About this Course
4.2
736 ratings
191 reviews

Course 2 of 6 in the

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Flexible deadlines

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Approx. 15 hours to complete

Suggested: 3 hours/week...

English

Subtitles: English, Spanish

Skills you will gain

Motion PlanningAutomated Planning And SchedulingA* Search AlgorithmMatlab

Course 2 of 6 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 15 hours to complete

Suggested: 3 hours/week...

English

Subtitles: English, Spanish

Syllabus - What you will learn from this course

Week
1
4 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 videos (Total 27 min), 4 readings, 4 quizzes
5 videos
1.2: Grassfire Algorithm6m
1.3: Dijkstra's Algorithm4m
1.4: A* Algorithm6m
Getting Started with the Programming Assignments3m
4 readings
Computational Motion Planning Honor Code10m
Getting Started with MATLAB10m
Resources for Computational Motion Planning10m
Graded MATLAB Assignments10m
1 practice exercise
Graph-based Planning Methods8m
Week
2
2 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....
6 videos (Total 19 min), 3 quizzes
6 videos
2.2: RR arm2m
2.3: Piano Mover’s Problem3m
2.4: Visibility Graph3m
2.5: Trapezoidal Decomposition1m
2.6: Collision Detection and Freespace Sampling Methods4m
1 practice exercise
Configuration Space8m
Week
3
1 hour 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....
3 videos (Total 17 min), 2 quizzes
3 videos
3.2: Issues with Probabilistic Road Maps4m
3.3: Introduction to Rapidly Exploring Random Trees6m
1 practice exercise
Sampling-based Methods6m
Week
4
1 hour 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....
4 videos (Total 19 min), 2 quizzes
4 videos
4.2: Issues with Local Minima2m
4.3: Generalizing Potential Fields2m
4.4: Course Summary6m
1 practice exercise
Artificial Potential Fields6m
4.2
191 ReviewsChevron Right

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got a tangible career benefit from this course

Top Reviews

By FCNov 28th 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 ADJul 3rd 2018

The topic was very interesting, and the assignments weren't overly complicated. Overall, the lesson was fun and informative , despite the bugs in the learning tool(especially, the last assignment.)

Instructor

Avatar

CJ Taylor

Professor of Computer and Information Science
School of Engineering and Applied Science

About University of Pennsylvania

The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies. ...

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....
Robotics

Frequently Asked Questions

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  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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