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Robotics: Computational Motion Planning

Approx. 11 hours to complete

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Course Summary

This course covers the basics of robotics motion planning, including path planning, motion planning, and control. Students will learn about the algorithms and techniques used in robotics, as well as their practical applications.

Key Learning Points

Understand the fundamentals of robotics motion planning
Learn about the algorithms and techniques used in motion planning
Explore the practical applications of robotics motion planning

Learning Outcomes

Understand the basics of robotics motion planning
Learn about the different algorithms and techniques used in motion planning
Apply motion planning techniques to practical robotics applications

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with linear algebra and calculus

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Robotics: Perception
Robotics: Estimation and Learning
Robotics: Mobility

Related Education Paths

Notable People in This Field

Sebastian Thrun
Rodney Brooks

Related Books

Description

Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.

Outline

Introduction and Graph-based Plan Methods
1.1: Introduction to Computational Motion Planning
1.2: Grassfire Algorithm
1.3: Dijkstra's Algorithm
1.4: A* Algorithm
Getting Started with the Programming Assignments
Computational Motion Planning Honor Code
Getting Started with MATLAB
Resources for Computational Motion Planning
Graded MATLAB Assignments
Graph-based Planning Methods

Configuration Space
2.1: Introduction to Configuration Space
2.2: RR arm
2.3: Piano Mover’s Problem
2.4: Visibility Graph
2.5: Trapezoidal Decomposition
2.6: Collision Detection and Freespace Sampling Methods
Configuration Space

Sampling-based Planning Methods
3.1: Introduction to Probabilistic Road Maps
3.2: Issues with Probabilistic Road Maps
3.3: Introduction to Rapidly Exploring Random Trees
Sampling-based Methods

Artificial Potential Field Methods
4.1: Constructing Artificial Potential Fields
4.2: Issues with Local Minima
4.3: Generalizing Potential Fields
4.4: Course Summary
Artificial Potential Fields

Summary of User Reviews

This robotics motion planning course has received great feedback from students. They praise the thoroughness of the material and the real-world applications taught in the course.