Mobile Robot Self-Position Updating for Changing Map Conditions
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Solution Overview
Problem
Existing self-position estimation techniques for mobile robots struggle to maintain accuracy in changing environments, leading to degraded matching accuracy and consistency with environmental maps.
Innovation Solution
An information processing device that includes a detection section for acquiring distance and direction information from objects around the mobile robot, and a control section with a self-position updater that evaluates the reliability of self-positions estimated from both existing and current maps, and updates these positions accordingly to maintain consistency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If self-position estimation is performed using only an existing map prepared in advance, then the positioning process is simple, but the accuracy of self-position estimation degrades when the environment changes
Solution Approach 1:
The system dynamically switches between using an existing map and a current map based on environmental conditions. When environmental changes are detected, the system transitions from static existing map-based positioning to dynamic current map-based positioning, allowing the positioning approach to adapt to changing conditions while maintaining accuracy without permanently increasing system complexity
Solution Approach 2:
The system changes the map parameter state from static (existing map) to dynamic (current map) based on environmental change detection. This parameter change allows the system to maintain positioning accuracy in changing environments by updating the map data, while only activating the more complex current map mechanism when necessary
2Measurement precision
If self-position estimation is performed using both existing map and current map, then the accuracy of self-position estimation is maintained in changing environments, but the system complexity increases
Solution Approach 1:
The system implements dynamic map management where the current map is generated and maintained only when environmental changes are detected. This dynamic approach ensures high positioning accuracy in changing environments while avoiding the continuous overhead of maintaining dual map systems, thus managing complexity adaptively rather than statically
Solution Approach 2:
The system extracts and uses only the necessary map type (current map) when environmental changes are detected, rather than continuously maintaining both existing and current maps. This extraction principle reduces system complexity by activating the more complex current map mechanism only when its benefits are needed for accurate positioning
3Productivity
If matching is performed between detection information and existing map, then the positioning process is efficient, but matching accuracy degrades when the environment has changed
Solution Approach 1:
The system dynamically selects the map type for matching based on environmental conditions. When environmental changes are detected, it switches from efficient existing map matching to current map matching, ensuring accurate matching in changing environments while maintaining efficiency by using the simpler existing map when conditions are stable
Solution Approach 2:
The system changes the map data parameter from static existing map data to dynamic current map data when environmental changes occur. This parameter change ensures that matching is performed against up-to-date environmental information, maintaining matching accuracy while only incurring the additional processing cost when environmental changes are detected
Data Source
Figure 1~2
Figure 3(A)~3(C)
Figure 4(A)~4(C)
AI summary
Provided are an information processing device and a mobile robot capable of ensuring the accuracy of estimation of a self-position even with an environmental change and maintaining the consistency of the self-position on a map. The information processing device of the mobile robot (1) includes a control section (2) and a detection section (3) configured to detect a distance to a peripheral object and the direction thereof as detection information. The control section (2) includes a storage (28), a map producer (23) configured to produce a peripheral map, an existing map self-position estimator (24) configured to estimate a current self-position based on an existing map, a current map self-position estimator (25) configured to estimate the current self-position based on a current map, a reliability evaluator (26) configured to evaluate the reliability of each of the estimated self-positions, and a self-position updater (27) configured to update either one of the self-positions based on the reliability.