Papers
12
Total Citations
197
H-Index
8
About
Jinwen Hu is a robotics and autonomous systems researcher whose work spans multi-robot coordination, mobile sensor networks, path planning, and intelligent control. His most influential contribution, "Cooperative Search and Exploration in Robotic Networks" (2013, 85 citations), established foundational frameworks for deploying networked multi-robot systems in real-world applications such as environmental monitoring, battlefield surveillance, and search-and-rescue operations. Building on this, his 2011 work on energy-based target localization in mobile sensor networks (36 citations) addressed a critical limitation of conventional tracking methods by eliminating dependence on explicit observation models — a meaningful advance for unpredictable real-world deployments. Hu's research has progressively expanded into autonomous UAV collision avoidance, reinforcement learning applications, and advanced path planning, including an improved artificial potential field method that tackles the persistent local minima problem. More recently, his work on quadruped robot trajectory planning, LiDAR sensing in harsh environments, and decentralized multi-agent localization reflects a broadening commitment to robust, real-world autonomous systems. His exploration of transfer learning for mobile robot navigation further demonstrates an interest in bridging classical robotics with modern machine learning. With over 190 cumulative citations, Hu's body of work represents a sustained and evolving contribution to intelligent robotics research.
Research Focus
Key Achievements
Top Papers
- 1Cooperative Search and Exploration in Robotic Networks85 citations · 2013
- 2
- 3A review of rule-based collision avoidance technology for autonomous UAV15 citations · 2023
- 4
- 5A review of the applications and hotspots of reinforcement learning11 citations · 2017
- 6
- 7
- 83D ToF LiDAR for Mobile Robotics in Harsh Environments: A Review9 citations · 2024
- 9
- 10