Autonomous Robot Map Management for Multi-Floor Repositioning
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Solution Overview
Problem
Autonomous mobile robots face inefficiencies and inaccuracies in navigation and task execution due to the need to repeatedly compile temporary maps in changing environments, particularly in multi-story households, where they struggle with global self-localization and managing multiple maps.
Innovation Solution
The method involves permanently storing and managing maps, using a working copy for navigation while updating with sensor data, and employing SLAM to adapt to environmental changes, with features like air pressure analysis for altitude detection and movable object recognition to refine self-localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If permanently stored maps are used to improve navigation efficiency, then the need for repeated mapping is reduced, but the complexity of managing multiple maps and performing global self-localization increases
Solution Approach 1:
The patent segments the map management system into multiple components: a map storage unit that maintains permanently stored maps, a map selection unit that chooses the appropriate map based on sensor data, and a map updating unit that updates the working map. This segmentation reduces the complexity of managing multiple maps by dividing the task into manageable modules.
Solution Approach 2:
The patent performs preliminary actions by pre-storing multiple maps of different areas before the robot needs to navigate. The map storage unit maintains these maps in advance, and the map selection unit prepares to select the appropriate map based on predicted robot location, reducing the need for real-time mapping and improving navigation efficiency.
2Measurement precision
If global self-localization is performed to determine robot position on stored maps, then navigation accuracy is improved, but the time required for task execution is increased
Solution Approach 1:
Instead of performing complete global self-localization which compares the entire map with sensor data, the patent applies partial action by using the map selection unit to first identify the relevant area based on predicted robot location, then performing self-localization only in that specific area. This reduces the computational time while maintaining sufficient accuracy for navigation.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing map data in a structured format that facilitates quick comparison during self-localization. The map selection unit prepares candidate maps in advance, allowing the self-localization algorithm to work with pre-filtered data rather than entire maps, thus reducing computation time.
3Reliability
If the robot adapts to environmental changes by updating maps, then the reliability of navigation in dynamic environments is improved, but the complexity of distinguishing movable objects from permanent structures increases
Solution Approach 1:
The patent implements feedback mechanisms where the robot continuously compares sensor data with the stored map during navigation. When discrepancies are detected, the system analyzes whether the change is due to a movable object or a permanent structure by referencing the predicted robot location and comparing with historical map data. This feedback loop improves navigation reliability by adapting to environmental changes while maintaining the ability to distinguish between temporary and permanent features.
4Adaptability or versatility
If temporary maps are compiled for each work sequence, then the adaptability to changing environments is maintained, but the productivity of the robot is reduced due to repeated mapping
Solution Approach 1:
The patent merges the advantages of temporary and permanent maps by combining a map storage unit that maintains permanently stored maps with a map updating unit that creates and maintains a working map during each work sequence. The working map is initialized from the permanently stored map and updated with current sensor data, allowing the robot to benefit from pre-existing map information while still adapting to environmental changes. This merging approach eliminates the need to compile maps from scratch for each work sequence, thereby improving productivity while maintaining adaptability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the efficiency and reliability of autonomous robots by reducing the need for repeated mapping, improving navigation in dynamic environments, and ensuring accurate task execution across different levels and areas.
Implementation Method 1
air pressure analysis for altitude detection
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.