Study/final thesis: Dynamic Obstacle Prediction for Autonomous Outdoor Navigation

Call for applications for the field of study, such as: Computer Science, Software Engineering, Mechanical Engineering, Mechatronics or similar.

 

In the research group Navigation Mobile Robots, we develop autonomous, mobile robots for a variety of outdoor applications, such as agriculture, forestry and logistics. The focus is on the development of autonomous outdoor navigation solutions as well as the hardware of the robots.

 

Be part of change

Autonomous navigation in dynamic environments and higher speeds create challenges to typical mobile robots. To ensure safe and efficient path planning the robot must predict the future movement of dynamic obstacles and integrate that into its world understanding. This includes classifying the obstacle into typical dynamic obstacles like pedestrians, bikes, and vehicles based on LiDAR and/or camera data, predicting a realistic and/or conservative movement corridor and integrate this information efficiently into ROS2 costmaps. An important integration requirement lies in the compatibility with the ROS2 Nav2 Stack, which is widely used in the state of art of mobile robotics navigation.

 

First, you will select a suitable state-of-the-art detection and classification approach based on LiDAR and/or camera data. But your focus lies on the prediction and map integration part of the pipeline. Therefore, you will investigate different types of predictions like intent prediction, reachability approaches or Bayesian filtering and develop a robust prediction algorithm. These dynamic predictions must then be integrated into a map representation to be included and used in the path planning of the robot. An important integration requirement lies in the compatibility with the ROS2 Nav2 Stack, which is widely used in the state of art of mobile robotics navigation. You will implement your solution inside our ROS2, C++ navigation stack and validate it both in simulation and in real-world scenarios with our mobile CURT robots.