À propos de ce cours
4.2
714 notes
186 avis

Cours 2 sur 6 dans le

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Approx. 15 heures pour terminer

Recommandé : 3 hours/week...

Anglais

Sous-titres : Anglais, Espagnol

Compétences que vous acquerrez

Motion PlanningAutomated Planning And SchedulingA* Search AlgorithmMatlab

Cours 2 sur 6 dans le

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Approx. 15 heures pour terminer

Recommandé : 3 hours/week...

Anglais

Sous-titres : Anglais, Espagnol

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
4 heures pour terminer

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 vidéos (Total 27 min), 4 lectures, 4 quiz
5 vidéos
1.2: Grassfire Algorithm6 min
1.3: Dijkstra's Algorithm4 min
1.4: A* Algorithm6 min
Getting Started with the Programming Assignments3 min
4 lectures
Computational Motion Planning Honor Code10 min
Getting Started with MATLAB10 min
Resources for Computational Motion Planning10 min
Graded MATLAB Assignments10 min
1 exercice pour s'entraîner
Graph-based Planning Methods8 min
Semaine
2
2 heures pour terminer

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 vidéos (Total 19 min), 3 quiz
6 vidéos
2.2: RR arm2 min
2.3: Piano Mover’s Problem3 min
2.4: Visibility Graph3 min
2.5: Trapezoidal Decomposition1 min
2.6: Collision Detection and Freespace Sampling Methods4 min
1 exercice pour s'entraîner
Configuration Space8 min
Semaine
3
1 heure pour terminer

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 vidéos (Total 17 min), 2 quiz
3 vidéos
3.2: Issues with Probabilistic Road Maps4 min
3.3: Introduction to Rapidly Exploring Random Trees6 min
1 exercice pour s'entraîner
Sampling-based Methods6 min
Semaine
4
1 heure pour terminer

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 vidéos (Total 19 min), 2 quiz
4 vidéos
4.2: Issues with Local Minima2 min
4.3: Generalizing Potential Fields2 min
4.4: Course Summary6 min
1 exercice pour s'entraîner
Artificial Potential Fields6 min
4.2
186 avisChevron Right

33%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par 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!

par 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.)

Enseignant

Avatar

CJ Taylor

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

À propos de Université de Pennsylvanie

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. ...

À propos de la Spécialisation Robotique

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....
Robotique

Foire Aux Questions

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  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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