AUSPEX | 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.

AJCAI 2025 @ Canberra

Thu, 04 Dec 2025 00:00:00 +0000

At the 26th Australasian Joint Conference on Artificial Intelligence (AJCAI 2025), held in Canberra, Australia, Björn Döschl presented the paper “Planner-Independent Extraction of Goals and Constraints from Natural Language for Open-World Mobile Robot Missions.” The work introduces a planner-independent pipeline for extracting goals and constraints from natural-language mission instructions and projecting them into planner-specific representations for open-world mobile robot missions.

Conference Details

Conference: AJCAI 2025

Full name: 26th Australasian Joint Conference on Artificial Intelligence

Date: 1–5 December 2025

Presentation date: 4 December 2025

Location: Lowitja O'Donoghue Cultural Centre, Australian National University, Canberra, Australia

Session: Short Oral Session 8: Robotic AI

Summary: Björn Döschl presented research on planner-independent goal and constraint extraction for open-world mobile robot missions.

Paper Details

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

Authors: Björn Döschl and Jane Jean Kiam

Proceedings: AI 2025: Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol. 16371

Pages: 453–465

DOI: 10.1007/978-981-95-4972-6_35

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

Paper Summary

The paper addresses problem-instance acquisition for robot missions described in natural language. It combines an LLM-based goal and constraint extraction module with a planner-specific projection layer, allowing operator-defined mission instructions to be translated into structured representations for downstream planning systems. The approach is evaluated on mission instructions and demonstrated in AUSPEX for a Search and Rescue UAV scenario.

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.