Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
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vendor/ruvector/docs/research/agentic-robotics/architecture-synergy.md
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# Architecture Compatibility and Synergy Analysis
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**Document Class:** Technical Architecture Review
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**Version:** 1.0.0
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**Date:** 2026-02-27
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---
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## 1. Dependency Compatibility Matrix
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### Shared Dependencies (Exact or Compatible Versions)
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| Dependency | agentic-robotics | ruvector | Status | Resolution |
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|-----------|-----------------|----------|--------|------------|
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| tokio | 1.47 (full) | 1.41 (rt-multi-thread, sync, macros) | Minor mismatch | Upgrade ruvector to 1.47 |
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| serde | 1.0 (derive) | 1.0 (derive) | Compatible | No action |
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| serde_json | 1.0 | 1.0 | Compatible | No action |
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| rkyv | 0.8 | 0.8 | Compatible | No action |
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| crossbeam | 0.8 | 0.8 | Compatible | No action |
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| rayon | 1.10 | 1.10 | Compatible | No action |
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| parking_lot | 0.12 | 0.12 | Compatible | No action |
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| nalgebra | 0.33 | 0.33 (no-default-features) | Compatible | Unify feature flags |
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| thiserror | 2.0 | 2.0 | Compatible | No action |
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| anyhow | 1.0 | 1.0 | Compatible | No action |
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| tracing | 0.1 | 0.1 | Compatible | No action |
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| tracing-subscriber | 0.3 | 0.3 (env-filter) | Compatible | No action |
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| criterion | 0.5 (html_reports) | 0.5 (html_reports) | Compatible | No action |
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| rand | 0.8 | 0.8 | Compatible | No action |
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### agentic-robotics-Unique Dependencies
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| Dependency | Version | Size Impact | Feature-Gate Strategy |
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|-----------|---------|------------|----------------------|
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| zenoh | 1.0 | Large (~50+ transitive) | `feature = "robotics"` |
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| rustdds | 0.11 | Medium (~20 transitive) | `feature = "robotics-dds"` |
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| cdr | 0.2 | Small | `feature = "robotics"` |
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| hdrhistogram | 7.5 | Small | `feature = "robotics-rt"` |
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| wide | 0.7 | Small | `feature = "robotics-simd"` |
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| axum | 0.7 | Medium | `feature = "robotics-sse"` |
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### ruvector-Unique Dependencies
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| Dependency | Version | Notes |
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|-----------|---------|-------|
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| redb | 2.1 | Storage backend |
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| memmap2 | 0.9 | Memory-mapped files |
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| hnsw_rs | 0.3 (patched) | HNSW index (patched for WASM) |
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| simsimd | 5.9 | SIMD distance functions |
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| ndarray | 0.16 | N-dimensional arrays |
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| dashmap | 6.1 | Concurrent hashmap |
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| lean-agentic | 0.1.0 | Formal verification |
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| wasm-bindgen | 0.2 | WASM interop |
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### Version Conflict Resolution Plan
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**tokio 1.41 -> 1.47:**
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- Minor version bump, fully backward compatible
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- New features in 1.47 (improved multi-thread scheduling) benefit both
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- Change: `Cargo.toml` workspace `tokio = { version = "1.47", ... }`
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**napi 2.16 -> 3.0:**
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- Breaking change: napi 3.0 has different macro syntax
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- Strategy: Maintain separate NAPI versions per crate until coordinated upgrade
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- OR: Upgrade all ruvector -node crates to napi 3.0 (recommended)
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---
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## 2. Architecture Layer Mapping
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```
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+=========================================================================+
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| UNIFIED COGNITIVE ROBOTICS PLATFORM |
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+=========================================================================+
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| |
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| APPLICATION LAYER |
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| +----------------------------+ +------------------------------------+ |
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| | Robot Applications | | ML/AI Applications | |
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| | - Autonomous navigation | | - Vector search | |
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| | - Swarm coordination | | - Graph reasoning | |
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| | - Manipulation control | | - Attention inference | |
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| +----------------------------+ +------------------------------------+ |
