State Estimation and Mission Planning for Precision-critical Aerial Field Robotics
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Date
2022
Publication Type
Doctoral Thesis
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Abstract
Rotary-wing micro aerial vehicles (MAVs) are disrupting geomatics, logistics, and maintenance industries. Being airborne and easy to deploy they are a great tool to investigate large areas, inaccessible structures, and dangerous environments. Commercial platforms exist that automatically generate digital maps of cities, count warehouse inventory or perform non-destructive testing on industrial assets. Many more applications are envisioned but to turn those visions into reality, MAVs will have to fly closer to structures, physically engage with the environment, and integrate novel sensor modalities while providing ever more autonomy to make these advanced functionalities available to non-expert users. This thesis presents three precision-critical applications that contribute to the state of the art of industrial aerial robotics.
The first contribution is a multi-agent aerial robotic system to search, pick-up and relocate metallic objects. To interact with small, partly moving objects, the aerial robot requires precise navigation and detection capabilities as well as a compliant grasping process. Our system combines (i) GNSS empowered visual-inertial state estimation with (ii) collision avoiding, model predictive position control and (iii) geometric computer vision into a precise autonomous transportation system. The system was deployed in various environments, including successful participation in the Mohamed Bin Zayed International Robotics Challenge 2017. It shows that basic autonomous aerial physical interaction in outdoor environments is possible given well-defined task and environment constraints such as known target properties and workspace.
The second contribution is an MAV with ground-penetrating synthetic aperture radar (GPSAR) for humanitarian landmine detection. Airborne GPSAR is highly dependent on the flight path and the precision with which the radar antenna positions are determined. In this work we present a navigation framework that allows generating arbitrary circular and stripmap GPSAR missions controlled at low altitude above ground level. A self-calibrating, factor graph-based localization framework combines dual receiver RTK GNSS with inertial measurements to estimate the position of the radar antennas during flight. A custom hardware triggering mechanism ensures temporal correlation of the navigation sensors reaching sub-μs accuracy with respect to GNSS time. The system is self-contained and enables autonomous optical and radar surveys. In various experiments we show the advantages of the custom system design, including uniform radar sampling, self-calibration, and localization batch optimization. Finally, we validate mapping of buried objects to demonstrate the system's suitability for humanitarian landmine detection.
The final contribution is an automatic coverage path planner to enable aerial surveys with nadir imaging sensors in obstructed environments. The algorithm is based on exact cellular decomposition, which splits an admissible polygon flight area into simple polygons that can be covered efficiently with consecutive back-and-forth motions. Our algorithm improves the connection of the individual polygon coverage patterns by considering multiple starting points for each polygon. Formulating the problem as an equality generalized traveling salesman problem and basing it on a strong computational geometry foundation created an implementation that plans optimal coverage missions within seconds. The open-source software is popular in the robotics community and has been a valuable mission planning tool for our GPSAR missions.
Overall this thesis focuses on high-precision aerial robotic state estimation and MAV mission planning under spatial and motion constraints. All contributions have been tested in self-contained, real-world applications. The underlying software is available open-source to help bring forth a new generation of industrial aerial robots that operate autonomously in the vicinity of obstacles.
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published
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Examiner : Siegwart, Roland
Examiner : Kelly, Jonathan
Examiner : Saska, Martin
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ETH Zurich
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Software
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Subject
MAV; State estimation; Path planning; GNSS; IMU; Ground penetrating radar (GPR); Synthetic aperture radar (SAR); demining; UAV; Drone; Sensors; Time synchronisation; Field robotics; Aerial Interaction; Object detection; Autonomous robots; Autonomous navigation; Control; Coverage path planning; System integration
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.