On the concerted design and scheduling of multiple resources for persistent UAV operations

Abstract

Figure 1. A system of three service station candidates, four UAV candidates and three tasks.  Abstract-A fleet of unmanned aerial vehicles (UAVs)supported by logistics infrastructure, such as automated service stations, may be capable of long-term persistent operations. Typically, two key stages in the deployment of such a system are resource selection and scheduling. Here, we endeavor to conduct both of these phases in concert for persistent UAV operations. We develop a mixed integer linear program (MILP) to formally describe this joint design and scheduling problem. The MILP allows UAVs to replenish their energy resources, and then return to service, using any of a number of candidate service station locations distributed throughout the field. The UAVs provide service to known deterministic customer space-time trajectories. There may be many of these customer missions occurring simultaneously in the time horizon. Each customer mission may be addressed by several UAVs. Multiple tasks may be conducted by each UAV between visits to the service stations. The MILP jointly determines the number and locations of resources (design) and their schedules to provide service to the customers. We then develop a modified receding horizon task assignment algorithm including the design problem (RHTA d ) to address the computational complexity of the MILP. Numerical experiments assess the performance of RHTA d relative to the MILP solved via CPLEX. RHTA d is substantially faster with quite acceptable loss of optimality. As such, problems of much larger size can be addressed.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

“…Hong et al study the placement of recharging stations for delivery drones, and Kim and Morrison consider automatic service centers for drones in a general context. In the latter paper, the authors simultaneously plan tours of drones to perform a given set of tasks and decide on the placement of automatic service centers where the drones can replenish their energy.…”

On the computational complexity of the patrol boat scheduling problem with complete coverage

“…This area of research is concerned with planning mission trajectories for a system of resource‐constrained unmanned aerial vehicles (UAVs). The research was first undertaken by Kim, Song, and Morrison (2013), followed by the publication of two closely related studies by Kim and Morrison (2014) and Song et al (2014). The context for the problem is a given field of operations, which consists of automated recharging stations and preplanned space‐time mission trajectories.…”

A Generic Spatiotemporal UAV Scheduling Framework for Multi-Event Applications

“…Although both studies manage multiple UAVs, the proposed solutions do not consider the temporal characteristics of the tasks and/or the battery lifetime issue. Some studies have dealt with the energy availability challenge [24], [25]. The authors of these studies developed a MILP problem to model the UAV scheduling procedure.…”

“…Constraints (24) and (25) are added to ensure that all drones start their journey from the docking station and return to it at the end of the makespan. Notice that the above optimization problem is non-linear due to some non-linear constraints, e.g., products of decision variables, and existing logical conditions (logical OR).…”

“…Constraints (39) are added to replace (5) and (8). Finally, constraints (40) are used to replace constraints (24) and (25). The problem's feasibility is subject to many parameters including the battery capacityB d , the length of the events p e , their spatial and temporal distribution, the power levels of the UAVs P hov d , P tr d , and P ser d , and the charging power of the docking station.…”