Qingwei Chen
Papers
7
Total Citations
143
H-Index
4
About
Qingwei Chen is a robotics and autonomous systems researcher whose work spans motion planning, multi-robot coordination, and optimization-based control strategies. His most influential contribution, "Car-like Mobile Robot Path Planning in Rough Terrain Using Multi-Objective Particle Swarm Optimization Algorithm" (2017, 97 citations), demonstrates his expertise in applying evolutionary computation techniques to complex real-world navigation challenges. This work, alongside his MOPSO-based trajectory planning research for robot manipulators (2015, 19 citations), highlights his sustained focus on multi-objective optimization frameworks, particularly using particle swarm and genetic algorithms to achieve smooth, physically feasible motion profiles with continuous velocity, acceleration, and jerk characteristics. Chen's research extends naturally into multi-robot systems, where he has tackled cooperative task allocation, formation control for nonholonomic robots in three-dimensional terrain, and collision-free path planning through generalized shared potential fields. His early work on biology-inspired behavior selection using genetic algorithms (2007, 14 citations) reflects a longstanding interest in nature-inspired computing. Additionally, his co-design approach for networked robotic systems addresses practical challenges of bandwidth and energy constraints in wireless robot coordination. Collectively, Chen's portfolio represents a coherent research vision: making autonomous robotic systems more efficient, coordinated, and deployable in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2MOPSO Based Multi-objective Trajectory Planning for Robot Manipulators19 citations · 2015
- 3Biology Inspired Robot Behavior Selection Mechanism: Using Genetic Algorithm14 citations · 2007
- 4
- 5Hybrid dynamic formation control for nonholonomic mobile robots2 citations · 2014
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- 7