Mohamed Ennaji
Papers
1
Total Citations
18
H-Index
1
About
Mohamed Ennaji is a researcher at the forefront of multi-robot systems and bio-inspired artificial intelligence, with a particular focus on cooperative hunting strategies. His most-cited work, "Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field" (2022), has garnered 18 citations, reflecting its significance in advancing the coordination and efficiency of robotic teams. Ennaji’s major contribution lies in integrating wolf swarm algorithms with artificial potential fields, enabling robots to autonomously collaborate in dynamic environments—a breakthrough for applications in search-and-rescue, surveillance, and autonomous exploration. By mimicking the predatory behavior of wolf packs, his approach enhances task allocation and obstacle avoidance, addressing a key challenge in multi-agent systems. This work underscores his broader impact on robotics and AI, where his research bridges theoretical models and practical implementations. Ennaji’s achievements highlight his role in solving complex problems that single robots cannot handle, making his contributions invaluable for students and researchers exploring decentralized control, swarm intelligence, and cooperative robotics.
Research Focus
Key Achievements
Top Papers
- 1