Faculty of Engineering

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Scavenger 01_Automatic ping pong collector

SCAVENGER-01 uses AI and computer vision to perform object detection and pick up ping pong balls, without interrupting athletes’ training.

It saves time, improves training efficiency, and prevents injuries.

It can be deployed in ping pong arenas, ideal for ping pong training centres, schools, colleges, clubs, and home settings. Where the robot will pick up the ping pong balls while athletes can focus on their training.

SCAVENGER-01 is a prototype, in the future, we plan to deploy a human avoidance algorithm to avoid colliding into the path of athletes, improve the routing algorithm so it can effectively go through the entire map/arena, and automatic wireless charging so the unmanned robot can head to the wireless charging station for automatic recharging.

Smart PV Panel Cleaning Robot

Solar farms require flat and open spaces, however, Hong Kong is a relatively mountainous region. With Hong Kong’s geographical structure, it is not feasible for Hong Kong to develop hugely in solar farms. Nevertheless, we can expect a rise in the use of solar PV panels on village house rooftops and building rooftops by individuals for having partially self -sufficient energy systems to reduce electricity charges. Therefore, we have a great business opportunity here. Supporting the maintenance of solar PV panels with a cleaning robot is a meaningful idea at the right time. Moreover, doing manual cleaning for solar PV panels on rooftops is a slow and risky process. People’s safety is a major concern as working from heights and in different weather conditions can hinder people’s safety when cleaning the solar panels. Moreover, cleaning done by people is hard and slow process. With a cleaning robot, the cleaning work becomes simple and a fast process.
With a great number of policies and measures being implemented to encourage the use of renewable energy, it is a time where we could expect more installations of solar PV systems. This would be a fantastic opportunity for our team to make a solar PV panel cleaner to enhance the maintenance of the solar PV panels. With our product, it brings convenience to 6 people having solar PV systems on their home’s rooftops or buildings. This could further encourage people to opt for renewable energy from solar PV panels.

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Machine Learning in Archaeology

Digitization of archaeology is in great demand. Since 2009, an HKU archaeological team of researchers and students led by Dr. Cobb has been investigating the area around Vedi, Armenia, aiming at understanding human life and mobility in the ancient landscapes of the Near East. A large volume of sherds was excavated and documented with photography. Inspired by the recent advancement in computer vision and machine learning, this project attempts to explore various deep learning models to classify and compare those sherds unearthed. It is hoped that insights gained from the project can help archaeologists of manage the massive quantity of ancient artifacts in the future.

SLAM robot with monocular depth estimation

This project aim to construct a robot using SLAM and a monocular camera for both SLAM and depth estimates. The system will be able to map the environment correctly and robustly, determining the robot’s location and orientation inside it. The robot can then estimate the depth using a deep learning model and pictures recorded by the monocular camera as input. This research intends to develop robotics and computer vision by investigating the potential of monocular cameras for SLAM and depth estimation, which might have practical applications in autonomous navigation and depth estimation through the use of a neural network model.

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BREED HKU Agile Robotic Fish with 3D Manoeuvrability for Open Water Swim @ 8th Inno Show

Our robotic fish swimming across the harbour from Central to TST will prove its applicability in seawater. Along with its prior speed record and additional features, it is beneficial to society because its biomimicry nature doesn’t interfere with marine life like other technology automation such as boats or submarines. Compared to the previous model, it is much more energy efficient, less polluting, constructed with less material, and thus environmentally friendly.

HKU Racing @ 8th Inno Show

HKU Racing Aims to complete the production of HKU01 in the winter of 2022 getting it ready for fine tuning in the spring of 2023 and reaching race ready state before summer 2023 which we are planning to ship it to UK for FSUK 2023 class 1 dynamics event. At the mean time the design work of HKU02 will begins at the first quarter of 2023 simultaneously. Sightseeing and recruitment event will also be hosted throughout this period, for team promotion and learning purposes.

Deadline Fighter

Deadline Fighter, a Unity 3D Game designed for COMP3329, is a First-person Shooter game that puts you in the shoes of a lone survivor in post-apocalyptic Hong Kong. You must use your wits and skills to fend off the relentless zombie attacks and expand your safe zone by clearing out the infected areas. Purchase powerful weapons that will help you boost your firepower and survive longer. Deadline Fighter features immersive gun play mechanics as well as advanced zombie AI movement that makes the enemies unpredictable and challenging. Deadline Fighter offers a thrilling and exhilarating gaming experience through the immersive gun play mechanics and advanced zombie AI movement.

HKU Unmanned Aerial System Team @ 8th Inno Show

HKU Unmanned Aerial System Team (HKU UAS) is dedicated to form a community of Unmanned Aerial Vehicles (UAV) enthusiasts and its associated systems, consisting of two technical teams: Mechanical Team and Computer Science Team. The team aims to join the Student Unmanned Aerial System (SUAS) competition, situated in Maryland, U.S.A. The competition requires the team to design a UAS capable of Autonomous Flight, Obstacle Avoidance, Object Detection, Classification and Localization, and Package Delivery. HKU UAS is also developing FPV (First person view) racing drone and promotes the drone racing activities to the students. For future development, the team aims to develop real-time obstacle avoidance for the UAV and build new types of UAV, such as hybrid VTOL (Vertical take-off and landing) UAV.