Multi-Robot Task Region Distribution Using Concave-Corner Segmentation
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Solution Overview
Problem
Existing cleaning devices, such as sweeping robots, face inefficiencies due to the existing method of dividing cleaning regions into fixed-sized areas, leading to unnecessary turning actions and increased cleaning time in non-rectangular home environments.
Innovation Solution
A method and system for distributing task regions by acquiring a task map, dividing it into sub-regions based on concave corners, combining adjacent sub-regions with equal sides, and calculating cleaning times to optimize region distribution among multiple cleaning devices, ensuring each device performs tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the cleaning region is divided into fixed-sized task regions, then the task allocation is simple, but the cleaning device performs too many unnecessary turning actions and cleaning time increases
Solution Approach 1:
The cleaning region is divided into multiple sub-regions based on concave corners of the environment map, creating natural segmentation points that reduce unnecessary turns. Each sub-region is then assigned to cleaning devices, optimizing the path planning by aligning task boundaries with environmental features rather than using fixed-size divisions.
2Ease of manufacture
If the cleaning region is divided into fixed-sized task regions, then the region division is simple, but the number of turning actions increases and cleaning efficiency decreases
Solution Approach 1:
The environment map is segmented at concave corners to create task sub-regions, which naturally align with environmental features. This segmentation approach maintains relative simplicity while significantly reducing turning actions and improving cleaning efficiency compared to fixed-size divisions.
Solution Approach 2:
Different regions are treated differently based on their geometric properties. Concave corners are identified as natural division points, and task regions are configured to align with these local features, optimizing the cleaning path for each specific region rather than applying a uniform fixed-size division.
3Productivity
If multiple cleaning devices are used, then the cleaning coverage increases, but the task distribution must be optimized to minimize average cleaning time
Solution Approach 1:
The cleaning region is segmented into multiple sub-regions based on concave corners, which can then be distributed among multiple cleaning devices. This segmentation enables parallel cleaning operations while optimizing the assignment to minimize the average completion time across all devices.
Solution Approach 2:
The task distribution algorithm calculates and compares different assignment scenarios, selecting the configuration that minimizes average cleaning time. By dynamically adjusting which sub-region is assigned to which device based on calculated metrics, the system optimizes multi-device coordination.
Data Source
AI summary
A method of distributing task regions for a plurality of cleaning devices, including: dividing a task map into a plurality of basic sub-regions according to concave corners corresponding to the shape of the task map; combining each two adjacent basic sub-regions, and calculating basic cleaning time corresponding to each of the combined basic sub-regions; repeatedly combining each two adjacent basic sub-regions according to the basic cleaning time, and obtaining a basic partition result; selecting starting blocks according to positions of the plurality of task sub-regions in the basic partitioning result; combining the task sub-regions according to the position of each starting block, the position of each task sub-region, and the cleaning time corresponding to each task sub-region, and obtaining the task region distribution result; enabling cleaning devices to perform cleaning tasks according to the position of each cleaning device and the task region distribution result.