Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service

TL;DR

This paper introduces a novel behavior tree-based task planning framework for multiple mobile robots that utilizes a data distribution service for asynchronous communication, enabling efficient task assignment and fault recovery in real-world scenarios.

Contribution

A new type of behavior tree action and improved nodes are proposed for multi-robot coordination using DDS, with a unified task planning unit for simultaneous task assignment.

Findings

Successfully coordinated three robots to visit four goal positions.

Demonstrated fault recovery through embedded behavior trees.

Validated framework's feasibility in real-world experiments.

Abstract

In this study, we propose task planning framework for multiple robots that builds on a behavior tree (BT). BTs communicate with a data distribution service (DDS) to send and receive data. Since the standard BT derived from one root node with a single tick is unsuitable for multiple robots, a novel type of BT action and improved nodes are proposed to control multiple robots through a DDS asynchronously. To plan tasks for robots efficiently, a single task planning unit is implemented with the proposed task types. The task planning unit assigns tasks to each robot simultaneously through a single coalesced BT. If any robot falls into a fault while performing its assigned task, another BT embedded in the robot is executed; the robot enters the recovery mode in order to overcome the fault. To perform this function, the action in the BT corresponding to the task is defined as a variable, which is shared with the DDS so that any action can be exchanged between the task planning unit and robots. To show the feasibility of our framework in a real-world application, three mobile robots were experimentally coordinated for them to travel alternately to four goal positions by the proposed single task planning unit via a DDS.