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

arXiv: https://arxiv.org/abs/2201.10918 · HTML: https://mlsanity.com/paper/2201.10918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Towards Blended Reactive Planning and Acting using Behavior Trees](https://mlsanity.com/paper/1611.00230/) (2016)
- [Learning of Behavior Trees for Autonomous Agents](https://mlsanity.com/paper/1504.05811/) (2015)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [On the Implementation of Behavior Trees in Robotics](https://mlsanity.com/paper/2106.15227/) (2021)
- [Task Planning with Belief Behavior Trees](https://mlsanity.com/paper/2008.09393/) (2020)
- [Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions](https://mlsanity.com/paper/2008.09427/) (2020)
- [Improving the Parallel Execution of Behavior Trees](https://mlsanity.com/paper/1809.04898/) (2018)
- [Improving the Modularity of AUV Control Systems using Behaviour Trees](https://mlsanity.com/paper/1811.00426/) (2018)
- [Asynchronous Behavior Trees with Memory aimed at Aerial Vehicles with Redundancy in Flight Controller](https://mlsanity.com/paper/1907.00253/) (2019)
- [A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions](https://mlsanity.com/paper/2104.05305/) (2021)

## Abstract (arXiv)

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.