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Field Log · Entry 002
The rover brain goes outside: lawn to road
28 Sep 2026
Peoria, Illinois
Outdoor · sunny · ~50 ft
BrainQwen3-VL 2B · 4-bit · on-deviceComputeJetson Orin Nano 8 GBEyesUSB webcam · 12 in above grassTrutha tape measure
Summary

Objects: yes. The road: only up close. Distance on a slope: no.

We took the hand-pushed rig from last week's hallway tests onto the front lawn and pointed it down the shortest line to the road, about 50 feet. From one camera frame, the on-board model found the pickup across the street, the mailbox, a trash bin and the tree trunks, and planned a route that put the big tree on the correct side at a distance within 13% of the tape. It recognized the road itself only from about 10 feet. And the lawn's slope tilted the camera enough to break the single-camera distance method that worked well indoors.

Every one of those failures points at a specific, cheap fix: a tilt sensor, a stereo camera, and a small model that separates road from grass. That is what derisking is for.

The rig and what it saw

The hand cart with cameras and a Jetson on a lawn, extension cord trailing, road and trees beyond.
Start line. Hand cart, cameras, Jetson, one extension cord. The road is about 50 ft ahead.
Front camera view across the lawn toward the road with the model's detection boxes.
pickup
mailbox
trash bin
trunk
trunk
trunk
trunk?
“road”
What the model found from the start line. Green boxes are correct: pickup, mailbox, trash bin, three trunks. Red boxes are wrong: an extra trunk that isn’t there, and the house on the left labelled as the road.

Walking up to the road

We stopped every ten feet or so, asked the model to find only the road, and taped the true distance. The box is the model's answer.

Front camera 40 ft from the road with the model's road box.
road?
40 ft to the road. Miss. Boxed sunlit lawn.
Front camera 29.5 ft from the road with the model's road box.
road?
29.5 ft to the road. Miss. Nearly identical box to 40 ft: guessing, not looking.
Front camera 19.5 ft from the road with the model's road box.
road?
19.5 ft to the road. Partial. One loose box over road and lawn.
Front camera 11 ft from the road with the model's road box.
road?
11 ft to the road. Hit. A clean band on the road.
Tape to roadRoad found?
50 ftmiss boxed a sunny patch of lawn and driveway
40 ftmiss
29.5 ftmiss same box as 40 ft
19.5 ftpartial
11 fthit

Look at the road in the four frames: it runs diagonally and sits high. The lawn slopes, so the camera was pitched up and rolled sideways. Our single-camera distance method assumes a level camera on flat ground. It measured a basket within a few percent in a hallway. On this lawn its numbers were meaningless.

The route plan

From the start line, the model located the obstacles and a few lines of plain rules turned them into a plan.

ROUTE MANIFEST · lawn to the road
Goal    stop before the road, do not enter it
Road    not seen from here (~50 ft); model sees it within ~10 ft
Hazard  big tree trunk, RIGHT (+33°), ~7.8 ft → off our line, hold heading
Far     middle and left trunks near the road, not in the path
Plan    straight ahead · 0.5 m/s · slow to 0.3 m/s for the last 10 ft
        STOP when the road is detected within 10 ft
CheckPlanTape
Big tree siderightright
In our path?nono
Distance7.8 ft9 ft · 13% short
Road distancenot estimated50 ft
Front camera view used for the route plan, with real and invented tree-trunk boxes.
big trunk · right · ~7.8 ft
trunk
trunk
The route-plan snapshot. Green: the three real trunks. Faint red: six evenly spaced “trunks” the model invented after it fell into a repetition loop. The frame is also washed out by direct sun.

Every purchase traces to a measured failure

What we measuredWhat it justifies
The lawn slope tilted the camera and broke distanceTilt sensor (IMU), so every frame knows its own pitch and roll
Distance error grows fast past ~15 ft with one cameraStereo camera, true depth without assuming flat ground
The road only recognized within ~10 ftRoad/grass segmentation model, software only
The model invented six extra trunks in one replyA duplicate filter in the rules layer
Objects found well; route side and decision correctKeep the current model for objects and planning

Next: the stereo camera and tilt sensor, then this exact route plan again from the same spot with the same tape, side by side.

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