Motion Planning Under Temporal Logic Specifications In Semantically Unknown Environments

TL;DR

This paper introduces a new automata-theoretic method for motion planning in uncertain environments with semantic labels, enabling robots to achieve complex spatio-temporal tasks expressed in scLTL logic through online replanning.

Contribution

It presents a novel product automaton construction that incorporates semantic uncertainty and employs value iteration for real-time motion replanning in unknown environments.

Findings

Effective handling of semantic label uncertainty in motion planning.

Successful online replanning demonstrated through simulations.

Theoretical guarantees on the proposed approach.

Abstract

This paper addresses a motion planning problem to achieve spatio-temporal-logical tasks, expressed by syntactically co-safe linear temporal logic specifications (scLTL\next), in uncertain environments. Here, the uncertainty is modeled as some probabilistic knowledge on the semantic labels of the environment. For example, the task is "first go to region 1, then go to region 2"; however, the exact locations of regions 1 and 2 are not known a priori, instead a probabilistic belief is available. We propose a novel automata-theoretic approach, where a special product automaton is constructed to capture the uncertainty related to semantic labels, and a reward function is designed for each edge of this product automaton. The proposed algorithm utilizes value iteration for online replanning. We show some theoretical results and present some simulations/experiments to demonstrate the efficacy of the proposed approach.