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This paper considers the combination of temporal logic (TL) specifications and local objective functions to create online, multiagent, motion plans. These plans are guaranteed to satisfy a persistent mission TL specification and locally optimize an objective function (e.g. in this paper, a cost based on information entropy). The presented approach decouples the two tasks by assigning sub-teams of agents to fulfill the TL specification, while unassigned agents optimize the objective function locally. This paper also presents a novel decoupling of the classic product automaton based approach while maintaining satisfaction guarantees. We also qualitatively show that optimality loss in the local greedy minimization due to the TL constraints can be approximated based on specification complexity. This approach is evaluated with a set of simulations and an experiment of 6 robots with real sensors. 

In this work, we consider the multi-image object matching problem in distributed networks of robots. Multi-image feature matching is a keystone of many applications, including Simultaneous Localization and Mapping, homography, object detection, and Structure from Motion. We first review the QuickMatch algorithm for multi-image feature matching. We then present NetMatch, an algorithm for distributing sets of features across computational units (agents) that largely preserves feature match quality and minimizes communication between agents (avoiding, in particular, the need to flood all data to all agents). Finally, we present an experimental application of both QuickMatch and NetMatch on an object matching test with low-quality images. The QuickMatch and NetMatch algorithms are compared with other standard matching algorithms in terms of preservation of match consistency. Our experiments show that QuickMatch and Netmatch can scale to larger numbers of images and features, and match more accurately than standard techniques.