Robotics: Computational Motion Planning from Coursera

What's inside

Syllabus

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.

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.

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.

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.

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Taught by CJ Taylor, who are recognized for their work in robotics

Explores motion planning, which is used to solve complex problems in the design of robotic systems

Provides a theoretical foundation and practical approaches to effectively plan and control robot behavior

Covers essential topics including graph-based methods, randomized planners, and artificial potential fields

Emphasizes the challenges faced in motion planning and provides strategies to address them

May require students to have basic knowledge of linear algebra and differential equations

Reviews summary

Quality robotics: computational motion planning

Learners say this course is a quality introduction to robotics motion planning and fun to learn. The course mainly uses MATLAB, so familiarity with MATLAB is helpful. Lectures are short and concise, but the code exercises are difficult and time-consuming. There are some issues with auto grading and the lack of available support in forums.

Short and clear

"The lectures were very short and concise."

"The course was clear and concise."

Difficult, but good learning tool

"The programming exercises were quite easy. It would be great if there is a final exercise for 2-tree RRT and graph-like A*."

"I learnt every concept from this course"

Familiarity with MATLAB required

"The programming code was so great!!! I learnt a lot in programming"

Not much engagement and little help available

"The level of learner engagement in the forums is minimal."

"If you have difficulties with the assignments, you may find yourself combing through years-old posts, hoping that someone in the past dealt with the same issue that you are having and posted some useful information."

Can be buggy and not flexible

"assignment 4 needs some plugins if you want to run it on local system."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Robotics: Computational Motion Planning with these activities:

Review the concept of motion planning

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Recall what is motion planning and why it's important in robotics.

Browse courses on Motion Planning

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Read the course description and syllabus.
Watch the first lecture video.

Practice using the graph-based methods

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Solve motion planning problems using the graph-based methods taught in the course.

Browse courses on Motion Planning

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Solve the practice exercises at the end of each lecture.
Implement the graph-based algorithms in a programming language of your choice.

Follow tutorials on sampling-based methods

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Enhance your understanding of sampling-based methods through guided tutorials.

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Find tutorials on sampling-based methods online.
Follow the tutorials and implement the sampling-based methods in a programming language of your choice.

Four other activities

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Practice using the artificial potential field methods

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Gain proficiency in using the artificial potential field methods.

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Solve the practice exercises at the end of each lecture.
Implement the artificial potential field algorithms in a programming language of your choice.

Create a visualization of a motion planning problem

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Deepen your understanding of motion planning by creating a visualization of a problem.

Browse courses on Motion Planning

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Choose a motion planning problem.
Design and create a visualization of the problem.

Contribute to an open-source motion planning library

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Enhance your skills and contribute to the open-source community by working on a motion planning library.

Browse courses on Motion Planning

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Find an open-source motion planning library that you'd like to contribute to.
Read the library's documentation and contribute to the project.

Review 'Planning Algorithms' by Steven LaValle

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Expand your knowledge of motion planning algorithms through a comprehensive book.

View Virtual Reality on Amazon

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Read the book and take notes on key concepts.
Solve the exercises at the end of each chapter.

Career center

Learners who complete Robotics: Computational Motion Planning will develop knowledge and skills that may be useful to these careers:

Robotics Software Developer

Robotics Software Developers design, develop, and test software that controls the behavior of robots. Graduates of Robotics: Computational Motion Planning may be well-suited for this career, as the course provides a strong foundation in the principles of robot motion planning. The skills and knowledge gained in this course can help Robotics Software Developers ensure that robots move safely and efficiently through complex environments.

See salaries and explore the career path for Robotics Software Developer

Robotics Systems Engineer

Robotics Systems Engineers design, develop, and maintain robotic systems. The course Robotics: Computational Motion Planning can be beneficial for those interested in this career, as it covers the fundamentals of robot motion planning and control. This knowledge can help Robotics Systems Engineers develop more effective and efficient robotic systems.

See salaries and explore the career path for Robotics Systems Engineer

Robotics Researcher

Robotics Researchers conduct research to develop new and improved robotic technologies. The course Robotics: Computational Motion Planning can be a valuable resource for those interested in this career, as it provides a deep understanding of the mathematical and computational principles underlying robot motion planning. This knowledge can help Robotics Researchers make significant contributions to the field of robotics.

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Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of artificial intelligence and machine learning. This knowledge can help Artificial Intelligence Engineers develop more intelligent and capable robotic systems.

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Control Systems Engineer

Control Systems Engineers design, develop, and maintain control systems for a variety of applications, including robotics. The course Robotics: Computational Motion Planning can be beneficial for those interested in this career, as it provides a strong foundation in the principles of control theory. This knowledge can help Control Systems Engineers develop more effective and efficient control systems for robotic systems.

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Mechanical Engineer

Mechanical Engineers design, develop, and maintain mechanical systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of mechanics and dynamics. This knowledge can help Mechanical Engineers design more efficient and effective robotic systems.

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Electrical Engineer

Electrical Engineers design, develop, and maintain electrical systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of electricity and electronics. This knowledge can help Electrical Engineers design more efficient and effective robotic systems.

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Computer Scientist

Computer Scientists design, develop, and maintain computer systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of computer science. This knowledge can help Computer Scientists develop more efficient and effective robotic systems.

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Software Engineer

Software Engineers design, develop, and maintain software systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of software engineering. This knowledge can help Software Engineers develop more efficient and effective robotic systems.

See salaries and explore the career path for Software Engineer

Systems Engineer

Systems Engineers design, develop, and maintain systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of systems engineering. This knowledge can help Systems Engineers develop more efficient and effective robotic systems.

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Data Scientist

Data Scientists collect, analyze, and interpret data, including data from robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of data science. This knowledge can help Data Scientists develop more efficient and effective methods for analyzing data from robotic systems.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve problems, including problems related to robotics. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of operations research. This knowledge can help Operations Research Analysts develop more efficient and effective solutions to problems related to robotics.

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Industrial Engineer

Industrial Engineers design, develop, and maintain industrial systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of industrial engineering. This knowledge can help Industrial Engineers design more efficient and effective robotic systems.

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Manufacturing Engineer

Manufacturing Engineers design, develop, and maintain manufacturing systems, including robotic systems. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of manufacturing engineering. This knowledge can help Manufacturing Engineers design more efficient and effective robotic systems.

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Quality Assurance Engineer

Quality Assurance Engineers ensure that products, including robotic systems, meet quality standards. The course Robotics: Computational Motion Planning can be helpful for those interested in this career, as it provides a strong foundation in the principles of quality assurance. This knowledge can help Quality Assurance Engineers develop more effective methods for testing and evaluating robotic systems.

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