Nicholas Lawrance

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Lawrance

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Nicholas

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0000-0003-2167-7427

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Publications 1 - 10 of 28

Decentralised finite-time consensus for second-order multi-agent system under event-triggered strategy
Zhang, Liang; Zhang, Zexu; Lawrance, Nicholas; et al. (2020)
IET Control Theory & Applications
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
Achermann, Florian; Kolobov, Andrey; Dey, Debadeepta; et al. (2020)
Proceedings of Machine Learning Research ~ Proceedings of the 2020 Conference on Robot Learning
While optical cameras are ubiquitous in robotics, some robots can sense the world in several sections of the electromagnetic spectrum simultaneously, which can extend their capabilities in fundamental ways. For instance, many fixed-wing UAVs carry both optical and thermal imaging cameras, potentially allowing them to detect temperature difference-induced atmospheric updrafts, map their locations, and adjust their flight path accordingly to increase their time aloft. A key step for unlocking the potential offered by multi-spectral data is generating consistent, multi-spectral maps of the environment. In this work, we introduce MultiPoint, a novel data-driven method for generating interest points and associated descriptors for registering optical and thermal image pairs without knowledge of the relative camera viewpoints. Existing pixel-based alignment methods are accurate but too slow to work in near-real time, while feature-based methods such as SuperPoint are fast but produce poor-quality cross-spectral matches due to interest point instability in thermal images. MultiPoint capitalizes on the strengths of both approaches. An offline mutual information-based procedure is used to align cross-spectral image pairs from a training set, which are then processed by our generalized multi-spectral homographic adaptation stage to generate highly repeatable interest points that are invariant across viewpoint changes in both spectra. These are used to train a MultiPoint deep neural network by exposing this model to both same-spectrum and cross-spectral image pairs. This model is then deployed for fast and accurate online interest point detection. We show that MultiPoint outperforms existing techniques for feature-based image alignment using a dataset of real-world thermal-optical imagery captured by a UAV during flights in different conditions and release this dataset, the first of its kind.
Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar
Girod, Rik; Lawrance, Nicholas; Streichenberg, Lucas; et al. (2022)
Field Robotics
Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8μs and as precise as 0.1μs with localization rates of 1kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.
Circling Back: Dubins set Classification Revisited
Lim, Jaeyoung; Achermann, Florian; Girod, Rik; et al. (2023)
Dubins paths are commonly used in robot motion planning for generating minimal-length fixed-curvature motions between two states. Existing analytical approaches generate the Dubins set - the set of paths consisting of different sequences of arcs and straight lines that contains the optimal solution for travelling between a state pair. Typically, the length for each path in the set is evaluated and the shortest path is selected. Dubins set classification approaches use an additional pre-calculation phase to further reduce the Dubins set before evaluating path length. This can significantly reduce computational costs, especially for sampling based planners in the Dubins space that perform many path length evaluations during a search. This paper addresses the issue of degenerate solutions from the Dubins set classification method presented in~\cite{shkel_classication_2001} when solving for a shortest path using Dubins paths. The results show that a Dubins set classification approach can result in 58$\%$ reduced computation time for computing a Dubins path but still return the optimal path when compared to evaluating the full Dubins set.
Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV
Rohr, David; Studiger, Matthias; Stastny, Thomas; et al. (2021)
IEEE Robotics and Automation Letters
This letter presents the modeling, system identification and nonlinear model predictive control (NMPC) design for longitudinal, full envelope velocity control of a small tiltwing hybrid unmanned aerial vehicle (H-UAV). A first-principles based dynamics model is derived and identified from flight data. It captures important aerodynamic effects including propeller-wing interaction and stalled airfoils, but is still simple enough for on-board online trajectory optimization. Based on this model, a high-level NMPC is formulated which optimizes throttle, tilt-rate and pitch-angle setpoints in order to track longitudinal velocity trajectories. We propose and investigate different references suitable to regularize the optimization problem, including both offline generated trims as well as preceding NMPC solutions. In simulation, we compare the NMPC with a frequently reported dynamic inversion approach for H-UAV velocity control. Finally, the NMPC is validated in flight experiments through a series of transition maneuvers, demonstrating good tracking capabilities in the full flight envelope.
Reinforcement Learning for Outdoor Balloon Navigation: A Successful Controller for an Autonomous Balloon
Jeger, Simon L.; Lawrance, Nicholas; Achermann, Florian; et al. (2024)
IEEE Robotics & Automation Magazine
