Path Planning & Navigation: Guiding Intelligent Robots Through the World
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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
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