Autonomous Vacuum Path Tracking for Adaptive Suction Control
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Solution Overview
Problem
Existing autonomous vacuum cleaners inefficiently clean areas as they frequently overlap paths, leading to incomplete cleaning and excessive energy consumption, as they lack a system to dynamically adjust suction capacity based on dust levels and travel history.
Innovation Solution
A tracking system that includes a position determination device to detect position data, a storage device to store travel paths and dust thresholds, and a micromechanical device to adjust suction capacity, allowing the vacuum cleaner to optimize its path and energy usage by varying suction based on previously cleaned areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the autonomous vacuum cleaner frequently overlaps paths to clean areas, then the degree of cleanliness is increased, but the energy consumption increases and the cleaning time is extended
Solution Approach 1:
The system performs preliminary cleaning actions and stores the cleaning status in memory. When re-visiting areas, it checks the stored threshold values to determine if additional cleaning is needed, avoiding redundant cleaning operations and reducing energy consumption.
Solution Approach 2:
The system uses feedback from stored threshold values of sucked particles to dynamically adjust suction capacity. By monitoring cleaning results and comparing against stored thresholds, the vacuum cleaner optimizes its cleaning strategy for subsequent passes through the same area.
2Productivity
If the autonomous vacuum cleaner operates with constant suction capacity, then it can clean the predetermined area systematically, but the operational time is extended due to repeated cleaning of already cleaned areas
Solution Approach 1:
The system dynamically adjusts the suction capacity based on stored threshold values from previous cleaning operations. Instead of maintaining constant suction, the vacuum cleaner adapts its cleaning intensity to the actual dust levels detected in previously cleaned areas, optimizing both productivity and operational time.
Solution Approach 2:
The system changes the operational parameters (suction capacity) based on stored cleaning data. By modifying the suction threshold dynamically according to stored particle concentration data, the system achieves faster cleaning completion without compromising cleanliness standards.
3Use of energy by moving object
If the autonomous vacuum cleaner stops cleaning after predetermined time, then energy is conserved, but dusty zones are not sufficiently cleaned
Solution Approach 1:
The system performs preliminary cleaning and stores the cleaning status and particle threshold values in memory. This preliminary action enables intelligent decision-making for subsequent cleaning cycles, allowing the system to target only areas that require additional cleaning while conserving energy in already clean zones.
Solution Approach 2:
The system uses feedback from stored cleaning data to determine when to continue or stop cleaning operations. By monitoring whether stored threshold values indicate sufficient cleaning, the system can stop operations at optimal points, ensuring both energy conservation and cleaning completeness.
4Area of stationary object
If the autonomous vacuum cleaner uses random path travel, then it can cover large areas, but the cleaning coverage is inefficient with frequent path overlaps
Solution Approach 1:
The system performs preliminary path planning and cleaning operations, storing the traveled paths and cleaning status. This preliminary action enables subsequent passes to be optimized based on stored data, reducing redundant overlaps and improving overall cleaning efficiency while maintaining comprehensive coverage.
Solution Approach 2:
The system uses feedback from stored path and cleaning data to optimize future cleaning routes. By analyzing where cleaning has already occurred and where threshold values indicate insufficient cleaning, the system can plan more efficient paths that maximize coverage while minimizing redundant overlaps.
Data Source
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AI summary
The present invention provides a tracking system (T1). The tracking system (T1) for operating an autonomous vacuum cleaner (X1) comprises a position determination device (10) configured to detect position data (101, 101 a, 101b, 101c, 101d, 101x, 101 y) of the autonomous vacuum cleaner (X1) within a spatially defined area (A1). The tracking system further comprises a storage device (11) configured to store a traveled path (102, 201, 301) of the autonomous vacuum cleaner (X1) based on the determined position data (101, 101 a, 101b, 101c, 101d, 101x, 101y) within the spatially defined area (A1). The storage device (11) is further configured to store at least one threshold value of sucked particles (P1) along the traveled path (102, 201, 301). Finally, the tracking system (T1) is further configured to adjust a sucking capacity of the autonomous vacuum cleaner (X1) depending on the at least one stored threshold value in case that the autonomous vacuum cleaner (X1) at least partially overlaps the traveled path (102, 201, 301). The present invention further provides a corresponding method.