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Autonomous systems

Energy-efficient flight guidance for rescue helicopter missions

The Challenge

Every day, emergency services around the world are faced with the important task of providing efficient and rapid assistance in critical situations. One component of these missions is air rescue, where it is crucial to provide help to patients as quickly as possible and transport them to appropriate hospitals for further treatment. However, the locations are often in unfamiliar terrain where helicopters cannot land safely. We want to make this challenging landing situation safer and faster for the helicopter crew with the help of technical assistance systems.

How we will help

We want to use suitable sensor technology and artificial intelligence to better capture and understand the helicopter’s surroundings during flight, approach and landing in order to enable the pilot and crew to land even more safely and quickly. To this end, we talk to helicopter pilots, technical crew members and emergency paramedics to better understand their day-to-day work and the challenges they face. On the one hand, we are increasing the robustness of AI algorithms that are responsible for detecting the environment through data and, on the other hand, we are ensuring the safe operation of the system through a robust software architecture.

Technologies

Computer Vision Deep Learning Generative AI Self adaptive system ROS2 CI/CD User Research Qualitative Interviews User Journeys Requirements Engineering

User-centered research

To get to know our target group and their day-to-day work better, we conduct qualitative interviews

  • with various air rescue and police pilots
  • with all crew members of a rescue helicopter, including TC HEMS (Technical Crew Member Helicopter Emergency Medical Services) and emergency doctors
  • with people who carry out operations on the ground, such as paramedics

The findings from the target group interviews and research work show the biggest obstacles and challenges faced by the crew in the respective flight phases and make it possible to derive requirements for possible technical solutions.

Prozessansicht aller Beteiligten Notfalleinsatz-Personen und deren Kommunikation miteinander

Robust perception

We are helping to develop a more robust perception of the environment by expanding partly public data sets with synthetically generated realistic weather conditions and real obstacles. As a result, the AI algorithms in the project are trained on a broader database and deliver reliable results even in rare but dangerous edge cases. This significantly reduces the effort required to collect and annotate real project-specific edge case data.

Bildaufnahme einer Straße mit vielen Autos aus der Vogelperspektive
Grafik der Straßenszene klassifiziert in die verschiedenen sichtbaren Objekte
Bildaufnahme einer Straße mit vielen Autos aus der Vogelperspektive mit künstlich hinzugefügtem Schnee

Software architecture

We are developing a software system that integrates the AI algorithms in the consortium, monitors itself and adapts. The adaptations are carried out so that the system works as efficiently as possible in different flight phases and at the same time can recover independently from possible errors.

Robustness is achieved not only at runtime through the self-adaptive system, but also at design time through extensive testing of individual software modules and the overall system with hardware-in-the-loop.

Schaubild des Self-adaptive software system

Project participants and funding

Project partners

  • DLR (German Aerospace Center)
  • Fraunhofer FHR
  • Fraunhofer FKIE
  • Fraunhofer IMS
  • Fraunhofer IVI
  • Airbus Helicopters Deutschland GmbH
  • Airbus Defence and Space GmbH
  • BIT Technology Solutions GmbH
  • IMST GmbH
  • Kappa optotronics GmbH
  • Lake Fusion Technologies GmbH
  • X2E GmbH
  • Technische Hochschule Ingolstadt
  • Technische Universität München

Project sponsor

Funding program

Civil aeronautics research program LuFo VI-3

Project management

Aeronautics research at DLR e.V. (PT-LF)

Other projects

Are you interested in a project, a service or do you have any other questions?

Dr. Alwin Hoffmann

Tel. +49 821 885882-231
alwin.hoffmann@xitaso.com