Multi-agent systems can be extremely efficient when solving a team-wide task in a concurrent manner. However, without proper synchronization, the correctness of the combined behavior is hard to guarantee, such as to follow a specific ordering of sub-tasks or to perform a simultaneous collaboration. This work addresses the minimum-time task planning problem for multi-agent systems under complex global tasks stated as Linear Temporal Logic (LTL) formulas. These tasks include the temporal and spatial requirements on both independent local actions and direct sub-team collaborations. The proposed solution is an anytime algorithm that combines the partial-ordering analysis of the underlying task automaton for task decomposition, and the branch and bound (BnB) search method for task assignment. Analyses of its soundness, completeness and optimality as the minimal completion time are provided. It is also shown that a feasible and near-optimal solution is quickly reached while the search continues within the time budget. Furthermore, to handle fluctuations in task duration and agent failures during online execution, an adaptation algorithm is proposed to synchronize execution status and re-assign unfinished subtasks dynamically to maintain correctness and optimality. Both algorithms are validated rigorously over large-scale systems via numerical simulations and hardware experiments, against several strong baselines.
翻译:多试剂系统在以同时方式解决整个团队任务时可能极为高效。然而,如果没有适当的同步,则很难保证综合行为的正确性,例如遵循子任务的具体顺序或同时开展合作。这项工作解决了复杂全球任务(线性时空逻辑(LTL)公式)下多试剂系统的最低时间任务规划问题。这些任务包括独立当地行动和直接分队协作的时间和空间要求。提议的解决方案是一种随时随地算法,它将任务拆解的基本任务自动图的局部排序分析与任务任务分配的分支和约束(BnB)搜索方法结合起来。在提供最起码的完成时间时,分析其健全性、完整性和最佳性。还表明,在继续搜寻的同时,将很快达成一个可行和接近最佳的解决办法。此外,为了处理任务期限的波动和在网上执行过程中的代理失败,建议采用适应性算法,使执行状态和未完成的子任务配置同步,以进行任务拆解,分支和约束(BnB)任务分配方法。在提供最短的完成时间时,分析其健全、完整和最佳的基数级测试时,这些算法都适用于稳妥的大型的硬件和最佳的系统。