Path Planning & Navigation: Guiding Intelligent Robots Through the World

web-development.

Course Modules:

Module 1: Introduction to Robotic Navigation

What is path planning and why it matters

Overview of motion planning, localization, and control

Common navigation tasks: point-to-point, patrol, map exploration

Module 2: Environment Representation

Occupancy grids and cost maps

Static vs. dynamic environments

Global vs. local maps in navigation

Module 3: Classical Path Planning Algorithms

Breadth-First Search and Dijkstra’s Algorithm

A* Algorithm and heuristic search

Comparison of path optimality, speed, and efficiency

Module 4: Sampling-Based and Advanced Planning

Rapidly-exploring Random Trees (RRT)

Probabilistic Roadmaps (PRM)

Dealing with high-dimensional and complex spaces

Module 5: Obstacle Avoidance and Real-Time Navigation

Local path planning and reactive control

Dynamic Window Approach (DWA)

Combining planning and perception in live environments

Module 6: Capstone Project – Simulate a Navigation Task

Use a simulation platform (e.g., Gazebo, Webots, or RViz)

Implement a path planner with obstacle avoidance

Submit map snapshots, path outputs, and performance evaluation

Tools & Technologies Used:

Python

ROS (Robot Operating System) with Navigation Stack

OpenCV, NumPy, Matplotlib

Gazebo or Webots (for simulation)

Target Audience:

Robotics and AI learners

Engineers working on autonomous vehicles and drones

Developers building warehouse, service, or delivery robots

Students studying motion planning and intelligent systems

Global Learning Benefits:

Learn how to navigate real or simulated worlds with confidence

Combine perception, planning, and control for intelligent motion

Master industry-relevant tools like ROS and A*

Build robotics projects that can operate in unknown or changing environments

?Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

?Shop our library of over one million titles and learn anytime

?‍? Learn with our expert tutors