Off-road environments are among the most challenging operating conditions for autonomous robotic systems. Unlike structured road or industrial environments, they are characterized by unpaved paths, uneven terrain, varying ground conditions, and environmental conditions that are difficult to predict. Mud, sand, debris, vegetation, and steep inclines not only affect the passability of the terrain but also place high demands on the perception, navigation, and control of autonomous vehicles. Added to this are changing weather and lighting conditions, which can significantly impair the reliability of the sensors used.

A particular challenge lies in the fact that autonomous systems must not only recognize their environment but also assess its characteristics and potential risks on a case-by-case basis. For example, depending on its nature, the vehicle type, and current environmental conditions, an obstacle may be passable or critical. At the same time, incomplete, contradictory, or unreliable sensor data can lead to erroneous assessments of the environment. Autonomous robots must therefore be able to evaluate the reliability of their perception and adjust their decisions accordingly.

At the Chair of Adaptive Autonomy and Off-Road Robotics, we are researching methods that enable autonomous systems to address these challenges. Our focus is on robust environmental perception, adaptive decision-making, knowledge-based environmental models, and reliable system architectures. In particular, we investigate how robots can detect uncertainties, utilize experience gained from past observations, and dynamically adapt their behavior to changing operational conditions.

Our research approaches are developed and evaluated both in real-world field trials and in simulation-based environments. Using various robotic platforms and autonomous utility vehicles, we investigate the interplay between sensor technology, software architecture, and autonomous decision-making processes. The goal is to improve the safety, robustness, and adaptability of autonomous systems, thereby enabling their reliable deployment in demanding off-road applications.