  This paper presents a hierarchical framework to solve the multi-robot
temporal task planning problem. We assume that each robot has its individual
task specification and the robots have to jointly satisfy a global
collaborative task specification, both described in linear temporal logic.
Specifically, a central server firstly extracts and decomposes a collaborative
task sequence from the automaton corresponding to the collaborative task
specification, and allocates the subtasks in the sequence to robots. The robots
can then synthesize their initial execution strategies based on locally
constructed product automatons, combining the assigned collaborative tasks and
their individual task specifications. Furthermore, we propose a distributed
execution strategy adjusting mechanism to iteratively improve the time
efficiency, by reducing wait time in collaborations caused by potential
synchronization constraints. We prove the completeness of the proposed
framework under assumptions, and analyze its time complexity and optimality.
Extensive simulation results verify the scalability and optimization efficiency
of the proposed method.
