Feedback Results

Comments:

• To recognize a chair, it can use some other distinguishable properties, instead of color.
• Didn’t see wandering. However, the idea behind random “goals” seemed very functional.
• Use of ACTS to detect the difference in chair/obstacle worked efficiently.
• Chair didn’t seem to end up where it should at the desk. Some improvement seems needed to place the chair somehow and not the robot.
• Watch out for the laser’s status lights or change your color to something not normally visible.

Which aspects of the RAFS project particularly impressed you? Please explain.

• Smooth navigation – It’s fast to detect obstacles in the closed world.
• Grabbing a chair – It has a pretty strong grabber and performed well.
• Path planning and movement of chair to the desk. To see it when fully functional would be quite interesting.
• Path finding, chair recognition – [fair].
• Chair gripping seems like one of the hardest modules to implement. The robot’s reorientation was impressive.
• The complex control of the robot from a PC.
• I saw you are using color recognition to detect chair, don’t you think coupling that with edge detection would help to find a chair or desk.
• ACTS recognition seemed to work well.

Which aspects of the RAFS project did you feel needed significant improvement? Please explain.

• Objects Orientation Recognition – It should figure out the shape of an object.
• Localization Precision – It should place a chair in the right direction.
• Again, just the movement of the chair. Also the integration of movement with the chair and forward sensing, seems objects dead on could be easily hit instead of avoided.
• It had some problems (getting stuck) before reaching the chair. This could be improved, though it was working previously.
• Code just needs a little more work.
• You might want to rethink the Placement Algorithm.