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Robotics: Computational Motion Planning(으)로 돌아가기

펜실베이니아 대학교의 Robotics: Computational Motion Planning 학습자 리뷰 및 피드백

4.3
별점
959개의 평가
245개의 리뷰

강좌 소개

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....

최상위 리뷰

FC
2018년 11월 27일

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!

AD
2018년 7월 2일

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.)

필터링 기준:

Robotics: Computational Motion Planning의 240개 리뷰 중 151~175

교육 기관: Mohammed A

2016년 4월 2일

The videos are short and to the point, and the Matlab home works are great.

교육 기관: Neel P

2016년 12월 23일

Could be improved. There is more need of involvement of mentors and TAs.

교육 기관: Shuai W

2016년 6월 23일

The last part is a bit boring.

Overall I like this course a lot! Thanks!

교육 기관: Joaquin R

2018년 9월 5일

Good course. Easy to understand and with reasonable mat lab assigments

교육 기관: Nhan T

2016년 6월 23일

I learned a lot of brilliant techniques in this course. Thank you.

교육 기관: rajas j

2019년 9월 22일

Week 1 dijkstra assignment took 1month to get acess to

교육 기관: SONG

2017년 7월 4일

Quite good, It can be better if the content is richer

교육 기관: Jeff

2016년 5월 19일

It is only introductory course. not a lot of content.

교육 기관: Anil S

2016년 3월 27일

Taught me many planning algorithms in an easy way.

교육 기관: Rahul N

2017년 6월 12일

Very useful introductory course to path planning

교육 기관: Vikalp M

2017년 2월 3일

assignment grading feedback can be made better.

교육 기관: rao s y

2016년 3월 14일

Should letting us do more programming stuff.

교육 기관: AMIT S P

2016년 9월 1일

Good Introductory Course but can be better.

교육 기관: Manikandan R

2016년 2월 27일

Damn good!! and bit difficult

교육 기관: Orlando B

2016년 3월 15일

It was a great course!!!

교육 기관: Abdullah B

2016년 8월 13일

Good but can do better.

교육 기관: MAssimo S

2017년 9월 6일

Basic concept course

교육 기관: jinxz

2018년 2월 28일

forum is useful !!!

교육 기관: Fabio B

2017년 6월 26일

Very good course!

교육 기관: Md I S

2017년 12월 23일

it was great.

교육 기관: Prabin K R

2018년 8월 13일

awesome!!!

교육 기관: Deep P

2021년 7월 13일

I would like to thank Coursera team, university of Penn and Prof. CJ Taylor for providing this course. Please take this as a constructive feedback and not a complain. I personally felt that lectures were too short and didn't do justice to the topics for all 4 weeks. Even though lectures were crisp and to the point for learning the algorithm, still I feel that more comprehensive knowledge about the topics should be shared. For ex- applications of these algorithms. Also a little more focus on implementation part please. It seemed that Prof. Taylor was screen reading the lecture content. I was very disappointed when I realized this (in week1 only). Unlike other courses where instructor engages with students as if they are really talking to us, this felt plain. As for the assignments, for week1 and 3 the pseudo code displayed in the lecture video wasn't tested in the assignment. It was more like complete the code and make it working rather than program the core steps of pseudo code. To conclude, this course needs some improvement but crucial ones.

교육 기관: Sj

2016년 3월 13일

Overall decent course.

This course focused less on the theory aspects in the course videos, which bothered me a lot considering I am paying for it. But the explanations were still good for those algorithms.

The assignments were good as well. I liked how they made us work on them instead of the first course where we were mostly tuning parameters. Hopefully MOOCs start having challenging assignments too.

The instructor explained really well too!

I didn't really end up visiting the Discussion Forums for this course at all. So can't comment on the participation from other students or TAs.

Future Advice -

Considering how other courses offer about 1-2 hours of course videos, I think this course could offer a lot more. One assignment problem focusing on one algorithm, while having other challenging algorithms taught in those videos to be left for our own implementation would help students a lot more i believe.

교육 기관: Glenn B

2016년 3월 8일

The material is interesting, however there is not enough information provided by the course to effectively implement the algorithms in the allotted time of each week's assignments. It relies on deferring to external reading materials as primary sources, and these resources were not specified in advance to secure copies in a timely manner.

Additionally, there is a big disconnect between the knowledge provided by the weekly material and what is required to easily do the programming assignments in the suggested time of 3 hours.

Overall the course material needs to provide more background material to be more effective in delivering the knowledge expected each week. This may be an artifact of trying to cram what other online course provide in 7-10 weeks down into 4 weeks. If the intention is to give a "flavor" in 4 weeks, then the material needs to be distilled down into more of a cookbook format.

교육 기관: Manoj R

2018년 6월 2일

Very good overview of basic topics in Computational Motion Planning. The material is nicely and intuitively presented in short video lectures and is a rapid overview of the first 5-6 chapters in the book by Choset et. al.

Some of the assignments were too simple and required us to work on the non-critical parts of the problem. For example, only focusing on descending along gradients of artificial potential fields, instead of constructing them and seeing the effect of different types of potentials.

Also, a dominant portion of my time was spent fighting the autograder. There are tips on the forums to help deal with this but sometimes an almost-complete solution is presented by some of the earlier students in a frustrated attempt to get help with the autograder.

Many of these autograder related problems have not been addressed for many months.