用4D BIM调度数据提升工地机器人对工人动作的识别准确率
Adaptive Robot Perception in Construction Environments using 4D BIM
- 将4D BIM计划分解为实时低层任务，约束动作预测空间
- 相比全局预测，识别置信度提升18.6%（在真实工地数据集上）
- 适合需要高可靠人机协作的智能建造场景
人体活动识别（HAR）是物理人机交互（pHRI）任务中机器人感知的核心。在建筑机器人领域，机器人需具备对工人活动的精准且鲁棒的感知能力，这是实现工业环境中可信、安全的人机协作（HRC）的基础。现有大多数HAR算法缺乏鲁棒性与适应性，难以保障无缝的人机协作。近期研究采用多模态方法以增强特征表达。本文进一步拓展此前工作，引入4D建筑信息模型（BIM）进度数据。我们构建了一个实时将高层级BIM进度活动转化为低层级任务序列的流程。该框架利用这些任务子集作为工具，限制HAR算法可预测的动作范围。通过4D BIM调度数据缩小解空间，算法在局部场景下从更小的可能性池中预测真实动作，相较全局遍历预测，显著提升准确性。实验结果表明，该方法在真实工地数据集上使预测置信度较基线模型平均提升18.6%。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) is a pivotal component of robot perception for physical Human Robot Interaction (pHRI) tasks. In construction robotics, it is vital that robots have an accurate and robust perception of worker activities. This enhanced perception is the foundation of trustworthy and safe Human-Robot Collaboration (HRC) in an industrial setting. Many developed HAR algorithms lack the robustness and adaptability to ensure seamless HRC. Recent works have employed multi-modal approaches to increase feature considerations. This paper further expands previous research to include 4D building information modeling (BIM) schedule data. We created a pipeline that transforms high-level BIM schedule activities into a set of low-level tasks in real-time. The framework then utilizes this subset as a tool to restrict the solution space that the HAR algorithm can predict activities from. By limiting this subspace through 4D BIM schedule data, the algorithm has a higher chance of predicting the true possible activities from a smaller pool of possibilities in a localized setting as compared to calculating all global possibilities at every point. Results indicate that the proposed approach achieves higher confidence predictions over the base model without leveraging the BIM data.
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