Research: deep reinforcement learning and autonomous systems
Autonomous drones
His research develops deep reinforcement learning agents for drones and asks how they should act when part of their sensing degrades or fails in flight.
See the researchAutonomous cars
Vehicles exchange bird's-eye views over vehicle-to-vehicle (V2V) links, so the affected car can still detect what a parked truck hides. Reconstructing that view without inventing obstacles is the question behind a graduate project he supervises.
See the researchSensor fusion and computer vision
A camera degrades in fog well before LiDAR does. Fusing the two keeps detections confident as conditions worsen, the kind of multimodal perception on which his drone research depends.
See the researchGraph neural networks
Each layer of a graph neural network lets every node aggregate information from its neighbours. His graduate students have applied the approach to misinformation and sentiment analysis on social media.
See the researchIllustration: simulated LiDAR from a drone crossing a city block. Sensor: nominal.
Illustration: Ego LiDAR nominal. A parked truck hides the curb ahead.
Illustration: clear night. Detection confidence: camera 0.95, LiDAR 0.90, fused 0.99.
GCN-style label propagation from 8 seeds (misinformation or reliable). Layer 0 of 3: only the seeds are known.
His research aims to give unmanned aerial vehicles autonomy that is robust, reliable and trustworthy in complex, GPS-denied and degraded environments, moving them from lab prototypes to field-ready systems for infrastructure inspection, search and rescue, and environmental monitoring.
The guiding question
How can autonomous agents sense, decide and coordinate under uncertainty, when sensors fail, conditions change and maps are incomplete, while remaining safe and reliable?
Three connected directions form one autonomy stack: sensing, deciding under uncertainty, and acting as a team. Uncertainty is measured and passed along at every level.
Multimodal fusion that keeps a drone aware of its surroundings when a sensor fails, using generative models to repair missing signals in real time and reporting how confident each estimate is.
Reinforcement learning controllers that act on that uncertainty: slowing down in clutter, keeping wider clearance in poor visibility, and backed by lightweight safety layers.
Teams of drones that coordinate to build accurate maps rather than simply cover ground, and that reorganize when an agent fails or communication drops.
Longer term: natural-language tasking and explanation of drone decisions, cooperative perception across networked UAVs, and swarm coordination that accounts for energy and mission time.
This work develops deep reinforcement learning agents that navigate unmanned aerial vehicles (UAVs) through cluttered, dynamic environments using onboard camera input alone. Attention mechanisms direct the agent to the regions of the scene most relevant to avoiding obstacles.
This work examines how an autonomous system should perceive and act when a sensor degrades or fails, and how the missing information can be recovered without introducing spurious detections. For connected vehicles, cooperative perception over vehicle-to-vehicle (V2V) communication allows a vehicle whose LiDAR has failed to reconstruct its view of the road from data shared by nearby vehicles.
The same deep learning methods extend beyond aerial robotics to social network analysis, anomaly detection in Internet of Things data, and medical image analysis.
Agile DQN: Adaptive deep recurrent attention reinforcement learning for autonomous UAV obstacle avoidance. AlMahamid, F. and Grolinger, K. Scientific Reports, 15(1).
VizNav: A modular off-policy deep reinforcement learning framework for vision-based autonomous UAV navigation in 3D dynamic environments. AlMahamid, F. and Grolinger, K. Drones, 8(5), 173.
Autonomous unmanned aerial vehicle navigation using reinforcement learning: A systematic review. AlMahamid, F. and Grolinger, K. Engineering Applications of Artificial Intelligence, 115, 105321.
Size-aware learning and contrast enhancement for multiple sclerosis lesion segmentation in brain MRI. Aref, Y., Awadallah, O., Charaf, N., AlMahamid, F., Sadhu, A. and Grolinger, K. IEEE SmartNets.
A multi-step comparative framework for anomaly detection in IoT data streams. Al-Qudah, M. and AlMahamid, F. IEEE International Conference on New Trends in Computing Sciences.
Agglomerative hierarchical clustering with dynamic time warping for household load curve clustering. AlMahamid, F. and Grolinger, K. IEEE CCECE.
Virtual sensor middleware: Managing IoT data for the fog-cloud platform. AlMahamid, F., Lutfiyya, H. and Grolinger, K. IEEE CCECE.
Reinforcement learning algorithms: An overview and classification. AlMahamid, F. and Grolinger, K. IEEE CCECE.
Deep reinforcement learning for autonomous unmanned aerial vehicle navigation. Ph.D. thesis, Western University.
Virtual sensor middleware: A middleware for managing IoT data for the fog-cloud platform. M.Sc. thesis, Western University.
Also listed on Google Scholar and ORCID.