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Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent’s trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR.

 

In this paper, we introduce an agent-based model of lost person behavior that may be used to improve current methods for wilderness search and rescue (SAR). The model defines agents moving on a landscape with behavior considered as a random variable. The behavior uses a distribution of four known lost person behavior strategies in order to simulate possible trajectories for the agent. We simulate all possible distributions of behaviors in the model and compute distributions of horizontal distances traveled in a fixed time. By comparing these results to analogous data from a database of lost person cases, we explore the model’s validity with respect to real-world data.

 

For human autonomy teaming, information for promoting transparency could lead to information overload, negatively impacting performance and workload. This paper presents an empirical study investigating how different level of details (LODs) about the autonomy represented on the user interface would influence speed, accuracy, and workload. Specifically, we compared visualizations of a lost person model at four different LODs to aid in directing human and unmanned aerial vehicles searchers in search and rescue missions. The lowest LOD was found to support higher accuracy but at the expense of speed. The highest LOD induced the highest workload, while the other three LODs induced lower and similar levels of workload. The results indicate that the LOD in transparent displays could induce a speed and accuracy tradeoff. 

Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy.