Optimizing the Dispatch Topology of a 911 Response Drone Network

This thesis adapts and applies methodologies for optimizing the sensing topology of a counter-UAS (CUAS) network to the problem of optimizing the geospatial distribution of emergency response drone bases subject to resource limitations while ensuring alignment with emergency response requirements. The specific context for this work is a 911 call incident response.

Drone response time, time on scene, and sensor effectiveness are used as network performance metrics to develop a mission planning algorithm that attempts to maximize network response effectiveness. A composite objective function utilizes network response effectiveness and customer-defined region weights that indicate the probability of an incident occurring to represent the performance of the geospatial distribution of 911 drone bases. A Greedy Algorithm iterates upon this objective function to optimize the network topology.

Previous work [1] suggests that a heuristic based approach utilizing a hexagonal network topology centered around suburban/urban focal points is the preferred method for optimizing the dispatch topology of a 911 response drone network. The optimization strategy deployed here demonstrated an 11% improvement on the objective function compared to this heuristic when tested in Tippecanoe County, IN.

Previous work [2] also suggests that, of all drones in the design space compliant with FAA Part 107, a single Vertical Take-off and Landing (VTOL) type drone with an ability to transition into fixed wing horizontal flight adhering to specific performance requirements is the preferred drone for executing the emergency response mission. This thesis utilizes the optimization strategy deployed here to test this supposition by comparing the performance of a network with access to only this single drone type to a network with access to multiple types of fixed-wing VTOL drones. Findings indicate that access to only the single type of optimally-sized drone outperforms a network with access to multiple drone types; however, improvements to the greedy algorithm that consider the marginal value of each drone type and across diverse mission types may modify this conclusion.