Nicholas R. J. Lawrance — accepted AI-conference papers

2026

Towards Robust Optimization-Based Autonomous Dynamic Soaring With a Fixed-Wing UAV

Marvin Harms, Jaeyoung Lim, David Rohr, Friedrich M. Rockenbauer, Nicholas R. J. Lawrance, Roland Siegwart

RA-L 2026

Dynamic soaring is a flying technique to exploit the energy available in wind shear layers, enabling potentially unlimited flight without the need for internal energy sources. We propose a framework for autonomous dynamic soaring with a fixed-wing uncrewed aerial vehicle (UAV). The framework makes u

Cited by 0 Source ↗Scholar ↗

2024

Safe Low-Altitude Navigation in Steep Terrain With Fixed-Wing Aerial Vehicles

Jaeyoung Lim, Florian Achermann, Rik Girod, Nicholas R. J. Lawrance, Roland Siegwart

RA-L 2024

Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications. By design, they also require large open spaces due to limited maneuverability. However, strict regulatory and safety altitude limits constrain the available

Cited by 9 Source ↗code ↗Scholar ↗

2024

Under-Canopy Navigation Using Aerial Lidar Maps

Lucas Carvalho de Lima, Nicholas R. J. Lawrance, Kasra Khosoussi, Paulo V. K. Borges, Michael Brünig

RA-L 2024

Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aeria

Cited by 2 Source ↗Scholar ↗

2022

Power-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents

Eugenio Cuniato, Nicholas R. J. Lawrance, Marco Tognon, Roland Siegwart

RA-L 2022

As the performance of autonomous systems increases, safety concerns arise, especially when operating in non-structured environments. To deal with these concerns, this work presents a safety layer for mechanical systems that detects and responds to unstable dynamics caused by external disturbances. T

Cited by 22 Source ↗Scholar ↗

2021

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

David Rohr, Matthias Studiger, Thomas Stastny, Nicholas R. J. Lawrance, Roland Siegwart

RA-L 2021

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 dat

Cited by 30 Source ↗Scholar ↗

2019

Learning to Predict the Wind for Safe Aerial Vehicle Planning

Florian Achermann, Nicholas R. J. Lawrance, René Ranftl, Alexey Dosovitskiy, Jen Jen Chung, Roland Siegwart

ICRA 2019poster

Obtaining an accurate estimate of the local wind remains a significant challenge for small unmanned aerial vehicles (UAVs). Small UAVs often operate at low altitudes near terrain, where the wind environment can be more complex than at higher altitudes. Combined with their relatively low mass, this m…

Cited by 19 Source ↗Scholar ↗

2017

Fast Marching Adaptive Sampling

Nicholas R. J. Lawrance, Jen Jen Chung, Geoffrey A. Hollinger

RA-L 2017

A challenging problem for autonomous exploration is estimating the utility of future samples. In this paper, we consider the problem of placing observations over an initially unknown continuous cost field to find the least-cost path from a fixed start to a fixed goal position. We propose the adaptiv

Cited by 9 Source ↗Scholar ↗

2016

Informative soaring with drifting thermals

Joseph L. Nguyen, Nicholas R. J. Lawrance, Robert Fitch, Salah Sukkarieh

ICRA 2016

The informative soaring (IFS) problem involves a gliding unmanned aerial vehicle (UAV) exploiting energy from thermals to extend its information gathering capability. In this paper, we address the realistic situation of detecting new thermals drifting with the wind in the search environment. We cons

Cited by 2 Source ↗Scholar ↗