Image-Based Localization Models for Scalable Robot Fleet Deployment
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Solution Overview
Problem
Existing mobile robot localization technologies face challenges in scaling from single instance deployments to large fleets across diverse environments, requiring substantial manual adjustments and increasing technical complexity as the fleet size grows, especially in environments like restaurants and hospitals.
Innovation Solution
An automated robot training and deployment system for image-based localization models that includes data collection, retention strategies, and model generation pipelines capable of scaling to larger fleets, utilizing a model system with components like data collection and preparation, model training, and deployment modules to create and maintain image-based localization models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustments are made to customize navigation models for each environment, then localization accuracy is improved, but device complexity and time consumption increase substantially when scaling to large robot fleets
Solution Approach 1:
The system creates standardized navigation models that can be copied and deployed across multiple robots and environments. Instead of manually customizing each model, a single validated model serves as a template that can be replicated fleet-wide, maintaining localization accuracy while eliminating repetitive manual configuration work
Solution Approach 2:
The navigation models are designed with universal applicability across diverse environments and robot types. A single model architecture serves multiple functions and environments through standardized interfaces and configurable parameters, reducing the need for environment-specific customizations and simplifying fleet-wide deployment
2Reliability
If manual adjustments are made for each environment, then navigation performance is improved, but productivity decreases due to substantial time requirements for model tuning
Solution Approach 1:
Navigation models are pre-configured, pre-trained, and pre-validated in a controlled environment before deployment. This preliminary preparation ensures optimal performance is achieved without requiring time-consuming manual tuning during actual deployment, thereby maintaining high navigation performance while significantly improving deployment speed
Solution Approach 2:
The system implements automated model selection and deployment mechanisms that eliminate the need for manual intervention. The deployment system automatically matches robots with appropriate pre-configured models based on environment type and robot specifications, maintaining navigation performance while enabling rapid fleet-wide deployment
3Measurement precision
If data is retained for all service locations, then model accuracy is improved, but data management complexity and storage requirements increase
Solution Approach 1:
Training data is segmented and organized by service location, environment type, and robot category. This segmentation allows the system to retrieve only relevant data subsets for specific deployment scenarios, maintaining model accuracy through targeted data usage while reducing overall data management complexity and storage requirements through selective data retention
4Measurement precision
If custom models are developed for each location, then localization precision is improved, but loss of time increases due to extensive data collection and model training requirements
Solution Approach 1:
Data collection and model training processes are merged into a centralized cloud-based system that serves the entire robot fleet. Individual robots contribute data to a shared pool, and trained models are distributed to all relevant robots. This merging approach maintains high localization precision through comprehensive training data while dramatically reducing the time each individual robot or location needs to spend on data collection and model development
Data Source
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AI summary
A computer-implemented method and apparatus to generate an image-based localization model for mobile robot navigation. The method includes performing data collection at a plurality of different service locations, to which a fleet of mobile robots is deployable, to generate collected data, performing a data retention operation with respect to the collected data based on a data retention policy, generating a first image- based localization model and a second image-based localization model for a first and respectively, second service location of the plurality of different service locations, using the collected data. The method further includes deploying the first image-based localization model and the second image- based localization model to a first and, respectively, second mobile robot of the fleet of mobile robots, the first image-based localization model and the second image-based localization model being used to navigate the first and, respectively, the second service location of the plurality of different service locations.