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| | | |
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| MCP LAYER (AI TOOL INTERFACE) |
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| +-------------------------------------------------------------------+ |
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| | agentic-robotics-mcp + ruvector MCP tools | |
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| | - robot_move, sensor_read | vector_search, gnn_classify | |
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| | - path_plan, status_query | attention_focus, memory_recall | |
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| +-------------------------------------------------------------------+ |
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| | | |
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| SCHEDULING LAYER |
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| +-------------------------------------------------------------------+ |
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| | agentic-robotics-rt (Dual Runtime) | |
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| | HIGH-PRIORITY (2 threads) | LOW-PRIORITY (4 threads) | |
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| | - Control loops (<1ms) | - Planning (>1ms) | |
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| | - Sensor processing | - Index rebuilds | |
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| | - GNN inference (urgent) | - Batch vector ops | |
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| | - Attention (time-critical) | - Training updates | |
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| +-------------------------------------------------------------------+ |
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| | | |
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| MESSAGING LAYER |
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| +----------------------------+ +------------------------------------+ |
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| | agentic-robotics-core | | ruvector-cluster | |
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| | - Publisher<T>/Subscriber<T>| | - Raft consensus | |
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| | - Zenoh pub/sub | | - Replication | |
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| | - CDR/JSON serialization | | - Delta consensus | |
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| +----------------------------+ +------------------------------------+ |
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| | | |
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| COMPUTE LAYER |
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| +----------------------------+ +------------------------------------+ |
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| | Robotics Compute | | ML Compute | |
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| | - Kinematic solvers | | - HNSW indexing (ruvector-core) | |
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| | - Path planning | | - GNN forward (ruvector-gnn) | |
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| | - State estimation | | - Attention (ruvector-attention) | |
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| | | | - Graph transformer | |
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| | | | - Sparse inference | |
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| +----------------------------+ +------------------------------------+ |
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| | | |
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| STORAGE LAYER |
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| +-------------------------------------------------------------------+ |
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| | ruvector-core (redb + memmap2) | ruvector-postgres | |
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| | - Vector persistence | - SQL storage backend | |
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| | - Index snapshots | - Graph persistence | |
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| +-------------------------------------------------------------------+ |
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| |
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| BINDING LAYER |
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| +----------------------------+ +------------------------------------+ |
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| | NAPI (Node.js) | | WASM (Browser/Edge) | |
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| | agentic-robotics-node | | ruvector-*-wasm (20+ crates) | |
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| | ruvector-*-node (10+) | | agentic-robotics-embedded | |
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| +----------------------------+ +------------------------------------+ |
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| |
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+=========================================================================+
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```
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---
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## 3. Data Flow Integration
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### Sensor-to-Decision Pipeline
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```
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[LiDAR Sensor]
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|
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v
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[PointCloud Message] ──> agentic-robotics-core Publisher
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|
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| (zero-copy via shared memory / crossbeam channel)
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v
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[BRIDGE: PointCloud -> Vec<f32>] ──> ruvector-robotics-bridge
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|
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├──> [HNSW Spatial Index] ──> ruvector-core
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| |
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| v
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| [Nearest Obstacles] (k-NN search, <500us)
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| |
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├──> [Scene Graph Build] ──> ruvector-graph
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| |
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| v
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| [Graph Transformer] ──> ruvector-graph-transformer
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| |
