论文标题
可重新配置的行为树：走向执行框架，达到高级决策和控制层功能
Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features
论文作者
论文摘要
行为树构成了一种广泛的AI工具，该工具已成功地用机器人技术开发。它们的优势包括代码的简单性，模块化和可重复性。但是，行为树仍然是高级决策引擎。控制功能不能轻易集成。本文提出了可重新配置的行为树（RBT），这是传统BTS的扩展，该扩展在决策过程中考虑了机器人环境的物理约束。我们赋予RBT的连续感官信息，允许在线监视任务执行。由此产生的刺激驱动的体系结构能够在执行环境中动态处理更改，同时保持执行时间较低。在一组机器人实验上评估了所提出的框架。结果表明，RBT是机器人任务表示，监视和执行的有前途的方法。
Behavior Trees constitute a widespread AI tool which has been successfully spun out in robotics. Their advantages include simplicity, modularity, and reusability of code. However, Behavior Trees remain a high-level decision making engine; control features cannot be easily integrated. This paper proposes the Reconfigurable Behavior Trees (RBTs), an extension of the traditional BTs that considers physical constraints from the robotic environment in the decision making process. We endow RBTs with continuous sensory information that permits the online monitoring of the task execution. The resulting stimulus-driven architecture is capable of dynamically handling changes in the executive context while keeping the execution time low. The proposed framework is evaluated on a set of robotic experiments. The results show that RBTs are a promising approach for robotic task representation, monitoring, and execution.
