Fault-Tolerant Six-DoF Pose Estimation for Tendon-Driven Continuum Mechanisms

Antonin Raffin, Bastian Deutschmann, Freek Stulp
<span title="2021-04-30">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t4zwwbshrrfd3hjbg4s3bysm7q" style="color: black;">Frontiers in Robotics and AI</a> </i> &nbsp;
We propose a fault-tolerant estimation technique for the six-DoF pose of a tendon-driven continuum mechanisms using machine learning. In contrast to previous estimation techniques, no deformation model is required, and the pose prediction is rather performed with polynomial regression. As only a few datapoints are required for the regression, several estimators are trained with structured occlusions of the available sensor information, and clustered into ensembles based on the available
more &raquo; ... By computing the variance of one ensemble, the uncertainty in the prediction is monitored and, if the variance is above a threshold, sensor loss is detected and handled. Experiments on the humanoid neck of the DLR robot DAVID, demonstrate that the accuracy of the predicted pose is significantly improved, and a reliable prediction can still be performed using only 3 out of 8 sensors.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/frobt.2021.619238">doi:10.3389/frobt.2021.619238</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33996921">pmid:33996921</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8120429/">pmcid:PMC8120429</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/65dfpydxwjcktbxojgtpelbumq">fatcat:65dfpydxwjcktbxojgtpelbumq</a> </span>
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