The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the development of an autonomous mobile robot (AMR) software stack designed to safely optimize material movement throughput in warehouses. Currently, AMRs in warehouses operate at less than half the speed of their human-driven counterparts due to the robot?s limited perception capabilities. A consequence of this decision is a dramatic reduction in the vehicle?s potential throughput, translating to a smaller facility fleet size with associated cost and material savings. Improved AMR operation will address warehouse labor shortages and improve safety. According to the Occupational Safety and Health Administration (OSHA), there are roughly 85-90 forklift fatalities each year, and over 7,000 injuries requiring days away from work. By automating forklift trucks with the proposed software stack, these safety incidents will be significantly reduced. This project will develop software associated with safer AMRs based on systems used in autonomous cars. This Small Business Innovation Research (SBIR) Phase I project is developing a software stack for autonomous mobile robots (AMRs) in warehouses to improve AMR agility and spatial awareness to human-like levels. The proposed innovation takes inspiration from advances in self-driving cars through the use of high-definition (HD) mapping and three-dimensional (3D) perception. These HD maps will leverage the latest advances in 3D LiDAR systems and deep learning approaches for object detection, enabling operation at higher speeds. This project will enable safe increases in AMR speeds from 2.0 to 3.0 m/s. To ensure vehicle safety is not compromised, systems will be tested to and meet the American National Standards Institute/Industrial Truck Standards Development Foundation (ANSI/ITSDF) B56.5 safety standard for object detection requirements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
