Autonomous Mobile Robot Navigation for Construction Progress Tracking

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Solution Overview

Problem

There is a need for autonomous mobile robots (AMRs) to efficiently navigate and document construction sites, track progress, and monitor safety and housekeeping within defined spaces, particularly in dynamic environments like construction sites, where traditional methods lack precision and real-time data collection.

Innovation Solution

An autonomous mobile robot system equipped with a machine vision system, including LIDAR and discrete cameras, navigates via predefined paths or GPS coordinates, acquires imagery, and uses ML models to define completion percentages and detect safety hazards, integrating data collection, processing, and reporting functionalities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional manual methods are used for construction site documentation and progress tracking, then human judgment and flexibility are maintained, but precision, real-time data collection, and operational efficiency deteriorate

Engineering Contradiction:

Improveprogress tracking precisionVSAvoidoperational efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The autonomous mobile robot performs self-navigation, self-documentation, and self-analysis of construction sites. The robot independently captures imagery, processes it through ML models to determine progress percentages, and generates reports without human intervention, enabling continuous autonomous operation that improves both measurement precision and productivity

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical documentation methods are replaced with an automated system combining mobile robotics, machine vision cameras, LIDAR, and machine learning algorithms. This substitution enables precise, real-time progress tracking and eliminates the limitations of manual measurement and documentation processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If autonomous mobile robots are deployed for construction site monitoring, then real-time data collection and operational efficiency improve, but device complexity increases

Engineering Contradiction:

Improveoperational efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The autonomous mobile robot is designed as a multi-functional platform that performs navigation, imagery capture, machine learning-based progress analysis, safety monitoring, and report generation within a single integrated system. This universal design consolidates multiple functions into one device, improving productivity while managing complexity through functional integration

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

Machine learning models serve as intermediaries between the raw imagery data captured by sensors and the progress determination output. The ML models process and interpret complex visual data, translating it into meaningful progress percentages, which simplifies the overall system architecture and manages data processing complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If comprehensive imagery acquisition is performed at multiple defined locations, then measurement precision and data quality improve, but loss of time and data processing complexity increase

Engineering Contradiction:

Improveprogress assessment accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system pre-defines multiple strategic locations within the construction site where imagery should be captured. By establishing these predetermined capture points before deployment, the robot efficiently collects comprehensive data without unnecessary wandering or redundant captures, improving measurement precision while minimizing time loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The robot captures imagery at multiple defined locations, which may be more than the absolute minimum needed. This excessive action ensures comprehensive coverage and redundancy, improving progress assessment accuracy through multiple viewing angles and perspectives, while the ML model efficiently processes this abundant data

Inventive Principle:

Principle #16Partial or excessive action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables precise navigation, documentation, progress tracking, and safety monitoring within construction sites, providing real-time data and enhancing operational efficiency and safety through automated data collection and analysis.

Implementation Method 1

The machine vision system may include one or more of: a LIDAR system; and a plurality of discrete machine vision cameras.

Methodology Applied

Scientific EffectLIDAR: LIDAR

Data Source

PatentUS20230324922A1Autonomous Robotic Platform

Publication Date: 2023.10.12 DIDGE NX INC D B A NEXTERA ROBOTICS

AI summary

A computer-implemented method, computer program product and computing system for: navigating an autonomous mobile robot (AMR) within a defined space; acquiring imagery at one or more defined locations within the defined space; processing the imagery using an ML model to define a completion percentage for the one or more defined locations within the defined space; and reporting the completion percentage of the one or more defined locations within the defined space to a user.