This course is part of the Robotics Specialization

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1,009 ratings

CJ Taylor

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Offered By

Robotics SpecializationUniversity of Pennsylvania

About this Course

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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.

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Course 2 of 6 in the

Robotics Specialization

Approx. 11 hours to complete

English

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Skills you will gain

Motion Planning
Automated Planning And Scheduling
A* Search Algorithm
Matlab

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 6 in the

Robotics Specialization

Approx. 11 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.41/5 (48 Ratings)

CJ Taylor

Professor of Computer and Information Science

School of Engineering and Applied Science

37,153 Learners

1 Course

Offered by

University of Pennsylvania

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Syllabus - What you will learn from this course

Content Rating94%(1,607 ratings)

Week1

Week 1

5 hours to complete

Introduction and Graph-based Plan Methods

Welcome to Week 1! In this module, we will introduce the problem of planning routes through grids where the robot can only take on discrete positions. We can model these situations as graphs where the nodes correspond to the grid locations and the edges to routes between adjacent grid cells. We present a few algorithms that can be used to plan paths between a start node and a goal node including the breadth first search or grassfire algorithm, Dijkstra’s algorithm and the A Star procedure.

5 hours to complete

5 videos (Total 27 min), 4 readings, 4 quizzes

Week2

Week 2

3 hours to complete

Configuration Space

Welcome to Week 2! In this module, we begin by introducing the concept of configuration space which is a mathematical tool that we use to think about the set of positions that our robot can attain. We then discuss the notion of configuration space obstacles which are regions in configuration space that the robot cannot take on because of obstacles or other impediments. This formulation allows us to think about path planning problems in terms of constructing trajectories for a point through configuration space. We also describe a few approaches that can be used to discretize the continuous configuration space into graphs so that we can apply graph-based tools to solve our motion planning problems.

3 hours to complete

6 videos (Total 19 min)

Week3

Week 3

2 hours to complete

Sampling-based Planning Methods

Welcome to Week 3! In this module, we introduce the concept of sample-based path planning techniques. These involve sampling points randomly in the configuration space and then forging collision free edges between neighboring sample points to form a graph that captures the structure of the robots configuration space. We will talk about Probabilistic Road Maps and Randomly Exploring Rapid Trees (RRTs) and their application to motion planning problems.

2 hours to complete

3 videos (Total 17 min)

Week4

Week 4

2 hours to complete

Artificial Potential Field Methods

Welcome to Week 4, the last week of the course! Another approach to motion planning involves constructing artificial potential fields which are designed to attract the robot to the desired goal configuration and repel it from configuration space obstacles. The robot’s motion can then be guided by considering the gradient of this potential function. In this module we will illustrate these techniques in the context of a simple two dimensional configuration space.

2 hours to complete

4 videos (Total 19 min)

Reviews

4.3

261 reviews

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4 stars
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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM ROBOTICS: COMPUTATIONAL MOTION PLANNING

by FCNov 27, 2018

The course was challenging, but fulfilling. Thank you Coursera and University of Pennsylvania for giving this wonderful experience and opportunity that I might not experience in our local community!

by RRMar 29, 2018

The course material and videos are very good. Small bugs in the exercise can be a bit of headache. Luckily, digging the community forum there is always a high chance to solve your issue.

by NNAug 4, 2016

Good Introduction to some of the Algorithms in Computational Planning . More of training in assignment than explanation in video

by GGMay 8, 2016

A very good course in motion planning. Here are introduced some basic approach to motion and the Matlab assignments are very helpful to understand the topics of the course. Absolutely suggested.

View all reviews

About the Robotics Specialization

The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects.

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Robotics: Computational Motion Planning

About this Course

Skills you will gain