Yiguo Peng
Papers
1
Total Citations
3
H-Index
1
About
Yiguo Peng is a researcher specializing in robotics, artificial intelligence, and autonomous navigation, with a particular focus on target localization in complex environments. His most-cited work introduces the TLSE-PPO (Target Localization in Staircase Environments-Proximal Policy Optimization) method, a novel approach that combines object detection with reinforcement learning to enable swing-arm tracked robots to accurately locate soldiers in challenging staircase settings. This contribution addresses critical challenges in urban warfare and rescue scenarios, where traditional localization methods often fail due to constrained spaces and irregular terrain. The paper has garnered 3 citations since its 2024 publication, reflecting growing interest in integrating deep reinforcement learning with robotic perception systems. Peng’s work stands out for its practical application to real-world military and emergency response operations, demonstrating how advanced AI techniques can enhance robotic autonomy in high-stakes environments. His research bridges the gap between theoretical reinforcement learning algorithms and deployable robotic systems, offering a scalable solution for target identification in non-standard indoor architectures.
Research Focus
Key Achievements
Top Papers
- 1