🚀🔬 Research Experience 📈🧪

🎓👨‍🔬Graduate Assitant

Jan’23 - Present
AI4CE Lab, New York University

Supervisor: Dr. Chen Feng, NYU

• Proposed a spatiotemporal backbone to harness the sequential information for Visual Place Recognition(VPR)
• Implementing self-attention and cross-attention mechanisms to enable the model to focus on the most relevant regions in each frame and across the sequence
• Evaluating different network configurations on benchmark VPR datasets to quantify accuracy gains compared to baseline approaches in Pytorch

🛠️Research Intern

Feb’21 - June’21
Formal Control and Autonomous Systems(FOCAS) Lab
Robert-Bosch Centre for Cyber-Physical Systems (RBCCPS)
Indian Institute of Science (IISc), Bangalore

Supervisor: Dr. Pushpak Jagtap, IISc, Bangalore

• Led the setup and calibration of PhaseSpace Motion Capture System and developed ROS support for integrating the system with Turtlebot3 robots
  
• Deployed Turtlebot3 robots with differential and mecanum drive configurations and utilized motion capture for accurate robot position tracking and real-time control adjustments
  
• Implemented a Control Lyapunov Function (CLF) based controller in Python to guide unicycle modeled agents (robots) to desired poses
  
• Implemented a barrier certificate-based collision avoidance algorithm for multi-agent systems using ROS(Python)
  
• Achieved a 30cm safety radius in a 6m x 5m arena
  

(Video 1) (Video 2)

🛠️Research Intern

Robotics Innovations Lab(RIL), Indian Institute of Science, Bangalore
Jul’21 – Dec’21
Supervisor: Dr. Abhra Roy Chowdhury, Indian Institute of Science

• Constructed a differential drive rover robot platform with DC motors, encoders, microcontroller, and forklift
  
• Developed models and control programs in MATLAB to enable autonomous navigation:
  • Characterized gear motor response through PWM speed sweeps and data analysis
    
  • Implemented differential drive kinematics for low-level control of driving and arm motions
    
  • Derived and simulated kinematic and dynamic models of rover motion and used for open loop control
    
  • Designed and tuned a PID controller with wheel encoder feedback for precise closed loop position and heading control
    
  • Added webcam and localization algorithms to determine rover location within the test arena
    
  • Integrated navigation and vision systems for high-level arena navigation and object manipulation tasks
    
  • Enhanced autonomy with line following and obstacle avoidance behaviors
    
• This advanced robotic system incorporated mechanical design, modeling, control theory, computer vision, PID control, path planning, and autonomous decision-making
• Demonstrated expertise in mechatronics, autonomous robotics, and advanced control algorithms
  (Project Report) (Project Video)