UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. Their equipment and mission plan are carefully selected to minimize the carried load an overall resource consumption. Typically, several single task UAVs are dispatched to perform different missions. In certain cases, (part of) the geographical area of operation may be common to these single task missions (such as those supporting post-disaster recovery) and it may be more efficient to have multiple tasks carried out as part of a single UAV mission using common or even additional specialized equipment. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.
Beside military and security operations, the usage of UAVs is envisioned in a plethora of civil applications, ranging from agriculture to environmental monitoring and disaster management (see @cite_19 for a thorough taxonomy and survey). In the following, we focus on the three types of tasks encompassed in the scenario under study.
@cite_19
2141154005
Unmanned aerial vehicles (UAVs) are well-suited to a wide range of mission scenarios, such as search and rescue, border patrol, and military surveillance. The complex and distributed nature of these missions often requires teams of UAVs to work together. Furthermore, overall mission performance can be strongly influenced by vehicle failures or degradations, so an autonomous mission system must account for the possibility of these anomalies if it is to maximize performance. This paper presents a general health management methodology for designing mission systems that can anticipate the negative effects of various types of anomalies on the future mission state and choose actions that mitigate those effects. The formulation is then specialized to the problem of providing persistent surveillance coverage using a group of UAVs, where uncertain fuel usage dynamics and strong interdependence effects between vehicles must be considered. Finally, the paper presents results showing that the health-aware persistent surveillance planner based on this formulation exhibits excellent performance in both simulated and real flight test experiments.