Melika Ataollahi
Papers
1
Total Citations
4
H-Index
1
About
Melika Ataollahi is a researcher whose work lies at the intersection of robotics, artificial intelligence, and autonomous systems, with a particular focus on multi-robot coordination in complex, unknown environments. Her most-cited paper, "Online path planning of cooperative mobile robots in unknown environments using improved Q‐Learning and adaptive artificial potential field" (2023), introduces a novel hybrid algorithm that combines reinforcement learning with adaptive potential fields to enable real-time, collision-free navigation for teams of mobile robots. This work, which has garnered 4 citations, addresses a critical challenge in robotics: how to make cooperative robots robust and efficient when they cannot rely on pre-mapped surroundings. Ataollahi’s contribution is significant because it bridges the gap between theoretical learning methods and practical, dynamic control, offering a scalable solution for applications such as warehouse automation, search-and-rescue, and exploration. By integrating improved Q-learning with adaptive artificial potential fields, she has demonstrated a pathway for robots to learn and adapt their paths on the fly, enhancing both safety and mission speed. Her research is particularly valuable for students and engineers working on autonomous navigation, as it provides a clear, implementable framework for overcoming the unpredictability of real-world environments.
Research Focus
Key Achievements
Top Papers
- 1