Robot Path Planning With Raster Mapping for Full Room Coverage
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Solution Overview
Problem
Current path planning methods for household sweeping robots face challenges in building accurate maps, navigation, and obstacle avoidance, particularly in ensuring complete room coverage without environmental mapping and precise positioning.
Innovation Solution
A low-cost path planning method that divides the operating space into rectangular virtual regions, using sensors to build a raster map through rectilinear-shaped behavior, marking pass, obstacle, and collision points, and adjusting the path to ensure thorough traversal and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If random traversal method is used, then positioning and environmental mapping are not needed, but coverage efficiency and cleaning speed are reduced
Solution Approach 1:
The patent segments the operating space into multiple virtual regions (e.g., 5x5 grid) and divides the cleaning task into region-by-region traversal. This allows the robot to systematically cover each region while maintaining low system complexity, achieving both efficient coverage and simplified positioning requirements.
Solution Approach 2:
The patent performs preliminary actions by pre-dividing the space into virtual regions and pre-planning traversal sequences before actual cleaning. The robot builds a simplified raster map during traversal and uses pre-defined region boundaries to guide movement, enabling efficient coverage without complex real-time positioning systems.
2Productivity
If planning traversal with environmental mapping is used, then cleaning efficiency is improved, but system complexity and cost increase
Solution Approach 1:
The patent uses a simplified, low-cost raster map representation instead of complex environmental maps. The map is built incrementally during traversal using basic sensor data (distance, angle, collision, obstacle information) and is discarded or updated continuously, avoiding the need for expensive, high-precision positioning and mapping systems while maintaining cleaning efficiency.
Solution Approach 2:
The patent changes the representation parameters from complex continuous environmental maps to discrete raster grid cells with simple attributes (visited, obstacle, collision). This parameter simplification reduces system complexity while preserving the ability to plan efficient traversal paths and achieve complete coverage.
3Ease of operation
If rectilinear-shaped behavior is used for traversal, then path planning simplicity is improved, but coverage completeness may be compromised
Solution Approach 1:
The patent makes the rectilinear traversal dynamic by allowing real-time adjustments based on detected obstacles and region boundaries. The robot dynamically modifies its path within each virtual region while maintaining the overall rectilinear structure, ensuring complete coverage without sacrificing the simplicity of the base path planning approach.
Solution Approach 2:
The patent incorporates feedback mechanisms where the robot continuously monitors obstacle detection, collision information, and region traversal progress. This feedback allows the robot to adjust its rectilinear path in real-time, backtrack when necessary, and ensure complete coverage of all virtual regions while maintaining the simplicity of rectilinear motion patterns.
Data Source
AI summary
A path planning method of an intelligent robot includes: (1) dividing the operating space into a plurality of rectangular virtual regions having areas of n*m square meters; (2) controlling the intelligent robot to traverse each rectangular virtual region with bow-shaped behavior; (3) when moving in bow-shape, building a raster map with the distance information acquired by distance information sensor, angle information acquired by angle information sensor, collision information acquired by collision detection sensor and obstacle information acquired by obstacle detection sensor, and marking the pass points, obstacle points and collision points; (4) analyzing the raster map in real time to judge if the entire operating space has been fully traversed: if so, moving to step (5); otherwise, returning to step (2); (5) controlling the intelligent robot to make an alongst behavior, and marking the alongst behavior points on the raster map.