  This paper investigates the task coordination of multi-robot where each robot
has a private individual temporal logic task specification; and also has to
jointly satisfy a globally given collaborative temporal logic task
specification. To efficiently generate feasible and optimized task execution
plans for the robots, we propose a hierarchical multi-robot temporal task
planning framework, in which a central server allocates the collaborative tasks
to the robots, and then individual robots can independently synthesize their
task execution plans in a decentralized manner. Furthermore, we propose an
execution plan adjusting mechanism that allows the robots to iteratively modify
their execution plans via privacy-preserved inter-agent communication, to
improve the expected actual execution performance by reducing waiting time in
collaborations for the robots. The correctness and efficiency of the proposed
method are analyzed and also verified by extensive simulation experiments.
