Multi-Robot Task Planning under Individual and Collaborative Temporal Logic Specifications
This addresses task coordination for multi-robot systems, offering an incremental improvement in handling temporal logic specifications.
The paper tackles the problem of coordinating multi-robot systems with both individual and collaborative temporal logic tasks by proposing a hierarchical planning framework and an iterative adjustment mechanism, resulting in reduced waiting times and verified efficiency through simulations.
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.