Start Date
5-12-2025 12:00 PM
End Date
5-12-2025 1:00 PM
Description
Autonomous sidewalk navigation on campuses remains a significant challenge due to irregular ge- ometries, varying surface textures, and dynamic obstacles found in real-world settings. This project develops a ROS2-based system that enables the robot to navigate locally on the campus sidewalk and globally between campus buildings. We employ FastSCNN, a Semantic Convolutional Net- work trained on the Cityscapes dataset, allowing the robot to perform pixel-level classification of sidewalk and non-sidewalk in real time navigation. Applying transfer learning to a small, locally labeled dataset of our campus improved sidewalk segmentation, effectively adapting the pretrained model to our campus environment.
To translate these segmentations into movement, we developed an edge-based motion algorithm that constructs a ”virtual rail” by calculating the trajectory from the sidewalk edge to the vanishing point. This trajectory serves as the input for a PID controller, which optimizes steering commands for smoother local navigation.
Another topic the study explores is sidewalk intersection classification using Support Vector Machines (SVM) to classify the images based on certain characteristics such as geometric cues. Results have shown that it is possible to develop a vision-based project with low-cost resources. This research contributes to the broader goal of developing autonomous delivery robots on college campus environments, with potential applications in exploration, mapping, and assistive robotics.
Last-Mile Autonomous Delivery Robot
Autonomous sidewalk navigation on campuses remains a significant challenge due to irregular ge- ometries, varying surface textures, and dynamic obstacles found in real-world settings. This project develops a ROS2-based system that enables the robot to navigate locally on the campus sidewalk and globally between campus buildings. We employ FastSCNN, a Semantic Convolutional Net- work trained on the Cityscapes dataset, allowing the robot to perform pixel-level classification of sidewalk and non-sidewalk in real time navigation. Applying transfer learning to a small, locally labeled dataset of our campus improved sidewalk segmentation, effectively adapting the pretrained model to our campus environment.
To translate these segmentations into movement, we developed an edge-based motion algorithm that constructs a ”virtual rail” by calculating the trajectory from the sidewalk edge to the vanishing point. This trajectory serves as the input for a PID controller, which optimizes steering commands for smoother local navigation.
Another topic the study explores is sidewalk intersection classification using Support Vector Machines (SVM) to classify the images based on certain characteristics such as geometric cues. Results have shown that it is possible to develop a vision-based project with low-cost resources. This research contributes to the broader goal of developing autonomous delivery robots on college campus environments, with potential applications in exploration, mapping, and assistive robotics.