Floor Treatment Route Planning With Boundary-Based Area Mapping
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Solution Overview
Problem
Existing automatic floor treatment machines face inefficiencies in navigation and data storage due to the need for precise mapping and complex data processing, especially in dynamic environments with movable obstacles, leading to high operational effort and limited use in variable floor areas.
Innovation Solution
A floor treatment machine that determines a treatment direction and drives along perpendicular lines, using sensors like laser scanners, ultrasonic, and infrared sensors to detect obstacles and boundaries, allowing for efficient route planning and minimal data storage by treating the boundary as an obstacle and recording path segments with start and end points and treatment status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If precise mapping and grid-based route planning are used, then cleaning completeness is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent segments the cleaning area into simple rectangular zones defined by walls and fixed elements, rather than using fine-grained grids. Each zone is represented by minimal boundary data (wall positions), dramatically reducing storage requirements while maintaining complete coverage through systematic zone-by-zone cleaning patterns.
Solution Approach 2:
Instead of creating a detailed map of the entire floor area and planning routes based on that map, the patent inverts the approach by using wall and fixed element boundaries to directly define cleaning zones. The machine navigates by following these boundaries rather than referencing a comprehensive stored map, reducing data storage needs.
2Measurement precision
If grid-based mapping with high resolution is used, then position determination accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the environment into discrete rectangular zones bounded by walls and fixed elements. Position determination is simplified by identifying which zone the machine is in and its position relative to zone boundaries, rather than calculating coordinates on a fine grid, significantly reducing computational complexity.
Solution Approach 2:
The patent creates a simplified topological representation of the environment that copies only the essential boundary information (walls and fixed elements) rather than a complete geometric map. This simplified model is sufficient for route planning and position determination, reducing processing requirements.
3Quantity of substance
If random navigation without mapping is used, then data storage requirements are reduced, but cleaning efficiency and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary identification of wall and fixed element boundaries to establish cleaning zone definitions before actual cleaning begins. This preliminary structuring of the environment into zones enables efficient systematic cleaning without requiring continuous complex navigation decisions, improving productivity with minimal data storage.
4Manufacturing precision
If detailed route planning with obstacle avoidance is implemented, then cleaning coverage is improved, but adaptability to movable obstacles decreases
Solution Approach 1:
The patent implements dynamic obstacle handling by allowing the machine to detect and adapt to movable obstacles during cleaning operations. When obstacles are detected, the system dynamically adjusts the cleaning path within the current zone without requiring complete re-planning, maintaining both coverage and 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 enables efficient and adaptive floor treatment with reduced data storage and processing requirements, allowing the machine to effectively cover entire areas with variably positioned obstacles, optimizing cleaning routes and minimizing redundant cleaning.
Implementation Method 1
at least one scan sensor (8), which makes it possible to carry out distance measurements in a substantially horizontal plane over a predetermined angular range
Implementation Method 2
using sensors like laser scanners, ultrasonic, and infrared sensors to detect obstacles and boundaries
Implementation Method 3
using sensors like laser scanners, ultrasonic, and infrared sensors to detect obstacles and boundaries
Data Source
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Figure 3~5
Figure 6
AI summary
A floor treatment machine (1) for treating floor surfaces comprises a housing (2), two drive wheels (3), at least one support wheel (4), a drive device (5), a controller (6), at least one scan sensor (8), which makes it possible to carry out distance measurements in a substantially horizontal plane over a predetermined angular range, and a soil treatment device (9) which makes it possible to treat the soil. The controller (6) has a treatment mode that guarantees a simple and safe selection of a successful route with just a few driving and storage steps. The boundary of the floor area is treated as an obstacle. In order to record the treated area, the start and end points of traveled path segments and the end points of one of the states “completely treated” or “incompletely treated” as well as direction information are stored.