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Motion planning, also path planning (also known as the navigation problem or the piano mover's problem) is a computational problem to find a sequence of valid configurations that moves the object from the source to destination.
The probabilistic roadmap [1] planner is a motion planning algorithm in robotics, which solves the problem of determining a path between a starting configuration of the robot and a goal configuration while avoiding collisions. An example of a probabilistic random map algorithm exploring feasible paths around a number of polygonal obstacles
OMPL (Open Motion Planning Library) is a software package for computing motion plans using sampling-based algorithms.The content of the library is limited to motion planning algorithms, which means there is no environment specification, no collision detection or visualization.
A free course can be "upgraded" to the paid version of a course, which includes instructor's feedback and grades for the submitted assignments, and (if the student gets a passing grade) a certificate of completion. [57] [60] Other Coursera courses, projects, specializations, etc. cannot be audited—they are only available in paid versions ...
Real-Time Path Planning is a term used in robotics that consists of motion planning methods that can adapt to real time changes in the environment. This includes everything from primitive algorithms that stop a robot when it approaches an obstacle to more complex algorithms that continuously takes in information from the surroundings and creates a plan to avoid obstacles.
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Path planning is an important issue as it allows a robot to get from point A to point B. Path planning algorithms are measured by their computational complexity. The feasibility of real-time motion planning is dependent on the accuracy of the map (or floorplan ), on robot localization and on the number of obstacles.
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