Qianjie Liu

Xiamen University, East China Jiaotong University

Papers

Total Citations

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About

Qianjie Liu is a robotics researcher whose work spans autonomous navigation, path planning, and soft robotics. Liu’s most cited contribution, “Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain” (2020, 16 citations), introduces a laser-based approach that integrates the Sobel operator for edge detection in 3D point clouds, enabling robots to safely navigate challenging, uneven environments. Building on this, Liu proposed an improved ant colony algorithm with a Gaussian-distributed pheromone mechanism (GD-ACO) for mobile robot path planning (2022, 9 citations), effectively reducing tangential collision risks in complex settings. Most recently, Liu has ventured into soft robotics with the design and optimization of a vacuum-powered soft bending actuator (VSBA, 2025), leveraging coordinated chamber collapse for safe, durable, and reliable actuation. This work demonstrates Liu’s growing versatility, bridging traditional rigid robotics with emerging soft systems. With a research portfolio that advances both perception and actuation, Qianjie Liu is contributing foundational tools for the next generation of autonomous and adaptive robotic systems.