside from perception and planning algorithms, deploying perception-guided manipulators also requires substantial effort in integration. One approach is writing scripts in a traditional language (such as Python) to construct the planning problem and perform integration with other algorithmic modules & external devices. While scripting in Python is feasible for a handful of robots and applications, deploying perception-guided manipulation at scale (e.g., more than 10000 robot workstations in over 2000 customer sites) becomes intractable. To resolve this challenge, we propose a Domain-Specific Language (DSL) for perception-guided manipulation applications. To scale up the deployment,our DSL provides: 1) an easily accessible interface to construct & solve a sub-class of Task and Motion Planning (TAMP) problems that are important in practical applications; and 2) a mechanism to implement flexible control flow to perform integration and address customized requirements of distinct industrial application. Combined with an intuitive graphical programming frontend, our DSL is mainly used by machine operators without coding experience in traditional programming languages. Within hours of training, operators are capable of orchestrating interesting sophisticated manipulation behaviors with our DSL. Extensive practical deployments demonstrate the efficacy of our method.

机器人编程领域语言工业自动化视觉引导

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