Mohammad Khan
Papers
1
Total Citations
5
H-Index
1
About
Mohammad Khan is a researcher specializing in mobile robotics and deep learning, with a focus on vision-based navigation in complex indoor environments. His most cited work, "Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network" (2019), introduces a robust obstacle avoidance system for mobile robots operating in tight, dynamic spaces. By applying deep convolutional neural networks to low-quality raw RGB images, Khan developed a refined classifier that enables real-time decision-making without reliance on expensive sensors. His innovative fine-tuning approach significantly improves the network’s adaptability to unpredictable surroundings, demonstrating how deep learning can enhance autonomous navigation in constrained settings. Though his citation count is modest, this foundational paper has garnered 5 citations, reflecting its early impact on the field. Khan’s contributions are particularly valuable for researchers seeking cost-effective, vision-based solutions for indoor robotics, bridging the gap between theoretical deep learning models and practical deployment. His work underscores the potential of lightweight neural architectures in resource-limited applications, making him a promising voice in the intersection of computer vision and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1