From f2fe75b778009a830de6fc4401f27439c30afc7a Mon Sep 17 00:00:00 2001 From: Swannn <153203896+ProgrammingDevelopment@users.noreply.github.com> Date: Sat, 28 Feb 2026 00:56:54 +0700 Subject: [PATCH] Add production architecture, API, whitepaper, and hardware roadmap docs --- README.md | 110 +++---------- docs/api-contract-spec.md | 196 +++++++++++++++++++++++ docs/hardware-integration-roadmap.md | 61 +++++++ docs/system-architecture-diagram-spec.md | 153 ++++++++++++++++++ docs/technical-whitepaper.md | 67 ++++++++ 5 files changed, 496 insertions(+), 91 deletions(-) create mode 100644 docs/api-contract-spec.md create mode 100644 docs/hardware-integration-roadmap.md create mode 100644 docs/system-architecture-diagram-spec.md create mode 100644 docs/technical-whitepaper.md diff --git a/README.md b/README.md index ff0e220..8720b8b 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,18 @@ -# System Architecture Overview +# SigmaPrompt Robotics -SigmaPrompt Robotic OS follows a distributed cognitive microservices architecture: +SigmaPrompt is a distributed cognitive robotic operating system blueprint for production-scale humanoid deployment. -Physical Robot - ↓ -Telemetry Ingestion Service - ↓ -Real-Time Analyzer - ↓ -SigmaPrompt Cognitive Core - ↓ -Decision Arbitration Engine - ↓ -Actuator Command Layer - ↓ -Monitoring Dashboard +## Core Architecture Summary ---- - -## Core Services +Physical Robot +-> Telemetry Ingestion Service +-> Real-Time Analyzer +-> SigmaPrompt Cognitive Core +-> Decision Arbitration Engine +-> Actuator Command Layer +-> Monitoring Dashboard +### Core Services - Telemetry Service - Digital Twin Engine - Swarm Coordinator @@ -28,90 +21,25 @@ Monitoring Dashboard - Authentication Service - Dashboard API ---- - -## Data Layer - +### Data Layer - Neon Serverless PostgreSQL - Drizzle ORM schema management - Redis event streaming - Partitioned telemetry storage ---- - -## Infrastructure - +### Infrastructure - Docker containers - Kubernetes orchestration - Horizontal Pod Autoscaling - CI/CD pipeline ---- - -# 12-Month Roadmap - -## Phase 1 (Month 1–3) -- Core telemetry ingestion -- Neon + Drizzle schema stabilization -- Real-time anomaly detection MVP -- Basic dashboard - -## Phase 2 (Month 4–6) -- Digital Twin integration -- Swarm coordination prototype -- Advanced AI reasoning layer -- Performance optimization - -## Phase 3 (Month 7–9) -- Edge deployment mode -- Federated robotic learning -- Predictive maintenance engine -- Distributed consensus refinement - -## Phase 4 (Month 10–12) -- Production-grade Kubernetes scaling -- Multi-robot fleet management -- Enterprise security hardening -- Observability & telemetry analytics expansion - ---- - -# Enterprise Integration Strategy - -SigmaPrompt Robotic OS is designed for industrial-grade adoption. - ---- - -## Target Integration Domains - -- Industrial automation -- Humanoid robotics manufacturers -- Autonomous fleet systems -- Research institutions -- AI infrastructure providers - ---- - -## Integration Capabilities - -- REST + gRPC APIs -- Event-driven architecture -- Cloud-native deployment -- Hybrid on-prem + cloud model -- Secure multi-tenant support - ---- - -## Enterprise Features (Planned) - -- SLA-backed deployment model -- Dedicated cluster mode -- Private AI routing -- Advanced telemetry analytics -- Enterprise observability dashboards +## New Production Planning Documents ---- +- [Full system architecture diagram specification](docs/system-architecture-diagram-spec.md) +- [Detailed API contract specification](docs/api-contract-spec.md) +- [Formal technical whitepaper](docs/technical-whitepaper.md) +- [Hardware integration roadmap](docs/hardware-integration-roadmap.md) ## Long-Term Vision -To establish SigmaPrompt as a distributed cognitive backbone for humanoid robotics and intelligent autonomous systems worldwide. +Distributed Cognitive Robotic Operating System with Digital Twin simulation, swarm intelligence, real-time monitoring, defensive safety framework, and AGI-ready cognitive architecture. diff --git a/docs/api-contract-spec.md b/docs/api-contract-spec.md new file mode 100644 index 0000000..41cd29c --- /dev/null +++ b/docs/api-contract-spec.md @@ -0,0 +1,196 @@ +# SigmaPrompt API Contract Specification (v1) + +## 1. Protocol Standards +- External client APIs: