Autonomous Robotics Master Class

Training workshop on advanced Advanced Autonomous Robotics with hands-on practicals

Autonomous Robotics

What you will learn and the outcome

  • Insightful and conceptual live training on the concepts of Autonomous Robotics 
  • Hands on session for Autonomous Robotics using modern AI and ML
  • Access to the course material
  • Access to online platform for AI/ML operations
  • Certificate of achievement

Delivery mode 

  • You can choose to join either on-site training in Adelaide, or join online from anywhere virtually
  • 2 hours x 5 sessions 

Best suited for

  • Professionals (Engineering, Healthcare, Education, Industrial, Government, etc) who is looking forward to develop Autonomous Robotics solutions
  • Business executives (C-level, directors, management) who would like to have advanced understanding of Autonomous Robotics
  • University students
  • School leavers
  • High school students
  • Any enthusiasts

Pre-requisite

  • Ability to understand scientific concepts
  • Basic understanding with Artificial Intelligence and Machine Learning
  • Coding with Python

Your trainer

 Dr. Kalana Withanage has Ph.D in Computer Vision / Machine Learning BSc. (Hons) in Electrical and Information Engineering 16+ years industrial experience   in architecting and developing solutions with machine learning, computer vision, robotics, and embedded systems. He is an inspiring trainer and   international community educational event speaker.

Course content

Session 1

 AI and ML Overview

 Classic programmatic robotics vs Autonomous Robotics

 Levels of autonomy

 Applications of autonomous robotics - drones, self-driving cars, industrial robots, home companion robots

 Autonomous robotic tasks: perception, planning, decision-making, control

 Sensors: Cameras, LIDAR, IMU, etc

 Actuators: Motors, Engines, Brakes

 Communication and Computational Systems

 Introduction to ROS 

 URDF - Universal Robot Definition Format

 Hands-on session on communication between robotics sub systems


Session 2

 Computer Vision for Autonomous Robotics

 Object detection, tracking, recognition

 Scene understanding for navigation and manipulation

 Data sources from cameras, LIDAR, IMUs.

 Sensor fusion

 SLAM - Simultaneous Localization and Mapping

 Hands-on session on building robot model in simulator, add cameras, LIDAR, and setup navigation using SLAM


Session 3

 Trajectory planning - A*, Dijekstra, RRT

 Dynamic motion planning with real-time re-planning

 Collision avoidance and reactive motion planning

 MoveIt simulator for robotic arm motions

 Hands-on session on object grasping using real-time path planning in the Moveit simulator


Session 4

 Reinforcement Learning for Autonomous Robotics

 Policy gradients, Q-learning, Deep reinforcement learning

 Hands-on session in RL applied in simulation


Session 5

 Real-Time Control for Autonomous Robotics

 Closed loop feedback control

 PID for position, velocity, acceleration and force control

 Decision making framework

 Finite State Machine (FSM) and behaviour trees

 Human robot collaboration

 Hands-on session feedback control and decision making applied in simulator