XTRON Core™
Testing robotic autonomy in real-world settings introduces significant cost, risk, and limitations in repeatability. Additionally, training AI-driven control systems requires large-scale, annotated, and diverse datasets across edge conditions which are difficult to capture physically.
XTRON Core™ addresses this with a modular, high-performance simulation framework built for precision, interoperability and encrypted data traceability. It enables development, testing and refinement of autonomous agents under controlled yet dynamic environments with outputs usable both in-the-loop and onchain.
Core Capabilities
High-Resolution Physical Simulation Real-time, accurate modeling of vehicle dynamics, sensor feedback and environment interactions using Unreal Engine with a physics update rate of up to 1000Hz, suitable for HITL and SITL pipelines.
Cross-Platform Controller Integration Native support for PX4, ROS2, MavLink, and custom flight stacks. Simulated agents can be plugged into real firmware stacks with full signal routing from virtual sensors to actuators.
AI Behavior Evaluation Loop Optimized for reinforcement learning and policy evaluation across thousands of simulation runs. Agents can be trained to generalize across conditions like varying payload, wind profiles or hardware constraints.
Deterministic and Stochastic Runs Offers both controlled deterministic testing for debugging and stochastic mode for robustness training via randomized environmental noise injection.
Encrypted Data Output All simulation telemetry can be anonymized, signed and exported as structured datasets. This allows the creation of verified, reproducible R&D-grade logs for training or on-chain publishing.
System Architecture
XTRON Core™ is composed of discrete modules designed for flexibility and scalability:
ModuleFunction
Environment Generator
Models terrains, wind fields, lighting changes, GPS shadowing, magnetic interference
Vehicle Dynamics Model
Simulates 6-DOF rigid body motion, actuator delays, center of mass shifts
Sensor Stack
Provides accurate models for IMU, barometer, GPS, LiDAR, RGB/IR camera with parameterized noise
Controller Interface
Supports flight stacks and actuator feedback loops through simulated ESCs or digital twin interfaces
Data Logger & Encoder
Records telemetry at high resolution, compresses and hashes for reproducibility and traceability
Example: Quadrotor Simulation in XTRON Core™
To validate AI-based flight control strategies, XTRON Core™ includes a highly accurate quadrotor dynamics module:
Control Inputs (u₁, u₂, u₃, u₄): Direct mapping of motor commands.
Generated Forces (F₁–F₄): Calculated from rotor thrust coefficients and aerodynamic drag.
Torques (τ₁–τ₄): Result from rotor inertia and differential rotation, affecting pitch, roll, and yaw.
External Forces: Includes wind gusts, turbulence models, ground effect, and sensor delays.
This setup allows for fine-tuned testing of:
Fault-tolerant controllers
PID vs. nonlinear adaptive control comparisons
Flight under degraded IMU/GPS conditions
Multi-agent formation and collision-avoidance protocols
📌 Technical Rationale for XTRON Core™
Reproducible Autonomy Experiments Enables the same simulation to be run under exact initial conditions with controlled variables, essential for algorithm benchmarking.
Bridges Simulation-to-Reality Gap Physics-correct sensor modeling and controller feedback integration allows trained policies to generalize to real-world performance with reduced simulation to reality loss.
Modular Deployment Components (e.g., vehicle models, sensor stacks) can be used independently for unit testing or plugged into larger multi-agent scenarios.
Data Provenance and Cryptographic Logging Each simulation session produces signed output logs, ensuring traceability for research audit, training dataset licensing or downstream AI alignment tasks.
Last updated