Development, implementation, and investigation of admittance control algorithms for physical human-robot interaction scenarios

Date

2024-11

Author

Özkara, Efe

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This thesis presents a novel approach to physical Human-Robot Interaction (pHRI) that incorporates real-time estimation of human "behavior" and "intention". After inspection of previous applications of admittance control, it is notable that human intention is used for improving the admittance control strategy. While some recent studies incorporate human intention into admittance control strategies, they rely solely on sensor readings and overlook the interaction impedance between the human and the robot. To address this limitation, we propose a method that models the dynamic interaction between the human arm and the robot using a simple spring-mass system to represent induced compliance. This impedance modeling enables the estimation of "human" velocity via a dynamic observer (i.e., Kalman filter) based on force and velocity measurements of the robot. We believe this approach improves the accuracy of human velocity and intention estimation, offering more reliable inputs for admittance control policies. To validate its effectiveness, we designed an adaptive admittance controller that generates reference velocity commands from estimated human behavior and feeds these trajectories to the robot’s internal velocity control module. This method is adaptable to any robotic system with force measurement capabilities and a closed-loop velocity controller, avoiding the need to model the robot's full dynamics. Experiments with the Franka Emika Panda cobot manipulator demonstrate that our methodology enhances pHRI by enabling more intuitive and responsive robot behavior, achieving significant improvements over existing approaches.

Subject Keywords

Admittance Control, Human Intention, Physical Human-Robot Interaction, Adaptive Admittance Control, Haptics

URI

https://hdl.handle.net/11511/112717

Collections

Graduate School of Natural and Applied Sciences, Thesis