Abstract
Compliant robots with constant joint stiffness (Serial Elastic Actuators - SEA), on the contrary to ones with variable joint stiffness (Variable Stiffness Actuators – VSA), have limited capabilities for modulating robot mechanical impedance in the interaction task. However, in the case of kinematic redundancy in specific tasks, robots can exploit the null space to adjust End-Effector (EE) Cartesian stiffness. Thus, prior knowledge of the task path or the operational workspace can be used to pre-compute joint stiffness that can enable maximal ratio between maximal and minimal stiffness of the robot’s EE during the task execution, and therefore shape achievable EE stiffness to best fit the task execution. In that light, this paper elaborates on the preselection of joint stiffnesses which influences the achievable robot’s Cartesian stiffness in a specific task. Besides optimizing the available operational EE stiffness, by pre-computed joint stiffness values, the robot will be able to adapt better to specific tasks and provide a better framework for safe and efficient physical human-robot interaction. The paper presents an approach to the selection of predefined joint stiffness values of the 7-DOFs KUKA LWR, where joint stiffness is achieved/emulated with torque feedback. In the simulation experiments, the approach is depicted in the preselection of two joint stiffness values within the prescribed range, while other joint stiffness is set constant.
This paper was funded by the Program for excellent projects of young researchers (PROMIS), Science Fund and Ministry of Education, Science and Technological Development of the Republic of Serbia, and bilateral project ARRS BI-RS/18-19-053.
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Lukić, B., Jovanović, K., Knežević, N., Žlajpah, L., Petrič, T. (2020). Maximizing the End-Effector Cartesian Stiffness Range for Kinematic Redundant Robot with Compliance. In: Zeghloul, S., Laribi, M., Sandoval Arevalo, J. (eds) Advances in Service and Industrial Robotics. RAAD 2020. Mechanisms and Machine Science, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-48989-2_23
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