AI Robot Navigation for Data Quality Feedback Control

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Solution Overview

Problem

Existing autonomous roaming robotic devices (REDs) face challenges in collecting high-quality data due to dynamic operational environments, sensor positioning errors, and the inability to evaluate data quality in real-time, leading to suboptimal decision-making in AI pipelines.

Innovation Solution

A computer-implemented method using AI to analyze data quality, adjust navigation, and update policies for robotic devices to improve data collection by comparing vector representations to a model, and initiating actions such as moving to new locations or adjusting sensors when data quality falls below a threshold.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a robotic device navigates autonomously to collect data, then mobility and versatility are improved, but positioning and orientation errors increase leading to reduced measurement precision

Engineering Contradiction:

ImprovemobilityVSAvoidpositioning and orientation accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system continuously receives feedback about data quality metrics (positioning errors, orientation accuracy, signal-to-noise ratio) and uses this feedback to adjust navigation actions. The control system monitors measurement precision in real-time and modifies the robotic device's movement to maintain acceptable error thresholds, resolving the contradiction between mobility and measurement accuracy.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The navigation policy is made dynamic rather than static. The system adapts navigation parameters (speed, position, orientation) in real-time based on current environmental conditions and data quality requirements. This allows the robotic device to maintain measurement precision while moving through the environment, rather than being constrained to fixed positions.

Inventive Principle:

Principle #15Dynamics

2Productivity

If the robotic device collects more data to improve decision-making quality, then productivity increases, but energy consumption increases due to continuous navigation and data collection

Engineering Contradiction:

Improvedata collection qualityVSAvoidenergy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system collects data selectively rather than continuously. It performs partial actions (navigation moves, sensor activations) only when necessary to improve data quality metrics. The control system evaluates whether additional data collection is needed based on current quality thresholds, avoiding unnecessary energy expenditure while maintaining productivity.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The robotic device autonomously determines when and where to collect data based on quality metrics without requiring continuous human intervention or energy-intensive monitoring. The system self-regulates its data collection activities to optimize the balance between productivity and energy consumption.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If fixed sensors are embedded in the environment, then measurement precision is improved, but adaptability to different locations and dynamic environments decreases

Engineering Contradiction:

Improvesensing accuracyVSAvoidenvironmental flexibility

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The robotic device serves as a universal sensing platform that can be deployed to multiple locations and adapt to different environmental conditions. Rather than having fixed sensors for each location, one mobile robotic device with adjustable sensors can perform the function of multiple fixed sensors, maintaining measurement precision while providing environmental flexibility.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If raw data is shared with edge or cloud servers for quality evaluation, then measurement precision improves, but communication costs and latency increase

Engineering Contradiction:

Improvedata quality evaluation accuracyVSAvoidcommunication latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data quality evaluation using the trained machine learning model before transmitting raw data to edge or cloud servers. This preliminary assessment allows the system to filter out low-quality data locally, reducing the amount of data that needs to be transmitted and the associated latency and communication costs.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12564964B2Adaptive navigation using artificial intelligence for enhancing task performance in autonomous roaming robotic devices

Publication Date: 2026.03.03 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Optimizing sensing capabilities of a roaming robotic device using Artificial Intelligence (AI) includes receiving data at a control system having a computer from a robotic device. The control system communicating a policy to the robotic device for choosing navigation actions for the robotic device. The received data is analyzed using the control system for determining when the received data meets a threshold for determining quality of the data. The analysis can include generating a model based on the received data where the model includes vector representation of inputs detected by a sensor array at the location. In response to the received data at the control system not meeting the threshold for determining quality, the robotic device communicating with the control system to collaborate in updating the policy to choose a next action.