Multi-Robot Task Planning under Individual and Collaborative Temporal Logic Specifications

TL;DR

This paper presents a hierarchical framework for multi-robot task planning that combines centralized allocation of collaborative tasks with decentralized synthesis of individual plans, incorporating privacy-preserving adjustments to optimize performance.

Contribution

It introduces a novel hierarchical planning approach with an execution plan adjustment mechanism that enhances efficiency while preserving privacy.

Findings

Framework effectively generates feasible plans for multi-robot tasks.

Plan adjustment reduces waiting times and improves execution efficiency.

Method is validated through extensive simulations.

Abstract

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.