Learn Mobile Robotics with WeBots

Discover how a robot functions as an intelligent machine that understands its surroundings, makes decisions from sensor data, operates automatically, and is reprogrammable for different tasks.

Classify robots into mobile and industrial types; mobile includes land, aerial, and underwater robots, while industrial covers serial and parallel robots with applications like welding, material handling, and painting.

Understand service robotics, including ISO and IFR definitions, with human–robot interaction, teleoperation, and autonomous systems shaping application-based robots with full mobility, safety, and productivity across environments.

Explore the applications of service robotics across domestic and industrial settings, including warehouse robots, social robots in hospitality, aerial robots for surveying and delivery, and bio-inspired designs.

Explores popular robot simulation software, including ABB Robot Studio for industrial robots, ROS for libraries and tools, Cappella SIM and Bee Bots for service robotics, and WeBots for course simulations.

Download Python 3.8.6 from python.org, run the Windows x86-64 executable installer. Check add Python 3.8 to path during install, approve prompts, and complete the setup for WeBots on Windows.

Install Webots robot simulator on Windows by downloading the latest version, running the installer, choosing install for me, and finishing setup, while noting Mac and Linux installations follow official docs.

Master the software handling of Webots by configuring themes in preferences and exploring the four windows—scene tree, simulation, text editor, and console, with Python programming in the editor.

Learn to build a base environment in Webots by adding a textured background, lighting, a floor with tiles, and surrounding walls, then save and reuse the world for simulations.

Explore Webots' base environment and learn to add robots and objects via proto nodes, move and rotate with arrow heads and precise translation, and resize models for practice.

Learn to add passive objects in Webots, configure appearance and geometry for shapes, and use solids to enable translation, rotation, and physics like density and mass.

Explore the types of robots in Webots, including aerial drones, bio-inspired and humanoid ground robots, automated ground vehicles, and industrial robots from ABB and Universal Robots.

Add universal robots UR3 and UR5E to the WeBots scene and run a simulated industrial workflow where they grasp cans from a conveyor and place them into cartons.

Explore the building blocks of a mobile robot, from problem design and material selection to 3d cad design, machining, procurement, circuitry, programming, and iterative testing.

Learn the four core elements of a mobile robot: controller, sensor, actuator, and power, and how the microcontroller, sensors, actuators, and DC power interconnect to sense, think, and act.

Explore robot locomotion types, from wheeled systems—caster, standard, ball, and Swedish 45/90 omnidirectional wheels—to legged designs, highlighting gate coordination, terrain handling, and the factorial-based gate count.

Explore the geometric design of mobile robots, balancing chassis and wheel choices with caster placement to ensure stable turning, while considering robot type, load, and ground clearance.

Learn how a differential drive robot uses two independently actuated wheels, a three-point contact, and an instantaneous center of curvature, with R denoting the distance from base midpoint to ICC.

Explore the mathematics of differential drive robots, linking wheel speeds to omega and turning radius, and examine three cases: straight line, rotation about the midpoint, and vr zero.

Learn what a controller does in Webots and how to create a new Python controller for the Spot robot, including selecting language and naming the controller.

Learn to build a two-tier robot in Webots by assembling chassis, wheels, joints, and sensors, including Arduino, GPS, distance sensor, and Kinect, with proper bounding objects and physics.

Learn to program basic movements for a four wheeled robot in Webots using a Python controller, including defining wheel motors, setting velocities, and implementing forward, backward, left and right turns.

Learn teleoperation of mobile robots from a distance via wired or wireless links, using joysticks, mobiles, or computers over Bluetooth or Wi‑Fi to map and explore hazardous areas.

Learn to build a Python controller in WeBots that uses the keyboard to drive the robot, mapping arrow keys to moving forward, backward, and drifting left or right.

Program tele-operation of a four-wheeled robot by mapping keyboard inputs to left and right wheel speeds in Webots, including motor setup, velocity control, and idle handling.

Explore how mobile robots use sensing, planning, and acting to drive decision making across hierarchical, reactive, and hybrid paradigms, with sensory data analysis and data distribution guiding sense-plan-act cycles.

Explore the reactive paradigm for mobile robots, where decisions rely on locally sensed data and two primitive organizations of behaviors as building blocks, a bio-inspired approach.

Explore the hierarchical paradigm in mobile robotics, from sensing the environment and building a centralized world model to planning, acting, and real-time control for deliberate robot behavior.

Explore the reactive paradigm in robotics, where actions arise from modular behaviors like direct mapping, with no plan, memoryless responses implemented on hardware or low-complexity algorithms.

Explore the Braitenberg principle as a reaction-based, behavior-based AI model, where a robot moves autonomously from sensor-to-motor connections, without internal memory or environmental inference.

Explore the Braitenberg principle for autonomous navigation by linking sensor inputs to motor outputs, with examples using infrared line sensors and a light-following robot.

Explore Braitenberg vehicle behaviors driven by sensor inputs, including inhibitory and excitatory circuits, with fear, aggression, love, and exploration modes that modulate motor speed toward or away from light.

Build and place cuboid obstacles in a simulated world to support robot path planning and obstacle avoidance, adjusting floor size, walls, and obstacle positions, sizes, and rotations.

Explore how robot sensors detect obstacles in the environment, including ultrasonic, infrared, camera/vision, and lidar, and how each sensor supports autonomous operation.

Explore obstacle avoidance algorithms for mobile robots, including bug zero, bug one, potential field, and vector field histogram, driven by sensor data to guide the end effector safely.

Explore obstacle avoidance with a range finder in WeBots, programming a robot to detect front obstacles via a range image and adjust left and right wheel speeds for safe navigation.

Explore how images form—from the eye to the pinhole and digital cameras—and how sampling, quantization, and pixels shape robotics vision and depth perception.

Learn how vision sensors in robotics use camera images to determine part orientation and accuracy, combine sensors to improve precision, reduce cycle time, and store inspection data for reference.

Explore how vision sensors drive robotics, from multi-point inspections in industries to warehouse robots sorting and handling packages, including blob detection.

Explore robot vision with Webots by adding a camera and importing an STL, then align it in front of the chassis and configure lens, recognition, and frame thickness.

Explore object recognition in Webots by enabling recognition colors on solids, converting objects to base nodes, and watching red bounding boxes track items as the robot moves with the keyboard.

Explore motion sensors for robots, including position sensors, wheel encoders, and proximity sensors, and learn how linear and rotary position sensors, encoder counts, and ultrasonic or infrared proximity methods work.

Learn the principles of wheel encoders as position sensors that provide feedback on rotation, position, speed, and direction. Explore rotary and optical encoders, code, and applications in robotics.

Explore how dead reckoning uses a known position to determine current location and how wheel odometry updates position from wheel sensors, including delta x, delta y, and phi.

Program a Pbot robot in Webots to use wheel encoders and position sensors for four-wheel positioning. Configure a Python controller to enable sensors and print wheel positions for motion.