Course

Introduction to Robotics with Webots

University of Colorado Boulder

The Introduction to Robotics with Webots specialization offers comprehensive learning in the design and control of intelligent robotic systems. Through a series of modules, you will delve into forward and inverse kinematics, trajectory planning, and uncertainty management in robotic sensing and actuation.

Throughout the program, you will gain hands-on experience using Python and the Webots simulator to implement robotic behaviors, map trajectories, and execute complex tasks. The course is designed to provide a deep understanding of mechanisms, sensors, and control, enabling you to develop the skills necessary for a career in robotics.

  • Model mechanisms, sensors, and actuators in a physics-based online simulator
  • Understand basic reactive and discrete controllers
  • Perform forward kinematics computations for simple (non-)Holonomic mechanisms
  • Apply coordinate transforms to multi-dimensional sensor signals
  • Use discrete planning techniques such as Dijkstra and A* to compute optimal robot trajectories
  • Implement complex sequences of behaviors using behavior trees

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Introduction to Robotics with Webots
Course Modules

The Introduction to Robotics with Webots course is divided into three modules that cover topics such as basic robotic behaviors and odometry, robotic mapping and trajectory generation, and robotic path planning and task execution. Each module provides hands-on experience and theoretical knowledge essential for understanding robotic systems.

Basic Robotic Behaviors and Odometry

Module 1: Basic Robotic Behaviors and Odometry

  • Model mechanisms, sensors, and actuators in a physics-based online simulator
  • Understand basic reactive and discrete controllers
  • Perform forward kinematics computations for simple (non-)Holonomic mechanisms

Robotic Mapping and Trajectory Generation

Module 2: Robotic Mapping and Trajectory Generation

  • Use basic feedback control techniques for inverse kinematics of (non-)Holonomic mechanisms
  • Apply coordinate transforms to multi-dimensional sensor signals
  • Understand basic probabilistic representations to deal with uncertainty from measurement noise

Robotic Path Planning and Task Execution

Module 3: Robotic Path Planning and Task Execution

  • Use discrete planning techniques such as Dijkstra and A* to compute optimal robot trajectories
  • Implement complex sequences of behaviors using behavior trees
  • Plan and implement a complex robotic controller for autonomous mobile manipulation behavior
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