Mobile Robot Control 2024 Ultron:Solution 3: Difference between revisions
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=== Answers of the questions === | === Answers of the questions === | ||
1. How could finding the shortest path through the maze using the A* algorithm be made more efficient by placing the nodes differently? Sketch the small maze with the proposed nodes and the connections between them. Why would this be more efficient? | |||
2. Would implementing PRM in a map like the maze be efficient? | |||
3. What would change if the map suddenly changes (e.g. the map gets updated)? | |||
4. How did you connect the local and global planner? | |||
5. Test the combination of your local and global planner for a longer period of time on the real robot. What do you see that happens in terms of the calculated position of the robot? What is a way to solve this? | |||
6. Run the A* algorithm using the gridmap (with the provided nodelist) and using the PRM. What do you observe? Comment on the advantage of using PRM in an open space. | |||
=== Simulation Results === | === Simulation Results === | ||
=== Real-Robot Results === | === Real-Robot Results === | ||
Revision as of 12:18, 24 May 2024
Methodology
Answers of the questions
1. How could finding the shortest path through the maze using the A* algorithm be made more efficient by placing the nodes differently? Sketch the small maze with the proposed nodes and the connections between them. Why would this be more efficient?
2. Would implementing PRM in a map like the maze be efficient?
3. What would change if the map suddenly changes (e.g. the map gets updated)?
4. How did you connect the local and global planner?
5. Test the combination of your local and global planner for a longer period of time on the real robot. What do you see that happens in terms of the calculated position of the robot? What is a way to solve this?
6. Run the A* algorithm using the gridmap (with the provided nodelist) and using the PRM. What do you observe? Comment on the advantage of using PRM in an open space.