School of Engineering Department of Electronic and Computer Engineering 145 Dirt Classification Scheme on a built-up Robot Platform Supervisor: SHI Ling / ECE Student: CHANG Mincheol / ELEC Course: UROP1100, Fall Developing a well-performed dirt classification scheme is a fundamental task for cleaning robots nowadays. The technique of dirt detection allows locomotion robots to identify and fulfill the task of cleaning in the first place, and avoid hard-to-clean objects. This report proposes a method to build a dirt dataset and dirt classification scheme using a neural network model for the embedded system of a locomotion robot. The report illustrates several detection model candidates such as Tiny YOLOv2, Mask R-CNN, and simple MobileNet, which can be applied to embedded systems with the robot's KPU chip. Multi-agent System Control Supervisor: SHI Ling / ECE Student: LIU Hanmo / CPEG Course: UROP1100, Spring During the actual operation of the unmanned vehicle, the laser radar input is used to determine the location of unknown obstacles, so as to further calculate obstacle avoidance and path planning. The lidar input is the feedback length of the target point and the angle between the target point and the car. A current simulation environment is a number of cylindrical target obstacles with a radius of 0.05m. The trolley needs to identify and record these obstacles during the traveling process for subsequent elfslam calculation. Multi-agent System Control Supervisor: SHI Ling / ECE Student: WANG Peiqi / ELEC Course: UROP1100, Fall UROP2100, Spring The last decade has seen a rapid development of the Automated Guided Vehicle (AGV). AGVs can help improve the throughput of warehouses while reducing the labor cost. In this report, we will be focusing on some of the common methods of indoor AGV path planning. The survey will serve as a foundation for further research in mobile robot navigation and help the researchers build more advanced vehicles for complex tasks.