Mobile Robotics: From Sensors to Autonomous Navigation

Description

Course Structure

Module 1: Introduction to Mobile Robotics

Topics Covered:

• What is Mobile Robotics?

• Types of Mobile Robots (Wheeled, Tracked, Legged)

• Applications in Industries, Defense, Healthcare, Exploration

• Overview of the Course and Learning Roadmap

Project:

• Project Preview: Autonomous Navigation Robot

Module 2: Robot Platform & Wiring Architecture

Topics Covered:

• ESP-WROOM-32 Pinout and Capabilities

• Power Management and 7.4V Battery Integration

• Complete Hardware Wiring Plan

• Line Sensor (TCRT5000L)

• Motion Tracking (MPU6050)

• Precision Driving (N20 Motors + Encoders + TB6612FNG)

• Obstacle Avoidance (VL53L0X on Servo)

• Servo Scanning + Pin Management

Hands-on Task:

• Wiring and Assembling the Complete Bot

Module 3: Mobile Robot Dynamics

Topics Covered:

• Kinematics of Differential Drive Robots

• Velocity Control using Encoders

• Forward and Inverse Kinematics

• Wheel Slippage and Correction Models

• Center of Mass and Stability (for Self-Balancing)

Python Simulations:

• Differential Drive Model

• Trajectory Estimation with Encoder Data

Module 4: Sensor Integration & Data Fusion

Topics Covered:

• IMU (MPU6050) – Angle Estimation using Kalman Filter / Complementary Filter

• Encoder Readings – RPM and Distance Calculation

• Line Sensor Calibration and Logic Implementation

• ToF Sensor (VL53L0X) Scanning and Obstacle Detection

Python Simulations:

• Sensor Modeling

• Kalman Filter Demo for Orientation

Microcontroller Tasks:

• Integrate IMU and Read Real-Time Orientation

• Tune and Test Line Sensor

• Test Encoder Data for Speed Control

• Servo-Controlled Scanning using VL53L0X

Module 5: Control Systems for Mobile Robots

Topics Covered:

• PID Control Fundamentals

• PID for Motor Speed Control using Encoder Feedback

• PID for Orientation Balancing (using MPU6050)

• Line Following with Proportional Control

• SLAM Basics using 2D Lidar-style ToF Sweep

Python Simulations:

• PID Controller Visualization

• Simulate Line-Following on Grid

• Obstacle Avoidance Algorithm with Angle Mapping

ESP Tasks:

• Implement PID for Wheel Speed

• Self-Balancing Demo using MPU6050

• Line Following on Track

• Sweep and Detect Walls using Servo-Mounted VL53L0X

Module 6: Mapping & Navigation

Topics Covered:

• Introduction to Grid Mapping and SLAM (Basic 2D Map)

• Data Logging via Serial or MicroSD

• Creating Simple Maps from ToF Data

• Path Planning Overview (A*, Dijkstra – Conceptual)

Simulation Task:

• Create Grid Map from Sensor Readings

• Simulate Obstacle Detection and Rerouting

Module 7: Capstone Projects

Participants Can Pick One Project and Work End-to-End:

• Self-Balancing Robot using MPU6050 + PID

• Maze Solver Bot using ToF + Line Sensor

• SLAM Explorer Bot with Servo-Mounted VL53L0X

• Speed Controlled Delivery Bot using Encoder Feedback

• Simulation-Driven Robot (Use Python + Hardware Sync)

Each Project Involves:

• Sensor Calibration

• System Dynamics Tuning

• Control Loop Design

• Real-World Testing

Tools & Languages Used

• Microcontroller Programming: ESP-IDF or Arduino IDE (C++)

• Simulation: Python (NumPy, matplotlib, PyGame for Visual Demos)

• Data Handling: Serial Monitor, CSV Logging, Optional MicroSD

• Visualization: Matplotlib, Plotly, Real-Time Dashboard (Optional)

Components Used