🎓 Robotics and Intelligent Systems Engineer | 🤖 AI & Control Systems Enthusiast | 🧠 Researcher in Intelligent Robotics

I am a Robotics and Intelligent Systems Engineering graduate from Manara University, passionate about Robotics, Artificial Intelligence, and Control Systems.
My mission is to bridge theory and real-world applications through intelligent robotic systems that perceive, decide, and act autonomously.

For my undergraduate thesis, I designed and implemented a monocular camera-equipped differential-drive mobile robot for indoor 3D reconstruction, integrating hybrid control and path-tracking approaches — combining AI optimization, control theory, and computer vision.

I’m currently preparing to pursue a Master’s in Robotics & AI to further specialize in autonomous mobile robotics, focusing on:

• 🗺️ Path planning & localization
• ⚙️ Adaptive & intelligent control
• 🧭 AI-based decision making for autonomy

• 🤖 Autonomous Mobile Robotics
• 🧠 Artificial Intelligence & Machine Learning
• 🎯 Control Systems (LQR, Lyapunov, PID, Adaptive Control)
• 🧩 Computer Vision & 3D Reconstruction
• 🧰 Embedded Systems & Real-Time Control

An end-to-end autonomous indoor 3D reconstruction system using a monocular camera-equipped differential-drive robot. Combines Lyapunov–LQR hybrid control, neural network optimization, and hierarchical localization (DISK + LightGlue) to generate high-fidelity 3D models via COLMAP and OpenMVS on a ROS-based Raspberry Pi–Arduino platform.

Tech: Python, ROS, OpenCV, PCL, NumPy

A hybrid control architecture integrating Lyapunov-based stability and LQR precision tracking, enhanced by a neural network for adaptive gain tuning. Enables a differential-drive robot to achieve trajectory accuracy, smooth orientation transitions, and minimal traversal time through intelligent control optimization.

Tech: MATLAB, Simulink, Python (Optimization & Simulation)

A benchmarking framework for hierarchical localization pipelines, evaluating feature extractors and matchers based on keypoint density, inlier ratios, and pose accuracy. Guides the selection of optimal visual components to enhance multi-view stereo (MVS) performance and high-quality mesh generation.

Tech: OpenCV, NumPy, Matplotlib, Python

A fine-tuned YOLOv8n model with contrastive learning (CL) on the Google Landmarks dataset (10 classes). Delivers a lightweight and efficient cultural landmark recognition model, supporting AI-driven cultural heritage preservation.

Tech: PyTorch, Ultralytics YOLOv8, CL, OpenCV

🎓 B.Sc. in Robotics and Intelligent Systems Engineering
Manara University

• Graduated with Excellence — GPA 3.46/4.00, Ranked 4th in class
• Focus: Control Systems, Embedded Systems, Artificial Intelligence, and Mobile Robotics
• Thesis: “Design and Implementation of an Autonomous Monocular Mobile Robot for 3D Reconstruction and Path Tracking”

• 🔬 Pursuing a Master’s in Robotics & AI
• 🤝 Collaborating on autonomous systems research
• 💡 Innovating at the intersection of AI, control, and robotics
• 🌍 Advancing a humanitarian mission to preserve cultural heritage in regions affected by war and conflict, where identity and history are at risk.
  Witnessing the destruction of cultural landmarks has driven my commitment to develop autonomous aerial robotics for rapid, high-fidelity 3D documentation—ensuring that the world’s shared heritage endures beyond the reach of conflict.

Languages: Python, C++, MATLAB, ROS
Tools & Frameworks: OpenCV, PyTorch, NumPy, TensorFlow, Simulink, Gazebo
Specialties: Control Systems, Computer Vision, Robot Simulation, AI Optimization

🌐 Portfolio / Projects: github.com/Bisher-Alsaleh
💼 LinkedIn: https://www.linkedin.com/in/bisher-alsaleh-739a89352/
📧 Email: bisher.alsaleh@outlook.com

⭐ “Advancing intelligent autonomy to transform industries, preserve culture, and enhance human life.”