Abdelhak Bougouffa
Papers
2
Total Citations
28
H-Index
2
About
Abdelhak Bougouffa is a researcher whose work sits at the intersection of robotics, optimization, and autonomous navigation. His primary contributions focus on advancing **simultaneous localization and mapping (SLAM)** —a critical challenge for mobile robots operating in human environments. Bougouffa’s key innovation lies in integrating **particle swarm optimization (PSO)** with the **normal distributions transform (NDT)** to solve scan-matching problems, a core component of SLAM. His most-cited paper, "Particle swarm optimization for solving a scan-matching problem based on the normal distributions transform" (2021, 20 citations), demonstrates how PSO can robustly align laser scans to build accurate maps and estimate robot poses. This work builds on his earlier foundational paper, "NDT-PSO, a New NDT based SLAM Approach using Particle Swarm Optimization" (2020, 8 citations), which directly tackles the dual problem of mapping and localization. By replacing traditional gradient-based methods with swarm intelligence, Bougouffa’s approach offers improved resilience to local minima and noisy sensor data—key for safe human-robot interaction. His research remains highly relevant as autonomous systems become more prevalent, and his citation record reflects a growing interest in bio-inspired algorithms for real-world robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2NDT-PSO, a New NDT based SLAM Approach using Particle Swarm Optimization8 citations · 2020