Home > Center for Automation & Robotics Research (CARR) > Our Projects

Our Projects

  • CARR

The Future of Automation

The Center for Automation & Robotics Research (CARR) is where innovation in smart manufacturing, digital twin, and human-robot collaboration comes to life. Designed as a bridge between academia and industry, CARR empowers students and researchers to solve complex engineering problems through interdisciplinary collaboration and advanced prototyping. Backed by cutting-edge infrastructure and guided by expert faculty, the center fosters industry partnerships and fuels the development of industry-ready talent and solutions.
  • ONGOING PROJECTS

Innovation at Work

CIM’s projects are built on a foundation of research excellence and future-focused innovation. We combine the power of AI, deep learning, and sensor fusion to address real-world mobility challenges—from making autonomous navigation safer to enabling precise spatial understanding. Our work is rooted in practical experimentation and collaboration, pushing the limits of what connected and intelligent transportation can achieve.

  • Swarm Robotics: Towards Decentralized Intelligence in Multi-Robot Systems
  • As industries move from mass customization to mass individualization, Multi-Robot Systems (MRS) offer scalable, flexible solutions for evolving shop floor needs. This project focuses on developing decentralized intelligence in heterogeneous MRS. It involves creating robust algorithms for planning, task allocation, coordination, and navigation in both Single Robot-Single Task (SRST) and Multi-Robot-Single Task (MR-ST) configurations. The goal is to enable these systems to self-adapt and efficiently fulfill customer-specific production demands.
  • Vision-Based Quality Control Assurance
  • This project enhances Additive Manufacturing (AM) processes through real-time machine vision. By integrating sensors and cameras into the production line, irregularities are detected and corrected immediately. Machine learning algorithms analyze patterns, adjust parameters on the fly, and ensure precision — reducing waste, improving speed, and ensuring consistent quality. The solution has high relevance for aerospace, healthcare, and automotive sectors.
  • Digital Twin Projects

The Power of Virtual Precision

  • Digital Twin for Diagnostics of Ball Bearing
  • To improve maintenance of rotating machinery like gearboxes and pumps, this project uses physics-based digital twins enhanced with sensor data. A model of the ball bearing and shaft assembly is built using finite element and discrete element methods. Real-time vibration data from sensors is used to calibrate this model. Multi-Category Support Vector Machines (MSVM) are trained to predict maintenance needs, reducing downtime and optimizing machine performance in Industry 4.0 environments.
  • Virtual Twin of Smart Assembly Line
  • This project develops a dynamic, real-time digital replica of a custom smart manufacturing line. The setup integrates Omron’s COBOT TM5, SCARA, and Viper robots, an Automated Storage and Retrieval System (ASRS), and a Chain Conveyor system. Designed using Dassault Systèmes 3DEXPERIENCE CATIA and DELMIA, this virtual twin allows:
  • Advanced production planning and optimization
  • Real-time monitoring and analytics
  • Immersive training and collaboration
  • Industry 4.0 adoption for enhanced productivity
  • Human-Robot Collaboration Projects

Shared Spaces, Smarter Work

  • Robots’ Movement in Human Spaces: Dynamic Path Planning and Navigation
  • This project addresses the challenge of enabling robots to function safely in human-centric environments like homes, hospitals, and manufacturing units. It explores real-time, adaptive path-planning using SLAM (Simultaneous Localization and Mapping), sensor fusion, and reinforcement learning. The key focus is on achieving smooth, non-disruptive robot behavior that balances safety, comfort, and efficiency while dynamically responding to changing environments.
  • Object Affordance and Actions
  • Manual disassembly in product maintenance can be time-consuming and error-prone. This project aims to automate the disassembly process using AI-driven object recognition and robotic manipulation. Through image-based recognition and affordance-based interaction, robots will learn to identify various screw patterns and remove them autonomously. This solution reduces dependency on skilled labor, ensures consistency, and accelerates service processes in manufacturing industries.
  • RESEARCH FOCUS AREAS

Domains of Impact

01

Perception and Control

02

Security

03

Connectivity