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Jakub Jirkal

dblp:399/7748 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 61% Robot navigation and mapping · 39%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › mobile robot navigation › mapless navigation
navigation in unknown environments
0.912025
FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy · ICRA 2025
Machine learning › Reinforcement learning › reinforcement learning environment › environment design
procedural environment generation
0.912025
FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy · ICRA 2025
Machine learning › Reinforcement learning › reinforcement learning environment
simulation environment
0.912025
FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy · ICRA 2025

Methods — techniques the papers use, named apart from their topics

sensor simulation · 0.9procedural generation · 0.9photorealistic rendering · 0.9
YearPublicationVenuePosition
2025 FlightForge: Advancing UAV Research with Procedural Generation of High-Fidelity Simulation and Integrated Autonomy
abstract
Robotic simulators play a crucial role in the development and testing of autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles (UAV). However, existing simulators often lack high-level autonomy, hindering their immediate applicability to complex tasks such as autonomous navigation in unknown environments. This limitation stems from the challenge of integrating realistic physics, photorealistic rendering, and diverse sensor modalities into a single simulation environment. At the same time, the existing photorealistic UAV simulators use mostly hand-crafted environments with limited environment sizes, which prevents the testing of long-range missions. This restricts the usage of existing simulators to only low-level tasks such as control and collision avoidance. To this end, we propose the novel FlightForge UAV opensource simulator. FlightForge offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments. Moreover, the simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments. The key innovation lies in novel procedural environment generation and seamless integration of high-level autonomy into the simulation environment. Experimental results demonstrate superior sensor rendering capability compared to existing simulators, and also the ability of autonomous navigation in almost infinite environments.
David Capek, Jan Hrncír, Tomás Báca, Jakub Jirkal, Vojtech Vonásek, Robert Penicka, Martin Saska
ICRA4