scholarly journals Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 266
Author(s):  
Lorenzo Rapetti ◽  
Yeshasvi Tirupachuri ◽  
Kourosh Darvish ◽  
Stefano Dafarra ◽  
Gabriele Nava ◽  
...  
Keyword(s):  
Real Time ◽  
Inverse Kinematics ◽  
Motion Tracking ◽  
Lyapunov Analysis ◽  
System Configuration ◽  
Method Performance ◽  
Model Based ◽  
Time Motion ◽  
Velocity Correction ◽  
Time Critical

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.

scholarly journals Real-time motion tracking of cognitive Baxter robot based on differential inverse kinematics

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110240
Author(s):  
Shaobo Li ◽  
Xingxing Zhang ◽  
Jing Yang ◽  
Qiang Bai ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Real Time ◽  
Inverse Kinematics ◽  
Motion Tracking ◽  
Control Method ◽  
Time Estimation ◽  
Automatic Target Recognition ◽  
The Real ◽  
Time Motion ◽  
Wide Range ◽  
Real Time Tracking

The tracking motion of the robot is realized based on a specific robot or relying on an expensive movement acquisition system. It has the problems of complex control procedures, lack of real-time performance, and difficulty in achieving secondary development. We propose a robot real-time tracking control method based on the control principle of differential inverse kinematics, which fuses the position and joint angle information of the robot’s actuators to realize the real-time estimation of the user’s movement during the tracking process. The motion coordinates of each joint of the robot are calculated and the coordinate conversion between man and machine is realized with the combination of the Kinect sensor and the robot operating system. We have demonstrated the robustness and accuracy of the tracking method through the real-time tracking experiment of the Baxter robot. Our research has a wide range of application value, such as automatic target recognition, demonstration teaching, and so on. It provides an important reference for the research in the field of cognitive robots.

Real-time motion tracking in image-guided oral implantology

2008 ◽  
Vol 4 (4) ◽  
pp. 339-347 ◽  
Author(s):  
Xiaojun Chen ◽  
Yanping Lin ◽  
Yiqun Wu ◽  
Chengtao Wang
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Image Guided ◽  
Time Motion ◽  
Oral Implantology

scholarly journals Evaluation of real time motion tracking accuracy of customised IMU sensor for application in a mobile badminton virtual reality training system

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Zahari Taha ◽  
Mohd Yashim Wong ◽  
Hwa Jen Yap ◽  
Amirul Abdullah ◽  
Wee Kian Yeo
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Tracking ◽  
Training System ◽  
Microsoft Kinect ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Improve Performance ◽  
Motion Data ◽  
Time Motion

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.

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