Emergency Response & Recovery Management
Our goal is to develop artificial intelligence-based solutions to support disaster response management. One of the core questions is, how should robotic systems efficiently support humans in situation assessment and the progressive creation of joint situation awareness in dangerous environments. Depending on the concrete mission, environmental factors, e.g. underground or underwater with polluted or radiated areas, require morphologically different robots to meet the challenges of a dynamic and complex environment, often collaborating with humans or other robots. How such teams should work efficiently and communicate, which information needs to be collected, how it needs to processed and presented to humans, are some of the main questions we are addressing.
We have competences on a broad range of topics, including the following:
- User-centric approach to disaster response technology-development
- Human-robot teaming in disaster-response scenarios
- Verbal interaction processing for disaster response management
- Situation awareness support tools for disaster response management
- Long-term human-robot interaction modeling
- Collision avoidance
- Autonomous orientation in unknown areas (SLAM)
- Secure communication and control
- Teleoperation, operator monitoring and support for disaster response
- Robotic navigation, localization, mapping and planning in disaster-response scenarios
- Autonomous and semi-autonomous robots, complex behaviours
- Reconfigurable cooperative teams
- Robust object recognition and pose estimation
- Autonomous robotic manipulation
- Exploration in unknown and unstructured terrain (HW and SW)
- Sensor fusion and online signal processing, wearable computing
- Human-Robot communication via gestures
- Hybrid robots
- Electric mobility and road trains (as robotic shuttles)
- Security research, crisis prevention & intervention
- Intelligent-adaptive action support
- (Educational) Datamining, analysis of user interaction
- Information Management and Modeling
- Sensor-data-based Conformance and Compliance Checking of Processes
- Process Mining and Big Data Analytics for Process Prediction
Key People
- Dr. Eric Rietzke, SDS
- Dr. Sirko Straube, RIC
- Prof. Dr. Dieter Hutter, CPS
- Prof. Dr. Peter Fettke, IWi
- Dr. Constantin Houy, IWi
- Jana Rehse, IWi
- Dr. Tim Schwartz, COS