perception-strategy

name	perception-strategy
description	Guide multimodal observation for spacecraft situational awareness. Use for perception-heavy tasks that combine image quality, segmentation, local inspection, and knowledge-base reasoning.
metadata	{"skill_kind":"helper","routing_summary":"Add when the mission depends on structured visual evidence gathering and multimodal inspection.","routing_keywords":["perception","observe","image analysis","multimodal evidence","visual summary","inspect components","component status","components status"]}

Perception Workflow

1. Start from the currently exposed observation source and the full image.
2. Evaluate image quality with image_bright() if visibility is questionable, and use set_exposure() when brightness itself is the bottleneck.
3. Inspect visible structure with part_segmentation() when segmentation is exposed.
4. Use image_zoom() when the target is centered but important details are too small to judge from the full frame.
5. Use image_crop() when you need a local view of a specific region such as an antenna, docking port, solar panel root, or damaged area.
6. If the [Sensor] LiDAR block shows data and geometry is still ambiguous, use it as an additional structural cue.
7. Query knowledge_base() after enough visual evidence exists to connect observed structure to type and function.

Viewpoint Control

Use set_attitude() when a different angle is needed to disambiguate spacecraft status or components.

Use image_zoom() or image_crop() before changing viewpoint when the needed evidence is already visible but too small in the current image.

Efficiency Rule

Do not loop on perception tools after the answer is already well-supported. Prefer the currently exposed visual tools first, and terminate once the evidence is sufficient.