Tracking Tagged Inventory in Unstructured Environments through Probabilistic Dependency Graphs

Abstract

Logging and tracking raw materials, workpieces and engineered products for seamless and quick pulls is a complex task in the construction and shipbuilding industries due to lack of structured storage solutions. Additional uncertainty is introduced if workpieces are stacked and moved by multiple stakeholders without maintaining an active and up-to-date log of such movements. While there are frameworks proposed to improve workpiece pull times using a variety of tracking modes based on deterministic approaches, there is little discussion of cases wherein direct observations are sparse due to occlusions from stacking and interferences. Our work addresses this problem by: logging visible part locations and timestamps, through a network of custom designed observation devices; and building a graph-based model to identify events that highlight part interactions and estimate stack formation to search for parts that are not directly observable. By augmenting the site workers and equipment with our wearable devices, we avoid adding additional cognitive effort for the workers. Native building blocks of the graph-based model were evaluated through simulations. Experiments were also conducted in an active shipyard to validate our proposed system.

Inventory Tracking for Unstructured Environments via Probabilistic Reasoning

“…As workpieces tend to stay in positions for long periods of time in unstructured environments, one limitation with accumulating linger weights for edges between workpieces is that all workpieces within proximity, given enough time, reach L max . While the time it takes for each pair of workpieces to reach L max is dependent on the distance D i,j between them, with larger periods of time, the effect of D i,j is lost in the system described in our previous work [15]. This has effects downstream in the rest of the system wherein all workpieces within a neighborhood of a missing workpiece become equally likely stack locations.…”

“…This work expands on our previous work described in [15] and focuses on addressing its limitation to prune search locations and extract more critical events from movers (Section 3.5). We also present methods to provide tighter search locations through knowledge insertion (Section 3.2), if available.…”

“…IoT architecture: The objective of the observation module in our approach is to collect information from the viewpoint of movers and from static cameras covering common inventory storage areas. While publishing images through a Robotic Operating System (ROS) [37] network in smaller spaces with strong Wi-Fi, as described in [15], is a solution that provides live updates on inventory movement, this breaks down in larger work sites that have weak or no Wi-Fi signals. A system that can scale and guarantee that observations are not lost despite weak communication channels vastly expands its application and reduces the implementation burden on the environment, making it more attractive for industries to adopt.…”

“…This makes it impractical to build structured storage solutions on-site for all the raw materials and leads to workpieces being stacked on by multiple stakeholders in the project. Long storage times, handling by multiple stakeholders and accidental displacement combined with the lack of an effortless information collection solution to log any displacements leads to poor pull times in these environments [4,8,[13][14][15]. Since workpiece displacement and movement decisions are not centralized and occur locally at worker's discretion, we focus on collecting information from their point of view and extracting the effect of such displacement decisions downstream through a reasoning model.…”

“…Section 2 discusses current work in literature that directly tackle the inventory tracking problem and those that deal with probabilistic and reasoning-based methods in the field of robotics from which we draw concepts. Section 3 describes the motivation and design of various components of our proposed approach, focusing on the contributions over our previous work [15]. Section 4 covers the setup, design and results for the experiments conducted to validate our approach.…”