Autonomous Machine Trust Table for Sensor Data Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous machines in collaborative groups face challenges in determining the reliability of other machines, which affects the accuracy and efficiency of their joint tasks.
Innovation Solution
Implementing a trust table system where autonomous machines compare their observed actions with expected actions, calculate accuracy scores, and store trust scores to determine the reliability of other machines within the group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous machines communicate sensor measurements to each other in a collaborative group, then task completion efficiency is improved, but reliability of information exchange deteriorates when sensor failures occur
Solution Approach 1:
The system implements a feedback mechanism where autonomous machines share sensor measurements with the group, and other machines verify these measurements against their own sensors. When discrepancies are detected, the system feeds back trust score adjustments to the offending machine, creating a self-regulating information exchange system that maintains reliability while enabling collaborative productivity.
Solution Approach 2:
The patent introduces trust scores as an intermediary mechanism that mediates information exchange between autonomous machines. Instead of directly accepting or rejecting sensor data, machines use trust scores as an intermediate evaluation layer that quantifies reliability, allowing the system to balance information sharing with verification needs.
2Reliability
If autonomous machines verify reliability of other machines through sensor comparison, then information exchange reliability is improved, but system complexity increases
Solution Approach 1:
The system transforms the complex verification process into a simplified parameter-based evaluation by introducing trust scores. Instead of complex continuous verification of all sensor data, machines adjust discrete trust score parameters based on measured discrepancies, reducing computational complexity while maintaining reliability monitoring.
Solution Approach 2:
Each autonomous machine independently calculates and maintains its own trust scores for other machines based on sensor comparison data it collects. This self-service approach distributes the verification complexity across all machines rather than requiring a centralized verification system, reducing overall system complexity while maintaining comprehensive reliability monitoring.
3Manufacturing precision
If autonomous machines use trust scores to filter sensor data, then task accuracy is improved, but information loss increases when reliable machines are excluded
Solution Approach 1:
The system applies partial filtering by using trust scores to weight or selectively exclude sensor data based on reliability thresholds. Rather than completely excluding data from machines with reduced trust scores, the system applies partial filtering that maintains data from machines above thresholds while excluding only clearly unreliable data, balancing accuracy improvement with information retention.
Data Source
AI summary
A device including a processor configured to detect an environment of an automated machine, wherein the environment comprises one or more further automated machines; determine an action taken by the one or more further automated machines; determine an action expected of the one or more further automated machines; compares the taken action with the expected action; determine an accuracy score associated with the one or more further automated machines based on the comparison.