Projects

[Research Projects]

1. Near-optimal Search using UAVs in 3D Environments, MOST, 2018/10/01~2019/09/30.

[Abstract]

Since the popularity of unmanned aerial vehicles (UAVs), robots can help humans solving practical and high-dimensional problems (e.g., detection of infected plants, search for structural failure, mountain rescue and search for illegal logging). These problems involve autonomously guidance in 3D environments and need to real-time process large data. These problems are proved as NP-hard problems. Hence, professional pilots are still necessary for UAVs. This project is to collect the UAV’s flying data via pilots’ control in complex 3D environments. And then, the UAV will learn from the data. It could make the flying ability of UAV be equal to or greater than the flying ability of pilots. The goal of this project is to explore three key issues of 3D search problems.

(1) The ability of UAV is better than human pilots?

(2) The UAV search problems are learnable? If yes, how much data the UAV need?

(3) What’s the optimality of the proposed solutions?

2. Deep Inverse Reinforcement Learning for Informative Path Planning, MOST, 2019/08/01~2022/07/31.

[Abstract]

The AI community has been paying more attention to the concept of informative path planning (IPP). The difference between path planning and IPP is that IPP is to maximize information gathering instead of avoiding obstacles. There are different applications depending on the definition of information (e.g., detection of infected plants, search for structural failure, mountain rescue and search, illegal logging, monitor of pollutions and 3D mapping). However, finding optimal solutions for these problems is NP-hard, so finding approximate solutions is a feasible way. To make a breakthrough of the IPP research status, this research proposed a deep inverse reinforcement learning approach to improve IPP performance of robots through analyzing how humans solve IPP problems in daily lives. The project will take three years. The focus of the first year is to explore the reward functions of that humans solve IPP problems via deep inverse reinforcement learning. The focus of the second year is to analyze the transfer learning of that humans solve different IPP problems. The focus of the third year is to explore human-robot cooperative IPP problems. The goal of this research is to explore three issues of IPP:

(1) IPP is learnable? If it is learnable, how much data robots need?

(2) How do humans transfer their knowledge for different IPP problems?

(3) What’s the difference and respective strengths of humans and robots?

[Teaching Projects]

1. Building and teaching of AI Maker Lab, Ministry of Education, 2018/08/01~2021/12/31.

[Abstract]

The goal of AI maker lab is to build a teaching lab at the Mathematics department in NCU. The difference between AI maker lab and other maker labs is that students only build AI programs instead of gears or circuits. The students in AI lab only focus on math and AI programs.

Currently, the AI maker lab has 10 Minibots (Mobile robots) and 8 bebop (UAVs). If the students took AI related courses, they must build their AI programs on these robots for final projects. After taking theses courses, the students can access to AI maker lab anytime to develop their own AI programs. The AI-related courses are as follows:

• Perception and estimation in robotics,

• Modern artificial intelligence

• Introduction to data science

2. Math Dog: AI teaching platform, Ministry of Education, 2021/08/01~2022/07/31.

[Abstract]

During the age of AI, it’s important to teach students AI. However, most of AI platforms are simulators, which cannot satisfy the requirement of AI industrial. If the general AI platform was built, students can implement AI algorithms on real robot dog platforms and sensors. These platforms will train AI engineers for AI industrial. The goal of this project is as follows:

(1) Develop 10 robot dogs with RGBD cameras, laser, and IMU.

(2) Develop supervised learning, unsupervised learning, and reinforcement learning lectures based on robot dog platform.

(3) Evaluate students’ study performance.

Key words: Robot dog, general platform, supervised learning, unsupervised learning, and reinforcement learning.