Distributed inference on networks: algorithms for agent self-localization
Operation of teams of artificial helpers is one of the hallmarks of technology for the near future, both in hazardous situations and in everyday life. In harsh environments as sea exploration and exploitation, search and rescue operations, and even in most indoor applications as butler robot groups, joint operation is critically supported by cooperative self-localization of each agent, and non-cooperative localization of targets.
Networks of agents typically rely on known node positions even if the main goal of the network is not localization. A network of agents may comprise a large set of miniature, low cost, low power autonomous sensing nodes, mixed with some mobile agents. In this scenario it is generally unsuitable to accurately deploy all static nodes in a predefined location within the network operation area, or trust dead reckoning for mobile nodes. GPS is also discarded as an option for indoor applications or due to cost and energy consumption constraints. On pursuit of distributed and scalable solutions, we develop approximations and tackle maximum-likelihood non-convex problems developing algorithms that have shown better results than the benchmark methods.