Abstract
Autonomous mobile robots are considered a valuable technology for search and rescue applications, where an initially unknown environment has to be explored to locate human victims. In this scenario, robots exploit exploration strategies to autonomously move around the environment. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using 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 some MCDM-based exploration strategies within an existing robot controller and we experimentally evaluated their performance in a simulated environment.
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