Jaeyoung Lim

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Lim

First Name

Jaeyoung

ORCID

0000-0002-5695-4511

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Publications1 - 6 of 6

PX4Space: PX4 for Spacecraft and Space Robotics
Roque, Pedro; Krantz, Elias; Lim, Jaeyoung; et al. (2024)
With the rise of interest in space robotics, both from an academic and industrial point of view, the need for standardization of testing facilities is ever greater. Providing an open-source solution that multiple parties can use and share improvements on becomes pivotal for supporting research and technology transfer roles, accelerating the progress in autonomy for this field. In this short note, we present an adaptation of the PX4 software stack for usage with spacecraft analogs and space robotics facilities. We showcase the proposed architecture, controllers, and interfaces both in simulation and at the space robotics laboratory in KTH.
Traversing Mars: Cooperative Informative Path Planning to Efficiently Navigate Unknown Scenes
Rockenbauer, Friedrich M.; Lim, Jaeyoung; Müller, Marcus G.; et al. (2025)
IEEE Robotics and Automation Letters
The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the wear on the wheels of a rover from abrasive terrain should be minimized at all costs as reparations are infeasible. On the other hand, utilizing a scouting robot such as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs and increase safety of a follower robot. This work proposes a novel cooperative informative path planning (IPP) framework that allows a scout (e.g., an MAV) to efficiently explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal. We derive theoretical guarantees for our algorithm, and prove that the algorithm always terminates, always finds the optimal path if it exists, and terminates early when the found path is shown to be optimal or infeasible. We show in thorough experimental evaluation that the guarantees hold in practice, and that our algorithm is 22.5% quicker to find the optimal path and 15% quicker to terminate compared to existing methods.
Autonomous Information Gathering Using Long Endurance Aerial Vehicles
Lim, Jaeyoung (2024)
Environmental monitoring is a crucial task for managing natural hazards, weather prediction, and improving our understanding of the environment. Unlike stationary sensor networks, robotic systems can autonomously navigate the environment, making it possible to choose where measurements are taken. In this thesis, we look into enabling an autonomous information-gathering use case using long-endurance aerial vehicles. We use a specific example of avalanche monitoring in mountainous environments which highlights the challenges of information-gathering applications in information quantification and safe navigation. This thesis consists of three contributions. First, a safe navigation strategy is proposed. As fixed-wing vehicles are limited in maneuverability, navigating in steep mountainous terrain may be challenging. The proposed method ensures the safety of planned paths by ensuring the terminal state is safe, using circular periodic paths. Safe positions are precomputed before flight in order to keep the evaluation of states efficient during operations. Second, an active mapping approach is proposed. As photogrammetry processes are computationally expensive optimization problems, it is impractical to plan viewpoints with reconstruction in the loop. We propose a real-time view planning based on a proxy metric based on Fisher Information. We show that by formally defining a measurement model of the camera, similar viewpoints that are sufficient for reconstruction can be acquired more efficiently compared to coverage planning methods. Third, we integrate the proposed safe planning method and active mapping method into an integrated system. We demonstrate a realistic mission of autonomously navigating to the region of interest and mapping an avalanche outline in Davos, Switzerland. Field tests demonstrate the system's ability to autonomously collect and process data in real-world environments, offering a significant step towards fully autonomous environmental monitoring using aerial vehicles.
Autonomous and efficient large-scale snow avalanche monitoring with an Unmanned Aerial System (UAS)
Lim, Jaeyoung; Hafner-Aeschbacher, Elisabeth; Achermann, Florian; et al. (2026)
Natural Hazards and Earth System Sciences
Current and accurate information about the location and extent of released avalanches is critical for public safety and decision-making. However, such data is difficult and expensive to obtain in remote locations. Uncrewed fixed-wing aerial vehicles, due to their low cost, long range, and high travel speeds, are promising platforms to gather aerial imagery to map avalanche activity. However, autonomous flight in mountainous terrain remains a challenge due to the complex topography, regulations, and harsh weather conditions. In this work, we present a proof of concept system that is capable of safely navigating and mapping avalanches using a fixed-wing aerial system (UAS) and discuss the challenges arising for operating such a system. We show in our field experiments that we can effectively and safely navigate in steep mountain environments while maximizing the map quality and efficiency while meeting regulatory requirements. We expect our work to enable more autonomous operations of fixed-wing vehicles in alpine environments to maximize the quality of the data gathered. By enabling the acquisition of frequent and high quality information on avalanche activity, such drone systems would have a large impact of safety critical applications such as avalanche warning, mitigation measure planning or hazard mapping.
Fisher Information Based Active Planning for Aerial Photogrammetry
Lim, Jaeyoung; Lawrance, Nicholas; Achermann, Florian; et al. (2023)
2023 IEEE International Conference on Robotics and Automation (ICRA)
Small uncrewed aerial systems (sUASs) are useful tools for 3D reconstruction due to their speed, ease of use, and ability to access high-utility viewpoints. Today, most aerial survey approaches generate a preplanned coverage pattern assuming a planar target region. However, this is inefficient since it results in superfluous overlap and suboptimal viewing angles and does not utilize the entire flight envelope. In this work, we propose active path planning for photogrammetric reconstruction. Our main contribution is a view utility function based on Fisher information approximating the offline recon struction uncertainty. The metric enables online path planning to make in-flight decisions to collect geometrically informative image data in complex terrain. We evaluate our approach in a photorealistic simulation. A viewpoint selection study shows that our metric leads to faster and more precise reconstruction than state-of-the-art active planning metrics and adapts to different camera resolutions. Comparing our online planning approach to an ordinary fixed-wing aerial survey yields 3.2 × faster coverage of 16 ha undulated terrain without sacrificing precision.
Safe Low-Altitude Navigation in Steep Terrain With Fixed-Wing Aerial Vehicles
Lim, Jaeyoung; Achermann, Florian; Girod, Rik; et al. (2024)
IEEE Robotics and Automation Letters
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 space. Especially in complex, confined, or steep terrain, ensuring the vehicle does not enter an inevitable collision state (ICS) can be challenging. In this letter, we propose a strategy to find safe paths that do not enter an ICS while navigating within tight altitude constraints. The method uses periodic paths to efficiently classify ICSs. A sampling-based planner creates collision-free and kinematically feasible paths that begin and end in safe periodic (circular) paths. We show that, in realistic terrain, using circular periodic paths can simplify the goal selection process by making it yaw agnostic and constraining yaw. We demonstrate our approach by dynamically planning safe paths in real-time while navigating steep terrain on a flight test in complex alpine terrain.

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Jaeyoung Lim

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