• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.133-140
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• 2008

In this paper, a small and effective attitude estimation system for a humanoid robot was developed. Four small inertial sensors were packed and used for inertial measurements(3D accelerometer and three 1D gyroscopes.) An effective 3D pose estimation algorithm for low cost DSP using an extended Kalman filter was developed and evaluated. The 3D pose estimation algorithm has a very simple structure composed by 3 modules of a linear acceleration estimator, an external acceleration detector and an pseudo-accelerometer output estimator. The algorithm also has an effective switching structure based on probability and simple feedback loop for the extended Kalman filter. A special test equipment using linear motor for the testing of the 3D pose sensor was developed and the experimental results showed its very fast convergence to real values and effective responses. Popular DSP of TMS320F2812 was used to calculate robot's 3D attitude and translated acceleration, and the whole system were packed in a small size for humanoids robots. The output of the 3D sensors(pitch, roll, 3D linear acceleration, and 3D angular rate) can be transmitted to a humanoid robot at 200Hz frequency.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.9
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• pp.783-790
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• 2016

One of the main factors related to the deterioration of estimation accuracy in inertial measurement unit (IMU)-based orientation determination is the object's acceleration. This is because accelerometer signals under accelerated motion conditions cannot be longer reference vectors along the vertical axis. In order to deal with this issue, some orientation estimation algorithms adopt acceleration-compensating mechanisms. Such mechanisms include the simple switching techniques, mechanisms with adaptive estimation of acceleration, and acceleration model-based mechanisms. This paper compares these three mechanisms in terms of estimation accuracy. From experimental results under accelerated dynamic conditions, the following can be concluded. (1) A compensating mechanism is essential for an estimation algorithm to maintain accuracy under accelerated conditions. (2) Although the simple switching mechanism is effective to some extent, the other two mechanisms showed much higher accuracies, particularly when test conditions were severe.