scholarly journals Industrial Robot Tool Position Estimation using Inertial Measurements in a Complementary Filter and an EKF

2017 ◽  
Vol 50 (1) ◽  
pp. 12748-12752 ◽  
Author(s):  
E. Hedberg ◽  
J. Norén ◽  
M. Norrlöf ◽  
S. Gunnarsson
Keyword(s):  
Industrial Robot ◽  
Position Estimation ◽  
Complementary Filter ◽  
Inertial Measurements ◽  
Tool Position

A nonlinear complementary filter for underwater navigation using inertial measurements

2015 ◽  
Author(s):  
Francesco Di Corato ◽  
Manuel Novi ◽  
Francesco Pacini ◽  
Giacomo Paoli ◽  
Andrea Caiti ◽  
...  
Keyword(s):  
Complementary Filter ◽  
Underwater Navigation ◽  
Inertial Measurements

scholarly journals VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4386
Author(s):  
Jingzhe Wang ◽  
Leilei Li ◽  
Huan Yu ◽  
Xunya Gui ◽  
Zucheng Li
Keyword(s):  
Large Scale ◽  
Linear Optimization ◽  
Position Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Non Linear ◽  
Outdoor Environments ◽  
Novel Method ◽  
And Performance ◽  
Inertial Measurements

Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system’s viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance.

Industrial Robot Forward Calibration Method and Results

1986 ◽  
Vol 108 (1) ◽  
pp. 1-8 ◽  
Author(s):  
D. E. Whitney ◽  
C. A. Lozinski ◽  
J. M. Rourke
Keyword(s):  
Least Squares ◽  
Search Algorithm ◽  
Industrial Robot ◽  
Calibration Method ◽  
Gear Transmission ◽  
Serial Link ◽  
Transmission Errors ◽  
Joint Compliance ◽  
Rms Error ◽  
Tool Position

This paper presents a forward calibration method for serial link manipulators. The procedure uses a model whose parameters represent link lengths, joint encoder offsets, the relative orientations of consecutive axes, and experimentally observed effects of joint compliance, backlash, and gear transmission errors. A least squares numerical search algorithm uses theodolite measurements of tool position and the robot’s joint encoder readings to estimate the complete model’s parameters. The calibrated robot model predicts theodolite readings with an rms error of 5.7 × 10−5 rad (0.13 mm). Other tests show that this technique improves the robot model from as much as 4.8 mm error to 0.3 mm error.

Design and Analysis of a Pose Estimator for Quadrotor MAVs With Modified Dynamics and Range Measurements

2017 ◽  
Author(s):  
Eranga Fernando ◽  
George K. Mann ◽  
Oscar De Silva ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Position Estimation ◽  
Estimation Task ◽  
Range Measurements ◽  
Aerial Vehicles ◽  
Accurate Position ◽  
Inertial Measurements ◽  
Modified Dynamics ◽  
Estimation Range

This paper presents the design and analysis of a pose estimator for quadrotor micro aerial vehicles (MAVs). The proposed design uses the dynamic model of the quadrotor with aerodynamic effects and uses the extended Kalman filter (EKF) formulation for state estimation. Range measurements to known locations, inertial measurements and height measurements are used for the estimation task. The purpose of the study is to evaluate the performance of the estimator when navigating through a changing indoor setting. The study investigates the effect of changing number of rannge measurements, different geometrical arrangements of range sensors and changing availability of confident height information on the performance of the estimator. Performance of the estimator for each scenario is numerically analyzed. Finally a criteria is proposed for selecting the sensors, number of range measurements, geometric location of sensors which facilitates accurate position estimation using the proposed method.

scholarly journals Robotic tooling calibration based on linear and nonlinear formulations.

2021 ◽  
Author(s):  
Mohamed Helal
Keyword(s):  
Industrial Robot ◽  
Kinematic Model ◽  
Position Estimation ◽  
Error Model ◽  
Linear Formulation ◽  
Experimental Result ◽  
Robot Calibration ◽  
Nonlinear Formulation ◽  
Self Calibration ◽  
Calibration Methods

Industrial robot calibration packages, such as ABB CalibWare, are developed only for robot calibration. As a result, the robotic tooling systems designed and fabricated by the user are often calibrated in an ad-hoc fashion. In this thesis, a systematic way for robotic tooling calibration is presented in order to overcome this problem. The idea is to include the tooling system as an extended body in the robot kinematic model, from which two error models are established. The first error model is associated with the robot, while the second error model is associated with the tooling. Once the robot is fully calibrated, the first error will be reduced to the required accuracy. Thus, the method is focused on the second error model. For the tool error calibration, two formulations were used. The first is a linear formulation based on conventional calibration as well as self-calibration methods while the second is a nonlinear formulation. The conventional linear formulation was extensively investigated and implemented while the self-calibration was proven to be inadequate for the tooling calibration. Moreover, the nonlinear formulation was demonstrated to be very effective and accurate through experimental result. The end-effector position estimation as well as the tool pose estimation were obtained using a 3D vision system as an off-line error measurement technique.

scholarly journals Novel Multi-IMU Tight Coupling Pedestrian Localization Exploiting Biomechanical Motion Constraints

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5364
Author(s):  
Dina Bousdar Ahmed ◽  
Estefania Munoz Diaz ◽  
Juan Jesús García Domínguez
Keyword(s):  
State Of The Art ◽  
Position Estimation ◽  
Position Error ◽  
Average Height ◽  
Tight Coupling ◽  
Localization System ◽  
Motion Constraints ◽  
Coupling System ◽  
Inertial Measurements ◽  
Better Than

In this article, we present a novel tight coupling inertial localization system which simultaneously processes the measurements of two inertial measurement units (IMUs) mounted on the leg, namely the upper thigh and the front part of the foot. Moreover, the proposed system exploits motion constraints of each leg link; that is, the thigh and the foot. To derive these constraints, we carry out a motion tracking experiment to collect both ground truth data and inertial measurements from IMUs mounted on the leg. The performance of the tight coupling system is assessed with a data set of approximately 10 h. The evaluation shows that the average 2D-position error of the proposed tight coupling system is at least 50% better than the average 2D-position error of two state-of-the-art systems, whereas the average height error of the tight coupling system is at least 75% better than the average height error of the two state-of-the-art systems. In this work, we improve the accuracy of the position estimation by introducing biomechanical constraints in an inertial localization system. This article allows to observe, for the first time, heading errors of an inertial localization system by using only inertial measurements and without the need for using maps or repeating totally or partially the walked trajectory.

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