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| v
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| [Scene Understanding] (spatial reasoning)
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| |
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└──> [GNN Classification] ──> ruvector-gnn
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|
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v
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[Object Classes + Confidence]
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|
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v
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[Decision Fusion] ──> ruvector-attention (weighted)
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|
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v
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[Action Command] ──> agentic-robotics-core Publisher -> /cmd_vel
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```
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### Data Type Mappings
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```rust
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// Bridge: PointCloud -> HNSW-indexable vectors
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impl From<&PointCloud> for Vec<Vec<f32>> {
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fn from(cloud: &PointCloud) -> Self {
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cloud.points.iter()
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.map(|p| vec![p.x, p.y, p.z])
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.collect()
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}
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}
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// Bridge: RobotState -> feature vector for temporal tensor
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impl From<&RobotState> for Vec<f64> {
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fn from(state: &RobotState) -> Self {
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let mut v = Vec::with_capacity(7);
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v.extend_from_slice(&state.position);
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v.extend_from_slice(&state.velocity);
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v.push(state.timestamp as f64);
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v
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}
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}
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// Bridge: Pose -> graph node features
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impl From<&Pose> for Vec<f64> {
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fn from(pose: &Pose) -> Self {
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let mut v = Vec::with_capacity(7);
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v.extend_from_slice(&pose.position);
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v.extend_from_slice(&pose.orientation);
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v
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}
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}
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```
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---
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## 4. Shared Pattern Analysis
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### Concurrency Patterns
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Both frameworks extensively use the same concurrency primitives:
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**Arc<RwLock<T>> (read-heavy shared state):**
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```rust
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// agentic-robotics-mcp: tool registry
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tools: Arc<RwLock<HashMap<String, (McpTool, ToolHandler)>>>
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// ruvector-core: vector index (conceptual)
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index: Arc<RwLock<HnswIndex>>
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```
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**Arc<Mutex<T>> (write-heavy shared state):**
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```rust
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// agentic-robotics-rt: scheduler queue
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scheduler: Arc<Mutex<PriorityScheduler>>
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// agentic-robotics-rt: latency histogram
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histogram: Arc<Mutex<Histogram<u64>>>
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```
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**Crossbeam channels (message passing):**
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```rust
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// agentic-robotics-core: subscriber
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let (sender, receiver) = channel::unbounded();
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// ruvector uses crossbeam for parallel processing pipelines
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```
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### Serialization Strategies
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Both frameworks support the same serialization stack:
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| Format | agentic-robotics | ruvector | Use Case |
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|--------|-----------------|----------|----------|
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| serde (JSON) | Primary for NAPI | Configuration | Debug, interop |
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| rkyv 0.8 | Derive macros on all types | Storage backend | Zero-copy persistence |
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| CDR | Robot message wire format | N/A | DDS compatibility |
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| bincode | N/A | Compact binary | Network transfer |
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**Key insight:** Both derive `rkyv::{Archive, Serialize, Deserialize}` on core types, enabling zero-copy data sharing.
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### Error Handling
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```rust
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// Both use thiserror for typed errors
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#[derive(Error, Debug)]
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pub enum Error {
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#[error("...")] Variant(String),
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#[error("...")] Io(#[from] std::io::Error),