REST/JSON over HTTPS +- Service-to-service APIs: gRPC over mTLS +- Realtime push: WebSocket and server-sent events for dashboard streams +- Event contracts: versioned JSON schema messages on event bus + +## 2. Global API Conventions +- Base URL: `/api/v1` +- Authentication: OAuth2/JWT bearer token +- Tenant scoping: `X-Tenant-ID` required for enterprise requests +- Robot scoping: `robot_id` required in all telemetry/control endpoints +- Traceability: `X-Request-ID` propagated through all services + +## 3. Authentication Service + +### POST `/auth/token` +Issue access token. + +**Request** +```json +{ + "client_id": "fleet-console", + "client_secret": "***", + "grant_type": "client_credentials", + "scope": "telemetry:read command:write" +} +``` + +**Response 200** +```json +{ + "access_token": "jwt", + "token_type": "Bearer", + "expires_in": 3600 +} +``` + +## 4. Telemetry Ingestion Service + +### POST `/telemetry/events` +Ingest batched robot telemetry. + +**Request** +```json +{ + "robot_id": "RB-1022", + "timestamp": "2026-01-11T05:12:00Z", + "joint_state": [{"name": "knee_l", "position": 0.42, "torque_nm": 6.1}], + "power": {"battery_soc": 0.77, "draw_w": 312.0}, + "thermal": {"motor_max_c": 64.2}, + "imu": {"pitch": 0.03, "roll": -0.01, "yaw": 1.22}, + "flags": ["nominal"] +} +``` + +**Response 202** +```json +{ + "accepted": true, + "event_id": "evt_01J...", + "ingested_at": "2026-01-11T05:12:00.124Z" +} +``` + +## 5. Real-Time Analyzer Service + +### GET `/robots/{robot_id}/health` +Returns computed health and risk summary. + +**Response 200** +```json +{ + "robot_id": "RB-1022", + "health_score": 0.93, + "anomaly_score": 0.04, + "risk_level": "low", + "updated_at": "2026-01-11T05:12:02Z" +} +``` + +## 6. Digital Twin Engine + +### POST `/twins/{robot_id}/simulate` +Run an on-demand simulation mode. + +**Request** +```json +{ + "mode": "joint_fatigue", + "horizon_minutes": 180, + "physics_backend": "mujoco", + "parameters": { + "payload_kg": 8.5, + "ambient_c": 36.0 + } +} +``` + +**Response 200** +```json +{ + "simulation_id": "sim_7f2", + "failure_probability": 0.28, + "maintenance_window_hours": 72, + "risk_heatmap_uri": "s3://sigma/twins/RB-1022/sim_7f2.png" +} +``` + +## 7. Swarm Coordination Service + +### POST `/swarm/tasks/allocate` +Allocate tasks across active robot cluster. + +**Request** +```json +{ + "swarm_id": "assembly-line-a", + "tasks": [ + {"task_id": "pick-1", "priority": "high", "required_capabilities": ["lift", "vision"]} + ], + "constraints": {"max_latency_ms": 120, "safety_mode": "strict"} +} +``` + +**Response 200** +```json +{ + "allocation_id": "alloc_112", + "leader_robot_id": "RB-2001", + "assignments": [{"task_id": "pick-1", "robot_id": "RB-2009"}] +} +``` + +## 8. Command and Failsafe Endpoints + +### POST `/robots/{robot_id}/commands` +Dispatch validated actuator-level command. + +### POST `/robots/{robot_id}/failsafe/trigger` +Trigger failsafe mode (`thermal_shutdown`, `geofence_lock`, `mechanical_lock`). + +### POST `/robots/{robot_id}/failsafe/recover` +Attempt controlled recovery from safe mode. + +## 9. Alert Service + +### GET `/alerts` +Query active and historical alerts (filter by severity, robot, window). + +### POST `/alerts/ack` +Acknowledge alert with operator identity and notes. + +## 10. Dashboard API + +### GET `/dashboard/fleet/overview` +Returns fleet KPIs: active robots, anomaly count, energy usage, and SLA status. + +### GET `/dashboard/stream` +WebSocket channel for live telemetry and incident updates. + +## 11. Event Schemas (Bus) +Mandatory event types: +- `telemetry.ingested.v1` +- `anomaly.detected.v1` +- `failsafe.triggered.v1` +- `swarm.leader_elected.v1` +- `twin.simulation.completed.v1` + +Each event includes: +- `event_id` +- `event_type` +- `occurred_at` +- `robot_id` or `swarm_id` +- `trace_id` +- versioned payload + +## 12. Error Model +Standard error envelope: +```json +{ + "error": { + "code": "INVALID_ARGUMENT", + "message": "robot_id is required", + "details": [{"field": "robot_id", "reason": "missing"}], + "request_id": "req_..." + } +} +``` + +## 13. SLO-aligned API Targets +- P95 read latency: <200 ms +- P95 command dispatch: <350 ms +- Critical failsafe API availability: 99.99% +- End-to-end decision window: <500 ms diff --git a/docs/hardware-integration-roadmap.md