Robotic Vacuum Cleaner Path Planning Using Dust Distribution Data
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Solution Overview
Problem
Robotic vacuum cleaners lack the ability to efficiently plan cleaning operations based on the spatial distribution of dust particles on a floor, often expending unnecessary power on areas with low dust concentrations.
Innovation Solution
A computer-implemented method that receives dust distribution data to plan a cleaning operation for a robotic vacuum cleaner, optimizing suction power, brush rotation speed, and cleaning speed based on the amount and location of dust particles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a robotic vacuum cleaner performs cleaning operations without dust distribution data, then it can clean the entire floor area, but it expends unnecessary power on locations that rarely collect dust
Solution Approach 1:
The system performs preliminary dust detection and mapping before the cleaning operation. A dust detection device scans the floor area ahead of the robotic vacuum cleaner to identify dust-prone locations, allowing the cleaning path to be pre-planned based on actual dust distribution rather than covering the entire area uniformly.
Solution Approach 2:
The cleaning operation is adapted to have different characteristics at different locations. The robotic vacuum cleaner concentrates cleaning resources on areas identified as dust-prone while reducing or skipping cleaning in areas with minimal dust accumulation, creating a non-uniform cleaning strategy matched to local dust conditions.
2Loss of time
If a robotic vacuum cleaner vacuumes the entire floor area uniformly, then it ensures comprehensive cleaning coverage, but it wastes time and energy on low-dust areas
Solution Approach 1:
Dust distribution mapping is performed in advance of the cleaning operation, allowing the system to identify which areas require cleaning attention before the robotic vacuum cleaner begins its work, thus avoiding unnecessary traversal of low-dust areas.
Solution Approach 2:
Instead of applying uniform cleaning action across the entire floor area, the system applies cleaning action selectively only to areas where dust accumulation exceeds certain thresholds, performing partial cleaning that is sufficient for dust-prone areas while omitting low-priority areas.
3Productivity
If a robotic vacuum cleaner uses dust distribution data to plan cleaning operations, then it improves energy efficiency and targets high-dust areas, but it requires additional dust detection and data processing systems
Solution Approach 1:
A dust detection device acts as an intermediary between the floor surface and the robotic vacuum cleaner's cleaning mechanism. This intermediary captures dust distribution information and transmits it to the control system, enabling intelligent cleaning planning without requiring complex sensors within the vacuum cleaner itself.
Solution Approach 2:
The system replaces purely mechanical cleaning approaches with a hybrid system that incorporates optical or electromagnetic dust detection (depending on the specific implementation) to inform cleaning decisions, substituting mechanical trial-and-error cleaning with data-driven cleaning path planning.
Data Source
AI summary
A computer-implemented method of controlling the operation of a robotic vacuum cleaner that includes the steps of: receiving dust distribution data indicating the spatial distribution of dust particles across the floor of a building; and planning a cleaning operation of the floor of the building to be executed by the robotic vacuum cleaner, based on the received dust distribution data. Additional computer-implemented methods relate to planning a cleaning operation manually via a client device, and an alert generation method based on dust distribution data.