Muhammad Shalihan
Papers
8
Total Citations
80
H-Index
6
About
Muhammad Shalihan is a robotics researcher specializing in multi-robot systems, simultaneous localization and mapping (SLAM), and sensor fusion for autonomous navigation in GPS-denied environments. His work addresses critical challenges in robot localization by integrating Ultra-Wideband (UWB), LiDAR, odometry, and WiFi signals to overcome the limitations of individual sensors. Shalihan’s most impactful contribution is his pioneering use of neural networks to mitigate non-line-of-sight (NLOS) ranging errors in UWB localization, achieving centimeter-level accuracy in indoor environments—a breakthrough documented in his highly cited 2022 paper (20 citations). He also developed distributed SLAM frameworks for multiple robots using UWB and odometry (23 citations), enabling efficient collaborative mapping in featureless spaces where traditional LiDAR fails. His research extends to multi-robot exploration with potential-field-based strategies and human-robot collaboration for search and rescue operations. With over 80 total citations across his publications, Shalihan’s work has significant practical implications for warehouse automation, disaster response, and large-scale indoor navigation, establishing him as a rising expert in resilient, multi-sensor robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2NLOS Ranging Mitigation with Neural Network Model for UWB Localization20 citations · 2022
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- 4Efficient WiFi LiDAR SLAM for Autonomous Robots in Large Environments8 citations · 2022
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