About this Course

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Learner Career Outcomes

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got a tangible career benefit from this course
Shareable Certificate
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100% online
Start instantly and learn at your own schedule.
Course 2 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 11 hours to complete
English

Skills you will gain

Motion PlanningAutomated Planning And SchedulingA* Search AlgorithmMatlab

Learner Career Outcomes

12%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 11 hours to complete
English

Offered by

Placeholder

University of Pennsylvania

Syllabus - What you will learn from this course

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Week
1

Week 1

5 hours to complete

Introduction and Graph-based Plan Methods

5 hours to complete
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 Methods30m
Week
2

Week 2

3 hours to complete

Configuration Space

3 hours to complete
6 videos (Total 19 min)
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 Space30m
Week
3

Week 3

2 hours to complete

Sampling-based Planning Methods

2 hours to complete
3 videos (Total 17 min)
3 videos
3.2: Issues with Probabilistic Road Maps4m
3.3: Introduction to Rapidly Exploring Random Trees6m
1 practice exercise
Sampling-based Methods30m
Week
4

Week 4

2 hours to complete

Artificial Potential Field Methods

2 hours to complete
4 videos (Total 19 min)
4 videos
4.2: Issues with Local Minima2m
4.3: Generalizing Potential Fields2m
4.4: Course Summary6m
1 practice exercise
Artificial Potential Fields30m

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About the Robotics Specialization

Robotics

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