Ghulam Farid

About

Ghulam Farid is a researcher specializing in autonomous aerial robotics, with a primary focus on the control, navigation, and motion planning of quadrotor unmanned aerial vehicles (UAVs). His work bridges classical nonlinear control theory with modern reinforcement learning (RL) to address the challenges of underactuated, coupled dynamics and obstacle-cluttered environments. Farid’s early contributions include the application of control law partitioning and inverse dynamics for robust altitude/attitude stabilization and trajectory generation, as seen in his 2018 papers on computationally efficient waypoint-based trajectories (8 citations). More recently, he has advanced autonomous navigation through improved deep Q-learning approaches, enabling UAVs to operate in 3D spaces with unseen random goals and multi-goal tasks—his 2025 papers have already garnered 10 and 3 citations, respectively. By integrating RL with classical control, Farid’s work offers scalable, real-time solutions for complex mission profiles, making him a notable figure in the evolution of intelligent UAV systems.

Research Focus

Key Achievements

Top Papers

1. 1
   On control law partitioning for nonlinear control of a quadrotor UAV

   11 citations · 2018
2. 2
   An Improved Deep Q-Learning Approach for Navigation of an Autonomous UAV Agent in 3D Obstacle-Cluttered Environment

   10 citations · 2025
3. 3
   Computationally efficient algorithm to generate a waypoints-based trajectory for a quadrotor UAV

   8 citations · 2018
4. 4
   A reinforcement learning approach for multi-goal motion planning of autonomous ground vehicles in cluttered environments

   3 citations · 2025
5. 5
   Inverse dynamics based altitude/attitude control and stabilization of a quadrotor UAV

   2 citations · 2017
6. 6
   A multi-goal reinforcement learning framework for motion planning of a quadrotor UAV in 3D cluttered environment with unseen random goals

   1 citations · 2025

Key Collaborators

Contact & Links