Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments

Complex mission specifications can be often specified through temporal logics; such as Linear Temporal Logic and its syntactically co-safe fragment; scLTL. Finding trajectories that satisfy such specifications becomes hard if the robot is to fulfil the mission in an initially unknown environment; where neither locations of regions or objects of interest in the environment nor the obstacle space are known a priori. We propose an algorithm that; while exploring the environment; learns important semantic dependencies in the form of a semantic abstraction; and uses it to bias the growth of an Rapidly-exploring random graph towards faster mission completion. Our approach leads to finding trajectories that are much shorter than those found by the sequential approach; which first explores and then plans. Simulations comparing our solution to the sequential approach; carried out in 100 randomized office-like environments; show more than 50% reduction in the trajectory length.