用多模态行为树让机器人自动封管并自检，提升实验室安全
Multimodal Behaviour Trees for Robotic Laboratory Task Automation
- 基于多模态感知的行为树，实时判断任务执行状态
- 封管成功率88%，插架成功率92%，可有效检测错误
- 适合高安全性要求的自动化实验场景
实验室机器人能以高精度和可重复性开展实验，有望变革科研方式。搬运样品、封盖等重复性任务非常适合机器人执行，若可靠完成，可使化学家专注于更关键的研究。当前机器人虽比人快，但可靠性如何？封盖不当可能导致有毒物质泄漏，危及生命。为确保机器人表现如人类般准确，需引入传感器反馈来评估任务进展。为此，我们提出一种基于多模态感知的行为树新方法，不仅实现任务自动化，还能验证任务成功执行，满足安全关键环境的基本需求。在样品瓶封盖与实验架插入两项任务上进行测试，结果表明：封盖成功率88%，插架成功率92%，具备强错误检测能力。实验验证了该方法的鲁棒性与可靠性，证明多模态行为树是迈向新一代机器人化学家的关键路径。
原文摘要 · Abstract (English)
Laboratory robotics offer the capability to conduct experiments with a high degree of precision and reproducibility, with the potential to transform scientific research. Trivial and repeatable tasks; e.g., sample transportation for analysis and vial capping are well-suited for robots; if done successfully and reliably, chemists could contribute their efforts towards more critical research activities. Currently, robots can perform these tasks faster than chemists, but how reliable are they? Improper capping could result in human exposure to toxic chemicals which could be fatal. To ensure that robots perform these tasks as accurately as humans, sensory feedback is required to assess the progress of task execution. To address this, we propose a novel methodology based on behaviour trees with multimodal perception. Along with automating robotic tasks, this methodology also verifies the successful execution of the task, a fundamental requirement in safety-critical environments. The experimental evaluation was conducted on two lab tasks: sample vial capping and laboratory rack insertion. The results show high success rate, i.e., 88% for capping and 92% for insertion, along with strong error detection capabilities. This ultimately proves the robustness and reliability of our approach and that using multimodal behaviour trees should pave the way towards the next generation of robotic chemists.
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