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Social Navigation in a Cognitive Architecture Using Dynamic Proxemic Zones
Jonatan Ginés1, Francisco Martín1, David Vargas1
1Intelligent Robotics Lab, Rey Juan Carlos University, Campus de Fuenlabrada, Camino del Molino s/n, 28943 Fuenlabrada, Spain.
Sensors (Basel, Switzerland)
|December 1, 2019
Summary
This study introduces a novel robot navigation algorithm that detects human personal space and perceives moods to minimize disruption. This social robot approach enhances human-robot interaction acceptability in shared environments.
Area of Science:
- Robotics
- Human-Robot Interaction
- Artificial Intelligence
Background:
- Social robots are increasingly present in human environments.
- Ensuring robot navigation is acceptable and non-disruptive to humans is crucial for widespread adoption.
- Existing navigation methods often fail to account for human social and personal space requirements.
Purpose of the Study:
- To propose and evaluate a novel robot navigation algorithm that enhances human acceptance.
- To develop a system where robots dynamically adjust their behavior based on human presence and emotional state.
- To integrate this adaptive navigation into a cognitive architecture for real-world applications.
Main Methods:
- A new navigation algorithm was developed, focusing on detecting human personal areas and proxemic zones.
- The algorithm incorporates a mood-perception system to dynamically adjust proxemic area sizes.
- The approach was integrated into a cognitive architecture and tested in controlled and uncontrolled environments.
Main Results:
- Quantitative results demonstrate improved social navigation metrics compared to traditional methods in controlled settings.
- Performance was validated in robotic competitions, measuring various social robotics indicators.
- The proposed algorithm showed a reduced impact on human activities during robot navigation.
Conclusions:
- The developed navigation algorithm significantly improves the acceptability of robots in human-populated areas.
- Perceiving human moods to adjust proxemic areas is a key innovation for seamless human-robot coexistence.
- This research contributes to the advancement of social robotics and human-centered AI systems.
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