Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Section 01
Day 01
Introduction
- What Makes a Smart Robot Smart?
Physical vs Virtual Smart Robots
- Smart Robots, Smart Machines, Sentient Machines and Robotic Process Automation (RPA), etc.
The Role of Artificial Intelligence (AI) in Smart Robots
- Beyond "if-then-else" and the learning machine
- The algorithms behind AI
- AI in Smart Robots: machine learning, computer vision, natural language processing (NLP), etc.
- Cognitive robotics
The Role of Big Data in Smart Robots
- Decision-making based on data and patterns
The Cloud and Smart Robots
- Linking robotics with IT
- Building more functional robots that access more information and collaborate
Case Study: Mechanical Smart Robots
- Industrial Smart Robots
- Baxter
- Personal Service Robots
- Domestic robots that assist the elderly, smart self-driving cars
- Professional Service Robots
- Agricultural robots in diary operations
Hardware components of a Smart Robot
- Motors, sensors, microcontrollers, cameras, etc.
Common Elements of Smart Robots
- Machine vision, voice recognition, speech synthesis, proximity sensing, pressure sensing, etc.
Development Frameworks for Programming a Smart Robot
- Open source and commercial frameworks
- Robot Operating System (ROS)
- Architecture: workspace, topics, messages, services, nodes, actionlibs, tools, etc.
Languages for Programming a Smart Robot
- C++ for low level controlling
- Python for orchestration
- Programming ROS nodes in Python and C ++
- Other languages
Tools for Simulating a Physical Smart Robot
- Commercial and open source 3D simulation and visualization software
Preparing the Development Environment
- Software installation and setup
- Useful packages and utilities
Day 02
Programming the Smart Robot
- Programming a node in Python and C ++
- Understanding ROS node
- Messages and topics in ROS
- Publication / subscription paradigm
- Project: Bump & Go with real robot
- Troubleshooting
- Simulation of robots with Gazebo / ROS
- Frames in ROS and reference changes
- 2D information processing of cameras with OpenCV
- Information processing of a laser
- Project: Safe tracking of objects by color
- Troubleshooting
Day 03
Programming the Smart Robot (Continued...)
- Services in ROS
- 3D information processing of RGB-D sensors with PCL
- Maps and Navigation with ROS
- Project: Search for objects in the environment
- Troubleshooting
Section 02
Day 04
Programming the Smart Robot (Continued...)
- ActionLib
- Speech Recognition and Speech Generation
- Controlling robotic arms with MoveIt!
- Controlling robotic neck for active vision
- Project: Search and collection of objects
- Troubleshooting
Testing Your Smart Robot
- Unit testing
Day 05
Extending a Smart Robot's Capabilities with Deep Learning
- Perception -- vision, audio, and haptics
- Knowledge representation
- Voice recognition through NLP (natural language processing)
- Computer vision
Crash Course in Deep Learning
- Artificial Neural Networks (ANNs)
- Artificial Neural Networks vs. Biological Neural Networks
- Feedforward Neural Networks
- Activation Functions
- Training Artificial Neural Networks
Day 06
Crash Course in Deep Learning (Continued...)
- Deep Learning Models
- Convolutional Networks and Recurrent Networks
- Convolutional Neural Networks (CNNs or ConvNets)
- Convolution Layer
- Pooling Layer
- Convolutional Neural Networks Architecture
Section 03
Day 07
Crash Course in Deep Learning (Continued...)
- Recurrent Neural Networks (RNN)
- Training an RNN
- Stabilizing gradients during training
- Long short-term memory networks
- Deep Learning Platforms and Software Libraries
- Deep Learning in ROS
Day 08
Using Big Data in Your Smart Robot
- Big data concepts
- Approaches to data analysis
- Big Data tooling
- Recognizing patterns in the data
- Exercise: NLP and Computer Vision on large data sets
Day 09
Using Big Data in Your Smart Robot (Continued...)
- Distributed processing of large data sets
- Coexistence and cross-fertilization of Big Data and Robotics
- The Smart Robot as a generator of data
- Range measuring sensors, position, visual, tactile sensors, and other modalities
- Making sense of sensory data (sense-plan-act loop)
- Exercise: Capturing streaming data
Section 04
Day 10
Programming an Autonomous Deep Learning Smart Robot
- Deep Learning robot components
- Setting up the robot simulator
- Running a CUDA-accelerated neural network with Cafe
- Troubleshooting
Day 11
Programming an Autonomous Deep Learning Smart Robot (Continued...)
- Recognizing objects in photographs or video streams
- Enabling computer vision with OpenCV
- Troubleshooting
Day 12
Data Analytics
- Using the Smart Robot to collect and organize new data
Building a Smart Robot Collaboratively
Deploying Your Smart Robot on Physical Hardware
Monitoring and Servicing Smart Robots in the Field
Securing Your Robot
- Preventing unauthorized tampering
- Preventing hackers from viewing and stealing sensitive business data (credit card, employee information, etc.)
Joining to the Robotics Community
Future Outlook for Smart Robots
Closing Remarks
Requirements
- Programming experience in C++
- Programming experience in Python
- Experience with Linux command line
84 Hours
Testimonials (1)
every time i wasn't sure about some exercise, the trainer explained to me in multiple ways, until I understood.