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#[error("...")] Other(#[from] anyhow::Error),
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}
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pub type Result<T> = std::result::Result<T, Error>;
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```
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### NAPI Binding Patterns
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```rust
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// agentic-robotics-node (napi 3.0)
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#[napi]
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pub struct AgenticNode { ... }
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#[napi]
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impl AgenticNode {
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#[napi(constructor)]
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pub fn new(name: String) -> Result<Self> { ... }
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#[napi]
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pub async fn create_publisher(&self, topic: String) -> Result<AgenticPublisher> { ... }
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}
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// ruvector-node (napi 2.16) - same pattern, older version
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#[napi]
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pub struct VectorIndex { ... }
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#[napi]
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impl VectorIndex {
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#[napi(constructor)]
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pub fn new(dimensions: u32) -> Result<Self> { ... }
|
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#[napi]
|
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pub async fn search(&self, query: Vec<f64>, k: u32) -> Result<SearchResults> { ... }
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}
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```
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### Benchmark Patterns
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Both use Criterion 0.5 with identical patterns:
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```rust
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fn benchmark_operation(c: &mut Criterion) {
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let mut group = c.benchmark_group("GroupName");
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group.bench_function("name", |b| {
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b.iter(|| { black_box(operation()); })
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});
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group.finish();
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}
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criterion_group!(benches, benchmark_operation);
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criterion_main!(benches);
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```
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---
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## 5. Integration Architecture Proposal
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### Tier 1: Bridge Layer (Minimal Integration)
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New crate: `ruvector-robotics-bridge`
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|
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```rust
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//! Bridge between agentic-robotics messages and ruvector operations
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use agentic_robotics_core::message::{PointCloud, RobotState, Pose, Message};
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use ruvector_core::types::Vector;
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/// Convert PointCloud to indexable vectors
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pub fn pointcloud_to_vectors(cloud: &PointCloud) -> Vec<Vector> {
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cloud.points.iter()
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.map(|p| Vector::from_slice(&[p.x as f64, p.y as f64, p.z as f64]))
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.collect()
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}
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|
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/// Convert RobotState to feature vector
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pub fn state_to_vector(state: &RobotState) -> Vector {
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let mut data = Vec::with_capacity(7);
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data.extend_from_slice(&state.position);
|
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data.extend_from_slice(&state.velocity);
|
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data.push(state.timestamp as f64);
|
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Vector::from_vec(data)
|
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}
|
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|
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/// Auto-indexing subscriber: indexes incoming PointClouds
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pub struct IndexingSubscriber {
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subscriber: Subscriber<PointCloud>,
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index: Arc<RwLock<HnswIndex>>,
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}
|
||||
|
||||
impl IndexingSubscriber {
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pub async fn run(&self) {
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loop {
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if let Ok(cloud) = self.subscriber.recv_async().await {
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let vectors = pointcloud_to_vectors(&cloud);
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let mut idx = self.index.write();
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for v in vectors { idx.insert(&v); }
|
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}
|
||||
}
|
||||
}
|
||||
}
|
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```
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|
||||
### Tier 2: Fusion Layer (Deep Integration)
|
||||
|
||||
New crate: `ruvector-robotics-perception`
|
||||
|
||||
```rust
|
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//! Perception pipeline: sensor data -> ML inference -> decisions
|
||||
|
||||
use agentic_robotics_rt::{ROS3Executor, Priority, Deadline};
|
||||
use ruvector_gnn::GraphNeuralNetwork;
|
||||
use ruvector_attention::AttentionMechanism;
