Sensing Robot Map Rectification for Outdated Environment Models
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Solution Overview
Problem
Unmanned robots face challenges in navigating and collecting data due to incomplete and outdated map data, as changes in the environment, such as moved obstacles or new objects, are not accurately reflected in their maps, leading to inefficient data collection and navigation.
Innovation Solution
A system that decomposes a stored map into polygonal sub-areas using a leaky ball algorithm to generate target locations and identifies navigable paths, allowing the robot to move autonomously and collect data while updating the digital model based on real-time sensor data, thereby addressing the inaccuracies in the map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot uses stored map data for navigation and data collection, then the operation can proceed with existing information, but the map data becomes incomplete and outdated when environmental changes occur
Solution Approach 1:
The system continuously collects sensor data during robot navigation and feeds this information back to update the digital model of the environment. This feedback loop ensures the map remains current by incorporating real-time observations of environmental changes, resolving the contradiction between using stored map data and maintaining its accuracy.
Solution Approach 2:
The system performs preliminary decomposition of the map into polygonal sub-areas and pre-calculates target locations using the leaky ball algorithm before the robot begins navigation. This preliminary preparation enables efficient real-time updates without compromising navigation performance, allowing the system to maintain reliable map data while adapting to environmental changes.
2Adaptability or versatility
If the robot navigates to obtain data based on changed or inaccurate map data, then the robot can adapt to environmental changes, but the navigation efficiency and data collection accuracy deteriorate
Solution Approach 1:
The map is decomposed into multiple polygonal sub-areas, and target locations are generated for each sub-area independently. This segmentation allows the robot to efficiently navigate through relevant regions without traversing the entire map, maintaining productivity while adapting to environmental changes through selective data collection in updated areas.
3Loss of information
If the robot traverses the entire area to ensure complete data collection, then all areas are covered, but the distance traveled and time required increase significantly
Solution Approach 1:
Target locations are pre-calculated using the leaky ball algorithm applied to each polygonal sub-area before the robot begins navigation. This preliminary action identifies optimal data collection points that ensure complete coverage of all areas while minimizing the total distance the robot must travel, resolving the contradiction between comprehensive data collection and navigation efficiency.
Data Source
AI summary
A system includes a computer that includes a processor and a memory storing instructions executable by the processor to decompose a stored map of an area into a plurality of polygonal sub-areas. The instructions include instructions to generate a set of target locations in the polygonal sub-areas according to a leaky ball algorithm. The instructions include instructions to actuate a robot to move to a first one of the target locations of the set. The instructions include instructions to actuate a sensor to collect data at the first one of the target locations.