Portable Python env + 360° LiDAR ablation flag

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>

herding/config.py

@@ -93,6 +93,19 @@ rays), a policy trained here can be deployed on a wider-FOV LiDAR (e.g.
 which can only improve tracker quality.
 """
 
+LIDAR_WEBOTS_360 = LidarConfig(
+    n_rays=360,
+    fov_rad=2.0 * math.pi,
+    max_range=15.0,
+)
+"""Matches ShepherdDog360.proto (360 rays, 360° FOV, 15 m range).
+
+Used by the FOV-ablation Webots launch (HERDING_LIDAR=360). The wider
+range and full surround visibility hand the tracker more detections
+per step, so the trained policy — already trained on 360° gym
+perception — sees an observation distribution closer to training.
+"""
+
 
 # ---------------------------------------------------------------------------
 # Cluster-detection pipeline