Robotic Cart Object Enrollment Using Simulated Sensor Data
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Solution Overview
Problem
Conventional techniques for robot object recognition and navigation in dynamic environments face challenges due to variations in object appearance, occlusion, and environmental changes, requiring extensive data collection and annotation, and are sensor-dependent, limiting adaptability and efficiency.
Innovation Solution
An environment-specific object recognition model is created using simulated sensor data from a 3D scanned environment, allowing robots to detect objects in context, and a 3D mesh is generated using consumer devices for mapping, enabling flexible and distributed mapping and localization without sensor dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional techniques are used for robot object recognition in dynamic environments, then robots can detect objects, but they require extensive data collection and annotation, and are sensor-dependent, limiting adaptability
Solution Approach 1:
The patent creates virtual copies of the physical environment using 3D scanning and generates synthetic sensor data from these digital twins. This copying approach eliminates the need for extensive real-world data collection and annotation while maintaining environmental fidelity, directly resolving the contradiction between adaptability and data complexity
Solution Approach 2:
The patent replaces physical sensor-dependent recognition systems with a simulation-based recognition model trained on synthetic data. This substitution eliminates sensor dependency and reduces the need for extensive physical data collection, thereby improving adaptability while reducing system complexity
2Reliability
If robots navigate dynamic environments with object variations and occlusions, then they can recognize objects, but conventional methods require extensive data collection and are sensor-dependent
Solution Approach 1:
By creating accurate virtual copies of the environment and objects, the system can generate unlimited synthetic training data that captures all possible object variations and occlusions without requiring corresponding real-world data collection, maintaining recognition accuracy while eliminating data complexity
Solution Approach 2:
The system performs preliminary environment scanning and 3D modeling before robot navigation begins. This advance preparation creates a complete digital twin that pre-contains all object information, eliminating the need for extensive real-time data collection during operation
3Adaptability or versatility
If environment-specific object recognition models are created using simulated sensor data, then robots can detect objects in context with reduced sensor dependency, but this requires 3D scanning and simulation infrastructure
Solution Approach 1:
The patent substitutes physical sensor systems with a simulation-based recognition model that operates on synthetic data. This replacement achieves sensor independence while the simulation infrastructure, though complex, is a one-time setup that eliminates ongoing sensor dependency
Data Source
AI summary
One or more simulated capture paths through a physical environment may be determined for a robot based on an environment navigation model of the physical environment. A plurality of simulated object parameter values may be determined for an object type. Simulated sensor data for a plurality of simulated instances of the object type may be determined based on the one or more simulated capture paths, the environment navigation model, and the simulated object parameter values. An object recognition model to recognize an object corresponding with the object type based on the simulated sensor data.