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Motion Planning

Motion planning is the process of finding a path for a moving body, such as a robot, to follow. It is a fundamental problem in robotics, as it is necessary for robots to be able to navigate their environment safely and efficiently.

Subfields

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Motion planning is the process of finding a path for a moving body, such as a robot, to follow. It is a fundamental problem in robotics, as it is necessary for robots to be able to navigate their environment safely and efficiently.

Subfields

Motion planning is a complex problem, and there are many different approaches to solving it. These approaches can be grouped into two main categories: local planning and global planning.

Local planning methods only consider the immediate surroundings of the robot, while global planning methods take into account the entire environment. Local planning methods are typically faster than global planning methods, but they can be less accurate. Global planning methods are typically more accurate than local planning methods, but they can be slower and lead to longer paths.

Careers

There are many different careers that involve motion planning, including:

  • Robotics engineer
  • Autonomous vehicle engineer
  • Motion control engineer
  • Control systems engineer
  • Computer scientist

These careers involve developing and implementing motion planning algorithms, and they can be found in a variety of industries, including robotics, automotive, and aerospace.

Benefits

There are many benefits to learning motion planning, including:

  • Improved problem-solving skills
  • Stronger understanding of robotics
  • Increased employability
  • Higher earning potential

Motion planning is a challenging and rewarding field, and it can lead to a successful career in robotics.

Projects

There are many different projects that you can pursue to learn more about motion planning. These projects can range from simple to complex, and they can be a great way to apply your knowledge and skills.

Some examples of motion planning projects include:

  • Developing a path planning algorithm for a robot to navigate a maze
  • Creating a motion planning system for an autonomous vehicle
  • Designing a control system for a robotic arm
  • Simulating the motion of a robot in a virtual environment

These projects can be a great way to learn more about motion planning, and they can also be a lot of fun.

Personality Traits

If you are interested in learning motion planning, it is important to have some key personality traits. These traits include:

  • Strong problem-solving skills
  • Analytical mindset
  • Creativity
  • Patience
  • Perseverance

These traits will help you to succeed in the field of motion planning.

Employer Benefits

Employers value employees who have a strong understanding of motion planning. This is because motion planning is a critical skill in many industries, including robotics, automotive, and aerospace.

Employees who are proficient in motion planning can help their employers to develop more efficient and effective products and systems.

Online Courses

There are many different online courses that can help you to learn motion planning. These courses can range from beginner to advanced, and they can be a great way to get started in the field.

Some of the benefits of taking an online course in motion planning include:

  • Flexibility
  • Affordability
  • Convenience
  • Access to expert instructors

If you are interested in learning more about motion planning, an online course is a great option.

Conclusion

Motion planning is a fundamental problem in robotics, and it is a challenging and rewarding field to work in. If you are interested in learning motion planning, there are many different resources available to help you get started.

Online courses are a great way to learn about motion planning, and they can be a helpful tool for developing a better understanding of the topic.

However, it is important to remember that online courses alone are not enough to fully understand motion planning. It is also important to practice implementing motion planning algorithms in real-world applications.

Path to Motion Planning

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We've curated 12 courses to help you on your path to Motion Planning. Use these to develop your skills, build background knowledge, and put what you learn to practice.
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Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Motion Planning.
Provides a comprehensive overview of motion planning for mobile robots, covering topics such as configuration space, path planning, and motion control. It is written by a leading expert in the field and is suitable for both students and researchers.
Provides a comprehensive overview of planning algorithms, including topics such as search, sampling-based planning, and optimization. It is written by a leading expert in the field and is suitable for both students and researchers.
Provides a comprehensive overview of motion planning for humanoid robots, covering topics such as kinematics, dynamics, and control. It is written by leading experts in the field and is suitable for both students and researchers.
Provides a comprehensive overview of autonomous mobile robots, covering topics such as sensing, control, decision-making, and applications. It covers motion planning in detail and is suitable for both students and researchers.
Provides a comprehensive overview of robotics, including topics such as kinematics, dynamics, and control. It covers motion planning in detail and is suitable for both students and researchers.
Provides a comprehensive overview of probabilistic robotics, covering topics such as localization, mapping, and planning. It covers motion planning in detail and is suitable for both students and researchers.
Provides a comprehensive overview of motion planning for underwater vehicles, covering topics such as kinematics, dynamics, and control. It is written by leading experts in the field and is suitable for both students and researchers.
Provides a comprehensive overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and policy optimization. It covers motion planning in detail and is suitable for both students and researchers.
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