Multi-UAV Planning in Search and Rescue Missions using Optimal Search Effort Allocation

In this paper we present a new search planning method for a coordinated swarm of UAVs based on the Theory of Search which provides a precise and robust probabilistic model for Search and Rescue (SAR) operations where lost victims need to be found as soon as possible. Using any “a priori” information about the victims' positions, a probability density function for each one is built and used to compute the optimal search effort allocation for the resources available. The priority assigned to each region of the area of interest is derived for such allocation using probability theory. This defines a set of priority sub-areas that span all the possible locations where a victim could be located. The optimal UAV distribution and order at which each sub-area is visited is computed using a Traveling Salesman Problem solver. The coverage paths within each sub-area are computed using an energy-aware path planner. We also address how to solve potential collisions with the terrain and/or other UAVs of the team. We have performed extensive simulations to validate our approach obtaining promising results in terms of probability of finding the victims and path feasibility.