Cloud-Edge Architecture for the Precise Positioning of Autonomous Mobile
Robots through a Fine-Tuned RNN
Abstract
The precise localization of mobile robots in unstructured
environments is of utmost importance for many industrial and field
applications, especially when the mobile robot is part of a more complex
kinematic chain, such as a mobile manipulator. Being able to precisely
localize affects the outcome of tasks that rely on an open-loop
kinematic computation, such as work-station docking procedures. To
achieve a repeatable and precise localization and positioning, mobile
robots generally rely on onboard sensors, most commonly 2D laser
scanners, whose readings are subjected to noise and numerous disturbing
factors (e.g., materials reluctance). Problems arise when precise
localization is needed in dynamic and unstructured environments where
generally applicable methods won’t perform adequately or might be
time-consuming to set up. In this work, we propose a cloud-edge
computing architecture to deploy a recurrent neural network (RNN) based
registration system, which uses a pair of consecutive LiDAR readings to
estimate a fixed transformation. The capability of RNNs to process
contiguous inputs will help neglect errors embedded in punctual laser
scanner readings and output a more precise registration estimation. In
such a way, the RNN can estimate a displacement error based on multiple
consecutive readings and act as a sensor to be employed in a closed-loop
control scheme. To tackle the dynamic and unstructured environments, the
model is firstly tuned on synthetic LiDAR data to embed rigid
transformations into the deep learning model, for then rapidly
fine-tuned on local scenarios. After model architecture and optimization
of hyperparameters, the devised model is tested in different scenarios,
comparing the precise positioning capability of the AMR(autonomous
mobile robot) with that of a classical registration algorithm. The
results suggest that an RNN model can greatly improve the registration
precision of laser scanner signals and, consequently, the precise
positioning efficiency of AMRs.
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