Zixiang Shen

About

Zixiang Shen is a researcher advancing the frontiers of autonomous robotics and intelligent warehouse systems. His work focuses on three interconnected areas: multi-robot path planning, digital twin technology, and bionic robotics. Shen’s most significant contribution is a novel path planning and tracking control algorithm for multi-autonomous mobile robot (multi-AMR) systems, which addresses spatiotemporal conflicts and nonholonomic constraints—a foundational paper with 6 citations. He further enhances this field by integrating deep reinforcement learning to solve scheduling inefficiencies in box storage environments, outperforming traditional dynamic programming methods (4 citations). Demonstrating versatility, Shen applies digital twin frameworks to both bionic climbing robots for transmission tower maintenance and warehouse operation scheduling platforms, tackling real-world challenges in power grid inspection and logistics. His work on digital twin-driven dynamic management systems (3 citations) highlights a commitment to bridging simulation and physical deployment. With a growing citation record and a focus on practical, scalable solutions, Shen is establishing himself as a key voice in the next generation of autonomous systems and smart manufacturing.

Research Focus

Key Achievements

Top Papers

1. 1
   A Path Planning and Tracking Control Algorithm for Multi-Autonomous Mobile Robot Systems Based on Spatiotemporal Conflicts and Nonholonomic Constraints

   6 citations · 2024
2. 2
   Deep Reinforcement Learning-based Multi-AMR Path Planning Algorithm

   4 citations · 2023
3. 3
   Research on Dynamic Management and Control System of Bionic Climbing Robot of Transmission Tower Based on Digital Twin

   3 citations · 2023
4. 4
   Warehouse Operation Scheduling Platform Based on Digital Twin

   1 citations · 2024

Key Collaborators

Contact & Links