このコースについて
4.2
730件の評価
191件のレビュー

次における6の2コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約15時間で修了

推奨:3 hours/week...

英語

字幕:英語, スペイン語

習得するスキル

Motion PlanningAutomated Planning And SchedulingA* Search AlgorithmMatlab

次における6の2コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約15時間で修了

推奨:3 hours/week...

英語

字幕:英語, スペイン語

シラバス - 本コースの学習内容

1
4時間で修了

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件のビデオ (合計27分), 4 readings, 4 quizzes
5件のビデオ
1.2: Grassfire Algorithm6 分
1.3: Dijkstra's Algorithm4 分
1.4: A* Algorithm6 分
Getting Started with the Programming Assignments3 分
4件の学習用教材
Computational Motion Planning Honor Code10 分
Getting Started with MATLAB10 分
Resources for Computational Motion Planning10 分
Graded MATLAB Assignments10 分
1の練習問題
Graph-based Planning Methods8 分
2
2時間で修了

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件のビデオ (合計19分), 3 quizzes
6件のビデオ
2.2: RR arm2 分
2.3: Piano Mover’s Problem3 分
2.4: Visibility Graph3 分
2.5: Trapezoidal Decomposition1 分
2.6: Collision Detection and Freespace Sampling Methods4 分
1の練習問題
Configuration Space8 分
3
1時間で修了

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件のビデオ (合計17分), 2 quizzes
3件のビデオ
3.2: Issues with Probabilistic Road Maps4 分
3.3: Introduction to Rapidly Exploring Random Trees6 分
1の練習問題
Sampling-based Methods6 分
4
1時間で修了

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件のビデオ (合計19分), 2 quizzes
4件のビデオ
4.2: Issues with Local Minima2 分
4.3: Generalizing Potential Fields2 分
4.4: Course Summary6 分
1の練習問題
Artificial Potential Fields6 分
4.2
191件のレビューChevron Right

33%

コースが具体的なキャリアアップにつながった

人気のレビュー

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.)

講師

Avatar

CJ Taylor

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

ペンシルベニア大学(University of Pennsylvania)について

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. ...

ロボット工学の専門講座について

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....
ロボット工学

よくある質問

  • 修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題(該当する場合)にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。

  • コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。