Internet of Things (IoT) is a system that connects sensors, machines, computing devices, etc. together to collect data without much human involvement. IoT allows a very massive amount of data to be collected, which were not available in the past. The data collected can then be used to enhance operation efficiency and performance of a system. The system then becomes “smart” in the sense that the system can now make decisions that are more optimized and intelligent without much human interaction. A home/building/city becomes smart when IoT is adopted in its maintenance and daily operations. IoT and smart city are still in infancies that there are not many real implementations. A complete IoT application consists of various components including sensors, power electronics, information processing, communication network, data analytics, data visualization, and data security etc. A lot of projects can be developed from these elements to provide experiential learning to our students.
Month: July 2021
Robomaster ICRA AI Challenge is a competition held by DJI and IEEE International Conference on Robotics and Automation. It emphasizes on writing AI algorithms and tuning and choosing and training machine learning models to power pure automatic robots to accomplish complex goals like building maps, avoiding collision and tracking. Our team aims to build AI Robot tuples that can work purely automatically. Implementing and design decision policies according to competition requirements. Training AI networks to recognize and track objects. Implement localization and motion planning on robots under uncertainty.
Robomaster is a national robotics competition for university students, hosted by DJI. The project is about forming a team to design various types of smart robots which can engage in face-to-face, videogame-style battles. The preparation process involves the knowledge in the following disciplines: mechanical and hardware design, control theory application, computer vision and smart algorithm, technical management. The ultimate purpose of the project is to develop the skills, quality and team spirit of engineering students that can be beneficial in their future career.
AI-Driving is an interdisciplinary endeavor in experiential learning, in which students apply technical knowledges in A.I. and robotics. In this project, students will learn knowledge about computer vision, artificial intelligence and robotics and implement AI algorithms to work on auto-driving vehicles running in a real miniature environment. The objective of the project is to allow students to gain insights about the development framework for autonomous vehicles, and to attempt problem such as lane following and object recognition. Students will also form a team representing HKU to participate in International competitions such as AI-Driving Olympics held by the DuckieTown Foundation and other contests related to AI-Driving, e.g., AWS DeepRacer.
HKU UAS is a student interest group focusing on Unmanned Aerial Systems (UAS). We aim to gather self-motivated drone lovers to learn and work on drone projects together. With the support of Innovation Wing, we plan to join competitions to test our knowledge and skills, also to learn from other teams and gain valuable experience. In 2020, the team build the system based on the rules of AUVSI SUAS 2021.
The AUVSI SUAS Competition is a yearly event, designed to stimulate interest in UAS technologies and careers, and to engage students in a challenging UAS mission.
This project aims to develop the set of technologies to achieve convenient-to-use mobility support for daily use of the elderly. In this project, we are developing a smart elderly walker which is intended to play an active role in an elderly person’s daily life, with three fundamental functionalities that do not exist or not well supported by (smart) walkers in the market: smart walking assistance; falling prevention and support; autonomous mobility.
A set of mechanical, control, sensory, and AI technologies is being developed including:
(1) novel walker mechanical structure with omnidirectional mobility and outrigger mechanisms;
(2) dual-mode actuation and control for walking/standing support and fall prevention/ recovery;
(3) multimodal sensory data collection through soft sensory skin, and data processing on device and in the cloud, for event detection and control such as user front following and fall detection;
(4) sound-source localization for elderly localisation and auto-navigation of walker.