Real-time reactive task allocation and planning of large heterogeneous multi-robot systems with temporal logic specifications

Existing methods for the task allocation and planning (TAP) of multi-robot systems with temporal logic specifications mainly rely on optimization-based approaches or graph search techniques applied to the product automaton. However, these methods suffer from high computational cost and scale poorly with the number of robots and the complexity of temporal logic tasks, thus limiting the applicability in real-time implementation, especially for large multi-robot systems. To address these challenges, this work develops a novel TAP framework that can solve reactive temporal logic planning problems for large-scale heterogeneous multi-robot systems (HMRS) in real time. Specifically, we develop a planning decision tree (PDT) to represent the task progression and task allocation specialized for HMRS with temporal logic specifications. Based on the PDT, we develop two key search algorithms—the planning decision tree search (PDTS) and the interactive planning decision tree search (IPDTS)—where PDTS generates an offline plan which will be modified online by PDTS and IPDTS jointly to enable fast reactive planning if environmental changes or temporary tasks occur. Such a design can generate satisfying plan for HMRS with multiple orders of magnitude more robots than those that existing methods can manipulate. Rigorous analysis shows that the PDT-based planning is feasible (i.e., the generated plan is applicable) and complete (i.e., a feasible plan, if exits, is guaranteed to be found). The algorithm complexity further indicates that the solution time is only linearly proportional to the robot numbers and types. Simulation and experiment results demonstrate that reactive plan can be generated for large HMRS in real-time, which outperforms the state-of-the-art methods.