Inventory tracking for unstructured environments via probabilistic reasoning

Workpiece location is critical to efficiently plan actions downstream in manufacturing processes. In labor-intensive heavy industries, like construction and shipbuilding, multiple stakeholders interact, stack and move workpieces in the absence of any system to log such actions. While track-by-detection approaches rely on sensing technologies such as Radio Frequency Identification (RFID) and Global Positioning System (GPS), cluttered environments and stacks of workpieces pose several limitations to their adaptation. These challenges limit the usage of such technology to presenting the last known position of a workpiece with no further guidance on a search strategy. In this work we show that a multi-hypothesis tracking approach that models human reasoning can provide a search strategy based on available observations of a workpiece. We show that inventory tracking problems under uncertainty can be approached like probabilistic inference approaches in localization to detect, estimate and update the belief of the workpiece locations. We present a practical Internet-of-Things (IoT) framework for information collection over which we build our reasoning. We also present the ability of our system to accommodate additional constraints to prune search locations. Finally, in our experiments we show that our approach can provide a significant reduction against the conventional search for missing workpieces, of up to 80% in workpieces to visit and 60% in distance traveled. In our experiments we highlight the critical nature of identifying stacking events and inferring locations using reasoning to aid searches even when direct observation of a workpiece is not available.