Mapping an environment around an autonomous vacuum
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Solution Overview
Problem
Conventional autonomous floor cleaning systems face limitations in adaptability, mobility, stain removal, waste storage, navigation, and user interaction, particularly when dealing with various surface types and messes, including obstacles and unpredictable environments.
Innovation Solution
An autonomous cleaning robot with a vertically-actuated cleaning head, sensor system, and waste bag that adjusts height based on surface and mess type, uses audiovisual sensors for navigation and user interaction, and includes a mop roller with abrasive material for effective stain removal, while mapping the environment and detecting messes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the cleaning head is set at a fixed optimal height for cleaning efficacy, then cleaning performance is improved, but mobility and adaptability to different surfaces are worsened
Solution Approach 1:
The cleaning head height is made dynamically adjustable through an actuator system that automatically modifies the height based on detected surface types and mess characteristics. This allows the system to optimize cleaning contact pressure for each specific situation rather than maintaining a fixed height setting.
Solution Approach 2:
The autonomous vacuum uses its own sensor data and processing capabilities to automatically determine optimal cleaning head height settings without requiring external intervention. The system self-adjusts based on real-time environmental perception, eliminating the need for manual user adjustment.
2Productivity
If the cleaning head is lowered to improve cleaning of messes, then cleaning performance is improved, but mobility and ability to move over obstacles are worsened
Solution Approach 1:
The cleaning head height is dynamically adjusted based on real-time detection of mess types and surface characteristics. When a mess is detected, the system lowers the cleaning head to optimize contact; when no mess is present or obstacles are detected, the head is raised to improve mobility and prevent damage.
Solution Approach 2:
The system changes the height parameter of the cleaning head based on detected conditions. By modifying this critical parameter in response to environmental feedback, the system optimizes the trade-off between cleaning effectiveness and mobility for different operating conditions.
3Productivity
If manual adjustment of cleaning head height is performed to optimize cleaning, then cleaning efficacy is improved, but user interaction complexity and time are increased
Solution Approach 1:
The autonomous vacuum performs self-adjustment of cleaning head height using its integrated sensors and control system. The device independently detects surface types, mess characteristics, and obstacle locations, then automatically modifies cleaning head position without requiring any manual intervention from the user.
Solution Approach 2:
The manual mechanical adjustment system is replaced with an automated electro-mechanical actuation system controlled by sensor feedback. This substitution eliminates the need for direct user manipulation of height controls while achieving optimized cleaning performance through automated decision-making.
4Productivity
If pressure is applied to the mop roller to remove tough stains, then stain removal is improved, but water retention capability of the microfiber cloth is worsened
Solution Approach 1:
The system applies pressure to the mop roller in periodic or intermittent cycles rather than continuously. During cleaning passes, pressure is applied to remove tough stains; during water pickup phases, pressure is reduced or removed to allow the microfiber cloth to retain water effectively. This periodic variation in pressure optimizes both stain removal and water retention at different stages of the cleaning cycle.
Data Source
AI summary
An autonomous cleaning robot (e.g., an autonomous vacuum) may use a sensor system to map an environment that may be used to determine where to clean. The autonomous vacuum receives visual data about the environment and determines a ground plane of the environment based on the visual data. The autonomous vacuum detects objects within the environment based on the ground plane. For each object, the autonomous vacuum segments a three-dimensional (3D) representation of the object out of the visual data and determines whether the object is static or dynamic. The autonomous vacuum adds static objects to a long-term level of a map of the environment and dynamic objects to an intermediate level of the map. The autonomous vacuum may further add virtual borders, flags, walls, and messes to the map.