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

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.

References in corpus (2)

• Optimal Target Assignment and Path Finding for Teams of Agents
• Multi-Agent Path Finding with Delay Probabilities

Cited by in corpus (8)

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• Searching with Consistent Prioritization for Multi-Agent Path Finding
• Multi-Agent Path Finding with Deadlines
• Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation
• Concurrent-Allocation Task Execution for Multi-Robot Path-Crossing-Minimal Navigation in Obstacle Environments