EdgeMind: Verifiable Onboard AI Planning Runtime for Embedded Robots

Overview

• EdgeMind is a modular, contract-driven runtime for autonomous planning on resource-constrained hardware (ARM, RISC-V). It enables offline-first planning with safety contracts, delta-sync updates, and a data-contract layer for cross-vendor interoperability.

Key components (minimal MVP, production-ready architecture):

• Planner: lightweight, bounded-complexity planning with per-goal action selection
• Safety contracts: pre/post conditions and simple budget-based risk controls
• EnergiBridge: a CatOpt-inspired data-contract bridge with LocalProblem, SharedSignals, PlanDelta, DualVariables, AuditLog, and AdapterContract
• Adapters: skeletons and bindings for sensors/actuators; TLS-ready communication in MVP
• Sandbox & governance: audit trails and deterministic replay
• Simulation hooks: Gazebo/ROS-based testbeds for validation

Usage

• Run tests: bash test.sh
• Package: python3 -m build
• You can import the package as: from idea15_edgemind_verifiable_onboard import EdgeMindPlanner, SafetyContract, EnergiBridge

This repository already ships a working Python MVP that passes tests and builds a wheel. This README documents the intended MVP roadmap and how to extend it further.

Roadmap (aligned with the Canonical Interop bridge concept)

• Phase 0: Skeleton MVP with 2 starter adapters (sensor gateway, navigator controller)
• Phase 1: Governance ledger scaffolding and adapter conformance tests
• Phase 2: Gazebo/ROS-based cross-domain demo and KPI dashboards