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Problem statement, on-board architecture, Parquet/DuckDB storage design, swarm sync strategy, zero-trust networking, environments, observability, CI/CD delivery with fleet release manifests, roadmap and open questions.
34 lines
4.4 KiB
Markdown
34 lines
4.4 KiB
Markdown
# 10 — Domain context
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Swarm autonomy has moved past biomimicry-driven research toward practical, operational systems. This proposal builds on a set of publicly discussed principles from that field; each maps directly onto a platform component.
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## Principles and where this design answers them
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| Principle | What it means | Where it lands in this design |
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| --- | --- | --- |
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| **Decentralization** | No single controller; every unit perceives locally and decides autonomously; no single point of failure | No swarm-wide orchestrator; each drone runs its own full data plane ([02](02-architecture.md)) |
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| **Local interactions** | Behavior emerges from neighbor-to-neighbor exchange, not top-down commands | State broadcast between peers; event hooks trigger local reactions ([04](04-swarm-sync.md)) |
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| **Self-organization** | The group coordinates without pre-planned orchestration and survives the loss of members | Opportunistic replication; store-and-forward through intermediate peers; automatic reconciliation after partitions ([04](04-swarm-sync.md)) |
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| **Sustainable pulsing** | The swarm repeatedly engages a target area from multiple directions and re-forms as conditions change | Requires every unit to know peer state with bounded staleness — exactly the state-sync contract ([04](04-swarm-sync.md)) |
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| **Ubiquitous sensing** | The swarm acts as one distributed sensor, fusing observations into shared "top sight" | Detections are first-class derived data: locally stored, selectively shared, fully preserved for post-flight fusion ([03](03-data-platform.md)) |
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| **Edge intelligence** | Detection and classification happen on the unit; a local event can autonomously cue nearby units without central validation | On-board YOLO-like inference + event hook that publishes detections to the mesh immediately ([02](02-architecture.md)) |
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| **Mesh networking** | Units relay data for each other; loss of any single link degrades nothing | Encrypted ad-hoc mesh, no infrastructure dependency ([05](05-network-security.md)) |
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| **GPS-denied operation** | Position comes from visual odometry and relative localization, not satellites | The state schema carries a frame-of-reference field and covariance, not just raw coordinates ([03](03-data-platform.md), [09](09-open-questions.md)) |
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| **Man-in-the-loop** | A supervisor defines intent and boundaries; the system distributes tasks itself | Narrow C2 plane for declarative goals; no per-vehicle steering ([02](02-architecture.md)) |
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| **Adaptive re-tasking** | A detected event re-prioritizes nearby units without manual replanning | Event-driven data plane: detections propagate as events, mission logic subscribes ([02](02-architecture.md)) |
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| **Ethical autonomy / traceability** | Autonomous actions must be explainable: event-driven sensing over indiscriminate collection, decisions logged and auditable | Every broadcast, sync, and re-tasking trigger is recorded in the flight data; the warehouse preserves the full decision trail for replay ([03](03-data-platform.md), [06](06-environments.md)) |
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| **Adaptive learning** | Experience gathered by one unit improves the whole team over iterations | Complete flights land in the ground warehouse; training reads from it and ships improved model weights through the fleet release cycle ([06](06-environments.md), [11](11-cicd-delivery.md)) |
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| **Multi-domain scaling** | The same swarm concepts apply to aerial, ground, surface, and underwater units — including sub-250 g platforms | The data plane is a set of small independent services; the minimal profile (writer + publisher, no GPU stack) fits constrained units ([02](02-architecture.md)) |
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## Why this matters for a data platform
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Every one of these principles quietly assumes a working data layer underneath:
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- *Pulsing* and *re-tasking* assume each unit **knows peer state** — that is a sync latency and staleness budget.
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- *Ubiquitous sensing* assumes observations are **fused later** — that is a warehouse with aligned timestamps and schemas.
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- *Edge intelligence* assumes detection events **reach neighbors fast** — that is an event-driven publish path, not a polling loop.
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- *Traceability* assumes actions are **replayable** — that is complete, immutable, partitioned flight data.
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- *Learning* assumes flights are **comparable across the fleet and across time** — that is schema versioning and a single storage format.
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The swarm behaviors are the visible product; the platform in this repository is the substrate they stand on.
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