25 lines
1.5 KiB
Markdown
25 lines
1.5 KiB
Markdown
EdgeMind: Verifiable Onboard AI Planning Runtime for Embedded Robots
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Overview
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- 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.
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Key components (minimal MVP, production-ready architecture):
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- Planner: lightweight, bounded-complexity planning with per-goal action selection
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- Safety contracts: pre/post conditions and simple budget-based risk controls
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- EnergiBridge: a CatOpt-inspired data-contract bridge with LocalProblem, SharedSignals, PlanDelta, DualVariables, AuditLog, and AdapterContract
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- Adapters: skeletons and bindings for sensors/actuators; TLS-ready communication in MVP
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- Sandbox & governance: audit trails and deterministic replay
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- Simulation hooks: Gazebo/ROS-based testbeds for validation
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Usage
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- Run tests: bash test.sh
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- Package: python3 -m build
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- You can import the package as: from idea15_edgemind_verifiable_onboard import EdgeMindPlanner, SafetyContract, EnergiBridge
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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.
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Roadmap (aligned with the Canonical Interop bridge concept)
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- Phase 0: Skeleton MVP with 2 starter adapters (sensor gateway, navigator controller)
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- Phase 1: Governance ledger scaffolding and adapter conformance tests
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- Phase 2: Gazebo/ROS-based cross-domain demo and KPI dashboards
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