A Decision-Theoretic Framework to Select Effective Observation Locations in Robotic Search and Rescue Scenarios

In some applications, like mapping and search and rescue, robots are autonomous when they are able to decide where to move next, according to the data collected so far. For this purpose, navigation strategies are used to drive the robots around environments. Most of the navigation strategies proposed in literature are based on the idea of evaluating a number of candidate locations according to an utility function and selecting the best one. Usually, ad hoc utility functions are used to provide a global evaluation of candidates by combining a number of criteria. In this paper, we propose to use a more theoretically-grounded approach, based on Multi Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented our MCDM-based exploration strategies within an existing robot controller and we experimentally evaluated their performance in environments used in the RoboCup Rescue Virtual Robots Competition.