Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
My First Blog Post
Published:
This is my first blog post for the personal website. Thanks for visiting!
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2 
publications
A Self-Supervised Learning Approach with Differentiable Optimizationfor UAV Trajectory Planning
Published in IROS 2025 Submission, 2025
IROS 2025 Submission. Will release the code and paper soon!
Recommended citation: ...
Download Paper
My Future First Journal Paper
Published in Somewhere, 2025
Nothing published yet. Hopefully I can publish my work soon.
Recommended citation: ...
Download Paper | Download Slides
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Fast Exploration of UAV Swarm in Unknown Environments
Undergraduate Thesis, Nankai University, 2024
Due to the good maneuverability of the drone swarm, it shows high efficiency in the exploration of unknown environment, which can be applied in domains such as disaster relief and forest exploration. The method can allocate exploration regions to individual drones, while considering the uneven distribution of obstacles and calculating the local environmental complexity to adjust the exploration cost. All the code has been open-sourced at https://github.com/Jiang-Yufei/Multi-agent-exploration.git
A Self-Supervised Learning Approach with Differentiable Optimizationfor UAV Trajectory Planning
Research Project, Penn State University, 2024
We propose a self-supervised UAV trajectory planning pipeline that integrates a learning-based depth perception with differentiable trajectory optimization. A 3D cost map guides UAV behavior without expert demonstrations or human labels. Additionally, we incorporate a neural network-based time allocation strategy to improve the efficiency and optimality. The system thus combines robust learning-based perception with reliable physics-based optimization for improved generalizability and interpretability. Both simulation and real-world experiments validate our approach across various environments, demonstrating its effectiveness and robustness. Our method achieves a 31.33% improvement in position tracking error and 49.37% reduction in control effort compared to the state-of-the-art.
