Papers
8
Total Citations
30
H-Index
3
About
Safa Ziadi is a robotics researcher whose work focuses on mobile robot motion planning and trajectory optimization, particularly through the application of Particle Swarm Optimization (PSO) to force field-based navigation methods. Ziadi's major contributions center on developing and refining novel path planning approaches that enable mobile robots to navigate complex environments while avoiding collisions with both static and dynamic obstacles. Her most influential work includes the PSO-CF² (Canonical Force Field) method and the PSO-DVSF² (Dynamic Variable Speed Force Field) approach, which optimize robot trajectories for known and unknown environments. Notably, Ziadi has extended these methods to track moving targets, as demonstrated in her PSO-CF²-mt and PSO-DVSF²-mt variants. Her research has accumulated over 30 citations across her most-cited papers, with individual works receiving up to 6 citations. Ziadi has also compared the effectiveness of PSO versus genetic algorithms for trajectory optimization, contributing valuable insights to the field. Her work represents a systematic effort to enhance autonomous navigation capabilities, making mobile robots more adaptable and efficient in dynamic real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 1
- 2PSO-CF2: A new method for the path planning of a mobile robot6 citations · 2015
- 3PSO-DVSF2: A new method for the path planning of mobile robots5 citations · 2015
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- 6
- 7Which is Better for Mobile Robot Trajectory Optimization: PSO or GA?2 citations · 2020
- 8PSO optimization of mobile robot trajectories in unknown environments2 citations · 2016