CAREER: Integrative Resource Optimization Framework for Large-scale Drone Delivery Systems

Wayne State University, Detroit MI

Investigators

Abstract

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by promoting safe, efficient and equitable use of low-altitude airspace for drone delivery operations. Drone delivery has the potential to reduce last-mile delivery time from hours to minutes and eliminate billions of individual trips that are otherwise unsafe, polluting and wasteful. However, safety and scalability challenges exist that currently impede commercial deployment at scale. This award supports research toward a fundamental understanding of how diverse resources can be managed to enable efficient city-scale drone delivery services. The design and operational challenges include optimal location of depots and rally points, capacity planning, fleet management, and dynamic vehicle routing under battery, traffic, and weather constraints. The research will support integration of aerial vehicle logistics, vehicle onboard intelligence, and air traffic management. The education program aims to generate new curricula, train new STEM talent and inspire new entrepreneurs in a new field at the intersection of aviation, robotics and operations research. The education program will also create undergraduate research opportunities, particularly for under-represented minorities in STEM. This project will introduce a general, optimization-based framework for modeling complex decision-making processes in aerial logistics operations, as well as new algorithms for solving problems modeled in such a framework. Three safety-critical resources, including ground stations, batteries, and the airspace, will be studied comprehensively and modeled holistically: the location and configuration of ground stations are informed by a suite of battery power prediction models, and in turn, determine the feasible paths of drones and hence the optimal dynamic allocation of airspace corridors. The research will develop (1) decomposable mathematical programs to tackle robust network design and continuous location problems, (2) new algorithms that combine evolutionary computing with convex optimization for solving nonlinear combinatorial problems, (3) a new framework for modeling stochastic multi-agent systems based on innovative extensions of Markov decision processes, and (4) improved algorithms for in-flight battery power management. The research approach integrates theoretical developments with practical implementation and is heavily informed by commercial application potential. Software apps will be developed and flight trials will be conducted to deliver practical relevance and impact. 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.