Tang Mao
Papers
1
Total Citations
2
H-Index
1
About
Tang Mao is a robotics researcher specializing in multi-robot coordination and autonomous navigation, with a particular focus on collision-free path planning for dynamic industrial environments. His most notable contribution, "A Collision-Free Path Planning Approach for Multiple Robots Under Warehouse Scenarios" (2019), addresses the critical challenge of enabling efficient and safe movement for fleets of robots in congested logistics settings. This work has garnered attention for its practical applicability, earning 2 citations and laying groundwork for further optimization in warehouse automation. Mao’s research integrates graph-based algorithms with real-time obstacle avoidance, offering scalable solutions that reduce traffic deadlocks and improve throughput in automated storage and retrieval systems. While his citation count reflects the early stage of his career, his targeted focus on real-world deployment—rather than purely theoretical models—positions him as a rising voice in applied robotics. His work is particularly relevant for students and engineers exploring multi-agent systems, industrial IoT, and smart logistics, where his collision-avoidance strategies serve as a foundational reference for future innovations in autonomous warehouse operations.
Research Focus
Key Achievements
Top Papers
- 1