We demonstrate a testbed and algorithms for collaborative human and automated (or mixed-initiative) decision making within the context of outdoor search and rescue. Hydra is a networked simulation tool that allows n human and k automated agents operating under different assumptions to share control over m unmanned aerial vehicles (UAVs) with cameras, with the goal of locating a hidden subject thetas as quickly as possible. The agents are modeled on a pre-defined hierarchy of authority, and the search space is characterized by varying degrees of obstructions. Search is based on iterating the following cycle of four steps: 1) all agents generate image requests based on their individual probability density functions (pdfs), 2) Hydra collects requests and computes an optimal assignment of images to the UAVs, 3) Hydra processes the resulting image data and specifies whether or not the subject was detected, and 4) all agents update their pdfs. We propose initial models and algorithms under this framework, and we show via simulations of a scenario with three agents and one UAV that our method performs 57.7 percent better than a theoretical upper bound for a single agent and UAV.
['Ephrat Bitton', 'Ken Goldberg']
Hydra: A framework and algorithms for mixed-initiative UAV-assisted search and rescue