Unmanned Aerial Vehicles Scheduling Research Articles

Incorporating Human Factor Considerations in Unmanned Aerial Vehicle Routing

Unmanned aerial vehicles (UAVs) have become increasingly valuable military assets, and reliance upon them will continue to increase. Despite lacking an onboard pilot, UAVs require crews of up to three human operators. These crews are already experiencing high workload levels, which is a problem that will be likely compounded as the military envisions a future where a single operator controls multiple UAVs. To accomplish this goal, effective scheduling of UAVs and human operators is crucial to future mission success. We present a mathematical model for simultaneously routing UAVs and scheduling human operators, subject to operator workload considerations. This model is thought to be the first of its kind. Numerical examples demonstrate the dangers of ignoring the human element in UAV routing and scheduling.

A Note on Integer Programming Formulations of the Real-Time Optimal Scheduling and Flight Path Selection of UAVs

In recent years, considerable attention has been given to research on various aspects of unmanned aerial vehicles (UAVs) applications. UAVs are currently used for various military and civilian missions in the air, sea, space, and on the ground. In two recent papers, Shima (2007) and Kim (2007) considered closely similar m-UAV problems. In Shima (2007), the problem is considered with each target being served by only one UAV to minimize the total travel distance across all UAVs (called load balancing), however, in Kim (2007), the problem is considered with maximum number of targets that each UAV can serve with the objective of minimizing load balancing. Kim presented mixed integer linear programming (MILP) formulations of the load balancing problem both for when the UAVs return to their original depot and when they do not. Shima presented a combinatorial optimization formulation of their model with a branch-and-bound solution procedure. The MILP formulation of the load balancing problem is also adaptable to Shima 's problem. However, there are major inefficiencies with the MILP formulation presented in Kim 's model. In fact, The MILP formulations presented in Kim are highly complicated with huge number of variables and constraints making them impractical for applications. The purpose of the present note is to provide explicit MILP formulations that dramatically reduce the number of variables and the number of constraints for variety of UAV tour assignment problems including the two models mentioned above and via simulation we show significance of these new formulations.

Operator Performance and Intelligent Aiding in Unmanned Aerial Vehicle Scheduling

Interest is increasing in designing systems for controlling unmanned vehicles that will invert the current many-to-one ratio of operators to UVs. Instead of the lower-level tasks that today's UV teams perform, a single operator would focus on high-level supervisory control. A key challenge in designing such single-operator systems is to minimize periods of excessive workload that arise when critical tasks for several UVs occur simultaneously. So, we need decision support that helps the operator evaluate different action alternatives for managing a multiple-UV mission schedule in real time. Two decision support experiments have attempted to provide operators of unmanned aerial vehicles with multivariate scheduling assistance, with mixed results. Those automated decision support tools that provided more local, as opposed to global, visual recommendations performed better. This result suggests that meta-information displays could saturate operators and reduce performance. This article is part of a special issue on Interacting with Autonomy.