Search and rescue operations can greatly benefit from the use of autonomous UAVs to survey the environment and collect evidence about the position of a missing person. To minimize the time to find the victim, some fundamental parameters need to be accounted for in the design of the search algorithms: 1) quality of sensory data collected by the UAVs; 2) UAVs energy limitations; 3) environmental hazards (e.g. winds, trees); 4) level of information exchange/coordination between UAVs.In this paper, we discuss how these parameters can affect the search task and present some of the research avenues we have been exploring. We then study the performance of different search algorithms when the time to find the victim is the optimization criterion.

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Abstract-Search is a fundamental task for Wilderness Search and Rescue that can greatly benefit from the use of a swarm of autonomous UAVs to survey the environment. The benefits are maximised if the UAVs coordinate their search activities with one another. In this poster, we present our preliminary work on developing coordination strategies for multiple UAVs. It is based on a distributed, grid-based probabilistic environmental model. We discuss the practicalities of the search task, present a simplified mathematical model of the environment and sensors, and present some preliminary simulation-based results. These clearly illustrate, even in a highly simplified case, the great benefits of coordinated search.

Abstract-This paper is motivated by the real world problem of search and rescue by unmanned aerial vehicles (UAVs). We consider the problem of tracking a static target from a bird'seye view camera mounted to the underside of a quadrotor UAV. We begin by proposing a target detection algorithm, which we then execute on a collection of video frames acquired from four different experiments. We show how the efficacy of the target detection algorithm changes as a function of altitude. We summarise this efficacy into a table which we denote the observation model. We then run the target detection algorithm on a sequence of video frames and use parameters from the observation model to update a recursive Bayesian estimator. The estimator keeps track of the probability that a target is currently in view of the camera, which we refer to more simply as target presence. Between each target detection event the UAV changes position and so the sensing region changes. Under certain assumptions regarding the movement of the UAV, the proportion of new information may be approximated to a value, which we then use to weight the prior in each iteration of the estimator. Through a series of experiments we show how the value of the prior for unseen regions, the altitude of the UAV and the camera sampling rate affect the accuracy of the estimator. Our results indicate that there is no single optimal sampling rate for all tested scenarios. We also show how the prior may be used as a mechanism for tuning the estimator according to whether a high false positive or high false negative probability is preferable.