Evaluation of Navigation in Mobile Robots for Long-Term Autonomy in Automotive Manufacturing Environments
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
In recent times, a number of reference implementations of Simultaneous Localization and Mapping (SLAM) and navigation techniques have been made publicly available via the ROS Community. Several implementations have transitioned to commercial products (vacuum robots, drones, warehouse robots, etc.). However, in such cases, they are specialized and optimized for their specific domains of deployment. In particular, their success criteria have been based primarily on mission completion and safety of humans around them. In this light, deployment in any new operational design domain (ODD) requires at least a careful verification of performance and often re-optimization. We seek the technological gaps that need to be addressed to ensure the mobile robots are fit for automotive manufacturing environments. Automotive final assembly environments pose significant additional challenges for mobile robot deployment. They are replete with relatively unstructured tasks with significant uncertainty, involve tasks with skills that require robots to work in collaboration with humans and are time sensitive. Currently, metrics for evaluating mobile robot functionalities have been based on accuracy, functionality and resource consumption. In addition to these, automotive assembly also requires consistency in execution times. This work evaluates the navigational capabilities of mobile robots in environments with static objects for time consistency as required by an automotive assembly process. The evaluation uses ASTM F3244-17 standard test method. It is performed on a simulated robot in Gazebo environment and Clearpath OTTO1500 robot in a laboratory environment.
CitationSingh Gill, J., Tomaszewski, M., Jia, Y., Pisu, P. et al., "Evaluation of Navigation in Mobile Robots for Long-Term Autonomy in Automotive Manufacturing Environments," SAE Technical Paper 2019-01-0505, 2019, https://doi.org/10.4271/2019-01-0505.
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