Papers
4
Total Citations
45
H-Index
4
About
Ruiquan Wang is a robotics researcher specializing in motion planning, obstacle avoidance, and intelligent control for robotic manipulators, with a particular focus on redundant and space-based systems. His major contributions lie at the intersection of reinforcement learning and robotics, where he has developed novel methods to enable manipulators to autonomously avoid obstacles while tracking desired trajectories—critical for safe human-robot collaboration and on-orbit operations. His most cited work, "Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators" (2022, 22 citations), introduces a reactive framework that ensures both efficiency and safety in dynamic environments. Building on this, his 2023 paper on obstacle-avoidance motion planning for redundant space robots (14 citations) addresses the unique challenges of unstructured, zero-gravity settings, ensuring task completability and safety. Wang has also explored genetic algorithm-based optimal design for modular robot topology (2023, 5 citations) and guided deep reinforcement learning for path planning (2021, 4 citations). His work is foundational for advancing autonomous robotic systems in complex, real-world applications, from factories to space missions.
Research Focus
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