Path Planning

Path planning, sometimes called "Motion Planning", is the act of finding a path to go from location A to B. Path planning is a major part in solving mazes for Tabletop Robotics or moving robots through big open fields with obstacles for Collegiate Robotics competitions. There are many approaches to solving path planning, but usually it involves a local and global path planner.

A global path planner usually generates a low-resolution high-level path from A to B, avoiding large obstacles and dealing with navigation around the arena. This is analogous to the path Google Maps might give you from home to a park. Local path planning usually gives a high-resolution low-level path only over a segment from global path A to B, avoiding small obstacles and dealing with motion planning: angles of turn, appropriate velocities, etc. This is analogous to choosing how fast to turn your car when moving around traffic while on your Google Maps path.

Path planning is a major topic in Computer Science, Electrical Engineering, and Mechanical Engineers. Many topics that serve as the basis for path planning include graph theory, geometric algorithms, potential fields, etc. Path planning is known to be a algorithmically intensive problem to solve.

Path planning should not be confused with path mapping, which is the act of saving properties of the environment the robot traverses, and may help with localization.

Tabletop Robotics
Tabletop Robotics usually does not require full featured path planning or mapping, and focuses on Maze Solving.

Collegiate Robotics
Collegiate Robotics competitions usually include the above mentioned combination of local and global path planning. The club, especially for the AUVSI IGVC, has commonly used a real-time mapping algorithm (to generate the arena map) which is then fed to the global path planner (based on D*, and recently D* Lite). While the robot moves around the competition arena based on the global path planner, its path is slightly corrected by a local path planner, avoiding smaller objects our global path planner misses.

Our experience has shown us that global path planners and a global map is important, though the resolution can be kept relatively low. A resolution of one to three feet for the global map is acceptable, and will greatly increase the speed of the path planner to execute. We also have learned that we don't need to update the global path planner that often, though the local path planner is critically important as it acts much more quickly than any normal global path planner. SLAM, or simultaneous localisation and mapping, is also important as a vehicle can quickly loose its own location and thus negatively affect the path planners.

The Mini Grand Challenge project does not use anything more than a local path planner as the global map is already known and waypoints are already given during the competition.

= External Links =
 * Wikipedia Motion Planning
 * Wikipedia Robot Mapping
 * Breadth-first Search Algorithm Commonly used for tabletop robotics
 * D* Search Algorithm Commonly used for collegiate robotics
 * Simple SLAM Explanation
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