Johnny Fernandes
d00da52c3c
Two small features.
(1) Portable interpreter
* `tools/setup_env.sh` exports HERDING_PYTHON (default points to the
project's conda env; override in your shell to retarget).
* Both `controllers/*/runtime.ini` files now use Webots' env-var
expansion: `COMMAND = $(HERDING_PYTHON)` so the Webots-launched
controllers pick up the same interpreter as the bash scripts.
* `tools/run_webots.sh`, `tools/webots_sweep{,_gt}.sh` and
`tools/calibrate_mecanum.sh` all source `setup_env.sh` at the top
instead of hard-coding `/home/jalf/miniconda3/envs/tir/bin`.
The hard-coded conda path is now exactly one line in `setup_env.sh`'s
fallback default — a single place to edit on a new machine, or
override-once via `export HERDING_PYTHON=...`.
(2) 360° LiDAR FOV ablation
* New `LIDAR_WEBOTS_360` preset matches the existing
`protos/ShepherdDog360.proto` (360 rays / 2π FOV / 15 m range).
* `tools/run_webots.sh` reads `HERDING_LIDAR=140|360` and swaps the
diff-drive proto accordingly (mecanum keeps 140° — the
ShepherdDogMecanum proto has its own LiDAR section). The variant
is written into `herding_runtime.cfg` so the controller can read
it even when Webots strips env vars.
* `controllers/shepherd_dog/shepherd_dog.py` picks the matching
`lidar_cfg` (`HERDING_WEBOTS.lidar` for 140°, `LIDAR_WEBOTS_360`
otherwise) and feeds it to `detections_from_scan` so the
perception pipeline interprets ray angles + max range correctly.
Smoke test: `HERDING_LIDAR=360 tools/run_webots.sh 5 strombom
differential field` launches with `ShepherdDog360.proto`, the
controller logs the new mode/drive/world line, and the dog is
penning sheep through 360° perception (4/5 at step 19200 before I
killed the test). No retraining required because the gym already
trains under `LIDAR_FULL` (360° preset).
126 pytest cases still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
58 lines
2.0 KiB
Bash
Executable File
58 lines
2.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Measure the actual velocity response of the Webots mecanum robot and
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# compare against the gym's first-order kinematics prediction.
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#
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# Uses HERDING_MODE=calibrate in the shepherd_dog controller, which applies
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# a known fixed action for N steps, records GPS displacement, and computes
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# the relative error vs gym prediction.
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#
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# Usage:
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# bash tools/calibrate_mecanum.sh [N_STEPS]
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# N_STEPS : steps to hold each action (default 150, ≈ 2.4 s real-time)
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#
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# Output:
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# calibrate_mecanum.log — per-axis results printed and written here
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#
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# Target: < 10% relative error on each axis.
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# If errors are high, tune coulombFriction / forceDependentSlip in
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# tools/run_webots.sh (mecanum contactProperties block).
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set -euo pipefail
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N_STEPS="${1:-150}"
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ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
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LOG="$ROOT/calibrate_mecanum.log"
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source "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )/setup_env.sh"
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echo "Running mecanum calibration (N_STEPS=$N_STEPS)..."
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echo "Results will be written to: $LOG"
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truncate -s 0 "$LOG"
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run_calib() {
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local vx="$1" vy="$2" om="$3"
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echo " Testing vx=$vx vy=$vy om=$om ..."
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rm -f "$ROOT/training/.run_done"
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timeout --kill-after=15s 60 \
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xvfb-run -a \
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env WEBOTS_HEADLESS=1 WEBOTS_EXTRA_ARGS="--stdout --stderr" \
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HERDING_MODE=calibrate HERDING_DRIVE=mecanum HERDING_WORLD=field \
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CALIB_VX="$vx" CALIB_VY="$vy" CALIB_OM="$om" \
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CALIB_N_STEPS="$N_STEPS" \
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bash "$ROOT/tools/run_webots.sh" 0 calibrate mecanum field \
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2>&1 | grep -E "cmd=|gym|webots|error" || true
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pkill -9 -f "webots-bin|Xvfb" 2>/dev/null || true
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sleep 1
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}
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# Three test vectors: pure-x, pure-y, diagonal
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run_calib 0.5 0.0 0.0
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run_calib 0.0 0.5 0.0
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run_calib 0.35 0.35 0.0
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echo ""
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echo "=== Calibration results ==="
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cat "$LOG" 2>/dev/null || echo "(no results written — check controller output above)"
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echo ""
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echo "Target: <10% error on each axis."
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echo "If errors are high, tune coulombFriction / forceDependentSlip in"
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echo "tools/run_webots.sh (mecanum contactProperties block)."
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