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Autonomous robots operating in large knowledgeintensive domains requireplanning in the discrete (task) space and the continuous (motion) space. Inknowledge-intensive domains, on the one hand, robots have to reason at thehighestlevel, for example the regions to navigate to or objects to be picked upand their properties; on the other hand, the feasibility of the respectivenavigation tasks have to be checked at the controller execution level.Moreover, employing multiple robots offer enhanced performance capabilitiesover a single robot performing the same task. To this end, we present anintegrated multi-robot task-motion planning framework for navigation inknowledge-intensive domains. In particular, we consider a distributedmulti-robot setting incorporating mutual observations between the robots. Theframework is intended for motion planning under motion and sensing uncertainty,which is formally known as belief space planning. The underlying methodologyand its limitations are discussed, providing suggestions for improvements andfuture work. We validate key aspects of our approach in simulation.

Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space