Autonomous Robot Map Management for Multi-Map Self-Localization
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Solution Overview
Problem
Autonomous mobile robots face challenges in efficiently managing permanently stored maps in changing environments and accurately self-localizing across multiple maps, leading to delays and inefficiencies in task execution.
Innovation Solution
The method involves permanently storing and managing maps, generating a working copy for navigation, and continuously updating it using sensor data. The robot determines its position on the map through global self-localization and adapts to changes in the environment by analyzing differences between the stored map and the actual environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If permanently stored maps are used for navigation, then productivity is improved by eliminating repeated exploratory mapping, but device complexity increases due to map management requirements
Solution Approach 1:
The patent segments map management into distinct functional modules: map storage unit for permanent map retention, map selection unit for choosing appropriate maps, and map update unit for maintaining map accuracy. This segmentation allows the system to handle complex map management tasks through specialized, independent components that can operate efficiently without overwhelming the overall system.
Solution Approach 2:
The system performs preliminary actions by pre-storing multiple maps of the environment before the robot begins its tasks. These maps are prepared in advance with various levels of detail and perspectives, allowing the robot to quickly select and use appropriate pre-prepared navigation data rather than exploring and mapping the environment repeatedly during task execution.
2Measurement precision
If global self-localization is performed to determine robot position on stored maps, then measurement precision is improved for navigation accuracy, but loss of time increases due to computation requirements
Solution Approach 1:
The patent applies partial action by performing self-localization computations only when necessary - specifically when the robot needs to determine its position on stored maps or when transitioning between different map regions. The system selectively activates global self-localization based on operational context rather than continuously computing position, thereby reducing unnecessary computation time while maintaining precision when needed.
Solution Approach 2:
The system uses feedback mechanisms where the robot continuously monitors its sensor data and compares it against stored map information. When discrepancies are detected or position uncertainty arises, the system triggers self-localization computations to correct the position estimate. This feedback-driven approach ensures measurement precision is maintained while minimizing computation time by only performing calculations when actually needed.
3Adaptability or versatility
If the robot adapts to changing environments by updating maps, then adaptability is improved for dynamic conditions, but reliability decreases due to potential map errors affecting navigation
Solution Approach 1:
The patent implements local quality by allowing different regions of the map to have different levels of update frequency and detail. When environmental changes are detected, the system updates only the specific local areas where changes occur rather than reconstructing the entire map. This selective updating maintains adaptability to local changes while preserving the reliability of unchanged map regions, preventing error propagation across the entire map.
Solution Approach 2:
The system prepares for potential map errors by maintaining multiple map versions and using confidence metrics to assess map reliability. Before making navigation decisions based on updated maps, the system validates the updates against existing map data and sensor observations. This beforehand cushioning approach allows the robot to adapt to environmental changes while having fallback mechanisms to prevent navigation errors from propagating.
Data Source
AI summary
A method for controlling an autonomous mobile robot. According to one exemplary embodiment, the method comprises the storage and management of at least one map associated with an area of use for the robot and the navigation of the robot through the area of use for the robot, wherein the robot continuously determines its position on the map. The method further comprises the detection of a repositioning procedure, during which the robot carries out a movement that the robot itself cannot control. During this repositioning procedure, the robot detects information about its position and/or its state of motion with the aid of sensors and, based on the detected information, determines an estimated value for its position.