Virtual Facility Simulation for Robotics Fleet Integration
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Solution Overview
Problem
Existing robotics systems lack efficient integration, control, simulation, and facilitation of operations, particularly in complex environments, due to limitations in data processing, sensor integration, and automation management.
Innovation Solution
A virtual facility robotics system is developed, comprising a storage system, integration system, virtual facility interface system, and simulator engine, which uses neural rendering models to create photorealistic 3D representations of real facilities, facilitating communication with control systems, simulating future states, and managing robot fleets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a virtual facility system with neural rendering models is implemented, then the integration and management of robotics operations is accelerated, but the device complexity and computational resources required increase
Solution Approach 1:
The patent creates a virtual facility that is a digital copy of the physical facility, including virtual robots, sensors, and environmental features. This virtual replica enables simulation and testing without affecting the physical system, accelerating integration by allowing parallel development and validation.
Solution Approach 2:
The virtual facility acts as an intermediary layer between the physical facility and the control systems. It provides a sandbox environment for testing workflows, validating robot behaviors, and predicting future states before deploying changes to the physical system, thereby managing complexity.
2Measurement precision
If photorealistic three-dimensional representations are generated using neural rendering, then the realism and accuracy of the virtual facility improve, but the computational time and processing power required increase
Solution Approach 1:
The system performs preliminary rendering and precomputes visual data during system setup and initialization. By pre-generating photorealistic representations and caching them, the system reduces real-time computational requirements while maintaining high accuracy in the virtual facility.
Solution Approach 2:
The rendering system dynamically adjusts the level of detail and photorealism based on the specific simulation needs. Different virtual facilities or different views within the same facility can have varying levels of rendering fidelity, optimizing the balance between accuracy and computational cost.
3Adaptability or versatility
If multiple layers of virtual facility information are maintained (photorealistic representation, dense reconstruction, 2D maps, facility rules), then the comprehensiveness of the virtual model improves, but the data storage and processing requirements increase
Solution Approach 1:
The virtual facility is divided into multiple distinct layers, each storing specific types of information: photorealistic 3D representations, dense geometric reconstructions, 2D topological maps, and facility rules. This segmentation allows efficient storage and retrieval of only the necessary data for each simulation task, managing data volume while maintaining comprehensiveness.
Solution Approach 2:
The multi-layered virtual facility structure serves multiple functions simultaneously: photorealistic layers support visual sensor simulation, dense reconstruction enables precise spatial reasoning, 2D maps provide topological navigation, and facility rules enforce operational constraints. This universal structure handles diverse simulation requirements without requiring separate systems.
4Reliability
If the simulator engine simulates future states of the real facility, then the predictive capability and workflow optimization improve, but the computational complexity and simulation accuracy requirements increase
Solution Approach 1:
The simulator engine uses feedback from the virtual facility state to iteratively refine future state predictions. By simulating robot actions, observing the resulting virtual facility state, and comparing with desired outcomes, the system learns and improves predictive accuracy while managing complexity through controlled iteration.
Solution Approach 2:
The system performs preliminary simulation of multiple potential future states before actual execution. By pre-evaluating different workflow scenarios, robot paths, and potential conflicts in the virtual environment, the system identifies optimal actions and avoids complex real-time decision-making, improving predictive reliability.
Data Source
AI summary
A virtual facility robotics system may include a storage system, a data engine, an integration system, a virtual facility interface system, and a simulator engine. The storage system may store video data of a real facility. The data engine may train a neural rendering model providing a photorealistic three-dimensional representation of the real facility. The integration system may provide facilitate communication with a robot fleet manager configured to control one or more robots operating within the real facility. The virtual facility interface system may provide access to information stored in a virtual facility that includes the photorealistic three-dimensional representation of the real facility. The virtual facility interface system may be configured to provide information to the robot fleet manager via the integration system upon request. The simulator engine may simulate a future state of the real facility including one or more novel views generated based on the neural rendering model.