UAV | AI LAB @ UniBw Munich

Flight Season 2026 Started

Thu, 12 Feb 2026 00:00:00 +0000

The 2026 flight season has officially started.

During the first test flights of the season, we successfully evaluated the changes made to AUSPEX over the winter on our heterogeneous multi-UAV fleet. The tests allowed us to validate the updated system in real flight conditions and to assess its performance across different UAV platforms.

After several adaptations and refinements, AUSPEX performed very well. These successful initial flights provide a solid foundation for the upcoming experiments and mark a promising start to the new flight season.

AUSPEX @ GAS 2025

Thu, 11 Dec 2025 00:00:00 +0000

At the German Autonomy Summit (GAS) 2025, held by the IEEE Intelligent Transportation Systems Society (ITSS) German Chapter, Maximilian Schnell and Nico Michel presented their teams research “AUSPEX: An Integrated Open-Source Decision-Making Framework for UAVs in Rescue Missions”. The event brought together Germanys leading researchers of intelligent transportation systems, with a focus on autonomous systems and perception of mobile systems. The summit fostered engaging discussions and offered valuable feedback from peers working at the forefront of perception, navigation, control, learning and autonomous systems.

ICAPS 2025 @ Melbourne

Mon, 10 Nov 2025 00:00:00 +0000

At the 35th International Conference on Automated Planning and Scheduling (ICAPS) 2025, held in Melbourne, Victoria, Australia, members of the AI Lab presented several contributions around UAV planning, simulation, dynamic replanning, and natural-language mission specification.

Max Sommer and Björn Döschl presented the system demonstration “Dynamic REAP: Bringing Life into Simulations for UAV Planning and Acting Frameworks.” Björn Döschl also presented the PlanRob workshop paper “Making Unified Planning Dynamic by Incorporating World State Updates via Feedback Loops” and a poster on “Planner-Independent Extraction of Goals and Constraints from Natural Language for Open-World Mobile Robot Missions.”

Conference Details

Conference: ICAPS 2025

Full name: 35th International Conference on Automated Planning and Scheduling

Date: 9–14 November 2025

Location: Melbourne, Victoria, Australia

Summary: MASC and BJDO presented research contributions on UAV simulation, dynamic planning, and natural-language goal and constraint extraction.

System Demonstration

Title: Dynamic REAP: Bringing Life into Simulations for UAV Planning and Acting Frameworks

Presented by: Max Schnell and Björn Döschl

Authors: Kai Sommer, Björn Döschl, Jane Jean Kiam

Demo page: ICAPS 2025 System Demonstrations

Paper: Download the demo paper

Original PDF: ICAPS demo paper PDF

Dynamic REAP extends UAV simulation for planning and acting frameworks by combining accurate vehicle physics with georeferenced semantic segmentation and dynamic background characters. The system demonstration shows how realistic Search and Rescue scenarios can be used to validate UAV decision-making, perception, and planning pipelines before deployment in real-world missions.

PlanRob Workshop Paper

Workshop: PlanRob: Planning and Robotics

Date: 10 November 2025

Title: Making Unified Planning Dynamic by Incorporating World State Updates via Feedback Loops

Presented by: Björn Döschl

Authors: Björn Döschl, Jane Jean Kiam

This workshop paper presents an architectural and functional extension to the Unified Planning framework for dynamic robot missions. The approach constructs planning problem instances from updated world states, integrates execution feedback, and enables replanning during mission execution. The work is demonstrated in the AUSPEX framework with the REAP simulation environment for UAV-based Search and Rescue missions.

Poster Presentation

Title: Planner-Independent Extraction of Goals and Constraints from Natural Language for Open-World Mobile Robot Missions

Presented by: Björn Döschl

Authors: Björn Döschl, Jane Jean Kiam

Publication: 38th Australasian Joint Conference on Artificial Intelligence

Project page: Publication page

The poster presents a planner-independent pipeline for extracting goals and constraints from natural-language mission instructions. The approach uses an LLM-based goal and constraint extraction module and a planner-specific projection layer, enabling operator-defined mission goals to be translated into structured representations for downstream planning systems.

