BodyCoM

@article{Knezevic2024,

title = {A Geometric Approach to Task-Specific Cartesian Stiffness Shaping},

author = {Nikola Knežević and Branko Lukić and Tadej Petrič and Kosta Jovanovič},

doi = {10.1007/s10846-023-02035-6},

issn = {15730409},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},

volume = {110},

number = {1},

abstract = {Controlling the exact Cartesian stiffness values of a robot end-effector (EE) is troublesome because of difficulties associated with estimating the stiffness and controllability of a full Cartesian stiffness matrix. However, most practical applications require only quantitative (high/low) stiffness values in the EE motion direction (or perpendicular direction). Full control of the stiffness matrix requiring too many control inputs which is hardly possible in practical applications. To ensure the efficiency of execution for a range of redundant robots, we present an algorithm for shaping a robot's Cartesian stiffness ellipsoid, a more intuitive and visual stiffness representation, using a nonlinear sequential least square programming optimization. The algorithm is designed to optimize the joint stiffness values and the trajectory of the robot's joints, using null-space exploration, for a given task. Using eigenvalue decomposition of the stiffness matrix, the algorithm minimizes the orientation difference between the major axis of the current and the desired stiffness ellipsoid and specify a scaling factor between the major and the minor axis. The presented approach allows the user to better understand and control of a robot, regardless of the user's knowledge of the achievable stiffness range and the interdependencies of the Cartesian stiffness matrix elements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Petric2023,

title = {Kinematic model calibration of a collaborative redundant robot using a closed kinematic chain},

author = {Tadej Petrič and Leon Žlajpah},

url = {https://doi.org/10.1038/s41598-023-45156-6},

doi = {10.1038/s41598-023-45156-6},

issn = {20452322},

year  = {2023},

date = {2023-01-01},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {1--12},

publisher = {Nature Publishing Group UK},

abstract = {In this paper, we propose a novel approach for the kinematic calibration of collaborative redundat robots, focusing on improving their precision using a cost-effective and efficient method. We exploit the redundancy of the closed-loop kinematic chain by utilizing a spherical joint, enabling precise definition of the robot end-effector position while maintaining free joint motion in the null space. Leveraging the availability of joint torque sensors in most collaborative robots, we employ a kinesthetic approach to obtain constrained joint motion for calibration. An optimization approach is utilized to determine the optimal kinematic parameters based on measured joint positions and a constrained end-effector position defined by the spherical joint. The effectiveness of the proposed method is demonstrated and validated on the Franka Emika Panda robot, a 7-DoF robot. Results indicate a significant enhancement in absolute accuracy, with comparable performance to more expensive sensor systems such as optical measurement systems. Our approach offers a practical and cost-effective solution for improving the precision of collaborative robots.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zlajpah2022,

title = {Kinematic calibration for collaborative robots on a mobile platform using motion capture system},

author = {Leon Žlajpah and Tadej Petrič},

url = {https://www.sciencedirect.com/science/article/pii/S0736584522001296},

doi = {https://doi.org/10.1016/j.rcim.2022.102446},

issn = {0736-5845},

year  = {2022},

date = {2022-09-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {79},

pages = {102446},

abstract = {For modern robotic applications that go beyond the typical industrial environment, absolute accuracy is one of the key properties that make this possible. There are several approaches in the literature to improve robot accuracy for a typical industrial robot mounted on a fixed frame. In contrast, there is no method to improve robot accuracy when the robot is mounted on a mobile base, which is typical for collaborative robots. Therefore, in this work, we proposed and analyzed two approaches to improve the absolute accuracy of the robot mounted on a mobile platform using an optical measurement system. The first approach is based on geometric operations used to calculate the rotation axes of each joint. This approach identifies all rotational axes, which allows the calculation of the Denavit–Hartenberg (DH) parameters and thus the complete kinematic model, including the position and orientation errors of the robot end-effector and the robot base. The second approach to parameter estimation is based on optimization using a set of joint positions and end-effector poses to find the optimal DH parameters. Since the robot is mounted on a mobile base that is not fixed, an optical measurement system was used to dynamically and simultaneously measure the position of the robot base and the end-effector. The performance of the two proposed methods was analyzed and validated on a 7-DoF Franka Emika Panda robot mounted on a mobile platform PAL Tiago-base. The results show a significant improvement in absolute accuracy for both proposed approaches. By using the proposed approach with the optical measurement system, we can easily automate the estimation of robot kinematic parameters with the aim of improving absolute accuracy, especially in applications that require high positioning accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}