XPndAI builds bespoke robot digital twin software for robotics manufacturers, system integrators, and enterprise fleet operators. Real-time 3D synchronisation with live robot telemetry, environment mapping, software update simulation, remote operation interface, failure scenario modelling, and fleet-level optimisation — giving operators a virtual window into every robot in their fleet. Source code ownership. From $80,000.
The robot digital twin is a live virtual replica of each physical robot, synchronised in real-time with sensor data. XPndAI builds the real-time synchronisation layer: robot 3D model (kinematic model of the robot — joint positions, actuator states, orientation, velocity — rendered in a 3D viewer that mirrors the physical robot's current pose), live sensor overlay (sensor readings — lidar scans, camera feed, ultrasonic distances — displayed on the 3D robot model), battery state visualisation (SoC%, charge rate, temperature — animated on the twin), joint torque and motor current display (each motor/actuator shows current load — immediately visible if any actuator is under unusual stress), error state display (any active fault code visualised on the affected robot component — technician can see which joint, which motor, which sensor triggered the fault), and update latency (twin synchronised with configurable update frequency — 10 Hz for real-time operation visibility, 1 Hz for fleet monitoring). Supports robots with URDF/SDF model definitions (ROS standard), proprietary manufacturer formats, or custom model build from CAD files.
Understanding where a robot is and what it perceives requires synchronising the robot's internal environment model with the digital twin. XPndAI builds environment synchronisation: SLAM map synchronisation (the robot's internally generated occupancy grid or 3D point cloud map synchronised to the digital twin — see the facility as the robot sees it), live robot position overlay (robot's estimated position on its internal map visualised on the digital twin — see exactly where in the facility the robot currently is), obstacle detection visualisation (dynamic obstacles detected by the robot's sensors displayed in real-time on the map — see what the robot is reacting to), mission path visualisation (planned navigation path shown on the map — see where the robot intends to go), coverage tracking (for cleaning robots — visualise which floor areas have been cleaned in the current mission, which are remaining, and coverage quality), and fleet-level facility map (all robots in a facility shown simultaneously on the facility map — fleet operations view: who is where, what is each robot doing, where are the congestion points).
Deploying untested software updates to a live robot fleet is a high-risk operation — a bad update can take an entire fleet offline. The digital twin provides a safe testing environment: simulation environment (identical physics and sensor model to the real robot — software update runs against the simulated robot before deploying to physical hardware), regression test suite execution (automated tests of core robot behaviours — navigation, obstacle avoidance, task execution, error handling — run against the digital twin after each software update), anomalous behaviour detection (AI compares robot behaviour before and after update in simulation — flags behaviours that deviate from pre-update baseline), staged rollout support (validate update on digital twin → deploy to 1-2 pilot robots → monitor physical robot performance → if nominal, roll out to full fleet — digital twin provides the validation gate), rollback decision support (if physical robot performance degrades after update — digital twin simulation confirms whether rollback will restore prior behaviour), and update approval workflow (simulation test results routed to engineering approval before deployment authorisation).
Where robots support teleoperation (remote human control), the digital twin provides the operator interface. XPndAI builds a remote operation layer: teleoperation interface (keyboard/joystick/gamepad control of the physical robot transmitted through the cloud platform — digital twin provides the visual feedback for the operator: what the robot sees, where it is, its current state), latency-aware control (remote operation compensates for network latency — control input prediction prevents jarring control responses over high-latency connections), camera feeds (robot's onboard cameras streamed to the operator — forward camera, arm camera, 360° view — integrated with the 3D twin view), emergency stop (immediate e-stop command available to operator and facility supervisor — overrides all remote commands), access control (which operators are authorised to remotely operate which robots — credential management, session logging), audit trail (every remote operation session logged — time, operator, commands issued, robot state — for safety and liability purposes), and handoff protocol (transition between autonomous and teleoperated modes — clear state handoff to prevent control conflicts).
When a robot fails, root cause analysis is often impeded by the fact that the failure condition no longer exists by the time engineers investigate. The digital twin preserves failure conditions for analysis. XPndAI builds a failure analysis capability: telemetry playback (digital twin plays back the recorded telemetry from any historical time window — rewind to the minutes before a failure, watch the robot state leading up to the fault), failure condition recreation (reproduce the exact sensor readings, motor states, and navigation state at the moment of failure — engineers can examine it from any angle, at any speed), hypothesis testing (change one parameter in the simulation and replay — if the failure disappears, that parameter was the root cause; if not, investigate further), failure mode correlation (across the fleet — are multiple robots showing similar pre-failure patterns? identifies systematic issues vs. individual hardware failures), maintenance procedure validation (simulate the proposed repair procedure in the digital twin before performing it on the physical robot — verify the repair will resolve the fault before the field engineer travels to site), and failure report generation (structured failure analysis report from the digital twin investigation — for engineering, quality, and customer communication).
