Robot Vacuum Route Planning for Quick and Precise Cleaning
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Solution Overview
Problem
Current robot vacuum cleaners lack an efficient method to plan cleaning routes that adapt to different cleaning modes and environmental changes, leading to suboptimal cleaning performance and time consumption.
Innovation Solution
A method where a robot vacuum cleaner divides indoor spaces into cleanable regions based on an indoor space map and adjusts cleaning routes according to a selected mode (quick or precise) by minimizing or maximizing direction changes, using sensors and AI to dynamically modify routes based on environmental factors and floor types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot vacuum cleaner uses a fixed cleaning route without adaptation to different cleaning modes, then the device complexity is reduced, but the cleaning efficiency and time consumption are suboptimal
Solution Approach 1:
The cleaning route is made dynamic by adapting it to different cleaning modes (quick mode and precise mode). The processor dynamically adjusts the route planning algorithm based on the selected mode, allowing the system to optimize between cleaning speed and coverage completeness without requiring multiple fixed route systems.
Solution Approach 2:
The indoor space is divided into multiple cleanable regions, and the cleaning route is segmented into multiple sub-routes corresponding to different regions. This segmentation allows the system to apply different routing strategies to different areas based on the cleaning mode, improving overall efficiency without overwhelming complexity.
2Loss of time
If the robot vacuum cleaner minimizes direction changes in quick mode, then cleaning time is reduced, but cleaning thoroughness may be compromised
Solution Approach 1:
The system changes the routing parameters based on the cleaning mode. In quick mode, the parameter for number of direction changes is minimized to reduce cleaning time. In precise mode, the same parameter is increased to ensure thorough coverage. This parameter adjustment allows the system to optimize for different priorities without compromising either mode's effectiveness.
3Manufacturing precision
If the robot vacuum cleaner maximizes direction changes in precise mode, then cleaning coverage is improved, but cleaning time increases
Solution Approach 1:
The routing parameters are adjusted based on cleaning mode requirements. For precise mode, the system increases the number of direction changes to maximize coverage, accepting the trade-off of increased cleaning time. This parameter adaptation allows the system to deliver comprehensive cleaning when needed while offering faster alternatives when time is constrained.
4Measurement precision
If the robot vacuum cleaner uses sensor-based recognition technology, then environmental recognition accuracy is improved, but the device complexity and cost increase
Solution Approach 1:
The sensor system is designed to perform multiple functions: generating indoor space maps, detecting obstacles, recognizing cleaning modes, and providing navigation data. This multi-functionality reduces the need for separate specialized sensors, thereby limiting complexity growth while maintaining high environmental recognition accuracy through unified sensor utilization.
Data Source
AI summary
A method, performed by a robot vacuum cleaner, of planning a cleaning route includes: dividing an indoor space into at least one cleanable region based on an indoor space map generated using at least one sensor included in the robot vacuum cleaner; dividing the at least one cleanable region into a plurality of partial regions based on a cleaning mode of the robot vacuum cleaner; and planning a first cleaning route to control a number of direction changes of the robot vacuum cleaner with respect to each of the plurality of partial regions based on the cleaning mode being a first mode.