2. Sensing and Mapping

Preliminary description! There can be changes during next weeks.

To be discussed in the teams meeting.

General description

The robots shall detect objects as weeds and beer cans (example for waste) and map or geo-reference them. Objects detected in the virtual field should be signalled by a ROS message in the real field by an acoustic signal. The coordinate system shall be locally in horizontal field dimensions. The reference point will be pillars with a QR code. This task is conducted in an environment similar to the previous task. Nevertheless, good row navigation is required. There will be nine (9) objects in total distributed across the virtual field.
The robot has to generate a file (*.csv) with classified objects and their coordinates relative to the given reference point. Each object should be reported on one line in the file including the coordinates x and y in horizontal plane in meters with 3 decimal points and its kind (table 5).

Bonus task for the field runs – Removal of waste

The robots can remove the cans and place them outside the crop area on the headland.
The robot is allowed to push the cans to the headland, but without a clear act of picking up, it will only earn points for the delivery.

Virtual and Field Environment

Objects are realistic weeds and cans e.g. of beer with different brands and colours. The objects will be placed randomly across the field. So they can be in between and in the rows. No objects are located on the headlands.

Rules for robots

Each robot has only one attempt. For starting, the robot is placed at the beginning of the first row without crossing the white line. The maximum available time for the run is 3 min.


The jury assesses the detection during the run:

Detected object during run (true positive) 5 point
Detected object during run (false positive) -5 (minus) points

And assesses the classification and accuracy of mapped objects:

x: Euclidean distance to object of the same kind* points
x ≤ 2cm 15
2 cm < x ≤ 37.5 cm 15.56 – 0.2817 * x
x > 37.5 cm (false positive) -5

*(distance error to the nearest object of the same kind)

And registers and assesses the number of objects where they are remaining after the run in the real field:

Cans picked up 3 points/object
Cans delivered to the headlands 6 points/object

Crop plant damage by the robot will result in a penalty of 4 points per plant.

The task completing teams will be ranked by the number of points as described above. The best 3 teams will be rewarded.

Picture 4 – Example of weed and obstacle locations.
X Y Kind
2.645 3.583 weed
3.804 3.537 weed
4.894 3.562 waste

Table 5 – Example for a map file.

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