Autonomous Fleet Memory

Share learned experience across a fleet with database snapshots.

Industry	Level	Difficulty	Recommended mode
Robotics & automotive	5 of 5	Frontier	sq8

Coming soon

A complete, runnable Autonomous Fleet Memory reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Each vehicle learns alone.
• Connectivity is intermittent.
• Storage is limited.

How SrvDB powers Autonomous Fleet Memory

1. Each unit keeps a local SrvDB memory.
2. Curated snapshots are shipped fleet-wide.
3. Compressed with sq8 or pq.
4. Works offline in mines, at sea or in orbit.

What changes

Today	With Autonomous Fleet Memory on SrvDB
Isolated learning	Shared experience
Always-online assumption	Offline-first
Large storage	Compressed

How it works

1. Collect local memories.
2. Curate centrally.
3. Build snapshots.
4. Distribute.
5. Measure impact.

SrvDB features it uses

• SQ8 compression (sq8): stores each value in one byte, 4× smaller than full precision.
• PQ compression (pq): stores vectors up to 32× smaller, at some cost in accuracy.
• Fully offline: SrvDB runs in-process and never opens a network connection.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Mining fleets
• Delivery robots
• Agricultural machines
• Ships
• Drones
• Space robotics

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./autonomous-fleet-memory-db", dimension=256, mode="sq8")

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def fleet_recall(situation, k=5):
    hits = db.search(embed(situation), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Curate snapshots carefully before distribution.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Robotics & automotive projects

Level	Project	What it does
1 · Beginner	In-car Manual Assistant	Answers drivers’ questions from the owner’s manual, offline.
2 · Intermediate	Diagnostic Assistant	Technicians match symptoms and trouble codes to past repairs.
3 · Advanced	Scenario Miner	Finds driving or robot episodes similar to a scenario of interest.
4 · Expert	Robot Episodic Memory	Robots remember places, objects and outcomes.
5 · Frontier	Autonomous Fleet Memory (this page)	Share learned experience across a fleet with database snapshots.

See also: Use cases · Live examples (coming soon)