---
pattern_id: pattern_blind_zones
applicable_symptoms: [blind_zones]
domain: Perception_Vision
---

# Target loss during navigation due to sensor blind zones from placement, FOV limits, or occlusions

**Domain**: `Perception_Vision`

## Fix

Active mitigation strategies such as multi-sensor fusion or predictive reacquisition to handle blind zones

## Anti-pattern

Relying solely on continuous visual tracking without coverage planning

## Cross-domain analogies

- **Planning_Decision** → Shared encoder-decoder architecture jointly trains perception and prediction to handle blind zones.
  - related fix: Unified architecture with shared route and language encoders feeding two decoders (action prediction and instruction generation), trained jointly on both objectives via pretrain-then-fine-tune.
- **Learning_Training** → Progressive multi-stage sensor fusion: first wide-FOV, then narrow-FOV to fill blind zones.
  - related fix: Two-stage progressive knowledge distillation: first distill from large teacher to medium student, then from medium to small student, achieving 1/7 model size with same accuracy.
- **Control_Locomotion** → Use reinforcement learning to map sensor inputs to adaptive camera control for blind zone avoidance.
  - related fix: Use reinforcement learning to learn a control policy that directly maps sensor observations to actuator commands for plasma shape and position control.

## Patch

```diff
--- blind_zones.before.py
+++ blind_zones.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: Target loss during navigation due to sensor blind zones from placement, FOV limits, or occlusions

+# Fix    : Active mitigation strategies such as multi-sensor fusion or predictive reacquisition to handle blind zones

+# Avoid  : Relying solely on continuous visual tracking without coverage planning

```
