AUTONOMOUS MOBILE ROBOT · TELEMETRY NORMALIZATION
16,908 confirmed mappings·18 OEM families·14 protocols·91% field coverage
Forge turns raw Fetch Robotics tags into one clean, governed schema your AI agent already understands. One MCP call, no per-AMR integration project.
Fleet state (battery, pose, velocity, e-stop) is published over ROS 2 topics or a proprietary REST fleet API, and no two vendors agree on the topic names.
Typical Fetch Robotics equipment communicates over ROS 2 (DDS), HTTP / REST. Each firmware revision, model line and integrator names the same physical quantity differently, so every AI or analytics project pays the integration tax again, the tax that cancels 40% of industrial AI deployments before they ship.
Forge normalizes Fetch Robotics telemetry into Forge's canonical field set, the same 366 canonical fields used across all 18 manufacturers. Point Forge at the AMR, pass oem: "fetch", and spindle speed is spindle_speed_rpm whether it came from a Fetch Robotics controller or anything else on the floor.
Confirmed canonical fields your agent gets from Fetch Robotics AMRs, drawn from Forge's 16,908-mapping corpus:
| Raw tag in | Canonical field out | Meaning | |
|---|---|---|---|
| battery_pct: 76 | → | amr.battery.charge: 76 | battery % |
| pose_x: 5.2 | → | amr.position.x: 5.2 | pose X |
| lin_vel: 0.9 | → | amr.velocity.linear: 0.9 | linear velocity |
| mode: "MOVING" | → | amr.operating_mode: "MOVING" | operating mode |
No SDK, no per-tag mapping, no historian project. Connect the MCP server and normalize live Fetch Robotics data:
Unknown tags never get force-mapped. Fields below the confidence floor abstain rather than guess, so your agent never acts on a bad reading.