Robotic Ground-Truth Mapping for Automated Checklist Verification
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Solution Overview
Problem
Existing methods for verifying the status of items in a space, such as hotel rooms or retail environments, require human intervention, which is costly, error-prone, and inefficient.
Innovation Solution
An autonomous device uses simultaneous localization and mapping (SLAM) techniques to create a map of a space, identify objects and their conditions, and compare this ground truth data to a desired state, flagging discrepancies for automated correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human managers manually verify the condition of items in a space, then they can complete checklist tasks, but it is costly, error-prone, and inefficient
Solution Approach 1:
The patent replaces manual mechanical verification by human managers with an automated robotic system equipped with sensors and processing units. The robot autonomously navigates the space, captures images and data, and compares them against checklist criteria without human intervention, thereby increasing productivity while reducing errors associated with manual checking
Solution Approach 2:
The robotic system performs self-verification by autonomously completing the entire checklist process. It independently navigates to required locations, captures necessary data, processes information, and generates verification reports without requiring human managers to perform the actual checking tasks, thus eliminating human error and improving consistency
2Ease of operation
If human managers manually complete checklists, then they can verify item status, but it is time-consuming and costly
Solution Approach 1:
The system replaces manual checklist completion with automated robotic verification. The robot uses sensors, cameras, and processing units to autonomously assess item conditions, navigate spaces, and generate verification reports, eliminating the time-consuming manual process while maintaining comprehensive coverage of checklist items
Solution Approach 2:
The robotic system operates continuously without interruption to perform verification tasks. Unlike human managers who require breaks and have variable speeds, the robot maintains consistent verification pace, continuously navigating and checking items throughout the entire verification process, thereby reducing total verification time
3Measurement precision
If manual verification is used, then human managers can assess object conditions, but tracking information over time becomes difficult due to human error
Solution Approach 1:
The patent replaces manual data collection with automated robotic systems that precisely capture and record object states. The robot's sensors and processing units ensure accurate, consistent data collection that can be reliably tracked over time, eliminating the information loss and errors associated with manual verification processes
Solution Approach 2:
The system incorporates feedback mechanisms where the robot compares captured data against checklist criteria and historical records. This feedback loop enables continuous monitoring and tracking of changes in object states over time, maintaining high measurement precision and ensuring reliable data tracking that improves with each verification cycle
Data Source
AI summary
Systems and methods are herein provided for automated robotic ground-truth, checklists, and physical traceability. In one example, a mobile autonomous device comprises an autonomous vehicle unit; a plurality of sensor devices; at least one processor; and memory that stores computer-executable instructions that, as a result of being executed by the at least one processor, cause the mobile autonomous device to: collect data of objects within the space, comprising: a map of the space indicating locations of objects within the space; identifications of objects within the space; a condition associated with each of the objects; compare the data to historical data; and based on the comparison between the data and the historical data, identify one or more discrepancies, comprising one or more of a change in location, a change in condition, and a change in presence of objects within the space; and generate a notification indicative of the discrepancies.