Abstract
Cost-effectiveness, management of risk, and simplicity of design are all arguments in favour of using heterogeneous multi-robot teams in dangerous domains. Robot losses are expected to occur and the loss of useful skills means that replacement robots—either released into the environment or previously lost and rediscovered—must be recruited for useful work. While teams of robots may eventually encounter replacements by chance, more active search strategies can be used to locate them more quickly, either to complete a single task or join a team. These searches, however, must be balanced with existing tasks so that the team can still perform useful work in the domain. This paper describes additions that we have made to an existing framework for managing dynamic teams in dangerous domains in order to support this goal.
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Nagy, G., Anderson, J. (2017). Active Team Management Strategies for Multi-robot Teams in Dangerous Environments. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_43
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DOI: https://doi.org/10.1007/978-3-319-57351-9_43
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