Papers
14
Total Citations
162
H-Index
6
About
Ziang Liu is a robotics and optimization researcher whose work sits at the intersection of energy-efficient motion planning, manufacturing automation, and intelligent robot control. His most influential contribution, "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization" (2022, 84 citations), established him as a leading voice in applying evolutionary computation to reduce industrial robot energy consumption — a pressing concern as Industry 5.0 demands more sustainable automation. Liu has systematically expanded this foundation, developing RRT-based sampling algorithms for robotic pick-and-place tasks, surrogate-assisted multi-objective frameworks for simultaneous packing and motion planning, and layout optimization strategies for robotic cellular manufacturing systems. His work on robotic lime picking, which treats dense foliage as permeable rather than hard obstacles, demonstrates creative problem-solving for agricultural robotics. More recently, Liu has pushed toward intelligent human-robot interaction, integrating large language models for natural language task planning and capsule neural networks for medical robot gesture recognition. Collectively, his publications reflect a researcher steadily broadening the scope of autonomous robotics — from industrial floors to orchards to operating rooms — while consistently prioritizing efficiency, practicality, and real-world applicability.
Research Focus
Key Achievements
Top Papers
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- 3Robotic Lime Picking by Considering Leaves as Permeable Obstacles13 citations · 2021
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