Autonomous Moving Machine Self-Location Reliability Recovery
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Solution Overview
Problem
Autonomous moving machines, such as drones, face challenges in maintaining reliable self-location estimation when environmental changes or sensor discrepancies occur, especially on ground surfaces where altitude adjustments are impractical, leading to failure in reaching destinations and performing operations.
Innovation Solution
The autonomous moving machine employs sensors, a processor, and a memory with a computer program that continuously estimates self-location, calculates reliability, and moves to positions with high reliability based on recorded data, ensuring stable self-location estimation through a reliability-recovering-action controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous moving machine uses sensor information to estimate self-location, then the machine can obtain location data, but the reliability of the estimated self-location becomes unstable when environmental changes or sensor discrepancies occur
Solution Approach 1:
The system continuously monitors the reliability of self-location estimation and uses this feedback to trigger recovery actions. When reliability drops below a threshold, the machine returns to a previously visited position with higher reliability, creating a closed-loop control system that maintains location accuracy despite environmental changes or sensor discrepancies.
Solution Approach 2:
The system pre-stores position information and reliability data from previously visited locations. When reliability degradation is detected, instead of attempting to estimate location again under poor conditions, the machine retrieves pre-recorded high-reliability position data and returns to that position, preparing a fallback solution in advance.
2Reliability
If the autonomous moving machine returns to a position with high reliability when self-location reliability decreases, then the machine can restore accurate location estimation, but the time required to reach the destination increases
Solution Approach 1:
Instead of returning to the very first high-reliability position, the system selects an appropriate recovery position based on the current situation and destination. It may choose a position that is not the absolute best in terms of reliability but is optimally positioned for continuing toward the destination, avoiding excessive detour time while still restoring sufficient location accuracy.
Solution Approach 2:
The system dynamically selects recovery positions based on real-time conditions, including the current location, destination, and available high-reliability positions in the stored map. The recovery strategy adapts to the specific situation rather than following a fixed protocol, optimizing the balance between reliability restoration and time efficiency.
Data Source
AI summary
An aspect of the present disclosure relates to an autonomous moving machine which may maintain the self-location with high reliability. Operations of the moving machine include acquiring sensor information about the self-location, estimating the self-location based on the sensor information, calculating the reliability of the estimated self-location, recording the reliability in association with the estimated self-location. When the reliability satisfies a given condition, the operations include moving the moving machine to a position at which the reliability is high, based on the reliability.