Presented Contributions

System demonstration: Dynamic REAP for realistic UAV planning and acting simulations.
Workshop paper: Dynamic UP extension with world-state updates and feedback-loop-based replanning.
Poster: Planner-independent goal and constraint extraction from natural-language mobile robot mission instructions.

Gallery

Replace the following placeholders with selected photos from the ICAPS presentations, demo session, workshop talk, and poster session.

SaynFly @ IEEE ROMAN 2025

Mon, 25 Aug 2025 00:00:00 +0000

At the IEEE International Conference on Robot and Human Interactive Communication (ROMAN) 2025, held in Eindhoven, Netherlands, Björn Döschl presented his latest research titled “SaynFly: An LLM-Modulo Online Planning Framework to Automate UAV Command and Control.” The conference brought together researchers from robotics, human-robot interaction, and automated systems. SaynFly contributes an LLM-modulo online planning framework for UAV command and control, with a particular focus on search and rescue missions.

Conference Details

Conference: IEEE ROMAN 2025

Date: 25 August 2025

Location: Eindhoven University of Technology, De Zaale, 5612 AZ Eindhoven, Netherlands

Summary: Björn Döschl presented his research on SaynFly at IEEE ROMAN 2025.

Paper Abstract

Command and Control (C2) for Unmanned Aerial Vehicle (UAV) missions requires the dynamic coordination of task planning, environmental perception, and navigation strategies. While Large Language Model (LLM-) modulo planning frameworks have shown promise in addressing such complexity, due to their capabilities to understand natural language and common sense, their limited robustness remains a challenge. Recent adaptions of such frameworks made online planning possible, but they do not scale well with the number of actions. This limits the applicability in robotic frameworks with large sets of parameterized actions, such as those encountered in UAV C2. We introduce SaynFly, an LLM-modulo online planning framework that streamlines the action space by discarding infeasible actions using domain-specific knowledge, while leveraging online heuristic search to mitigate reward uncertainty and automate the C2 processes for UAVs. Test results for our Search and Rescue (SAR) validation scenarios show that SaynFly is 70% more efficient compared to existing frameworks while maintaining or exceeding success rates.

LEAP @ CoRL 2024

Sat, 09 Nov 2024 00:00:00 +0000

At the LEAP (Learning Effective Abstractions for Planning) workshop, held as part of the Conference on Robot Learning (CoRL) 2024, Björn Döschl presented his latest research titled “Say-REAPEx: An LLM-Modulo UAV Online Planning Framework for Search and Rescue.” The event brought together leading researchers in planning and robot learning, with a focus on abstraction-based methods. The workshop fostered engaging discussions and offered valuable feedback from peers working at the forefront of AI and robotics.

LEAP @ CoRL 2024

Sat, 09 Nov 2024 00:00:00 +0000

Workshop Details

Event: LEAP Workshop @ CoRL 2024

Date: 9 November 2024

Location: Science Congress Center Munich, Walther-von-Dyck Str. 10, 85748 Munich, Germany

Summary: Björn Döschl presented his research at the LEAP workshop during CoRL 2024.

Event Abstract

The Learning Effective Abstractions for Planning (LEAP) workshop, part of the Conference on Robot Learning (CoRL), took place on 9 November 2024 at the Science Congress Center Munich. Björn Döschl presented his work titled "Say-REAPEx: An LLM-Modulo UAV Online Planning Framework for Search and Rescue" in the form of a poster and gained valuable insights into cutting-edge research in the field of abstractions for robot learning and planning.

Say-REAPEx: An LLM-Modulo UAV Online Planning Framework for Search and Rescue

Fri, 25 Oct 2024 00:00:00 +0000