Autonomous ballooning allows for energy-efficient long-range missions but introduces significant challenges for planning and control algorithms, due to their single degree of actuation: vertical rate control through either buoyancy or vertical thrust. Lateral motion is typically due to the wind; thus, balloon flight is both nonholonomic and often stochastic. Finally, wind is very challenging to sense remotely, and estimates are often available only via low-temporal-and-spatial-frequency predictions from large-scale weather models and direct in situ measurements. In this work, reinforcement learning (RL) is used to generate a control policy for an autonomous balloon navigating between 3D positions in a time- and spatially varying wind field. The agent uses its position and velocity, the relative position of the target, and an estimate of the surrounding wind field to command a target altitude. The wind information contains local measurements and an encoding of global wind predictions from a large-scale numerical weather prediction (NWP) model around the current balloon location. The RL algorithm used in this work, the soft actor-critic (SAC), is trained with a reward favoring paths that reach as close as possible to the target, with minimum time and actuation costs. We evaluate our approach first in simulation and then with a controlled indoor experiment, where we generate an artificial wind field and reach a median distance of 23.4 cm from the target within a volume of 3.5 x 3.5 x 3.5 m over 30 trials. Finally, using a fully autonomous custom designed outdoor prototype capable of controlling altitude, long-range communication, redundant localization, and onboard computation, we validate our approach in a real-world setting. Over six flights, the agent navigates to predefined target positions, with an average target distance error of 360 m after traveling approximately 10 km within a volume of 22 x 22 x 3.2 km.
In-Wing Pressure Measurements for Airspeed and Airflow Angle Estimation and High Angle-of-Attack Flight
Heinrich, Gian-Andrea; Vogt, Stephanie; Lawrance, Nicholas; et al. (2022)
Journal of Guidance, Control, and Dynamics
Small uncrewed aerial vehicles (UAVs) capable of stable, controlled stalled descent offer the possibility for a wider operating envelope and for precise, steep approaches and landings. However, the aerodynamics of stalled flight are inherently complex and underexplored compared to flight regimes with attached flow. This paper presents a number of advancements in these areas—particularly focusing on pressure sensors for estimating flow states. This paper presents a modular, open-source design for in-wing, flow-based differential pressure sensor arrays for estimating flow states without the requirement of fragile pitot tubes or airflow vanes. An extensive, publicly available set of wind tunnel and flight test results from a small UAV with 24 in-wing pressure sensors is provided. The data contain a wide range of incidence angles, including deep stalled states. This paper also presents data-driven regression models for predicting airflow angles and airspeed using only pressure information. Our results demonstrate predictions for airspeed, angle of attack (AoA), and angle of slip (AoS) with root mean square error (RMSE) of 0.07 m⋅s−1, 0.20°, and 2.83°, respectively, using wind tunnel data and all 24 sensors. Finally, this paper provides a placement analysis to determine the optimal sensor locations for estimating airflow data with fewer pressure sensors. Models trained and validated with real flight data show airspeed, AoA, and AoS predictions with RMSE of 0.44 m⋅s−1, 1.54°, and 4.07°, respectively, using data from only three pressure sensors each.
Differential Sweep Attitude Control for Swept Wing UAVs
Harms, Marvin; Kaufmann, Noah; Rockenbauer, Friedrich M.; et al. (2020)
2020 International Conference on Unmanned Aircraft Systems (ICUAS)
A novel approach for attitude control of swept wing unmanned aerial vehicles (UAVs) is presented, involving the use of only differential wing sweep and rudder deflection. An analytic aerodynamic model of the aircraft based on simple sweep theory is derived in a first step. The prediction of a vortex lattice method is then compared to the initial model. Based on the body moment analysis of the two models, design constraints and a control structure are proposed and implemented on a small scale UAV with variable sweep wings. The control structure involves a cascaded PID controller with a nonlinear mapping from controller output to sweep angles. The obtained simulation results show that simultaneous bank and elevation inputs can be tracked successfully by the attitude controller. Tracking of step inputs and dynamic inputs in the roll direction using only wing sweep is demonstrated in flight tests. The results show that the nonlinear mapping achieves decoupling of the roll and pitch movement, but performance is limited by the inertia of the moving wings. © 2020 IEEE.
Revisiting boustrophedon coverage path planning as a generalized traveling salesman problem
Girod, Rik; Lawrance, Nicholas; Chung, Jen Jen; et al. (2021)
Springer Proceedings in Advanced Robotics ~ Field and Service Robotics
In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs). Airborne systems can assist humanitarian demining by surveying suspected hazardous areas (SHAs) with cameras, ground-penetrating synthetic aperture radar (GPSAR), and metal detectors. Most available coverage planner implementations for MAVs do not consider obstacles and thus cannot be deployed in obstructed environments. We describe an open-source framework to perform coverage planning in polygon flight corridors with obstacles. Our planner extends boustrophedon coverage planning by optimizing over different sweep combinations to find the optimal sweep path, and considers obstacles during transition flights between cells. We evaluate the path planner on 320 synthetic maps and show that it is able to solve realistic planning instances fast enough to run in the field. The planner achieves 14% lower path costs than a conventional coverage planner. We validate the planner on a real platform where we show low-altitude coverage over a sloped terrain with trees.
Learning to Predict the Wind for Safe Aerial Vehicle Planning
Achermann, Florian; Lawrance, Nicholas; Ranftl, René; et al. (2019)
2019 International Conference on Robotics and Automation (ICRA)

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