|
||||
|
||||
pub struct PerceptionPipeline {
|
||||
executor: ROS3Executor,
|
||||
gnn: Arc<GraphNeuralNetwork>,
|
||||
attention: Arc<AttentionMechanism>,
|
||||
}
|
||||
|
||||
impl PerceptionPipeline {
|
||||
/// Process sensor data with RT-scheduled ML inference
|
||||
pub fn process(&self, cloud: PointCloud) {
|
||||
let gnn = self.gnn.clone();
|
||||
let attention = self.attention.clone();
|
||||
|
||||
// High-priority: real-time obstacle detection
|
||||
self.executor.spawn_high(async move {
|
||||
let scene_graph = build_scene_graph(&cloud);
|
||||
let gnn_output = gnn.forward(&scene_graph);
|
||||
let focused = attention.apply(&gnn_output);
|
||||
// Publish decision
|
||||
});
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Tier 3: Unified Cognitive Platform (Long-term)
|
||||
|
||||
```
|
||||
ruvector-cognitive-robotics
|
||||
|-- agentic-robotics-core (sensing + actuation)
|
||||
|-- agentic-robotics-rt (scheduling)
|
||||
|-- agentic-robotics-mcp (AI interface)
|
||||
|-- ruvector-core (vector memory)
|
||||
|-- ruvector-gnn (spatial reasoning)
|
||||
|-- ruvector-attention (selective focus)
|
||||
|-- ruvector-nervous-system (cognitive architecture)
|
||||
|-- ruvector-temporal-tensor (temporal reasoning)
|
||||
|-- sona (self-learning)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. Build System Integration
|
||||
|
||||
### Workspace Member Additions
|
||||
|
||||
Add to `Cargo.toml` `[workspace] members`:
|
||||
```toml
|
||||
members = [
|
||||
# ... existing 114 members ...
|
||||
"crates/agentic-robotics-core",
|
||||
"crates/agentic-robotics-rt",
|
||||
"crates/agentic-robotics-mcp",
|
||||
"crates/agentic-robotics-embedded",
|
||||
"crates/agentic-robotics-node",
|
||||
"crates/agentic-robotics-benchmarks",
|
||||
# New integration crates
|
||||
"crates/ruvector-robotics-bridge",
|
||||
]
|
||||
```
|
||||
|
||||
### Workspace Dependency Additions
|
||||
|
||||
```toml
|
||||
[workspace.dependencies]
|
||||
# New robotics dependencies
|
||||
zenoh = { version = "1.0", optional = true }
|
||||
rustdds = { version = "0.11", optional = true }
|
||||
cdr = { version = "0.2", optional = true }
|
||||
hdrhistogram = "7.5"
|
||||
wide = "0.7"
|
||||
```
|
||||
|
||||
### Feature Flag Strategy
|
||||
|
||||
```toml
|
||||
# In ruvector-core/Cargo.toml
|
||||
[features]
|
||||
default = ["storage"]
|
||||
robotics = ["agentic-robotics-core"]
|
||||
robotics-rt = ["robotics", "agentic-robotics-rt"]
|
||||
robotics-mcp = ["robotics", "agentic-robotics-mcp"]
|
||||
robotics-full = ["robotics-rt", "robotics-mcp", "agentic-robotics-embedded"]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. Performance Budget Analysis
|
||||
|
||||
### Latency Budget for Sensor-to-Decision Pipeline
|
||||
|
||||
| Stage | Budget | Mechanism |
|
||||
|-------|--------|-----------|
|
||||
| Sensor deserialization | 540ns | CDR zero-copy |
|
||||
| PointCloud -> vectors | ~100ns | Direct memory map, no allocation |
|
||||
| HNSW k-NN search (10K) | ~400us | O(log n) with SIMD distance |
|
||||
| Scene graph construction | ~50us | Pre-allocated graph structures |
|
||||
| GNN forward pass | ~200us | Small model, RT-scheduled |
|
||||
| Attention application | ~100us | Single-head, focused features |
|
||||
| Decision serialization | ~540ns | CDR output |
|
||||
| **Total** | **<1ms** | Meets hard RT requirement |
|
||||
|
||||
### Memory Layout Optimization
|
||||
|
||||
```
|
||||
Shared memory region (mmap):
|
||||
+------------------------------------------+
|
||||
| PointCloud (rkyv archived) |
|
||||
| points: [Point3D; N] <-- contiguous |
|
||||
| intensities: [f32; N] <-- SIMD-ready |
|
||||
+------------------------------------------+
|
||||
| HNSW Index Vectors |
|
||||
| [f32; 3] x N <-- same memory layout |
|
||||
+------------------------------------------+
|
||||
| GNN Graph (adjacency + features) |
|
||||
| nodes: [f32; D] x M |
|
||||
| edges: [(u32, u32)] x E |
|
||||
+------------------------------------------+
|
||||
```
|
||||
|
||||
Both Point3D `{x, y, z}: f32` and HNSW vectors `[f32; 3]` have identical memory layout, enabling zero-copy conversion via `unsafe { std::slice::from_raw_parts(...) }` when performance is critical.
|
||||
|
||||
---
|
||||
|
||||
## 8. NAPI/WASM Binding Unification Strategy
|
||||
|
||||
### Current State
|
||||
|
||||
| Binding | agentic-robotics | ruvector | Count |
|
||||
|---------|-----------------|----------|-------|
|
||||
| NAPI (Node.js) | 1 crate (napi 3.0) | 10+ crates (napi 2.16) | 11+ |
|
||||
| WASM | 0 (planned) | 20+ crates (wasm-bindgen 0.2) | 20+ |
|
||||
|
||||
### Unified TypeScript API Design
|
||||
|
||||
```typescript
|
||||
// @ruvector/platform - unified package
|
||||
import { RobotNode, VectorIndex, GnnModel } from '@ruvector/platform';
|
||||
|
||||
// Create robot node with integrated vector search
|
||||
const node = new RobotNode('perception_bot');
|
||||
const index = new VectorIndex({ dimensions: 3, metric: 'l2' });
|
||||
const gnn = await GnnModel.load('./scene_classifier.model');
|
||||
|
||||
// Subscribe to LiDAR, auto-index, classify
|
||||
const lidar = await node.subscribe('/lidar/points');
|
||||
lidar.onMessage(async (cloud) => {
|
||||
// Index points for spatial search
|
||||
await index.insertBatch(cloud.points);
|
||||
|
||||
// Find nearest obstacles
|
||||
const obstacles = await index.search(robot.position, { k: 20 });
|
||||
|
||||
// Classify scene
|
||||
const scene = gnn.classify(obstacles);
|
||||
|
||||
// Publish decision
|
||||
await node.publish('/nav/command', scene.safePath);
|
||||
});
|
||||
```
|
||||
|
||||
### WASM Build Strategy
|
||||
|
||||
```
|
||||
Phase 1: ruvector WASM crates work standalone (current)
|
||||
Phase 2: agentic-robotics-core builds to WASM (remove Zenoh, use web-sys channels)
|
||||
Phase 3: Combined ruvector-robotics-wasm with unified API
|
||||
Phase 4: Web-based robot simulator using combined WASM + WebGL
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The architectural compatibility between agentic-robotics and ruvector is exceptionally high:
|
||||
- 14/16 shared dependencies are version-compatible
|
||||
- Identical Rust edition, build profiles, and coding patterns
|
||||
- Complementary rather than overlapping functionality
|
||||
- Both use rkyv 0.8 enabling zero-copy data sharing
|
||||
- NAPI binding patterns are structurally identical
|
||||
|
||||
The primary integration challenges are manageable:
|
||||
1. Zenoh dependency tree size (mitigated by feature flags)
|
||||
2. NAPI version mismatch (coordinated upgrade to 3.0)
|
||||
3. tokio minor version bump (backward compatible)
|
||||
|
||||
The synergy potential is substantial: no existing framework combines real-time robotics middleware with native vector database operations, GNN inference, and MCP tool exposure in a unified Rust workspace.
|
||||
Reference in New Issue
Block a user