本课程是 Robotics 专项课程 专项课程的一部分
Robotics: Computational Motion Planning
提供方
课程信息
4.2
655 个评分
Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.
您将获得的技能
Motion PlanningAutomated Planning And SchedulingA* Search AlgorithmMatlab
教学大纲 - 您将从这门课程中学到什么
1
章节
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 个视频（共 27 分钟）, 4 个阅读材料, 4 个测验
5 个视频
1.2: Grassfire Algorithm6分钟
1.3: Dijkstra's Algorithm4分钟
1.4: A* Algorithm6分钟
Getting Started with the Programming Assignments3分钟
4 个阅读材料
Computational Motion Planning Honor Code10分钟
Getting Started with MATLAB10分钟
Resources for Computational Motion Planning10分钟
Graded MATLAB Assignments10分钟
1 个练习
Graph-based Planning Methods8分钟
2
章节
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 个视频（共 19 分钟）, 3 个测验
6 个视频
2.2: RR arm2分钟
2.3: Piano Mover’s Problem3分钟
2.4: Visibility Graph3分钟
2.5: Trapezoidal Decomposition1分钟
2.6: Collision Detection and Freespace Sampling Methods4分钟
1 个练习
Configuration Space8分钟
3
章节
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 个视频（共 17 分钟）, 2 个测验
3 个视频
3.2: Issues with Probabilistic Road Maps4分钟
3.3: Introduction to Rapidly Exploring Random Trees6分钟
1 个练习
Sampling-based Methods6分钟
4
章节
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 个视频（共 19 分钟）, 2 个测验
4 个视频
4.2: Issues with Local Minima2分钟
4.3: Generalizing Potential Fields2分钟
4.4: Course Summary6分钟
1 个练习
Artificial Potential Fields6分钟
4.2
83%
热门审阅
创建者 AD•Jul 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.)
创建者 LC•May 7th 2016
This course is supposed to be easier but somehow it also makes it difficult because implementations of the algorithms in Matlab are bit non-standard as I am used to. Altogether very challenging.
讲师
CJ Taylor
Professor of Computer and Information ScienceSchool of Engineering and Applied Science
关于 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.
关于 Robotics 专项课程
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.
常见问题
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
Is financial aid available?
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