Papers
4
Total Citations
54
H-Index
4
About
Ju Gao is a pioneering researcher at the intersection of autonomous navigation and intelligent sensing systems. His primary research areas encompass mobile robot path planning, simultaneous localization and mapping (SLAM), and self-powered multimodal sensing technologies. Gao’s most significant contributions include the development of a hybrid path planning method that integrates an improved A* algorithm with CSA-APF algorithms, effectively addressing the longstanding challenges of dynamic obstacle avoidance and local path optimization in complex environments. This work, published in 2024, has already garnered 20 citations for its practical applicability. His earlier foundational work on path planning using enhanced A* algorithms (2022, 12 citations) laid the groundwork for more efficient navigation solutions by reducing computational time and path curvature. Notably, Gao has also advanced the field of wearable technology through his 2025 paper on a flexible self-powered multimodal sensor capable of simultaneously detecting temperature, pressure, and humidity with low signal coupling. This innovation, cited 17 times, demonstrates significant potential for physiological monitoring and human-robot collaboration. Additionally, his 2022 work on integrating YOLOv7 with SLAM systems for unmanned vehicles operating in dynamic environments (5 citations) represents a crucial step toward robust autonomous navigation beyond static assumptions.
Research Focus
Key Achievements
Top Papers
- 1A Hybrid Path Planning Method Based on Improved A* and CSA-APF Algorithms20 citations · 2024
- 2
- 3Path Planning of Mobile Robot Based on Improved A* Algorithm12 citations · 2022
- 4