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.

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.

HKU Astar is a student group who participates in Robomaster University AI Challenge Competition. Robomaster University AI Challenge is an AI-based robotics competition for university students 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.