This C++ 14 library provides a framework to create BehaviorTrees. It was designed to be flexible, easy to use, reactive and fast.
Even if our main use-case is robotics, you can use this library to build AI for games, or to replace Finite State Machines in your application.
There are few features that make BehaviorTree.CPP unique, when compared to other implementations:
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It makes asynchronous Actions, i.e. non-blocking, a first-class citizen.
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You can build reactive behaviors that execute multiple Actions concurrently.
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Trees are defined using a Domain Specific Scripting scripting language (based on XML), and can be loaded at run-time; in other words, even if written in C++, Trees are not hard-coded.
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You can statically link your custom TreeNodes or convert them into plugins which can be loaded at run-time.
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It provides a type-safe and flexible mechanism to do Dataflow between Nodes of the Tree.
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It includes a logging/profiling infrastructure that allows the user to visualize, record, replay and analyze state transitions.
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Last but not least: it is well documented!
You can learn about the main concepts, the API and the tutorials here: https://www.behaviortree.dev/
To find more details about the conceptual ideas that make this implementation different from others, you can read the final deliverable of the project MOOD2Be.
The main goal of this project is to create a Behavior Tree implementation that uses the principles of Model Driven Development to separate the role of the Component Developer from the Behavior Designer.
In practice, this means that:
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Custom TreeNodes must be reusable building blocks. You should be able to implement them once and reuse them to build many behaviors.
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To build a Behavior Tree out of TreeNodes, the Behavior Designer must not need to read nor modify the C++ source code of a given TreeNode.
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Complex Behaviours must be composable using Subtrees
Many of the features and, sometimes, the apparent limitations of this library, might be a consequence of this design principle.
For instance, having a scoped BlackBoard, visible only in a portion of the tree, is particularly important to avoid "name pollution" and allow the creation of large scale trees.
Editing a BehaviorTree is as simple as editing a XML file in your favourite text editor.
If you are looking for a more fancy graphical user interface (and I know you do) check Groot out.
Click on the following image to see a short video of how the C++ library and the graphic user interface are used to design and monitor a Behavior Tree.
On Ubuntu, you are encourage to install the following dependencies:
sudo apt-get install libzmq3-dev libboost-dev
Other dependencies are already included in the 3rdparty folder.
To compile and install the library, from the BehaviorTree.CPP folder, execute:
mkdir build; cd build
cmake ..
make
sudo make install
If you want to use BT.CPp in your application a typical CMakeLists.txt file will look like this:
cmake_minimum_required(VERSION 3.5)
project(hello_BT)
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
find_package(BehaviorTreeV3)
add_executable(${PROJECT_NAME} "hello_BT.cpp")
target_link_libraries(${PROJECT_NAME} BT::behaviortree_cpp_v3)You can easily install the package with the command
sudo apt-get install ros-$ROS_DISTRO-behaviortree-cpp-v3
If you want to compile it with catkin, you must include this package to your catkin workspace.
This library was developed at Eurecat - https://eurecat.org/en/ (main author, Davide Faconti) in a joint effort with the Italian Institute of Technology (Michele Colledanchise).
This software is one of the main components of MOOD2Be, which is one of the six Integrated Technical Projects (ITPs) selected from the RobMoSys first open call. Therefore, MOOD2Be has been supported by the European Horizon2020 project RobMoSys. This software is RobMoSys conformant.
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Introductory article: Behavior trees for AI: How they work
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How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees. Michele Colledanchise and Petter Ogren. IEEE Transaction on Robotics 2017.
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Behavior Trees in Robotics and AI, published by CRC Press Taylor & Francis, available for purchase (ebook and hardcover) on the CRC Press Store or Amazon.
The Preprint version (free) is available here: https://arxiv.org/abs/1709.00084
The MIT License (MIT)
Copyright (c) 2014-2018 Michele Colledanchise Copyright (c) 2018-2020 Davide Faconti
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.