Max Robotics — Global Service Robot Directory + US Certification

About

Xiaoxu Shi is a leading researcher in mobile robotics, specializing in path planning and autonomous navigation. Their work focuses on overcoming fundamental limitations in robotic motion, particularly the inefficiencies of traditional algorithms in complex, continuous environments. Shi’s most impactful contribution is the development of an improved RRT_Connect algorithm, which optimizes node searching and path planning to significantly boost search efficiency—a paper that has garnered 24 citations. They have also pioneered the application of Double Deep Q Network (DDQN) reinforcement learning to robot path planning, introducing innovations like experience classification, multi-step learning, and average Q-value estimation with reward redistribution. These advances have made deep reinforcement learning more accurate and practical for real-world navigation, earning 21 and 9 citations respectively. Most recently, Shi has combined RRT-based autonomous detection with Karto SLAM algorithms to address frontier detection and drift distortion in mapping. Through this body of work—totaling over 58 citations—Xiaoxu Shi has established themselves as a key innovator at the intersection of sampling-based planning and deep reinforcement learning, pushing the boundaries of how robots perceive and move through their environments.