PMHT for multiple platform path exploration planning
Brian Cheung, Samuel J. Davey, Douglas Andrew Gray · 2009
This paper considers the problem of automatically coordinating multiple platforms to explore an unknown environment. The goal is a planning algorithm that provides a path for each platform in such a way that the collection of platforms cooperatively sense the environment in a globally efficient manner. A collection of discrete locales of interest is assumed to be known and the platforms use these as waypoints. The key feature of the method is to treat the assignment of locales to platforms as a target tracking problem and to use the probabilistic multi hypothesis tracker (PMHT) as a method of performing multi-platform batch data association. The locales are treated as measurements without time information, leading to a general framework also applicable in target tracking problems where the temporal information is noisy or unreliable.