b/docs/hardware-integration-roadmap.md new file mode 100644 index 0000000..cadb065 --- /dev/null +++ b/docs/hardware-integration-roadmap.md @@ -0,0 +1,61 @@ +# SigmaPrompt Hardware Integration Roadmap + +## Objective +Define a phased path for integrating heterogeneous humanoid hardware into SigmaPrompt with repeatable validation gates. + +## Phase 0 — Interface Baseline (Weeks 1-4) +- Finalize canonical robot interface spec (joint, power, thermal, IMU, vision, audio). +- Define control command schema with safety envelopes. +- Build hardware abstraction layer (HAL) adapter template. +- Acceptance gate: one reference robot streams full telemetry and accepts sandbox commands. + +## Phase 1 — Sensor and Actuator Bring-Up (Weeks 5-10) +- Integrate motor controller APIs and encoder feedback. +- Calibrate battery BMS reporting and thermal channels. +- Validate IMU + joint kinematics synchronization. +- Add emergency-stop and mechanical lock GPIO hooks. +- Acceptance gate: deterministic command roundtrip and hard-stop verified. + +## Phase 2 — Edge Runtime and Safety MCU (Weeks 11-16) +- Deploy edge runtime agent on robot compute module. +- Add local failover controller and watchdog heartbeat. +- Implement geofence and unsafe-movement local policies. +- Acceptance gate: robot enters safe mode autonomously under induced faults. + +## Phase 3 — Digital Twin Parity (Weeks 17-22) +- Map live telemetry into state replicator. +- Validate MuJoCo/Isaac/Gazebo adapter equivalence for key maneuvers. +- Run fatigue and battery degradation simulations against real operation logs. +- Acceptance gate: simulation prediction error within agreed tolerance band. + +## Phase 4 — Swarm Enablement (Weeks 23-30) +- Install secure communication module for peer mesh (gRPC/WebRTC). +- Enable distributed task engine and leader election participation. +- Validate cooperative obstacle avoidance in multi-robot trials. +- Acceptance gate: stable coordination under node churn and network jitter. + +## Phase 5 — Production Hardening (Weeks 31-40) +- Enable firmware integrity attestation and tamper alerts. +- Conduct thermal stress, overload, and long-run reliability tests. +- Tune energy optimization profiles for mission classes. +- Acceptance gate: meets latency, uptime, and failover SLO targets. + +## Hardware Compatibility Matrix (Initial) +- **Compute:** NVIDIA Jetson class / x86 edge IPC +- **Motor drivers:** CANopen / EtherCAT capable controllers +- **Sensors:** IMU, depth camera, force-torque, thermal probes +- **Connectivity:** Wi-Fi 6 / private 5G / wired Ethernet dock +- **Safety:** Independent MCU + hardware interlock circuit + +## Test and Validation Tracks +1. Functional hardware-in-the-loop (HIL) +2. Safety compliance and emergency procedures +3. Cybersecurity penetration and firmware integrity checks +4. Endurance and environmental stress (temperature, vibration) + +## Deliverables by Milestone +- Interface conformance reports +- Calibration package and tuning profiles +- Safety certification evidence pack +- Twin parity benchmark report +- Fleet readiness checklist diff --git a/docs/system-architecture-diagram-spec.md b/docs/system-architecture-diagram-spec.md new file mode 100644 index 0000000..5e3a37a --- /dev/null +++ b/docs/system-architecture-diagram-spec.md @@ -0,0 +1,153 @@ +# SigmaPrompt Full System Architecture Diagram Specification + +## 1. Purpose +This specification defines a production-scale architecture diagram for SigmaPrompt's Distributed Cognitive Robotic Operating System, including digital twin simulation, swarm coordination, defensive safety, AGI-ready cognition, and cloud-native microservices. + +## 2. Diagram Scope and View Layers +Use a layered C4-style view with the following sections on one canvas: + +1. **Physical Layer** (robot hardware and sensors) +2. **Edge Intelligence Layer** (local inference + failsafe) +3. **Realtime Platform Layer** (ingestion, stream processing) +4. **Cognitive & Simulation Layer** (reasoning + digital twin) +5. **Coordination Layer** (swarm orchestration) +6. **Data & Storage Layer** (Neon + Redis + cold archive) +7. **Control & Safety Layer** (defense and policy controls) +8. **Operations Layer** (CI/CD, observability, SRE) + +## 3. Primary End-to-End Dataflow +Represent this as the primary left-to-right flow: + +`Physical