Mohammed Qasim

About

Mohammed Qasim is a robotics researcher whose work centers on autonomous navigation, shared human-robot control, and collision avoidance for mobile platforms. His key contributions span trajectory tracking and obstacle avoidance for wheeled omnidirectional robots using Nonlinear Model Predictive Control (NMPC), as well as innovative approaches to brain-computer interfaces (BCI) for robot arm teleoperation. Notably, his 2024 NMPC paper (6 citations) demonstrates robust performance in dynamic environments, while his 2022 shared control work (6 citations) addresses the challenge of low-dimensional BCI commands for high-DOF robots. Qasim also developed a passivity-based adaptive controller for self-leveling landing platforms on UGVs (5 citations) and introduced super-ellipsoidal potential functions for UAV collision avoidance (4 citations). His indoor localization method using color-coded beacons and depth cameras (3 citations) further showcases his versatility. With over 26 total citations across his most-cited works, Qasim’s research is impactful for students and engineers interested in autonomous systems, human-robot interaction, and real-time control.

Research Focus

Key Achievements

Top Papers

1. 1
   NMPC Based-Trajectory Tracking and Obstacle Avoidance for Mobile Robots

   6 citations · 2024
2. 2
   Shared Control of a Robot Arm Using BCI and Computer Vision

   6 citations · 2022
3. 3
   Passivity-Based Adaptive Controller for Dynamic Self-Leveling of a Custom-Built Landing Platform on Top of a UGV

   5 citations · 2020
4. 4
   Super-Ellipsoidal Potential Function For Autonomous Collision Avoidance Of A Teleoperated Uav

   4 citations · 2016
5. 5
   Mobile Robot Indoor Localization Using Color-Coded Beacons and a Depth Camera

   3 citations · 2022
6. 6
   Autonomous Collision Avoidance for a Teleoperated UAV Based on a Super-Ellipsoidal Potential Function

   2 citations · 2016

Key Collaborators

Contact & Links