Determining the Effects of Coulomb Friction on the Dynamics of Bearings and Transmissions in Robot Mechanisms

1993 ◽  
Vol 115 (2) ◽  
pp. 231-240 ◽  
Author(s):  
A. Gogoussis ◽  
M. Donath
Keyword(s):  
Coulomb Friction ◽  
High Speed ◽  
Robot Motion ◽  
Robot Dynamics ◽  
Quantitative Characterization ◽  
Joint Coordinates ◽  
Dynamics Problems ◽  
And Control

In order to accurately model robots for precision applications where dynamics are significant, it is important to include the effects of Coulomb friction in the bearings and transmissions. The general guidelines for analyzing friction at the joints will be discussed. It will be shown that friction can be related to the joint coordinates and their first and second time derivatives. The resulting extended robot dynamics formulation will be investigated as it applies to the inverse and forward robot dynamics problems. The analytical dependency of Coulomb friction on joint interactions is explicitly examined. As an illustration of friction effects in transmissions, we elaborate on the friction in harmonic drives and develop a method for its evaluation. The effect of friction in the bearings on the dynamics is also considered and a quantitative characterization of several specific cases is provided. This study is significant to understanding the design and control issues as they relate to achieving high speed precision robot motion.

Image-based estimation, planning, and control for high-speed flying through multiple openings

2020 ◽  
Vol 39 (9) ◽  
pp. 1122-1137
Author(s):  
Dejun Guo ◽  
Kam K Leang
Keyword(s):  
Real Time ◽  
Motion Control ◽  
High Speed ◽  
Poor Performance ◽  
Image Features ◽  
Robot Dynamics ◽  
Planning And Control ◽  
Invariant Features ◽  
Experimental Trials ◽  
And Control

This article focuses on enabling an aerial robot to fly through multiple openings at high speed using image-based estimation, planning, and control. State-of-the-art approaches assume that the robot’s global translational variables (e.g., position and velocity) can either be measured directly with external localization sensors or estimated onboard. Unfortunately, estimating the translational variables may be impractical because modeling errors and sensor noise can lead to poor performance. Furthermore, monocular-camera-based pose estimation techniques typically require a model of the gap (window) in order to handle the unknown scale. Herein, a new scheme for image-based estimation, aggressive-maneuvering trajectory generation, and motion control is developed for multi-rotor aerial robots. The approach described does not rely on measurement of the translational variables and does not require the model of the gap or window. First, the robot dynamics are expressed in terms of the image features that are invariant to rotation (invariant features). This step decouples the robot’s attitude and keeps the invariant features in the flat output space of the differentially flat system. Second, an optimal trajectory is efficiently generated in real time to obtain the dynamically-feasible trajectory for the invariant features. Finally, a controller is designed to enable real-time, image-based tracking of the trajectory. The performance of the estimation, planning, and control scheme is validated in simulations and through 80 successful experimental trials. Results show the ability to successfully fly through two narrow openings, where the estimation and planning computation and motion control from one opening to the next are performed in real time on the robot.

On-Board Characterization of Engine Dynamics for Health Monitoring and Control

2005 ◽  
Author(s):  
Onder Uluyol ◽  
Kyusung Kim ◽  
Charles Ball
Keyword(s):  
Feature Extraction ◽  
Health Monitoring ◽  
High Speed ◽  
Monitoring And Control ◽  
Mechanical Assembly ◽  
Time Data ◽  
Feature Extraction Method ◽  
Limited Bandwidth ◽  
And Control

This paper introduces a feature extraction method for characterization of gas turbine engine dynamics for the purpose of engine health monitoring as well as optimum control. For a vehicle health monitoring system that is comprehensive in its scope, and timely and accurate in its diagnosis, high fidelity engine models and a large amount of high-speed data both in steady-state as well as in transients are needed. However, limited computational resources available on-board, and the limited bandwidth capacity and the high cost of real-time data transmission place serious barriers in fulfilling that need. The approach presented in the paper seeks to overcome these barriers by separating the initial feature extraction stage of diagnostics algorithms from the modeling and trending stages. The first part which includes the detection of time instances that are critical to diagnosis and control is performed on board, while the latter is performed on a ground station. The approach is applied to the startup transient in a propulsion engine. A 50-fold reduction in data size is realized while achieving a highly accurate prognosis of hydro-mechanical assembly (HMA) failures.

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