Buchumschlag

Enhancing Human-Robot Interaction through Ensemble Intention Recognition and Trajectory Tracking

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Veröffentlicht in:IISE Annual Conference. Proceedings (2025), p. 1-7
1. Verfasser: Alinezhad, Elnaz
Weitere Verfasser: Mohammadzadeh, Ali Kamali, Masoud, Sara
Veröffentlicht:
Institute of Industrial and Systems Engineers (IISE)
Schlagworte:
Leap Motion
Behavior
Accuracy
Collaboration
Deep learning
Adaptability
Trends
Artificial neural networks
Robots
Data processing
Manufacturing
Machine learning
Tracking
Time series
Virtual reality
Robustness
Artificial intelligence
Trajectories
Recognition
Neural networks
Decision making
Human motion
Algorithms
Human engineering
Real time
Ensemble learning
Industry 5.0
Human behavior
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Abstract:Effective intention recognition and trajectory tracking are critical for enabling collaborative robots (cobots) to anticipate and support human actions in Human-Robot Interaction (HRI). This study investigates the application of ensemble deep learning to classify human intentions and track movement trajectories using data collected from Virtual Reality (VR) environments. VR provides a controlled, immersive setting for precise monitoring of human behavior, facilitating robust model training. We develop and evaluate ensemble models combining CNNs, LSTMs, and Transformers, leveraging their complementary strengths. While CNN and CNN-LSTM models achieved high accuracy, they exhibited limitations in distinguishing specific intentions under certain conditions. In contrast, the CNN-Transformer model demonstrated superior precision, recall, and F1-scores in intention classification and exhibited robust trajectory tracking. By integrating multiple architectures, the ensemble approach enhanced predictive performance, improving adaptability to complex human behaviors. These findings highlight the potential of ensemble learning in advancing real-time human intention understanding and motion prediction, fostering more intuitive and effective HRI. The proposed framework contributes to developing intelligent cobots capable of dynamically adapting to human actions, paving the way for safer and more efficient collaborative workspaces.
DOI:10.21872/2025IISE_6188
Quelle:Science Database