Artificial Intelligence (AI) and Robotics Program
Join our Artificial Intelligence (AI) and Robotics Program to master the integration of AI with robotic systems. Gain hands-on experience in AI-driven robotic applications, including perception, motion planning, and autonomous navigation.
Application Deadline: 6th October 2024
Want to upskill?
Enroll today in our Artificial Intelligence (AI) and Robotics Program to elevate and expand your expertise.
Program Fee
Duration
0 Week(s)
Study Mode
Virtual classes
Introduction
The Artificial Intelligence (AI) and Robotics Mastery Program at Indepth Research Institute (IRES) is designed to equip participants with the essential knowledge and practical skills needed to excel in the dynamic fields of artificial intelligence and robotics. The Artificial Intelligence (AI) and Robotics Program is a comprehensive learning path designed to equip participants with a deep understanding of AI-driven robotics. This program integrates AI concepts with robotic systems, enabling learners to explore how AI enhances robotic capabilities in areas such as automation, decision-making, and human-robot interaction. Participants will engage in hands-on exercises and case studies that focus on the real-world applications of AI in robotics across various industries, from manufacturing and healthcare to autonomous systems.
Application Process
Register
Submit your registration by filling in the form online.
Make Payments
Receive Invoice upon registration and make payments.
Join program
Choose a mode of study and attend course.
Program Prerequisites
- Basic knowledge of programming (Python/C++ preferred).
- Familiarity with AI and machine learning fundamentals.
- Basic understanding of electronics and mechanical systems is recommended.
Program Modules
- What is AI-Driven Robotics?
- Overview of AI Algorithms in Robotics
- Key Concepts and Terminologies
- Case Study: AI-driven robotics in manufacturing automation.
- Autonomous Robots vs. Human-Operated Robots
- Mobile Robots, Industrial Robots, and Service Robots
- Robotic Operating Systems (ROS)
- Hands-on Exercise: Introduction to a simple robotic simulation using ROS.
- Types of Sensors (e.g., LIDAR, Camera, Ultrasonic)
- Sensor Fusion and Data Integration
- Environmental Perception in Robotics
- Hands-on Exercise: Implementing sensor data processing in a robotic system.
- Image Processing and Feature Detection
- Object Recognition and Tracking
- AI in Visual Perception for Robots
- Case Study: AI-based computer vision in autonomous vehicles.
- Kinematics and Dynamics of Robots
- Path Planning Algorithms (A*, RRT)
- Obstacle Avoidance Techniques
- Hands-on Exercise: Implement basic motion planning in a robotic simulator.
- PID Control and its Applications in Robotics
- Feedback Control Mechanisms
- Adaptive and Robust Control in AI-Driven Robots
- Case Study: Control system design for a robotic arm in a manufacturing setup.
- Supervised Learning for Robotic Tasks
- Reinforcement Learning in Robotics
- Imitation Learning for Robots
- Hands-on Exercise: Train a robot to perform a task using reinforcement learning.
- Learning from Environment and Experiences
- Real-Time Adaptation to Dynamic Environments
- AI in Robotic Manipulation
- Case Study: Adaptive robotics in warehouse automation.
- Understanding HRI and Its Importance
- Robot Behavior and Human Perception
- AI in Collaborative Robotics (Cobots)
- Hands-on Exercise: Design a simple HRI scenario using a simulated robot.
- Speech Recognition and Natural Language Understanding
- AI-Driven Conversational Agents for Robotics
- Multi-Modal Interaction (Voice, Gestures, Touch)
- Case Study: AI-driven HRI in healthcare robots.
- Basics of Localization and Mapping
- SLAM Algorithms (Kalman Filters, Particle Filters)
- 2D and 3D Mapping in Robotics
- Hands-on Exercise: Implement SLAM in a mobile robot simulation.
- Path Planning in Unknown Environments
- AI for Autonomous Decision-Making
- Real-Time Navigation and Dynamic Obstacle Avoidance
- Case Study: Autonomous drone navigation in delivery systems.
- Kinematics of Robotic Arms
- End Effectors and Grippers
- Manipulation in Structured and Unstructured Environments
- Hands-on Exercise: Program a robotic arm to perform pick-and-place tasks.
- Grasp Planning Algorithms
- Machine Learning for Object Handling
- Reinforcement Learning for Dexterous Manipulation
- Case Study: AI-driven robotic grasping in industrial automation.
- Concepts of Multi-Robot Systems
- Coordination and Communication in Robot Swarms
- Applications of Swarm Robotics in Real-World Scenarios
- Hands-on Exercise: Simulate a simple swarm robotics task.
- Distributed AI for Multi-Robot Coordination
- Reinforcement Learning for Swarm Optimization
- Collective Decision-Making in Swarms
- Case Study: Swarm robotics in environmental monitoring.
- AI-Driven Surgical Robots
- Assistive Robotics for Elderly Care
- Rehabilitation Robotics
- Case Study: AI in robotic surgery and patient care systems.
- Industrial Robotics for Automation
- AI for Predictive Maintenance in Robotics
- Autonomous Robots in Supply Chain and Logistics
- Case Study: Robotics in smart manufacturing and supply chain automation.
- Safety and Responsibility in AI-Powered Robotics
- Ethical Implications of Autonomous Robots
- Regulation and Compliance in AI Robotics
- Case Study: Ethical challenges in deploying autonomous robots in public spaces.
- Ensuring Transparency and Accountability
- Social Impact of AI Robotics
- Human-Centered Design in Robotics
- Hands-on Exercise: Develop an ethical framework for a hypothetical robotics project.
- Overview of Robotics Simulation Tools (e.g., Gazebo, V-REP)
- Simulating Robot Motion and Interaction
- Testing AI Algorithms in Simulated Environments
- Hands-on Exercise: Develop and test a robotic simulation using ROS and Gazebo.
- Virtual Prototyping and Simulation for AI Development
- Testing AI for Autonomous Navigation and Manipulation
- Performance Evaluation of AI Algorithms in Robotics
- Case Study: Testing autonomous navigation in a simulated urban environment.
- Deployment Challenges in Real-World Environments
- Scaling AI Solutions for Robotics
- Continuous Monitoring and Maintenance of Deployed Robots
- Hands-on Exercise: Deploy a robot with AI capabilities in a real-world scenario.
- AI for Edge Devices in Robotics
- AI-Enhanced Robotics for Space Exploration
- Emerging Trends: Quantum AI and Robotics
- Case Study: Future potential of AI-driven robotics in space missions.
Certifications
Upon successful course completion, participants will be awarded a certificate of program completion from Indepth Research Institute.
The Program also Includes
Program Delivery
Delivered via video lectures in form of zoom and google meet.
Real World Examples
Delivered through a combination of video and live online lectures.
Hands on Experience
Learn through individual assignments and feedback.
Debrief of Learning
A combination of recorded and live video lectures.
Tech Stack
No Technology needed
Upcoming Application Deadline
Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.
Deadline: 6 Oct 2024
Program Fees
Fees:
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