Andréa Cynthia Santos

Several major industrial disasters happen each year around the world. They usually involve limited accessibility, poor ground conditions, and toxic wastes. As a consequence, this reduces the efficiency of humanitarian operations. In such a context, flying drones may be a viable alternative: faster, no dependency on ground conditions, and larger areas scanned. They are also better suited for following the population and the crisis dynamic. For such a purpose, various issues have to be addressed such as defining and optimizing the drone's routes, their energy consumption, choosing the relay points for recharging equipment, among others. In this study, several additional features from existing works are considered: first, a probability of identifying individuals is defined. Thus, each node can be scanned several times in order to improve the observation. In addition, the nodes are prioritized according to a given heatmap. The probabilistic drone routing problem (PDRP) consists of finding a route, that is, a sequence of trips, for each drone such that the sum of the expected number of identified individuals on all routes is maximized. Constraints on energy consumption, collision avoidance and drone‐base assignment are considered. We propose a heuristic and metaheuristics based on the adaptive large neighborhood search for the PDRP. The methods are tested on theoretical instances, as well as on a case study of the Beirut Port explosion on August 4, 2020, in order to analyze the performance of the proposed methods.

This study presents the Drone Swarms Routing Problem (DSRP), which consists of identifying the maximum number of victims in post-disaster areas. The post-disaster area is modeled in a complete graph, where each search location is represented by a vertex, and the edges are the shortest paths between destinations, with an associated weight, corresponding to the battery consumption to fly to a location. In addition, in the DSRP addressed here, a set of drones are deployed in a cooperative drone swarms approach to boost the search. In this context, a V-shaped formation is applied with leader replacements, which allows energy saving. We propose a computation model for the DSRP that considers each drone as an agent that selects the next search location to visit through a simple and efficient method, the Drone Swarm Heuristic. In order to evaluate the proposed model, scenarios based on the Beirut port explosion in 2020 are used. Numerical experiments are presented in the offline and online versions of the proposed method. The results from such scenarios showed the efficiency of the proposed approach, attesting not only the coverage capacity of the computational model but also the advantage of adopting the V-shaped formation flight with leader replacements.