Muhammad Shahzad Alam Khan

National University of Sciences and Technology

Papers

Total Citations

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About

Muhammad Shahzad Alam Khan is a robotics researcher whose work centers on autonomous navigation, simultaneous localization and mapping (SLAM), and agricultural robotics. His primary contributions lie in advancing sensor selection and multi-sensor fusion for mobile robot navigation. In his most cited work (2022, 16 citations), Khan introduced a novel application of the Analytical Hierarchy Process to systematically evaluate widely used SLAM sensors, providing a quantitative framework for sensor selection that improves navigation accuracy in unstructured environments. His 2021 study (9 citations) demonstrated the integration of Extended Kalman Filter and RGBD-SLAM to achieve landmark localization without prior environmental knowledge, generating both 2D and 3D maps. Khan’s applied research includes implementing SLAM techniques for agricultural robots, or agribots, in simulated Gazebo environments (2021, 7 citations), addressing the critical need for autonomous operation in precision agriculture. His work bridges theoretical SLAM algorithms with practical deployment in agriculture, surveillance, and planetary exploration, offering researchers and engineers a systematic methodology for optimizing sensor configurations. Khan’s contributions are particularly valuable for advancing autonomous systems in GPS-denied and dynamic environments.