On the Implementation of Behavior Trees in Robotics

There is a growing interest in Behavior Trees (BTs) as a tool to describe and implement robot behaviors. BTs were devised in the video game industry and their adoption in robotics resulted in the development of ad-hoc libraries to design and execute BTs that fit complex robotics software architectures. While there is broad consensus on how BTs work, some characteristics rely on the implementation choices done in the specific software library used. In this letter, we outline practical aspects in the adoption of BTs and the solutions devised by the robotics community to fully exploit the advantages of BTs in real robots. We also overview the solutions proposed in open-source libraries used in robotics, we show how BTs fit in robotic software architecture, and we present a use case example

Extended behavior trees for quick definition of flexible robotic tasks

he requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using solely extended Behavior Trees (eBT), a model formalized and discussed in this paper. At run-time, the robot can use the more abstract skills to plan a sequence using a PDDL planner, expand the sequence into a hierarchical tree, and re-organize it to optimize the time of execution and the use of resources. The optimization is demonstrated on a kitting operation in both simulation and lab environment, showing up to 20% save in the final execution time