Robot -> Telemetry Stream -> Telemetry Ingestion Service -> Real-Time Analyzer -> SigmaPrompt Core -> Decision Arbitration -> Robot Command Gateway -> Actuators` + +Include branch flow: + +`Telemetry Stream -> Neon DB -> Digital Twin Engine -> Simulation Sandbox -> Predictive Output` + +## 4. Required Components and Grouping + +### 4.1 Physical + Edge Blocks +- Humanoid chassis +- Joint encoders, IMU, thermal sensor, battery BMS, vision/audio stack +- Edge Runtime Agent +- Local Edge AI +- Safety MCU / mechanical lock controller + +### 4.2 Core Microservices (Kubernetes) +- Telemetry Ingestion Service +- Real-Time Analyzer Service +- SigmaPrompt Core Service +- Swarm Coordination Service +- Digital Twin Engine Service +- Alert Service +- Dashboard API Service +- Authentication Service + +### 4.3 Data + Messaging +- Neon Serverless Postgres (partitioned telemetry) +- Redis Cluster (pub/sub, cache, distributed locks) +- Distributed Event Log bus +- Object Storage / Cold Archive (>90 days) + +### 4.4 Security + Compliance +- Identity provider and token issuer +- mTLS service mesh +- RLS enforcement at DB layer +- Firmware integrity validator +- Threat detection module + +### 4.5 Observability +- OpenTelemetry collectors +- Prometheus +- Grafana +- Centralized log store +- Alert routing (PagerDuty/email/webhook) + +## 5. Digital Twin Simulation Layer (DTSL) +For the digital twin zone, show the following sub-components: + +1. **State Replicator** + - Joint positions + - Motor torque state + - Power draw + - Sensor state mirror +2. **Physics Adapters** + - MuJoCo adapter + - Isaac Sim adapter + - Gazebo adapter +3. **Simulation Modes Engine** + - Stress test mode + - Extreme load mode + - Battery degradation projection + - Joint fatigue simulation + - Failure injection mode +4. **Predictive Output API** + - Failure probability (%) + - Maintenance window estimate + - Risk heatmap + +## 6. Swarm Robotics Coordination Layer +Show each robot as a node with: +- Local Edge AI +- Secure communication module +- Distributed task engine + +Show central and decentralized coordination paths: +- SigmaPrompt Central Orchestrator +- Raft-style consensus cluster +- Task allocation optimizer + +Mandatory arrows: +- Leader election updates +- Task load balancing signals +- Shared anomaly broadcast +- Cooperative obstacle avoidance messages +- Collective learning synchronization + +## 7. Autonomous Defense and Failsafe Path +Draw a vertical fallback chain: + +`Primary AI -> Secondary Edge AI -> Mechanical Fallback` + +Attach triggered actions: +- Motor overload cutoff +- Thermal shutdown +- Geofence restriction +- Unsafe movement override +- Human proximity override +- AI self-suspension +- SOS telemetry broadcast +- Low-power stability mode + +## 8. AGI-Ready Cognitive Stack +Represent SigmaPrompt Core with five internal modules: +1. Perception Layer (CV + NLP) +2. Reasoning Layer (LLM arbitration) +3. Planning Layer (task decomposition) +4. Action Layer (motor command synthesis) +5. Self-Evaluation Layer (feedback loop) + +Memory sidecar blocks: +- Short-term session state +- Long-term embedding memory +- Federated cross-robot memory sync + +## 9. NFR Annotations on Diagram +Place callouts on the right side: +- Swarm scale: 1-10,000 humanoids +- Telemetry throughput: 50,000 events/sec scalable +- Decision latency: <500 ms +- Failover recovery: <2 seconds + +## 10. Styling and Notation Requirements +- Use solid arrows for synchronous APIs, dashed arrows for async events. +- Use red borders for safety-critical components. +- Use lock icon markers for encrypted channels (gRPC mTLS/WebRTC DTLS/SRTP). +- Use cylinder icons for durable storage. +- Number each major flow path (F1..F12) and reference in legend. + +## 11. Suggested Diagram Outputs +Produce three artifacts from this single specification: +1. High-level executive architecture diagram (A3 landscape) +2. Engineering deployment topology diagram (Kubernetes + network zones) +3. Sequence diagram for fault event -> failsafe -> recovery diff --git a/docs/technical-whitepaper.md b/docs/technical-whitepaper.md new file mode 100644 index 0000000..f743feb --- /dev/null +++ b/docs/technical-whitepaper.md @@ -0,0 +1,67 @@ +# SigmaPrompt Distributed Cognitive Robotic Operating System +## Technical Whitepaper (Draft) + +## Abstract +SigmaPrompt is a production-oriented, distributed cognitive operating system for humanoid robotics fleets. The platform combines real-time telemetry, digital twin simulation, swarm coordination, safety-first failover, and AGI-ready cognition into a cloud-edge architecture designed for high reliability and low-latency decision loops. + +## 1. Problem Statement +Humanoid deployments face four persistent bottlenecks: +1. Incomplete observability of robot health under real workloads. +2. Difficult coordination of many robots with shifting tasks. +3. Safety and cybersecurity exposure under degraded conditions. +4. Limited scalability from prototype control loops to fleet operations. + +SigmaPrompt addresses these with a modular microservices platform and strong cyber-physical safety boundaries. + +## 2. System Design Principles +- **Safety-first execution:** Defensive controls override mission objectives. +- **Edge-cloud symmetry:** Core functions run centrally, while local edge fallback preserves safety. +- **Simulation-assisted operations:** Digital twin paths continuously test near-future outcomes. +- **Horizontal scale:** Stateless service design and event-driven flows support large swarm counts. +- **Auditability:** Structured event logs and distributed tracing support post-incident analysis. + +## 3. Reference Architecture +### 3.1 Functional Flow +Physical robot telemetry is ingested, normalized, scored by real-time analytics, and submitted to SigmaPrompt Core for cognitive arbitration. Decisions are sent to command gateways and enforced with safety policy checks before actuator execution. + +### 3.2 Digital Twin Layer +Each robot has a synchronized twin that mirrors joint state, torque, energy draw, and sensor signatures. Twin simulations run stress and failure-injection scenarios to estimate maintenance windows and risk probabilities. + +### 3.3 Swarm Intelligence +Robot nodes participate in secure mesh communication with dynamic leader election and task allocation optimization. Consensus-style coordination improves robustness when links fluctuate or nodes fail. + +## 4. Safety and Defensive Stability +SigmaPrompt uses multilayered safeguards: +- Motor overload cutoff and thermal shutdown. +- Human proximity override and unsafe motion detection. +- Geofencing and emergency mechanical lock. +- AI self-suspension trigger when policy confidence degrades. + +Graceful degradation follows a strict chain: +`Primary AI -> Secondary Edge AI -> Mechanical fallback`. + +## 5. Data Platform and Governance +The persistence model uses Neon Serverless Postgres with partitioning by `robot_id` and time range. Row-level security enforces device/role isolation. Hot telemetry is indexed for low-latency access, while data older than 90 days is archived to cold storage. + +## 6. Production Operations +Deployment targets Kubernetes with autoscaling, ingress control, and zero-downtime release patterns (canary + blue/green). Observability integrates OpenTelemetry, Prometheus, Grafana, and centralized logs to maintain strict SLO tracking. + +## 7. Performance Targets +- Swarm scale: 1-10,000 humanoids. +- Telemetry throughput: 50,000 events/sec scalable. +- Decision latency: <500 ms. +- Failover recovery: <2 s. + +These targets define acceptance gates for production readiness. + +## 8. AGI Readiness Path +SigmaPrompt's cognitive stack separates perception, reasoning, planning, action synthesis, and self-evaluation. Memory architecture combines session memory, long-horizon embeddings, and federated cross-robot knowledge transfer. This enables continual adaptation while preserving hard safety constraints. + +## 9. Risk Analysis and Mitigations +- **Model drift risk:** Continuous validation and shadow evaluation. +- **Sensor spoofing risk:** Cross-sensor consistency checks and cryptographic attestation. +- **Network partition risk:** Local autonomy with degraded command modes. +- **Operational complexity risk:** Strong service contracts and automated incident response. + +## 10. Conclusion +SigmaPrompt provides a practical path from single-robot autonomy to large-scale distributed humanoid intelligence. Its architecture combines simulation, cognition, and defense-in-depth into an operational foundation suitable for industrial, research, and enterprise robotics programs.