Visual robot-based historical map utilization method
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Solution Overview
Problem
Existing vision robots frequently rebuild or update maps during traversal, leading to inefficiencies and misalignment of physical positions and directions, making it difficult to utilize pre-built historical maps effectively for path planning.
Innovation Solution
A method that repositions the coordinate and direction of a historical map using a vision system, allowing independent processing of current and historical maps to maintain the effectiveness of the historical map, utilizing a transformation relationship formula for alignment and updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot frequently rebuilds or updates maps during traversal, then the map accuracy is improved, but the historical map effectiveness is lost and path planning intelligence is reduced
Solution Approach 1:
The system performs preliminary actions by collecting and processing road sign information from both current and historical maps before actual path planning. The transformation relationship formula is pre-calculated to enable quick alignment when historical maps need to be utilized, avoiding the need to rebuild maps during traversal while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism - the transformation relationship formula - that mediates between the current map and historical map. This formula acts as a bridge to align coordinate systems and directions, allowing the robot to utilize historical maps without losing accuracy, thus resolving the contradiction between map updates and historical map effectiveness.
2Productivity
If the robot utilizes pre-built historical maps for path planning, then the working efficiency is improved, but the misalignment between map coordinates and actual physical positions reduces navigation accuracy
Solution Approach 1:
The system changes parameters by calculating a transformation relationship formula that adjusts coordinate systems and directions between historical and current maps. This parameter transformation enables the robot to use historical maps for efficient path planning while maintaining navigation accuracy through mathematical alignment of map data with actual physical positions.
3Reliability
If the map is updated incrementally, then the map remains current, but the original map operations fail and tasks cannot be continued from breakpoints
Solution Approach 1:
The patent employs copying by maintaining both the original historical map and the updated current map in parallel. The transformation relationship formula enables the system to copy and adapt historical map data for new tasks while preserving the ability to continue previous tasks using the original map, thus maintaining both map currentness and task continuity.
Data Source
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AI summary
The disclosure discloses a historical map utilization method based on a vision robot, including that: S1, the vision robot is controlled to continuously collect at least one image of a preset road sign of a preset working area, and road sign information of the preset road sign is obtained; S2, the obtained road sign information is simultaneously transmitted to a first map positioning system and a second map positioning system; S3, the first map positioning system and the second map positioning system are controlled to respectively process corresponding road sign information, thereby obtaining first pose information and second pose information; S4, the first pose information and the second pose information under the same preset road sign are selected, and a transformation relationship formula of the first pose information and the second pose information is calculated; and 5, the historical map is controlled to perform a corresponding transformation operation according to the transformation relationship formula, and subsequent motion control is performed by utilizing a transformed historical map as a reference. The pet teasing control device has a high degree of intelligence, and the whole system has good accuracy and real-time performance.