Robot Doorway Detection Using ML-Based Indoor Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated robotic devices lack the capability to effectively identify doorways, leading to incomplete cleaning tasks and mapping errors, as they may enter new rooms without finishing the previous one, due to the inability to determine room boundaries.
Innovation Solution
The use of sensors like LIDAR, depth cameras, and TOF sensors in conjunction with machine learning algorithms to identify doorways by analyzing distance data and marking them in indoor maps, allowing the robot to execute specific actions such as completing tasks in one room before entering another.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If robotic devices use basic sensor data without machine learning analysis, then device complexity is reduced, but doorway identification accuracy deteriorates
Solution Approach 1:
The patent introduces machine learning models as an intermediary component between the sensors and the robotic device's decision-making system. The ML model processes sensor data to identify doorways, walls, and openings, acting as a mediator that transforms raw sensor inputs into meaningful spatial understanding without requiring complex hardware modifications.
Solution Approach 2:
The patent replaces mechanical or rule-based doorway detection systems with machine learning-based detection. Instead of using complex mechanical sensors or pre-programmed rules to identify doorways, the system uses trained ML models that automatically learn doorway patterns from sensor data, simplifying the mechanical complexity while improving detection accuracy.
2Productivity
If robotic devices enter doorways without identification capability, then productivity increases by cleaning more areas, but task completion quality deteriorates due to incomplete room cleaning
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously analyzes sensor data to identify doorways and provide feedback to the robotic device's navigation system. This feedback loop enables the robot to understand spatial boundaries and make informed decisions about when to enter or avoid doorways, ensuring complete room cleaning before transitioning to the next area.
Solution Approach 2:
The patent applies preliminary action by using machine learning to pre-identify doorways and map the environment before the robotic device begins cleaning tasks. This preliminary spatial understanding allows the robot to plan its cleaning path systematically, ensuring each room is completely cleaned before entering the next area through identified doorways.
3Measurement precision
If robotic devices use advanced machine learning algorithms, then doorway identification accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by using machine learning models that process only the necessary features from sensor data for doorway identification. Rather than analyzing all sensor data comprehensively, the ML models focus on specific patterns and features relevant to doorway detection, reducing computational overhead while maintaining high identification accuracy.
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
Enables robotic devices to accurately detect doorways, ensuring thorough cleaning of each workspace and preventing premature entry into new areas, thereby improving cleaning efficiency and map accuracy.
Implementation Method 1
The use of sensors like LIDAR, depth cameras, and TOF sensors in conjunction with machine learning algorithms to identify doorways by analyzing distance data
Implementation Method 2
The use of sensors like LIDAR, depth cameras, and TOF sensors in conjunction with machine learning algorithms to identify doorways by analyzing distance data
Data Source
AI summary
A method for identifying a doorway, including receiving, with a processor of an automated mobile device, sensor data of an environment of the automated mobile device from one or more sensors coupled with the processor, wherein the sensor data is indicative of distances to objects within the environment; identifying, with the processor, a doorway in the environment based on the sensor data; marking, with the processor, the doorway in an indoor map of the environment; and instructing, with the processor, the automated mobile device to execute one or more actions upon identifying the doorway, wherein the one or more actions comprises finishing a first task in a first work area before crossing the identified doorway into a second work area to perform a second task.