Robot Environment Mapping for Shelf Height Prohibited Areas
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Solution Overview
Problem
Existing reading systems for autonomous robots equipped with RFID tag readers face challenges in accurately determining prohibited areas, particularly in environments with shelves of varying heights, leading to potential collisions due to incomplete or incorrect environment mapping.
Innovation Solution
The system generates a superimposed image by combining detection data from different height ranges to create a comprehensive environment map, allowing for precise setting of prohibited areas and safe navigation paths for the autonomous robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LRF fixed at a predetermined height is used to generate environment map, then position estimation can be performed, but prohibited areas cannot be correctly set for shelves with varying heights
Solution Approach 1:
The patent transitions from two-dimensional environment mapping (horizontal plane only) to three-dimensional mapping by incorporating vertical height information. Multiple LRFs are positioned at different heights to capture vertical variations in shelf structures, enabling accurate prohibited area identification for shelves of varying heights while maintaining position estimation capability.
Solution Approach 2:
The environment mapping function is segmented across multiple LRF devices positioned at different vertical heights. Each LRF captures detection data for its specific height range, and the system integrates these segmented measurements to construct a comprehensive three-dimensional environment map that accurately represents shelves with varying heights.
2Device complexity
If single-height LRF detection is used, then device complexity is reduced, but collision risk increases due to incorrect prohibited area setting
Solution Approach 1:
The system adds vertical dimension detection by deploying LRFs at multiple heights, transforming the detection capability from two-dimensional to three-dimensional. This enables accurate identification of prohibited areas for shelves of varying heights, significantly improving collision avoidance reliability while maintaining relatively simple device architecture.
Solution Approach 2:
Multiple LRF detection results from different heights are merged and integrated to construct a comprehensive three-dimensional environment map. This combination of detection data from multiple sources enables accurate prohibited area identification and collision avoidance without requiring complex individual sensors.
3Ease of manufacture
If environment map is generated without vertical height consideration, then processing is simplified, but prohibited area determination becomes inaccurate
Solution Approach 1:
The environment map generation process is extended from two-dimensional to three-dimensional by incorporating vertical height information. Detection data from LRFs at multiple heights are integrated to create a comprehensive 3D environment map, enabling precise prohibited area determination for shelves of varying heights while maintaining systematic and organized processing.
Solution Approach 2:
The environment map generation is segmented into multiple height-range detections, with each LRF capturing data for its specific vertical range. These segmented measurements are then systematically integrated to construct the complete three-dimensional environment map, ensuring both processing organization and measurement precision.
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 enables accurate identification of prohibited areas and safe navigation paths, reducing the risk of collisions and ensuring efficient RFID tag reading operations in complex retail environments with varying shelf structures.
Implementation Method 1
the reading system uses an LRF (Laser Range Finder) fixed at a predetermined height of the autonomous robot to generate an environment map by scanning the surrounding environment
Data Source
AI summary
An information processing apparatus includes a processor configured to acquire first detection data for a first height range in a predetermined region. The processor generates a first environment map based on the first detection data and acquires second detection data for a second height range greater than the first height range in the predetermined region. The processor further acquires third detection data for a third height range included in the second height range and then superimposes a projected image of the third detection data on the first environment map to generate a superimposed image. The processor sets a prohibited area for an autonomous, mobile robot in the first environment map based on the superimposed image.