Distributed Behavior Trees for Heterogeneous Robot Teams

TL;DR

This paper introduces a novel distributed behavior tree framework for heterogeneous robot teams, enabling flexible, scalable, and coherent mission execution across diverse robots with dynamic team compositions.

Contribution

It presents a formal definition and open source implementation of distributed behavior trees tailored for heterogeneous robot teams, addressing team heterogeneity and dynamic mission requirements.

Findings

Effective distributed execution demonstrated in experiments

Supports complex real-world robotic missions

Open source ros_bt_py library available

Abstract

Heterogeneous Robot Teams can provide a wide range of capabilities and therefore significant benefits when handling a mission. However, they also require new approaches to capability and mission definition that are not only suitable to handle heterogeneous capabilities but furthermore allow a combination or distribution of them with a coherent representation that is not limiting the individual robot. Behavior Trees offer many of the required properties, are growing in popularity for robot control and have been proposed for multirobot coordination, but always as separate behavior tree, defined in advance and without consideration for a changing team. In this paper, we propose a new behavior tree approach that is capable to handle complex real world robotic missions and is geared towards a distributed execution by providing built in functionalities for cost calculation, subtree distribution and data wiring. We present a formal definition, its open source implementation as ros_bt_py library and experimental verification of its capabilities.