Continuous Mobile Manipulator Performance Experiment 06-07-2022
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https://data.nist.gov/od/id/mds2-3187
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Mobile manipulators, which are robotic systems integrating an automatic or autonomous mobile base with a manipulator, can potentially enhance automation in many industrial and unstructured environments. Namely, large-scale manufacturing processes, typical in the aerospace, energy, transportation, and conformal additive manufacturing fields, encompass a notable subset of potential future mobile manipulator use-cases. Utilizing autonomous mobility for manipulator re-positioning could allow for continuous, simultaneous arm and mobile base cooperation, which is referred to as i.e., continuous performance. Continuous mobile manipulator capabilities may hold particular benefit for large, curved, and complex workpieces. However, such flexibility can also introduce additional sources of performance uncertainty, preventing mobile manipulators from satisfying stringent pose repeatability and accuracy requirements. To identify and quantify this uncertainty, the Configurable Mobile Manipulator Apparatus (CMMA) was developed by the National Institute of Standards and Technology. Previous test implementations with the apparatus included non-continuous mobile manipulator performance, such as static and indexed performance, but continuous performance measurement had only been previously demonstrated in simulation and on proof-of-concept hardware. This dataset was obtained through the transfer of simulations and algorithms for continuous registration to an industrial mobile manipulator platform and through a subsequent 2^3 factorial designed experiment to compare the performance and robustness of two continuous localization methods: 1) A deterministic spiral search and 2) A stochastic Unscented Kalman Filter (UKF) search across two selected mobile base speeds and sides of the CMMA. Supplementary data obtained prior to the experiment, such as source code, calibration data, mobile base map and configuration data, coordinate system measurements, and robot/client to ground-truth system time synchronization is also included, along with the analysis source code and results files generated in conducting the performance evaluation. The experiment included the following improvements from the prior experiment conducted in February 2022: 1) Further manual tuning of the UKF hyper-parameters, 2) added retro-reflective tape edge detection to assist initial coordinate registration and to eliminate anomalies where the first fiducial was not detected, 3) eliminated infrared reflections on the CMMA and from the lab windows to improve ground-truth data capture quality, and 4) the coordinate system measurement between the cart transporter map and the ground truth system was re-done.*Certain commercial equipment, instruments, or materials are identified in this dataset to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.



