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Past research into robotic planning with temporal logic specifications,notably Linear Temporal Logic (LTL), was largely based on singular formulas forindividual or groups of robots. But with increasing task complexity, LTLformulas unavoidably grow lengthy, complicating interpretation andspecification generation, and straining the computational capacities of theplanners. By leveraging the intrinsic structure of tasks, we introduced ahierarchical structure to LTL specifications with requirements on syntax andsemantics, and proved that they are more expressive than their flatcounterparts. Second, we employ a search-based approach to synthesize plans fora multi-robot system, accomplishing simultaneous task allocation and planning.The search space is approximated by loosely interconnected sub-spaces, witheach sub-space corresponding to one LTL specification. The search ispredominantly confined to a single sub-space, transitioning to anothersub-space under certain conditions, determined by the decomposition ofautomatons. Moreover, multiple heuristics are formulated to expedite the searchsignificantly. A theoretical analysis concerning completeness and optimality isconducted under mild assumptions. When compared with existing methods onservice tasks, our method outperforms in terms of execution times withcomparable solution quality. Finally, scalability is evaluated by testing agroup of 30 robots and achieving reasonable runtimes.

Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications