Industry Challenges
Complex and Costly Testing Environments
Intelligent equipment operation scenarios involve all-weather (day/night), all-condition (sunny/rainy/snowy/foggy) and complex working conditions (road construction, obstacles, etc.). Actual testing requires significant resource investment and is difficult to reproduce frequently.
Difficulties in Closed-Loop Testing
Perception, decision-making, and control algorithms for intelligent equipment require closed-loop verification with physical devices (sensors, actuators). Precise variable control is challenging in real scenarios, affecting testing efficiency.
Scarcity of Edge Cases
Lack of data for extreme/fault/rare conditions (reflective/transparent/soft packaging/misalignment/jamming, etc.), resulting in insufficient coverage of edge cases.
High Training and Debugging Costs
Operator training relies on physical equipment, posing safety risks and high equipment occupation costs, making it difficult to rapidly develop proficiency at scale.
Customer Value
Controllable Reproduction of Extreme Conditions
Enables controlled and repeatable extreme/abnormal working conditions, significantly improving coverage of edge cases.
Enhanced Efficiency Through Hardware-Software Integration
Closed-loop coordination between software and hardware improves debugging efficiency and result reliability.
Cost and Risk Reduction in Training and Regression
Replaces partial real-equipment training and regression testing, reducing risks and costs.
Unified Metrics and Iterative Playback
Standardized metric dashboards and playback comparison accelerate solution iteration.
Product Capabilities
Scenarios and Assets
SimReady industrial assets including tooling fixtures, jigs, grippers/suction cups, pallets/containers, conveyors, and shelving systems. Supports CAD/URDF import and parameterization, with programmable placement, stacking, storage location, and obstacle generation.
Sensor Simulation
Multi-channel camera simulation (with distortion, noise, and weather effects), multi-channel LiDAR (reflectance intensity, noise, and distance simulation), and GNSS/IMU (position/velocity error simulation, including signal loss scenarios).
Synthetic Data
Multimodal outputs (RGB/depth/segmentation/normal maps/point clouds, etc.); label types (2D/3D bounding boxes, semantic/instance segmentation, 6D pose, keypoints, defect labels, temporal IDs); data versioning and sampling scripts.
System and Integration
ROS2/DDS, digital I/O, controller/HIL adaptation; multi-source timestamp synchronization; Python/C++ APIs for scenario and process control; real-time replay and visual dashboards.
