Using Behavior Trees in Risk Assessment

Cyber-physical production systems increasingly involve collaborative robotic missions, requiring more demand for robust and safe missions. Industries rely on risk assessments to identify potential failures and implement measures to mitigate their risks. Although it is recommended to conduct risk assessments early in the design of robotic missions, the state of practice in the industry is different. Safety experts often struggle to completely understand robotics missions at the early design stages of projects and to ensure that the output of risk assessments is adequately considered during implementation. This paper presents a design science study that conceived a model-based approach for early risk assessment in a development-centric way. Our approach supports risk assessment activities by using the behavior-tree model. We evaluated the approach together with five practitioners from four companies. Our findings highlight the potential of the behavior-tree model in supporting early identification, visualisation, and bridging the gap between code implementation and risk assessments' outputs. This approach is the first attempt to use the behavior-tree model to support risk assessment; thus, the findings highlight the need for further development.

Behavior Trees and State Machines in Robotics Applications

Autonomous robots combine a variety of skills to form increasingly complex behaviors called missions. While the skills are often programmed at a relatively low level of abstraction, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State Machines have been the go-to modeling language for decades, but recently, the language of Behavior Trees gained attention among roboticists. Originally designed for computer games to model autonomous actors, Behavior Trees offer an extensible tree-based representation of missions and are praised for supporting modular design and reuse of code. However, even though, several implementations of the language are in use, little is known about its usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are behavior tree and state machine concepts used in applications? We present a study of the key language concepts in Behavior Trees and their use in real-world robotic applications. We identify behavior tree languages and compare their semantics to the most well-known behavior modeling language in robotics: state machines. We mine open-source repositories for robotics applications that use the languages and analyze this usage. We find similarity aspects between the two behavior modeling languages in terms of language design and their usage in open-source projects to accommodate the need of robotic domain. We contribute a dataset of real-world behavior models, hoping to inspire the community to use and further develop this language, associated tools, and analysis techniques.

Behavior Trees in Action: A Study of Robotics Applications

Autonomous robots combine a variety of skills to form increasingly complex behaviors called missions. While the skills are often programmed at a relatively low level of abstraction, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. Recently, the language of Behavior Trees gained attention among roboticists for this reason. Originally designed for computer games to model autonomous actors, Behavior Trees offer an extensible tree-based representation of missions. However, even though, several implementations of the language are in use, little is known about its usage and scope in the real world. How do behavior trees relate to traditional languages for describing behavior? How are behavior tree concepts used in applications? What are the benefits of using them? We present a study of the key language concepts in Behavior Trees and their use in real-world robotic applications. We identify behavior tree languages and compare their semantics to the most well-known behavior modeling languages: state and activity diagrams. We mine open source repositories for robotics applications that use the language and analyze this usage. We find that Behavior Trees are a pragmatic language, not fully specified, allowing projects to extend it even for just one model. Behavior trees clearly resemble the models-at-runtime paradigm. We contribute a dataset of real-world behavior models, hoping to inspire the community to use and further develop this language, associated tools, and analysis techniques.