Chunxiao Song

Harbin University of Science and Technology

Papers

Total Citations

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About

Chunxiao Song is a robotics researcher specializing in locomotion and path planning for mobile robots operating on complex, uneven terrain. His work focuses on two key areas: wheeled robots navigating dynamic slopes and quadruped crawling robots maintaining stability on inclined surfaces. Song’s major contributions include developing a deep reinforcement learning-based dynamic path planning algorithm that addresses the slow convergence of Double Deep Q-Networks (DDQN) for wheeled robots on sloping ground with moving obstacles, and creating obstacle avoidance gait planning for quadruped robots that recognizes slope terrain to prevent collisions and overturns. His research has been published in high-impact venues, with his most-cited paper, “Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground” (2020), accumulating 15 citations. Additional notable works include “Research on Obstacle Avoidance Gait Planning of Quadruped Crawling Robot Based on Slope Terrain Recognition” (2022, 7 citations) and “Gait Planning and Control of Quadruped Crawling Robot on a Slope” (2019, 6 citations), which together establish Song as a leading figure in terrain-adaptive robotic mobility.