Estimation of Inertial Parameters of Manipulator Loads and Links

Atkeson, C.G.; An, Chae; Hollerbach, John M.

doi:10.1177/027836498600500306

Cited by 498 publications

(331 citation statements)

References 7 publications

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“…The kinematics used in the control is given in Supplementary Material. The force/torque measurements are transformed to the ultrasound probe tip and static forces caused by gravitation are compensated using Atkeson et al (1986). In the below sections, we use the transformed and compensated measurements of force and torque.…”

Section: Controlmentioning

confidence: 99%

An Ultrasound Robotic System Using the Commercial Robot UR5

et al. 2016

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The use of robots in health care has increased dramatically over the last decade. One area of research has been to use robots to conduct ultrasound examinations, either controlled by a physician or autonomously. This paper examines the possibility of using the commercial robot UR5 from Universal Robots to make a tele-operated robotic ultrasound system. Physicians diagnosing patients using ultrasound probes are prone to repetitive strain injuries, as they are required to hold the probe in uncomfortable positions and exert significant static force. The main application for the system is to relieve the physician of this strain by letting the them control a robot that holds the probe. A set of requirements for the system is derived from the state-of-the-art systems found in the research literature. The system is developed through a low-level interface for the robot, effectively building a new software framework for controlling it. Compliance force control and forward flow haptic control of the robot was implemented. Experiments are conducted to quantify the performance of the two control schemes. The force control is estimated to have a bandwidth of 16.6 Hz, while the haptic control is estimated to have a bandwidth of 65.4 Hz for the position control of the slave and 13.4 Hz for the force control of the master. Overall, the system meets the derived requirements and the main conclusion is that it is feasible to use the UR5 robot for robotic ultrasound applications.

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Section: Controlmentioning

confidence: 99%

An Ultrasound Robotic System Using the Commercial Robot UR5

et al. 2016

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“…As each leg has 3 Degrees of Freedom (DoFs), henceforth we will consider n = 3. The set of equations (1) are linear in the model parameters [6], [19] and the left-hand side of (1) can be rewritten in linear form with respect to a set of inertial parameters:…”

Section: Offline Leg Parameter Identificationmentioning

confidence: 99%

Online payload identification for quadruped robots

Tournois

Focchi

Prete³

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The identification of inertial parameters is crucial to achieve high-performance model-based control of legged robots. The inertial parameters of the legs are typically not altered during expeditions and therefore are best identified offline. On the other hand, the trunk parameters depend on the modules mounted on the robot, like a motor to provide the hydraulic power, or different sets of cameras for perception. This motivates the use of recursive approaches to identify online mass and the position of the Center of Mass (CoM) of the robot trunk, when a payload change occurs. We propose two such approaches and analyze their robustness in simulation. Furthermore, experimental trials on our 80-kg quadruped robot HyQ show the applicability of our strategies during locomotion to cope with large payload changes that would otherwise severely compromise the balance of the robot.

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“…As manipulators are designed to kinematic requirements, inertial parameters become a secondary property of the design. From an identification point of view, inertial parameters can be organized in to three groups: fully identifiable, identifiable in linear combinations, and completely unidentifiable according to Atkeson et al (1986). In the same publication, the authors provide methods for manipulator load and link parameter estimation.…”

Section: Model Parameter Estimationmentioning

confidence: 99%

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Gale¹,

Rahmati²,

Gravdahl³

et al. 2017

Front. Robot. AI

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A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input-output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t + 1) from known input reference up till and including time (t). A new partial least squares regression (PLSR) based method, nominal PLSR with interactions was developed and used to handle, unmodelled non-linearities. The performance of the new method is compared with least squares (LS). Different crossvalidation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.

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Estimation of Inertial Parameters of Manipulator Loads and Links

Cited by 498 publications

References 7 publications

An Ultrasound Robotic System Using the Commercial Robot UR5

An Ultrasound Robotic System Using the Commercial Robot UR5

Online payload identification for quadruped robots

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

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