Zhenyu Zhou

Unmanned aircraft systems service suppliers adhere to interoperability standards that require unmanned aircraft operators to submit an operational intent, which describes the planned flight path in four-dimensional space. To ensure fairness, the central database follows a first-come, first-served approach, accepting new operational intents as long as they do not conflict with any active ones. However, creating a viable operational intent is challenging due to moving obstacles. This paper introduces an innovative optimization-based procedure to automate the intent filing process. It utilizes a stacked hexagonal tessellation to model the airspace, offering adjustable granularity. Path finding is accomplished using integer programming on the hex grid. The integer program is solved on a grid canvas that includes only necessary cells, striking a balance between computational efficiency and optimality. Simulation experiments demonstrate the procedure’s effectiveness in generating feasible trajectories, even in scenarios with dense, omnidirectional air traffic. This procedure has the potential to become the foundational software core for low-altitude air traffic management systems, providing strategic deconfliction and constraint management services.

Due to the limitation imposed by hardware and form factor considerations, multirotor unmanned aircraft (drones) are unable to conduct preflight physical checks on their own capacity. Critical safety checks involve detecting various anomalies such as imbalanced payload, damaged propellers, mulfunctioning motors and poorly calibrated compass, etc. Human efforts are currently required for performing such tasks, which impedes large-scale deployments of drones and increases the operational costs. In this work, we propose a weight-measuring landing platform along with a set of statistical inference algorithms aimed at performing safety checks for any multicopter aircraft that lands on the platform. We develop a nonconvex nonlinear least squares model for estimating the center of gravity and orientation of the aircraft, and derive a recursive formula for calculating the optimal solution. In numeric experiments, our analytical solution method has been able to find the global solution orders-of-magnitude faster than a global optimization solver. We have conducted real-system tests on a quadcopter drone deliberately configured to carry misplaced payload, and to use damaged propellers. Experiment results show that the platform is able to detect and profile these common safety issues with high accuracy.

Drone fleet operators must be able to land the whole fleet in short notice. In practical operations, landing spots are usually much fewer than airborne drones. When many drones gravitate toward the limited landing spots simultaneously, congestion management becomes a challenge. This paper characterizes the fleet landing problem using mixed integer programming techniques and proposes a series of computational enhancements to reduce the solution time from hours to seconds. The solution algorithms are implemented in a software prototype for traffic management, and are thoroughly validated via extensive numerical examples and field simulations. For a fleet of 18 drones navigating at the same altitude layer within a 4-square kilometer area, all routing and trajectory computations can be completed in less than 5 seconds, and the entire fleet is able to complete landing at three pre-planned landing spots within about 3 minutes. Therefore, the models and algorithms are suitable for practical use.