This paper addresses a multi-robot mission planning problem in uncertain semantic environments. The environment is modeled by static labeled landmarks with uncertain positions and classes giving rise to an uncertain semantic map generated by semantic SLAM algorithms. Our goal is to design control policies for sensing robots so that they can accomplish complex collaborative high level tasks captured by global temporal logic specifications. To account for environmental and sensing uncertainty, we extend  Linear Temporal Logic (LTL) by including sensor-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification. The sensor-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that can be updated online to adapt to the map that is continuously learned by existing semantic SLAM methods . We provide extensive experiments that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale planning tasks in the presence of uncertainty .
This paper addresses a multi-robot planning problem in partially unknown semantic environments. The environment is assumed to have known geometric structure (e.g., walls) and to be occupied by static labeled landmarks with uncertain positions and classes . This modeling approach gives rise to an uncertain semantic map generated by semantic SLAM algorithms. Our goal is to design control policies for robots equipped with noisy perception systems so that they can accomplish collaborative tasks captured by global temporal logic specifications. To account for environmental and perceptual uncertainty, we extend a fragment of Linear Temporal Logic (LTL) , called co-safe LTL, by including perception-based atomic predicates allowing us to incorporate uncertainty-wise and probabilistic satisfaction requirements directly into the task specification. The perception-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that are updated online to adapt to a continuously learned semantic map . We provide extensive experiments to demonstrate the efficiency of the proposed planning architecture .
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mission
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uncertain
partially unknown
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modeled
assumed to have known geometric structure (e.g., walls) and to be occupied
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giving
. This modeling approach gives
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sensing robots
robots equipped with noisy perception systems
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complex collaborative high level
collaborative
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sensing
perceptual
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a fragment of
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by including sensor-based
, called co-safe LTL, by including perception-based atomic
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uncertainty
uncertainty-wise
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sensor-based
perception-based
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can be
are
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the map that is continuously learned by existing semantic SLAM methods
a continuously learned semantic map
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that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale planning tasks in the presence of uncertainty
to demonstrate the efficiency of the proposed planning architecture