The aggregate of individual robot digital twins creates a fleet-level simulation environment for operations optimisation. XPndAI builds fleet intelligence capabilities: mission allocation optimisation (simulate different mission allocation strategies across the fleet — which assignment algorithm maximises throughput given current robot states, facility map, and task queue?), charging strategy optimisation (simulate different charging schedules — when should each robot charge? — to maximise fleet utilisation while ensuring sufficient battery for peak demand periods), traffic flow simulation (identify congestion points in the facility map — multiple robots competing for the same corridor — and simulate layout changes or routing rule modifications to reduce congestion), new facility deployment simulation (before installing robots in a new facility — simulate the facility layout, robot count, and mission profile to predict throughput and identify issues before physical installation), what-if analysis (add 5 more robots: what happens to throughput and congestion? change mission profile: what changes? — operations managers can explore scenarios without committing resources), and continuous optimisation feedback (as the real fleet operates, digital twin learns from actual performance — simulated predictions vs. actual outcomes improve over time).
XPndAI's robot digital twin platform is designed to integrate with the robot's actual control system and existing simulation tools rather than requiring proprietary robot hardware. Integration categories: (1) Robot Operating System (ROS/ROS2) — for robots built on ROS/ROS2 (most modern AMRs, cobots, research robots), the digital twin integrates natively via ROS topics (telemetry subscription), tf (coordinate transforms for 3D visualisation), and URDF/SDF robot description files (kinematic model for 3D rendering); (2) Simulation frameworks — XPndAI can extend existing simulation environments (Gazebo, Isaac Sim, MuJoCo, PyBullet) where the manufacturer already uses them for development, adding the cloud connectivity and operations intelligence layer; or XPndAI can build a lightweight custom simulation layer if no existing framework is in use; (3) Proprietary robot platforms — for commercial robots with closed software architectures (Boston Dynamics Spot, various Chinese AMR manufacturers — Geek+, MUJIN, SYRIUS, ForwardX, SEER), integration uses the manufacturer's SDK/API to extract the telemetry and state data exposed; the richness of the digital twin depends on what the manufacturer's SDK exposes; (4) Industrial robots (KUKA, ABB, Fanuc, Universal Robots) — integration via OPC-UA or manufacturer-specific software interfaces (KUKA KRC, ABB OmniCore, Universal Robots URCaps, Fanuc FOCAS); 3D model from the manufacturer's URDF or CAD files; (5) Custom/proprietary robot — for robots without an existing software interface, XPndAI works with the manufacturer's engineering team to define a telemetry export layer. Integration complexity and achievable synchronisation fidelity depend heavily on what the robot platform exposes — assessed during scoping.
XPndAI builds both cloud-hosted and on-premise digital twin deployments — the choice depends on the operator's data sovereignty requirements, network architecture, and robot connectivity: (1) Cloud-hosted — the digital twin platform runs in a cloud region (AWS, Azure, or GCP — operator selects preferred provider and region); robots connect to the cloud platform via secure websocket or MQTT over cellular/Wi-Fi; advantages: no infrastructure management, global accessibility, scales easily; data residency: EU region for European operators, US region for US operators, Mumbai/Singapore for Asia-Pacific operators; (2) On-premise — for environments where robots cannot connect to the cloud (air-gapped manufacturing facilities, secure defence applications, data-sensitive healthcare), the digital twin platform runs on the operator's own servers; robots connect to the on-premise server on the local network; disadvantages: infrastructure management, capacity planning, backup responsibility; (3) Hybrid — digital twin runs on-premise with selective telemetry pushed to cloud for fleet analytics and remote access (remote operation, remote diagnostics, field engineer access); this architecture gives local control with cloud-enabled remote capabilities; (4) China deployment considerations — for Chinese robotics manufacturers with cloud infrastructure in China (Alibaba Cloud, Tencent Cloud, Huawei Cloud), XPndAI builds the platform on the manufacturer's chosen Chinese cloud provider rather than AWS/Azure/GCP; robot data stays within China network boundaries. Deployment architecture is determined during scoping based on robot connectivity, data requirements, and geography.
Tell us your robot type, fleet size, primary use case (simulation/remote ops/failure analysis/fleet optimisation), and deployment preference (cloud/on-premise/hybrid). We scope a bespoke digital twin platform and demo within 5 business days.
XPndAI · Robot Digital Twin Platform · AMR / Cobot / Industrial Robots · ROS2 / OPC-UA · Cloud or On-Premise · Source Code Ownership · From $80,000 · +91-9625368140 · 机器人数字孪生平台