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Primary Learning Objectives
Upon successful completion of this course, you will be able to:
- Understand the fundamental problem of path planning in robotics and its importance.
- Differentiate between various types of path planning algorithms, including search-based, sampling-based, and potential field methods.
- Implement common path planning algorithms, such as Dijkstra’s, A*, RRT, and PRM.
- Grasp concepts related to obstacle avoidance, path smoothing, and trajectory generation.
- Apply path planning techniques to practical robotics scenarios through simulations and hands-on examples.
- Critically evaluate and select appropriate path planning algorithms for different robotic applications.
Necessary Materials
- Computer with internet access
- Python 3 installation
- Integrated Development Environment (IDE) such as VS Code or PyCharm
- Access to a robotics simulation environment (e.g., Gazebo, CoppeliaSim, or a Python-based visualization library like Matplotlib for 2D grids)
- Basic understanding of linear algebra and geometry (recommended but not strictly required; concepts will be reviewed as needed)
Course Content
Week 1-2: Lesson 1 – Introduction to Path Planning and Grid-Based Search
Title: The Robot’s Roadmap: Navigating the Basics of Path Planning
Learning Objectives:
- Define path planning and its significance in robotics.
- Understand the different components of a path planning problem (start, goal, obstacles).
- Introduce the concept of a grid-based environment representation.
Key Vocabulary:
- Path Planning: The process of finding a sequence of valid configurations that moves a robot from a start configuration to a goal configuration while avoiding obstacles.
- Configuration Space (C-Space): The space of all possible configurations (positions and orientations) of a robot.
- Obstacle: An object or region in the environment that a robot cannot pass through.
- Grid Map: A discretized representation of an environment where the space is divided into a grid of cells.
Full Written Content:
Imagine you’re walking through a crowded room. You instinctively plan a route to get from one side to the other, avoiding people and furniture. Robots face a similar challenge: how do they get from point A to point B without bumping into anything? This is the essence of path planning.
Path planning is a fundamental problem in robotics, crucial for autonomous navigation, manipulation, and various other applications. Without effective path planning, a robot would simply wander aimlessly or get stuck.
At its core, a path planning problem involves a robot, a starting point, a destination, and obstacles that the robot must avoid. The environment can be represented in various ways. One of the simplest and most intuitive is the grid map. In a grid map, the environment is divided into a grid of uniform cells. Each cell can be marked as either free space (navigable) or occupied (an obstacle). This discretization simplifies the problem, allowing us to use search algorithms to find a path.
Consider a robot trying to move across a room. We can represent the room as a grid. If a cell contains a wall or furniture, it’s an obstacle. The robot’s goal is to find a sequence of free cells from its starting cell to its destination cell.
Practical Hands-on Examples:
- Representing a Simple Environment:
- Create a 2D array (list of lists in Python) to represent a small grid map.
- Initialize some cells as obstacles (e.g.,
1) and others as free space (e.g.,0). - Print your grid map to visualize it.
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- Defining Start and Goal:
- Add variables to your Python script for the start and goal coordinates (row, column).
- Mark these on your printed grid (you can use different symbols or colors for visualization if you wish).
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Week 3-4: Lesson 2 – Breadth-First Search (BFS) for Path Planning
Title: The Expansive Explorer: Finding Paths with Breadth-First Search
Learning Objectives:
- Understand the principles of Breadth-First Search (BFS).
- Apply BFS to find the shortest path in an unweighted grid.
- Implement BFS in Python for path planning.
Key Vocabulary:
- Breadth-First Search (BFS): A graph traversal algorithm that explores all the neighbor nodes at the present depth before moving on to the nodes at the next depth level.
- Queue: A First-In, First-Out (FIFO) data structure used in BFS.
- Visited Set: A collection to keep track of nodes that have already been explored to avoid cycles and redundant processing.
- Parent Dictionary (or Path Reconstruction): A way to store the predecessor of each node, enabling reconstruction of the path from the goal to the start.
Full Written Content:
Once we have our grid map, how do we actually find a path? One of the simplest and most intuitive algorithms is Breadth-First Search (BFS). BFS explores the environment level by level, like ripples spreading out from a stone dropped in water. It guarantees finding the shortest path in an unweighted grid (where moving from one cell to an adjacent cell has a cost of 1).
The core idea of BFS is to use a queue. We start by putting the initial cell into the queue. Then, we repeatedly:
- Dequeue a cell from the front of the queue.
- If this cell is the goal, we’ve found our path!
- Otherwise, we look at all its unvisited neighbors (up, down, left, right, and sometimes diagonals).
- For each unvisited,
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