Share Email Print

Proceedings Paper

Optimal accelerometer placement on a robot arm for pose estimation
Author(s): Indika B. Wijayasinghe; Joseph D. Sanford; Shamsudeen Abubakar; Mohammad Nasser Saadatzi; Sumit K. Das; Dan O. Popa
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The performance of robots to carry out tasks depends in part on the sensor information they can utilize. Usually, robots are fitted with angle joint encoders that are used to estimate the position and orientation (or the pose) of its end-effector. However, there are numerous situations, such as in legged locomotion, mobile manipulation, or prosthetics, where such joint sensors may not be present at every, or any joint. In this paper we study the use of inertial sensors, in particular accelerometers, placed on the robot that can be used to estimate the robot pose. Studying accelerometer placement on a robot involves many parameters that affect the performance of the intended positioning task. Parameters such as the number of accelerometers, their size, geometric placement and Signal-to-Noise Ratio (SNR) are included in our study of their effects for robot pose estimation. Due to the ubiquitous availability of inexpensive accelerometers, we investigated pose estimation gains resulting from using increasingly large numbers of sensors. Monte-Carlo simulations are performed with a two-link robot arm to obtain the expected value of an estimation error metric for different accelerometer configurations, which are then compared for optimization. Results show that, with a fixed SNR model, the pose estimation error decreases with increasing number of accelerometers, whereas for a SNR model that scales inversely to the accelerometer footprint, the pose estimation error increases with the number of accelerometers. It is also shown that the optimal placement of the accelerometers depends on the method used for pose estimation. The findings suggest that an integration-based method favors placement of accelerometers at the extremities of the robot links, whereas a kinematic-constraints-based method favors a more uniformly distributed placement along the robot links.

Paper Details

Date Published: 16 May 2017
PDF: 13 pages
Proc. SPIE 10216, Smart Biomedical and Physiological Sensor Technology XIV, 102160B (16 May 2017); doi: 10.1117/12.2262918
Show Author Affiliations
Indika B. Wijayasinghe, Univ. of Louisville (United States)
Joseph D. Sanford, The Univ. of Texas at Arlington (United States)
Shamsudeen Abubakar, Univ. of Louisville (United States)
Mohammad Nasser Saadatzi, Univ. of Louisville (United States)
Sumit K. Das, Univ. of Louisville (United States)
Dan O. Popa, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 10216:
Smart Biomedical and Physiological Sensor Technology XIV
Brian M. Cullum; Douglas Kiehl; Eric S. McLamore, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?