The theme of the meeting is AI related tools in Simulink. The event is organised in collaboaration with MathWorks.
1. Developing Autonomous Systems with MATLAB® and Simulink® by Martin Luo, Application Engineer, MathWorks
An autonomous robot involves a range of subsystems such as perception, decision making, motion planning, and controls. MATLAB® and Simulink® provides algorithms, tools and workflows for robotics and autonomous systems from design, simulation to test and deployment. In this presentation, you will learn how to develop an end-to-end workflow for an autonomous robot.
Perform robotics simulation and modeling in MATLAB® and Simulink®
Perception with fusion of sensor data (camera, Lidar, and radar) to maintain situational awareness
Mapping the environment and localizing the robot
Making decisions using supervisory logic
Path planning with obstacle avoidance
Interfacing to ROS networks and generating standalone ROS nodes for deployment
About the Presenter
Martin Luo is an application engineer with MathWorks, based in Sweden, where he focuses on robotics and autonomous systems, autonomous driving and model-based design. Prior to joining MathWorks, Martin worked on the flight control system for the civil aircraft and the navigation system for the quadcopter, conducting system and control law design, flight dynamics modeling and simulation. Martin holds an MS in Robotics from KTH Royal Institute of Technology, and an MS in Guidance, Navigation and Control from Beijing University of Aeronautics and Astronautics.
2. Reinforcement Learning Workflow with MATLAB® and Simulink® by Chris Setiadi , Application Engineer, MathWorks
Reinforcement learning allows you to solve control problems using deep learning but without using labeled data. Instead, learning occurs through multiple simulations of the system of interest. This simulation data is used to train a policy represented by a deep neural network that would then replace a traditional controller or decision-making system. In this session, you will learn how to do reinforcement learning using MathWorks products, including how to set up environment models, define the policy structure and scale training through parallel computing to improve performance.
About the Presenter
Chris Setiadi is an Application Engineer with MathWorks, based in Stockholm, Sweden, where he focuses on control system and model-based design. Prior to joining MathWorks, Chris worked in the field of advance driver assistance system and developed a lane keep assist system for heavy duty vehicle. Chris holds a PhD in Nuclear Fusion from KTH Royal Institute of Technology in Sweden and an MSc in System and Control from Eindhoven University of Technology in The Netherlands.