I am continuously looking for self-motivated M.S., PhD students, and PostDoc scholars with a strong mathematical or engineering background and interests in Distributed Optimization, Game Theory, Distributed Control, and Autonomous Robots, as detailed below.
If interested in the above areas, please drop me an email with your CV, a brief statement of your research interests, and transcript of bachelor and master courses.
Percetpion, Localization and Navigation. For autonomous robots (ARs), including robot manipulator, embodied robots, ground/aerial/underwater vehicles, environmental perception, localization, and navigation are of utmost importance for accomplishing complex autonoumous tasks, underpinning the feasibility of other high-level missions, such as atunomous driving. Roughly speaking, how to let an AR precisely perceive its surrounding environment, be aware of where it is, and plan what to do next adhering to specified objectives is fundamental for complex missions by ARs.
Multi-Robot Cooperation. Cooperation among a group of ARs (even human in the loop), inlcuding both homogeneous and heterogeneous ARs, is significant for practical large-scale problems (e.g., cooperative search, reconnaissance, surveillance, rescue, inspection and detection, due to the consideration of data privacy, capability limitation, attacking robustness, etc. In this respect, we are especially interested in the cooperation or collaboration for ARs, such as autonomous docking of UAVs onto stationary platforms or moving ground/aquatic vehicles.
Adversarial Games. Aside from cooperation among ARs, a vast array of realistic problems exhibit the opposition hallmark, for example, drone racing. This type of problems can genereally be modelled as adversarial games, consisting of multiple players (e.g., UAVs and human), which inevitably entail cutting-edge techniques, including game theory, etc.
AI methods. Artificial intelligence (AI) has been one of active and momentous fields all over the world, which is deemed to possess puissant fortes especially for complex problems with massive data and unclear models. Large language models (LLMs), reinforcement learning, deep neural networks, among others, are instances of AI methods. It is promising to leverage or come up with AI methods to conquer the aforementioned problems in ARs and other possible fields.