A Distributed Dynamic Framework to Allocate Collaborative Tasks Based on Capability Matching in Heterogeneous Multirobot Systems

Hoi-Yin Lee, Peng Zhou, Bin Zhang, Liuming Qiu, Bowen Fan, Anqing Duan, Jingtao Tang, Tin Lun Lam, David Navarro-Alarcon

Abstract: Collaboration among a group of robots with heterogeneous capabilities is an important research problem that enables to combine different robot functionalities, and thus, conducts complex tasks that may be difficult to achieve by a single robot with limited resources. In this article, we propose a new distributed task allocation framework based on the capability matching of heterogeneous robots. The framework is composed of an ontological dynamic knowledge graph model and a hardware control scheme to model the capability and optimize resource utilization for collaborative tasks. We introduce an intuitive hardware control scheme based on a dynamic knowledge graph that resolves possible conflicts between the hardware control of different types of robots. Action sequences are produced by a task and motion planning algorithm to collaboratively perform the assigned task. The performance of the proposed methodology is evaluated by both simulations and hardware experiments.