The authors developed a custom-built spool mechanism designed to actively adjust tether length using real-time sensor feedback. The tether system comprises a custom-built spool mechanism, integrated with sensor feedback for real-time tether length adjustments.

To coordinate these adjustments, the system was implemented within a standard ROS-based framework, ensuring efficient data management.

To evaluate the system’s effectiveness, the authors tested different slackness and control gain parameters while the Duckiebot followed a predefined square path. By analyzing the spool’s reactivity and the consistency of the tether’s behavior, they assessed the system’s performance across varying conditions.

Several challenges emerged during testing, e.g., maintaining the right balance of tether slackness was critical, as excess slack risked entanglement, while insufficient slack could restrict mobility.

Hardware limitations affected the spool’s responsiveness, requiring careful tuning of control parameters. Additionally, environmental factors, such as potential obstacles, underscored the need for a more adaptive control mechanism in future iterations.

The method involves training a DRL agent in a simulated Duckietown environment (Gym Duckietown Simulator) using an autoencoder for feature extraction. 

The encoder compresses image data into a latent space, reducing input dimensions for policy learning. The agent receives sequential encoded frames as observations and optimizes steering actions based on reward-driven updates. The trained model is then transferred to a real Duckiebot using a ROS-based communication framework. 

Challenges for pulling this off include accounting for discrepancies between simulated and real-world camera inputs, which affect performance and generalization. Differences in lighting, surface textures, and image normalization require domain adaptation techniques.

Moreover, computational limitations on the Duckiebot prevent direct onboard execution, requiring a distributed processing setup.

Reward shaping influences learning stability, and improper design of the reward function leads to policy exploitation or suboptimal behavior. Debugging DRL models is complex due to interdependencies between network architecture, exploration strategies, and training dynamics. 

The project addresses these challenges by refining preprocessing, incorporating domain randomization, and modifying policy structures.

The system processes images from a monocular RGB camera by applying inverse perspective mapping to generate a bird’s-eye view, assuming all pixels lie on the ground plane to simplify obstacle distortion detection. Obstacle detection involves HSV color filtering, image segmentation, and classification using eigenvalue analysis. The reaction strategies include trajectory planning or stopping based on the detected obstacle’s position and lane constraints.

Computational efficiency is a significant challenge due to the hardware limitations of Raspberry Pi, necessitating the avoidance of real-time re-computation of color corrections. Variability in lighting and motion blur impact detection reliability, while accurate calibration of camera parameters is essential for precise 3D obstacle localization. Integration of avoidance strategies faces additional challenges due to inaccuracies in pose estimation and trajectory planning.

The thesis involves implementing the MILE model (Model-based Imitation LEarning for urban driving), trained on the CARLA simulator, into the Duckietown environment to evaluate its performance in navigating unprotected intersections.

Experiments were conducted using the Gym-Duckietown simulator, where Duckiebots navigated a 4-way intersection across multiple trajectories. Metrics such as success rate, drivable area compliance, and ride comfort were used to assess performance.

The findings indicate that while the MILE model achieved state-of-the-art performance in the CARLA simulator, its generalization to the Duckietown environment without additional training was, as probably expected due to the sim2real gap, limited.

The BEVs generated by MILE were not sufficiently representative of the actual road surface in Duckietown, leading to suboptimal navigation performance. In contrast, the homographic BEV method, despite its assumption of a flat world plane, provided more accurate representations for intersection navigation in this context.

As for most approaches in robotics, there are limitation and tradeoffs to analyze.

Here are some technical challenges of the proposed approach:

• Generalization across environments: one of the challenges is ensuring that the 3D image feature representation generalizes well across different simulation environments, such as Duckietown and CARLA. The differences in scale, road structures, and dynamics between simulators can impact the performance of the navigation system.
• Accuracy of BEV representations: the transformation of camera images into Bird’s Eye View (BEV) representations has reduced accuracy, especially when dealing with low-resolution or distorted input data.
• Real-time processing: the integration of 3D image features for navigation requires substantial computational resources with respect to utilizing 2D features instead. Achieving near real-time processing speeds for navigation tasks such as intersection navigation, is challenging.