Sampling-based motion planning

Type of course:

Digital learning, Lesson

Language:

Duration:

20 minutes

Workload:

2 hours

Proficiency:

Intermediate

Target:

Professionals

This nugget will discuss popular sampling-based algorithms for robotics motion planning. Motion planning is the problem of finding a collision-free path for a robot to move from one point to another in an environment. Sampling-based methods such as Rapidly-exploring random trees (RRT) and probabilistic roadmaps (PRM) will be discussed.

Learning outcomes

The learner is able to describe the main features of sampling-based planning methods, such as RRT and PRM.
The learner is able to explain the advantages and disadvantages of sampling-based methods.
The learner is able to apply sampling-based methods to solve motion planning problems.