© Fraunhofer IOSB

Autonomous systems / robotics

Environment detection and motion planning for autonomous systems

An essential component of autonomous systems are methods for autonomous environment perception, position estimation as well as static and dynamic path planning. For autonomous environment detection, the data of different sensors have to be processed and transferred into environment models. AI methods help here, for example, to derive driveability maps from camera images and laser data. We have at our disposal powerful methods of sensor data fusion and Simulataneous Localization and Mapping (SLAM) to enable task- and environment-specific optimal environmental detection and navigation. This basis is used by our motion planning algorithms to optimally control robot systems in a wide variety of environments (structured, unstructured, underwater).

The Fraunhofer IOSB coordinates the Competence Center ROBDEKON ("Robots for decontamination in hostile environments"), which was founded in 2018. Here, complex autonomous systems for use in hostile environments are developed and demonstrated. Comprehensive solutions for autonomous movement and manipulation are demonstrated e.g. on autonomous construction machines.

In the project AKIT ("Autonomy KIT for near-series working vehicles for networked and assisted recovery of hazardous sources"), a kit for work machines was developed, with which excavators and tractors that are available on site in disaster scenarios can be quickly converted into autonomously and semi-autonomously operating recovery and transport vehicles to carry out work in particularly hazardous areas. This successfully demonstrated concept has been under further development in the AKITpro project since 2021.

In the project LocSens the institute participates in the development of multi-sensor systems for environmental perception under rough conditions. In areas where optical sensors cannot be used, sensors based on radar and UWB-radio help to detect the environment reliably. The solutions implemented here can be applied to a whole range of new applications for autonomous systems.

The solutions and algorithms developed for the different applications can be quickly transferred to customer-specific applications due to their modularity. In many cases ROS (Robot Operation System) acts as middleware. Partial solutions can also be offered as embedded implementations on powerful microcontroller platforms.