Shaharil Mad Saad

About

Shaharil Mad Saad is a robotics researcher whose work spans the critical intersection of perception, navigation, and bio-inspired systems. His primary research areas include mobile robot localization and mapping, gas sensing and plume tracking, and snake robot locomotion. Mad Saad’s most influential contribution is his method for converting Kinect 3D depth data into 2D maps for indoor SLAM (31 citations), a practical solution that helped bridge the gap between affordable depth sensors and real-time robotic navigation. He also developed a flexible, autonomous integrated system for characterizing metal oxide gas sensors in dynamic environments, addressing a long-standing challenge in chemical sensing. Demonstrating a flair for biologically inspired design, Mad Saad proposed Braitenberg swarm vehicles for odor plume tracking in laminar airflow, an algorithm that mimics natural strategies to coordinate multiple robots. His more recent work includes a comprehensive review on snake robot locomotion and control for challenging terrains, as well as a lane-keeping controller using image processing for driver-assistance systems. Through these contributions, Mad Saad has demonstrated a consistent focus on enabling robots to perceive, navigate, and operate effectively in complex, real-world environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Method to convert Kinect's 3D depth data to a 2D map for indoor SLAM

   31 citations · 2013
2. 2
   Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment

   9 citations · 2014
3. 3
   Braitenberg swarm vehicles for odour plume tracking in laminar airflow

   8 citations · 2013
4. 4
   A Review on Snake Robot Locomotion, Modelling, and Controlling in Challenging Environment

   2 citations · 2020
5. 5
   Development of Lane keeping Controller Using Image processing

   2 citations · 2018

Key Collaborators

Contact & Links