The paper presents an incremental replanning algorithm, dubbed LTL-D*, for temporal-logic-based task planning in a dynamically changing environment. The considered failures are categorized into two classes: (i) the desired LTL specification can be satisfied via replanning, and (ii) the desired LTL specification is infeasible to meet strictly and can only be satisfied in a "relaxed" fashion.
For feasible tasks, the algorithm leverages the D* Lite algorithm and employs a distance metric within the synthesized automaton to quantify the degree of the task violation and then replan incrementally. This ensures plan optimality and reduces planning time, especially when frequent replanning is required.
For infeasible tasks, the algorithm optimally revises the plan to minimally violate the desired task specifications by integrating the violation penalty into the key design of the incremental search. This allows the algorithm to efficiently find an optimal run that deviates the least from the original task specification.
The approach is implemented in a robot navigation simulation to demonstrate a significant improvement in the computational efficiency for replanning by two orders of magnitude compared to baseline methods.
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by Jiming Ren,H... lúc arxiv.org 04-02-2024
https://arxiv.org/pdf/2404.01219.pdfYêu cầu sâu hơn