University of Pennsylvania
Robotics: Computational Motion Planning
University of Pennsylvania

Robotics: Computational Motion Planning

This course is part of Robotics Specialization

Taught in English

Some content may not be translated

CJ Taylor

Instructor: CJ Taylor

40,216 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.3

(1,028 reviews)

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93%

11 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.3

(1,028 reviews)

|

93%

11 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Robotics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

5 videos4 readings1 quiz3 app items

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.

What's included

6 videos1 quiz2 app items

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.

What's included

3 videos1 quiz1 app item

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.

What's included

4 videos1 quiz1 app item

Instructor

Instructor ratings
4.4 (50 ratings)
CJ Taylor
University of Pennsylvania
1 Course40,216 learners

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