柔性制造中的自主机器人任务执行:在 ARIAC 2023 中整合 PDDL 和行为树。

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Biomimetics Pub Date : 2024-10-10 DOI:10.3390/biomimetics9100612
Ruikai Liu, Guangxi Wan, Maowei Jiang, Haojie Chen, Peng Zeng
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引用次数: 0

摘要

工业自动化敏捷机器人竞赛(ARIAC)旨在推进柔性制造,提高机器人装配系统在非结构化和动态工业环境中的敏捷性。ARIAC 2023 引入了八项敏捷性挑战,涉及故障部件、翻转部件、故障抓手、机器人故障、传感器停电、高优先级订单、部件不足和人类安全。鉴于这些情况的不可预测性,为每种可能的情况制定特定的策略是不切实际的。为了解决这些问题,本文提出了一个在动态场景中生成和执行自主机器人任务的分层框架。该框架分为任务层和执行层。首先,在任务层采用即时任务管理策略,合理分解动态任务,并将短期任务分配给地面机器人和天花板机器人。随后,在执行层面,每个机器人都设计了一个代理架构,将 PDDL 规划与行为树的快速响应相结合。最后,所提框架的有效性和实用性在 ARIAC 2023 中得到了充分验证。
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Autonomous Robot Task Execution in Flexible Manufacturing: Integrating PDDL and Behavior Trees in ARIAC 2023.

The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped parts, faulty grippers, robot malfunctions, sensor blackouts, high-priority orders, insufficient parts, and human safety. Given the unpredictability of these scenarios, it is impractical to develop a specific strategy for each possible situation. To address these issues, this paper presents a hierarchical framework for autonomous robotic task generation and execution in dynamic scenarios. The framework is divided into a task level and an execution level. Initially, an immediate task management strategy is adopted at the task level, which reasonably decomposes dynamic tasks and allocates short-term tasks to the floor robot and ceiling robot. Later, at the execution level, each robot is designed with an agent architecture that combines PDDL planning with the quick response of behavior trees. Finally, the effectiveness and practicality of the proposed framework were thoroughly validated in ARIAC 2023.

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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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