SBIR Phase I: Dense, Socially-Compliant, Autonomous Delivery Robot

Inception Robotics, Llc, College Park MD

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research Phase I project is to enable autonomous mobile robots (AMRs) to operate in densely crowded spaces in a safe and socially compliant/acceptable manner. A key potential outcome is the development of a collision avoidance method based on Deep Reinforcement Learning (DRL). This method would be capable of handling dense crowds and optimized to run on compact and power-efficient embedded processors. Such abilities would increase the commercial potential and adoption of learning-based navigation methods that have demonstrated excellent collision avoidance and noise handling capabilities. The technology may unlock commercial opportunities by deploying AMRs in the airport, retail, healthcare, and hospitality industries, where the environments are highly dense and dynamic. The airport industry may derive postive impacts from AMRs that can navigate in complex, indoor environments where global positioning systems (GPS) are not allowed by providing contactless deliveries of food, beverages, and other retail products to travelers at the gate. This Small Business Innovation Research (SBIR) Phase I project investigates a hybrid collision avoidance approach enabling autonomous mobile robots (AMRs) to operate safely in dense crowds, while being socially-compliant in sparse scenarios. Preliminary research has shown that Deep Reinforcement Learning (DRL)-based approaches can compute collision-free robot velocities with inaccurate, uncertain perception data. The proposed DRL-based model will be implemented as an optimized neural network that works on power and cost-efficient embedded processors. The key technical hurdles in this technology are: the DRL model trained in simulation may not perform well in real-world environments (known as sim-to-real gap), the fully-trained DRL model may have some performance degradation compared to the company’s current DRL models due to the lower number of parameters used to run on embedded processors, and the localization modules could compute erroneous locations when the AMR is navigating through a dense crowd due to occlusions. The key objectives of Phase I are to address these challenges. 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.