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Robot Motion Planning

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Robot motion planning is a subfield of robotics that deals with the problem of finding a path for a robot to move from one point to another, while avoiding obstacles and other constraints. It is a challenging problem, as the robot must take into account its own kinematics and dynamics, as well as the environment in which it is operating.

Motion Planning Algorithms

There are a number of different motion planning algorithms that can be used to find a path for a robot to move. Some of the most common algorithms include:

  • Dijkstra's algorithm is a greedy algorithm that finds the shortest path between two points on a graph. It is often used for motion planning in simple environments.
  • A* algorithm is a heuristic search algorithm that finds the shortest path between two points on a graph. It is often used for motion planning in more complex environments.
  • Rapidly-exploring random tree (RRT) is a randomized algorithm that finds a path between two points on a graph. It is often used for motion planning in very complex environments.

The choice of which motion planning algorithm to use depends on the specific environment in which the robot is operating. In simple environments, Dijkstra's algorithm is often sufficient. In more complex environments, A* or RRT may be necessary.

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Robot motion planning is a subfield of robotics that deals with the problem of finding a path for a robot to move from one point to another, while avoiding obstacles and other constraints. It is a challenging problem, as the robot must take into account its own kinematics and dynamics, as well as the environment in which it is operating.

Motion Planning Algorithms

There are a number of different motion planning algorithms that can be used to find a path for a robot to move. Some of the most common algorithms include:

  • Dijkstra's algorithm is a greedy algorithm that finds the shortest path between two points on a graph. It is often used for motion planning in simple environments.
  • A* algorithm is a heuristic search algorithm that finds the shortest path between two points on a graph. It is often used for motion planning in more complex environments.
  • Rapidly-exploring random tree (RRT) is a randomized algorithm that finds a path between two points on a graph. It is often used for motion planning in very complex environments.

The choice of which motion planning algorithm to use depends on the specific environment in which the robot is operating. In simple environments, Dijkstra's algorithm is often sufficient. In more complex environments, A* or RRT may be necessary.

Applications of Robot Motion Planning

Robot motion planning has a wide range of applications, including:

  • Industrial robotics: Motion planning is used to plan the paths of industrial robots that perform tasks such as welding, assembly, and painting.
  • Autonomous vehicles: Motion planning is used to plan the paths of autonomous vehicles that navigate through traffic and avoid obstacles.
  • Medical robotics: Motion planning is used to plan the paths of medical robots that perform tasks such as surgery and rehabilitation.
  • Space robotics: Motion planning is used to plan the paths of space robots that explore other planets and moons.

Benefits of Learning Robot Motion Planning

There are many benefits to learning robot motion planning. Some of the benefits include:

  • Increased understanding of robotics: Motion planning is a fundamental problem in robotics. By learning motion planning, you will gain a deeper understanding of how robots work.
  • Improved problem-solving skills: Motion planning is a challenging problem that requires creative thinking and problem-solving skills. By learning motion planning, you will develop your problem-solving skills.
  • Increased marketability: Motion planning is a valuable skill for robotics engineers. By learning motion planning, you will increase your marketability and make yourself more attractive to potential employers.

Online Courses on Robot Motion Planning

There are many online courses that can help you learn about robot motion planning. Some of the most popular courses include:

  • Modern Robotics, Course 4: Robot Motion Planning and Control
  • 현대 로봇공학, 강좌 4: 로봇 동작 계획 및 제어
  • 현대 로봇공학, 강좌 5: 로봇 매니퓰레이션 및 차륜형 이동 로봇

These courses cover a wide range of topics in robot motion planning, from the basics to advanced algorithms. They are taught by experts in the field and provide a great way to learn about robot motion planning.

Conclusion

Robot motion planning is a challenging but rewarding field. By learning motion planning, you will gain a deeper understanding of robotics and improve your problem-solving skills. You will also increase your marketability and make yourself more attractive to potential employers. If you are interested in robotics, I encourage you to learn about robot motion planning.

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Reading list

We've selected five 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 Robot Motion Planning.
This comprehensive textbook covers the fundamentals of robotics, including modeling, planning, and control. It is an excellent resource for students and researchers in the field.
Classic reference on robot motion planning. It provides a comprehensive overview of the field, including both theoretical and practical aspects.
Provides an in-depth overview of planning algorithms, including a variety of techniques for robot motion planning. It valuable resource for researchers and practitioners in the field.
Covers the topic of probabilistic robotics. It valuable resource for researchers and practitioners in the field.
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