AutoLoader ¢ An Augmented Reality Enabled Decision Support Tool with Genetic Algorithm Optimizer

Managing cargo loading for U.S. Navy and Marine Corps aircraft is a challenging task, requiring an understanding of elements such as aircraft limitations, aircraft center of gravity, cargo space dimensions, and tie-down procedures to name a few. These elements differ across aircraft and are documented in lengthy Cargo Loading Guides (CLGs). The primary objective of this SBIR topic is to develop apps that run on the Marine Air-Ground Tablet (MAGTAB) and assist aircrew in completing their loadmaster duties, helping to ensure that cargo is stowed efficiently and meets loading requirements specified in the CLGs. We propose to develop AutoLoader, which runs on a MAGTAB and performs calculations and provides feedback for efficient and effective cargo loading. The AutoLoader software has three primary requirements: First, enable the development of a 3D model of cargo placement and tie-down patterns. Second, evaluate the safety of the placement and tie-downs based on the information in the CLGs. Third, generate a complete solution, or finish a partial solution, to a specified cargo loading problem. By meeting these requirements, AutoLoader improves on existing processes by automating calculations, providing actionable feedback, and searching for optimizations to enable more efficient and effective cargo loading.