Vibrating Air Filter Mechanism for Robotic Vacuum Self-Cleaning
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Solution Overview
Problem
Autonomous robots face challenges in efficiently mapping and navigating complex environments due to the computational burden of traditional SLAM techniques, which require extensive data collection and perimeter tracing, making them unsuitable for service-oriented tasks like robotic vacuums that need to operate quickly and effectively.
Innovation Solution
The Light Weight Real Time SLAM Navigational Stack reduces computational load, allowing for faster mapping and navigation by using a Microcontroller Unit with a built-in 300 MHz clock, 8 MB RAM, and 2 MB flash memory, enabling real-time processing and efficient data collection with a 360-degree LIDAR and limited Field of View depth camera, and autonomously learning calibration of gyroscope and IMU wheel parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM techniques are used for mapping and navigation, then mapping accuracy is improved, but computational burden increases and operation speed decreases
Solution Approach 1:
The patent extracts and removes unnecessary computational steps from traditional SLAM algorithms, keeping only the essential functions needed for robotic vacuum navigation. This simplification reduces computational burden while maintaining sufficient mapping accuracy for the specific application, thereby improving operation speed without completely sacrificing precision.
Solution Approach 2:
The patent changes key parameters of the SLAM algorithm to optimize for speed rather than maximum accuracy. By adjusting parameters such as data collection frequency, processing intensity, and coverage requirements, the system achieves a balance where mapping is sufficiently accurate for navigation purposes while computational load is reduced to enable faster operation.
2Loss of information
If traditional SLAM techniques with extensive data collection are used, then mapping completeness is improved, but battery consumption increases
Solution Approach 1:
The patent applies partial action by collecting and processing only the minimum necessary data required for effective navigation and cleaning tasks. Instead of extensively mapping every detail of the environment, the system gathers sufficient information to navigate and perform cleaning, reducing computational processing and energy consumption while maintaining practical mapping completeness for the application.
Solution Approach 2:
The patent implements self-service through adaptive data collection where the system intelligently determines when sufficient mapping data has been gathered and when to stop collecting, avoiding unnecessary energy expenditure. The algorithm autonomously adjusts data collection based on current mapping completeness and operational needs, optimizing the balance between information quality and energy usage.
3Manufacturing precision
If perimeter tracing is performed for complete mapping, then coverage accuracy is improved, but operation time increases
Solution Approach 1:
The patent applies preliminary action by performing rapid initial mapping to establish basic coverage areas before executing cleaning tasks. Instead of completing exhaustive perimeter tracing before beginning cleaning operations, the system quickly gathers sufficient spatial information to plan and execute effective cleaning paths, achieving adequate coverage accuracy without the time penalty of complete perimeter exploration.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables robots to generate complete maps in minutes with minimal coverage, allowing for immediate task execution and improved performance, reducing battery consumption and costs, thus facilitating mass adoption in homes and commercial spaces.
Implementation Method 1
a piezoelectric transducer wired to the electronic circuit; wherein: the electronic circuit produces a high-frequency direct current; and the piezoelectric transducer converts the high-frequency direct current to high-speed vibration causing vibration of the coupled filter
Implementation Method 2
a direct current motor wired to the power source; a gearbox interfacing with the direct current motor; wherein the gearbox converts rotary motion of a shaft of the direct current motor to reciprocating motion
Implementation Method 3
efficient data collection with a 360-degree LIDAR
Data Source
AI summary
Provided is a vibrating filter mechanism, including: a power source; a metal wire attached on both ends to the power source; a filter; a connector coupled with the filter and interfacing with the metal wire; and a first permanent magnet; wherein: the power source delivers electric current pulses in alternating directions through the metal wire; and the first permanent magnet is positioned in a location where a magnetic field of the first permanent magnet and a magnetic field of the metal wire interact and cause vibration of the metal wire and the coupled filter.