This paper discusses a class of mission planning problems that generalizes the standard Multiple Depot Vehicle Routing Problem with Time Windows (MDVRPTW) to incorporate complicated technical constraints. These constraints are specified via Metric Temporal Logic (MTL). A tree search algorithm is provided to solve that novel MDVRPTW with MTL specifications (MDVRPMTL) to optimality. In this work, we use tree search algorithm to seek the optimal flyable trajectories for teams of UAVs starting from different depots to complete missions with complicated time and technical requirements. Then a Stochastic Dynamic Programming (SDP) based algorithm is proposed to dynamically tune the UAV teams in order to secure them against adversarial actions. Examples for practical mission planning problems, in which MTL is used as a high level language to specify complex mission tasks, are presented and discussed in the paper.
For many decades, the Vehicle Routing Problem (VRP) and its different variants have been studied and found applications in the real world. This paper briefly surveys VRP instances with applications to multi-objective Unmanned Aerial Vehicle (UAV) operations. Focusing on multi-objective multi-UAV mission planning problems, we try to take advantage of the literature in the VRP and its variants. We show that each military multi-UAV mission has its corresponding VRP variant. We present a novel algorithm that relies on an enhanced tree search algorithm to solve complex multi-UAV mission planning problems with complex constraints. In simulation, we introduce examples for practical problem sizes in military UAV applications.
We address a dynamic configuration strategy for teams of Unmanned Air Vehicles (UAVs). A team is a collection of UAVs which may evolve through different organizations, called configurations. The team configuration may change with time to adapt to environmental changes, uncertainty, and adversarial actions. Uncertainty comes from the stochastic nature of the environment and from incomplete knowledge of adversary behaviors. To each configuration, there corresponds a set of different properties for the UAVs in the team. The design for the configuration control problem involves a distributed hierarchical control architecture where the properties of the system can be formally analyzed. We do this in the framework of dynamic networks of hybrid automata. We present results from simulation to demonstrate different scenarios for adversarial response.
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