Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems

TL;DR

This paper introduces a hierarchical framework that combines task and motion planning in multirobot systems using simple temporal networks, enabling scalable plan generation and robust execution.

Contribution

It presents a novel hierarchical approach integrating task and motion planning with temporal reasoning for multirobot coordination.

Findings

Scalable plan generation method for high-level tasks.

Robust plan execution that reduces re-planning.

Application to multirobot path planning case study.

Abstract

The authors present an overview of a hierarchical framework for coordinating task- and motion-level operations in multirobot systems. Their framework is based on the idea of using simple temporal networks to simultaneously reason about precedence/causal constraints required for task-level coordination and simple temporal constraints required to take some kinematic constraints of robots into account. In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account. In the plan-execution phase, the framework provides a method for absorbing an imperfect plan execution to avoid time-consuming re-planning in many cases. The authors use the multirobot path-planning problem as a case study to present the key ideas behind their framework for the long-term autonomy of multirobot systems.