Limin Yu

About

Limin Yu is a robotics researcher focused on advancing autonomous navigation and multi-robot systems, with key contributions in path planning, visual tracking, and edge computing. Their work addresses critical challenges in mobile robotics, particularly in narrow passages and dynamic environments where traditional planners fail. Yu's most cited paper, "Comparison and Improvement of Local Planners on ROS for Narrow Passages" (2022, 7 citations), systematically evaluates and enhances the Dynamic Window Approach and Time Elastic Band planners, offering practical improvements for real-world deployment. Another notable contribution, "Improved Camshift Algorithm in AGV Vision-based Tracking with Edge Computing" (2021, 6 citations), integrates edge computing to boost tracking efficiency in automated guided vehicles. Yu also explores deep learning solutions for visual occlusion in multi-robot tracking (2022, 3 citations) and proposes a novel multi-robot planning algorithm using quad-tree map division for irregular obstacles (2022, 1 citation). These works demonstrate a consistent focus on bridging theoretical algorithms with practical robotic applications, making Yu's research valuable for students and engineers developing robust, real-time autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Comparison and Improvement of Local Planners on ROS for Narrow Passages

   7 citations · 2022
2. 2
   Improved Camshift Algorithm in AGV Vision-based Tracking with Edge Computing

   6 citations · 2021
3. 3
   Mobile Robot Tracking with Deep Learning Models under the Specific Environments

   3 citations · 2022
4. 4
   A Multi-Robot Planning Algorithm with Quad Tree Map Division for Obstacles of Irregular Shape

   1 citations · 2022

Key Collaborators

Contact & Links