A Formal Framework for Reactive Heterogeneous Multirobot Task Allocation in Uncertain Semantic Environments

Existing multirobot task allocation (MRTA) research primarily assumes an environment with known geometric and semantic information. However, in most real-world scenarios, semantic information, such as the locations and classifications of landmarks, is often uncertain. This uncertainty is exacerbated by dynamic requirements, like the sudden addition or removal of tasks, making it challenging for robots to respond reactively. Moreover, tasks in MRTA typically involve multiple constraints, including temporal requirements, diverse capabilities, varying resource needs, and inter-task dependencies. To address these challenges, we propose a reactive task allocation framework for heterogeneous multirobot systems that accounts for temporal requirements and multiple task constraints described by $\mathrm {LTL^{\mathcal {R}}}$ . Our approach assumes an environment with known geometry but unknown semantic landmarks. To efficiently solve task allocations, we encode $\mathrm {LTL^{\mathcal {R}}}$ along with the system's states into a proposed planning decision tree for exploration. Upon detecting a relevant semantic landmark, the reactive multiconstraint planning decision tree (RMC-PDT) is triggered for re-planning. Extensive experiments validate three key features of our method: 1) efficient reactive planning; 2) multiconstraint task solving; and 3) scalability.