A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions

Abstract

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

“…Depending on the sensors, systems used, and the time–frequency of the images obtained, different tasks are assigned and executed. Such changes can trigger follow-up activities to determine the cause or type of change, such as triggering additional image requests [ 3 , 4 ], direct actions such as search and rescue missions [ 5 ], or influencing decisions made in the area, e.g., threat avoidance [ 6 ]. In all of these cases, quick, precise, and interpretable change detection is critical to deriving timely information and properly reacting in the subsequent follow-up.…”

Multi-Criteria Decision Making in Complex Decision Environments

“…UAVs can navigate through three-dimensional airspace and reach remote or inaccessible locations. This flexibility allows for a vast number of potential routes, increasing the solution space dramatically [83,93].…”

“…Consequently, a natural solution to this problem is to completely avoid the multiobjective framework by only considering a single objective and implementing the other objectives through hard constraints as e.g., deployment time. This is done in Paper D [83], where the objective is to produce a set of routes that maximise the aggregated relative importance of the visited nodes in a SAR environment. Again, the relative importance of nodes is assumed calculated based on expert knowledge which brings us back to the main question of how to properly determine a decision framework for aggregating the objectives.…”