2 research outputs found
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband
Indoor autonomous navigation requires a precise and accurate localization
system able to guide robots through cluttered, unstructured and dynamic
environments. Ultra-wideband (UWB) technology, as an indoor positioning system,
offers precise localization and tracking, but moving obstacles and
non-line-of-sight occurrences can generate noisy and unreliable signals. That,
combined with sensors noise, unmodeled dynamics and environment changes can
result in a failure of the guidance algorithm of the robot. We demonstrate how
a power-efficient and low computational cost point-to-point local planner,
learnt with deep reinforcement learning (RL), combined with UWB localization
technology can constitute a robust and resilient to noise short-range guidance
system complete solution. We trained the RL agent on a simulated environment
that encapsulates the robot dynamics and task constraints and then, we tested
the learnt point-to-point navigation policies in a real setting with more than
two-hundred experimental evaluations using UWB localization. Our results show
that the computational efficient end-to-end policy learnt in plain simulation,
that directly maps low-range sensors signals to robot controls, deployed in
combination with ultra-wideband noisy localization in a real environment, can
provide a robust, scalable and at-the-edge low-cost navigation system solution.Comment: Accepted by ICAART 2021 - http://www.icaart.org
UWB-based system for UAV Localization in GNSS-Denied Environments: Characterization and Dataset
Small unmanned aerial vehicles (UAV) have penetrated multiple domains over
the past years. In GNSS-denied or indoor environments, aerial robots require a
robust and stable localization system, often with external feedback, in order
to fly safely. Motion capture systems are typically utilized indoors when
accurate localization is needed. However, these systems are expensive and most
require a fixed setup. Recently, visual-inertial odometry and similar methods
have advanced to a point where autonomous UAVs can rely on them for
localization. The main limitation in this case comes from the environment, as
well as in long-term autonomy due to accumulating error if loop closure cannot
be performed efficiently. For instance, the impact of low visibility due to
dust or smoke in post-disaster scenarios might render the odometry methods
inapplicable. In this paper, we study and characterize an ultra-wideband (UWB)
system for navigation and localization of aerial robots indoors based on
Decawave's DWM1001 UWB node. The system is portable, inexpensive and can be
battery powered in its totality. We show the viability of this system for
autonomous flight of UAVs, and provide open-source methods and data that enable
its widespread application even with movable anchor systems. We characterize
the accuracy based on the position of the UAV with respect to the anchors, its
altitude and speed, and the distribution of the anchors in space. Finally, we
analyze the accuracy of the self-calibration of the anchors' positions.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2020