Porting of Autoware Stack
Autoware is an open-source implementation of a complete automated driving stack. It provides a complete tool chain for mapping, localization, and navigation for automated vehicles. It is build up on ROS 2. In this project we are interested to port the Autoware stack to the Mercator robot platform of the AIS research group and to evaluate its performance in automated navigation in urban environments. The main tasks of the project are to become familiar with the Autoware stack and to implement and configure the interfaces to the actual sensor and platform setup of Mercator. If there is interested the project can be extended towards a master's thesis.
Weather Dependent Cost Representation for Routing of a Mobile Robot
Cost-based representations of the environment are frequently used in the path planning domain to obtain an optimized path based on various objectives, such as traversal time or energy consumption. However, obtaining such cost representations still heavily depends on the environment the robot navigates is, particularly in outdoor settings with diverse terrain types and slope angles. Furthermore, depending on the weather condictions, the conditions of the environment might change, and therefore also the capabilities of the robot on certain terrain. For example, while the robot might be able to traverse a grassy area on a sunny day, it might have problems after a heavy rainfall or when there is snow. This topic aims to address this problem by using weather dependent collected data of an outdoor environment to generate a cost-based representation fo the environment which a robot can use to navigate in. As basis, a data-driven approach (Deep Neural Networks) which generates a cost representation for various outdoor terrains on a dry day is provided. Using recorded data on a new weather condition (snow), the cost representation is to be adapted and the knowledge of the traversability of the terrain is to be transferred to the new weather condition.
Towards Explainable Off-road Navigation Systems
Path planning systems are critical elements for autonomous guided vehicles and provide the foundation for autonomous driving in all kinds of domains. While path planning on roads is usually quite intuitive (e.g., by seeing that the route goes onto the highway and therefore must be the fastest route), routes provided for off-road navigation can be unintuitive and may not contribute to a better understanding of the behavior of the system. Providing explanations and justifications for why a route was retrieved has the potential to address these issues. To provide this, this work should integrate an explanation algorithm based on properties retrieved from the environment and the robotic system. In essence, the explanation algorithm should be able to answer the question "Why is the provided path A the fastest, rather than B, which I expected?" Therefore, retrieved properties about the state (e.g., speed limit of the robot, steepness of the environment, impassable terrain) should be integrated into an explanation. A similar approach, which can be used as a basis for this work, has already been explored by Alsheeb and Brandao.