Jaber Karimpour
Papers
1
Total Citations
7
H-Index
1
About
Jaber Karimpour is a leading researcher in autonomous mobile robotics and neuromorphic computing, with a focus on developing biologically inspired control systems for real-world navigation. His most-cited work, "A Novel Approach for Target Attraction and Obstacle Avoidance of a Mobile Robot in Unknown Environments Using a Customized Spiking Neural Network" (2023, 7 citations), addresses critical limitations in reinforcement learning for autonomous mobile robots (AMRs), including long convergence times and high computational demands. Karimpour’s key contribution is a customized spiking neural network (SNN) that enables efficient target attraction and obstacle avoidance in unknown environments, mimicking the brain’s event-driven processing to reduce energy consumption and improve real-time performance. This work bridges the gap between theoretical neuromorphic models and practical robotics, offering a scalable solution for AMRs in dynamic settings. His research has significant implications for autonomous navigation, search-and-rescue operations, and industrial automation. Karimpour’s innovative SNN-based approach has been cited by peers exploring low-power, adaptive robotics, marking him as a rising voice in the integration of neural computation and mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1