Model Based Human Behaviour and Intention Estimation in Physical Human-Robot Interaction Scenarios

Date

2025-01-01

Author

Ozkara, Efe
Ankaralı, Mustafa Mert

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This research introduces a framework for physical Human-Robot Interaction (pHRI) scenarios that estimates human behavior and intention. By incorporating this estimation template, velocity-based adaptive controllers can enhance both the effectiveness and comfort of pHRI. Existing methods in the literature primarily rely on sensor readings from the robot, overlooking the dynamics and relationships between the human arm and the robot. In our approach, we model these dynamics and relationships as a spring-damper system and assume that human velocity is the system's input. This formulation enables the use of a Kalman filter to estimate human velocity from the robot's sensor measurements in real-time. With an accurate estimation of human velocity, our approach provides a more precise understanding of human intention, enabling smoother and more adaptive interactions. The real-time prediction of human velocity and intent allows for more intuitive and responsive robot behavior, leading to significant improvements in both the effectiveness and comfort of pHRI scenarios. We conducted systematic experiments using the Franka Emika Panda collaborative manipulator, demonstrating that our approach enhances pHRI performance compared to alternative methods, particularly in terms of responsiveness and user experience.

URI

https://hdl.handle.net/11511/117882

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Department of Electrical and Electronics Engineering, Conference / Seminar