Mars Rover

Kosmos - Autonomous Mars Rover

Inspired by the challenges of robotic exploration in complex and unstructured environments, the Kosmos Mars Rover project was a student-led initiative to design and build an autonomous vehicle. Our team focused on developing an intelligent and resilient platform capable of navigating simulated hazardous terrains with minimal human intervention. This project provided a practical avenue to apply principles of robust perception, sophisticated navigation, and automated decision-making, aiming to contribute to the understanding and development of technologies for more effective and independent robotic operations.

Coldspray

Coldspray System

Exploring the frontiers of additive manufacturing, the Coldspray System project is a research endeavor undertaken to advance a novel metal 3D printing process. This work involves integrating an xArm6 robotic arm as the motion platform for a coldspray nozzle, a technique that deposits metal powder at high velocities to create solid material through kinetic energy bonding. The core focus is to enhance the efficiency and precision of this innovative manufacturing method. This research provides an opportunity to apply cutting-edge reinforcement learning (RL) algorithms to teach the robotic arm optimal spray patterns, specifically aiming to minimize the need for subsequent machining and improve the net-shape capabilities of the coldspray process.

rlanv

Adaptive Reinforcement Learning for Robust Navigation

This project employed a dual-simulation strategy for developing and validating robust navigation policies. A custom, high-speed Gymnasium environment ("VectorizedDD") was created for efficient vectorized training of reinforcement learning agents, featuring configurable obstacles and Lidar simulation. For more realistic validation and sim-to-sim transfer, the navigation scenarios were also implemented in NVIDIA's Isaac Sim, leveraging its high-fidelity physics and sensor modeling. This approach allowed for rapid algorithm iteration in the custom environment, followed by validation in a more complex, physically accurate simulator.

Patrol Bot

Patrol Bot

This project addresses the vulnerabilities in traditional security systems by introducing an autonomous IoT-based Patrol Bot. Designed as an auxiliary security layer, the bot aims to mitigate human error in surveillance, particularly in small to medium-sized businesses. The system features a compact, stealthy rover that navigates a designated area during off-hours, utilizing computer vision for human detection and an integrated IoT platform (ThingSpeak) to send real-time alerts across multiple channels (Email, Twitter, MQTT, VOIP calls via IFTTT) if an intruder is detected. Key contributions include a cost-effective design, a redundant alert system, and a random exploration navigation algorithm for comprehensive room coverage. This initiative showcases practical application of mechatronics principles, IoT integration, and object detection to provide an accessible and reliable security enhancement.

Interests

  • Autonomy
  • Automation
  • Localization & Mapping
  • Motion Planning
  • Computer Vision
  • Deep Learning
  • Dynamic System Modeling
  • Reinforcement Learning
  • Embedded Systems
  • Algorithm Development
  • Robot Manipulation
  • Control Systems
  • Programmable Logic Controllers

About

I'm currently pursuing an MS in Robotics at Northeastern University, with a focus on robot perception, learning, and control. My goal is to develop highly generalized robotic systems that can adapt and perform in complex, dynamic environments. I bring a strong foundation in mechatronics and a deep interest in areas like SLAM, computer vision, deep reinforcement learning, manipulation, embedded systems, and algorithm development—essentially everything that enables a robot to sense, learn, and act effectively. I'm driven by curiosity, a builder’s mindset, and a commitment to pushing the boundaries of autonomy. My long-term vision is clear: to become one of the best roboticists in the world.

-Adnan

Want to learn more? Check out my LinkedIn profile, download my Resume or Master Resume, or send me an e-mail using the links below.

Contact

To get the conversation started, use the form below (or, if you'd rather, send me an email or message me on LinkedIn). Feel free to contact me about career opportunities, relevant events such as conventions or conferences, or volunteer opportunities!