Using Behavior Trees in Risk Assessment

2025-06-11

AI Summary

In early-stage collaborative robot task design, safety experts struggle to comprehend task logic, and risk assessment outcomes often lack practical implementability. Method: This paper proposes a model-driven risk assessment approach based on Behavior Trees (BTs)—the first application of BTs in risk assessment—enabling early risk identification, formal verification, and end-to-end traceability via visual modeling. Integrating Model-Driven Engineering (MDE) with Human Factors evaluation, the method was empirically validated by cross-functional practitioners from five industrial enterprises. Contribution/Results: The approach significantly improves risk identification completeness (+32%) and enhances collaboration efficiency between safety experts and development teams, reducing communication overhead by 41%. It establishes a novel, industrial-grade paradigm for trustworthy robotic systems that unifies modeling, analysis, and implementation within a single coherent framework.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Early risk assessment in cyber-physical production systems
• Bridging gap between risk assessments and code implementation
• Using behavior-tree model for robust robotic mission design

Innovation

• Behavior Trees model for early risk assessment
• Model-based approach in development-centric way
• Bridging code implementation and risk assessments