Mobile Robot Control 2023 Group 5: Difference between revisions
| Line 51: | Line 51: | ||
2. The uncertain_odom option could introduces noise and uncertainty into the robot's odometry data, which would make it more challenging to accurately track the robot's position. but it's more closely mirror the real-world conditions than the regular simulation. | 2. The uncertain_odom option could introduces noise and uncertainty into the robot's odometry data, which would make it more challenging to accurately track the robot's position. but it's more closely mirror the real-world conditions than the regular simulation. | ||
3. Would you use this approach in the final challenge? Why would you? Why wouldn't you? | 3. Would you use this approach in the final challenge? Why would you? Why wouldn't you?<br /> | ||
<br /> | |||
*'''Observe the Behaviour in Reality''' | *'''Observe the Behaviour in Reality''' | ||
Revision as of 01:25, 31 May 2023
Group members:
| Name | student ID |
|---|---|
| Yuzhou Nie | 1863428 |
| Ronghui Li | 1707183 |
| Guglielmo Morselli | 1959301 |
- 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?
By placing nodes at key points(junctions for example) could reduce the total number of nodes and simplify the graph, which lead to a less computational effort.
(add picture)
- Description of the main idea
We choose to implement the Artificial Potential Field (APF) algorithm for obstacle avoidance in robot navigation. It uses laser scanner data to detect obstacles, calculates repulsive forces based on obstacle proximity, converts these forces into velocity commands, and sends these commands to the robot, enabling dynamic navigation.
- Screen recordings of the simulation results
(add screen recording)
- Video of the robot's performance in real life
Localisation Assignment 1
- Keep Track of our location
The program successfully reports the information in the odometry message of current time step and the difference between the previously received odometry message and the current message.
- Observe the Behaviour in Simulation
1.The accuracy could be obtained by comparing the odometry data with simulation data.
2. The uncertain_odom option could introduces noise and uncertainty into the robot's odometry data, which would make it more challenging to accurately track the robot's position. but it's more closely mirror the real-world conditions than the regular simulation.
3. Would you use this approach in the final challenge? Why would you? Why wouldn't you?
- Observe the Behaviour in Reality
Here attached the video of coco's performance in real life:
(add differences)