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Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments
Oliver Avram1, Stefano Baraldo1, Anna Valente1
1Automation, Robotics and Machines Laboratory, Department of Innovative Technologies, Institute of Systems and Technologies for Sustainable Production, University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Viganello, Switzerland.
Frontiers in Robotics and AI
|July 18, 2022
Summary
This study introduces a framework using behavior trees to enhance robot autonomy in industrial settings. This improves human-robot interaction and safety by enabling robots to handle complex tasks and support human workers.
Area of Science:
- Robotics
- Artificial Intelligence
- Human-Robot Interaction
Background:
- Industrial environments (e.g., oil and gas, aeronautics) are unstructured and unpredictable.
- Automation alone cannot replace human dexterity and judgment in these settings.
- Robots need adaptable behaviors for dynamic environments and natural human interaction.
Purpose of the Study:
- To introduce a deliberative framework for mobile robotic platforms.
- To enhance robot autonomy and extend operational periods in maintenance, repair, and overhaul applications.
- To improve human-robot interaction and collaboration in industrial tasks.
Main Methods:
- Utilized behavior trees for the robot's high-level deliberative intelligence.
- Integrated social behaviors to interpret human emotional state and intention.
- Developed capabilities for task performance, support, and seamless human-robot shared work plans.
Main Results:
- The framework enables robots to interpret human health and cognitive load.
- Robots can collaborate with humans or take over tasks.
- Behavior trees provide modularity, reactivity, and deliberation for complex scenarios.
Conclusions:
- The proposed framework significantly enhances robot autonomy in harsh industrial environments.
- It improves safety, productivity, and efficiency by enabling robots to perform risky jobs.
- This leads to more natural and acceptable human-robot collaboration.
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