Mohammad Biglerbegian
Papers
1
Total Citations
10
H-Index
1
About
Mohammad Biglerbegian is a robotics researcher whose work focuses on autonomous navigation in complex, dynamic environments. His most-cited paper, "An Efficient Potential-Function Based Path-Planning Algorithm for Mobile Robots in Dynamic Environments with Moving Targets" (2015), introduces a novel approach to real-time path planning that addresses the challenge of avoiding moving obstacles while pursuing moving targets. This work, which has garnered 10 citations, is notable for its computational efficiency and practical applicability in scenarios such as search-and-rescue, warehouse automation, and autonomous driving. By refining potential-field methods, Biglerbegian’s algorithm reduces the risk of local minima and improves trajectory smoothness, offering a robust solution for robots operating in unpredictable settings. His contributions are particularly valuable for students and researchers interested in motion planning, control systems, and multi-agent coordination. While his citation count reflects a focused, emerging impact, the practical relevance of his work suggests growing influence in the field of intelligent robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1