The advancement of autonomous vehicles (AVs) has brought forth a substantial need for effective education in robotic operating systems, particularly ROS 2, which serves as the backbone for many autonomous vehicle (AV) applications. This paper explores the academic approach and instructional methodologies tailored for teaching ROS 2 in the context of autonomous vehicle technology. It highlights the critical components and architecture of ROS 2, emphasizing its modularity, real-time communication capabilities, and robust ecosystem which make it ideal for AV development. Through a detailed curriculum outline, we describe hands-on learning activities, simulation-based exercises, and project-driven modules that facilitate deep understanding and practical skills acquisition. The effectiveness of these teaching methods is evaluated through a mixed-methods study involving student feedback, performance assessments, and project outcomes. Results indicate significant improvements in student comprehension and proficiency in both ROS 2 and autonomous vehicle systems. This research contributes to the body of knowledge by providing a comprehensive framework for educators to effectively teach ROS 2, thereby fostering the next generation of engineers proficient in developing and deploying autonomous vehicle technologies.

Compact heavy-duty skid-steer robots are increasingly used for city logistics and intralogistics tasks where high payload capacity and stability are required. However, their limited maneuverability and non-negligible turning radius challenge conventional waypoint-tracking controllers that assume unconstrained motion. This paper proposes a curvature-constrained trajectory planning and control framework that guarantees geometrically feasible motion for such platforms. The controller integrates an explicit curvature limit into a finite-state machine, ensuring smooth heading transitions without in-place rotation. The overall architecture integrates GNSS-RTK and IMU localization, modular ROS 2 nodes for trajectory execution, and a supervisory interface developed in Foxglove Studio for intuitive mission planning. Field trials on a custom four-wheel-drive skid-steer platform demonstrate centimeter-scale waypoint accuracy on straight and curved trajectories, with stable curvature compliance across all tested scenarios. The proposed method achieves the smoothness required by most applications while maintaining the computational simplicity of geometric followers. Computational simplicity is reflected in the absence of online optimization or trajectory reparameterization; the controller executes a constant-time geometric update per cycle, independent of waypoint count. The results confirm that curvature-aware control enables reliable navigation of compact heavy-duty robots in semi-structured outdoor environments and provides a practical foundation for future extensions.

Simultaneous Localization and Mapping (SLAM) is a critical component of autonomous navigation, enabling mobile robots to construct maps while estimating their location. In this study, we compare the performance of SLAM Toolbox and Cartographer, two widely used 2D SLAM methods, by evaluating their ability to generate accurate maps for autonomous racing applications. The evaluation was conducted using real-world data collected from a RoboRacer vehicle equipped with a 2D laser scanner and capable of providing odometry, operating on a small test track. Both SLAM methods were tested offline. The resulting occupancy grid maps were analyzed using quantitative metrics and visualization tools to assess their quality and consistency. The evaluation was performed against ground truth data derived from an undistorted photograph of the racetrack.

One of the most significant challenges in sustainable autonomous mobile robot and vehicle development is the perception of stochastic environments. Various environmental perception methods have been proposed to address these challenges; however, these methods often lack general applicability. Many of these methods rely on environmental feature extraction, which can fail in specific scenarios, such as monochromatic environments. This article aims to evaluate existing SLAM (Simultaneous Localization and Mapping) methods that utilize camera or combined camera and LiDAR input data in predominantly monochromatic environments. Additionally, this study seeks to identify performance issues in such applications.