Method for robotic devices to identify doorways using machine learning
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Solution Overview
Problem
Autonomous robotic devices lack the ability to effectively detect doorways, leading to incomplete cleaning of rooms and inaccurate mapping, as they may enter a new room before finishing the previous one, causing mapping issues and inefficient cleaning patterns.
Innovation Solution
Equipping mobile robots with LIDAR sensors, depth cameras, or other distance measurement devices, combined with machine learning algorithms, to identify doorways by analyzing distance data and generating maps, allowing the robot to classify features and determine the presence of doorways for navigation and cleaning strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robotic devices use basic navigation without doorway detection, then the device can move freely between rooms, but cleaning coverage becomes incomplete and mapping becomes inaccurate
Solution Approach 1:
The patent segments the cleaning task by identifying doorways as boundary markers between rooms. The robotic device uses doorway detection to divide the cleaning workspace into distinct room zones, ensuring complete coverage of each room before transitioning to the next. This segmentation approach resolves the contradiction by providing reliable cleaning coverage through structured room-by-room cleaning while managing system complexity through modular sensor and algorithm integration.
Solution Approach 2:
The patent implements preliminary action by detecting and identifying doorways before the robotic device enters a new room. The system uses sensors to detect doorway features, machine learning algorithms to classify the detected features as doorways, and this preliminary identification informs navigation decisions. This preliminary action ensures complete cleaning of the current room by preventing premature entry into adjacent rooms, thereby resolving the contradiction between cleaning completeness and system complexity.
2Measurement precision
If robotic devices enter doorways without detection, then navigation between rooms is enabled, but mapping accuracy deteriorates due to incomplete room cleaning records
Solution Approach 1:
The patent applies preliminary action by detecting and classifying doorways before the robotic device navigates through them. The system uses sensors to detect doorway features, machine learning algorithms to classify these features as doorways, and this preliminary identification enables accurate mapping by marking room boundaries before transition. This ensures mapping precision is maintained while navigation remains relatively simple through automated doorway recognition.
Solution Approach 2:
The patent implements feedback by continuously monitoring sensor data for doorway features and using machine learning classification to provide feedback on room boundary detection. This feedback mechanism allows the robotic device to adjust its navigation and cleaning behavior based on accurate doorway identification, thereby maintaining high mapping accuracy while simplifying navigation through intelligent, adaptive decision-making.
3Measurement precision
If robotic devices use sensor data and machine learning for doorway identification, then doorway detection accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies partial action by using machine learning algorithms to classify only the most relevant sensor data features for doorway identification. Rather than processing all possible sensor inputs equally, the system focuses computational resources on key doorway features detected by sensors, achieving high detection accuracy while minimizing unnecessary computational energy consumption. This selective processing resolves the contradiction between accuracy and energy use.
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 robots to accurately detect doorways, ensuring thorough cleaning of each workspace before entering another, preventing incomplete mapping and improving cleaning efficiency by using the identified doorways as navigation boundaries.
Implementation Method 1
LIDAR sensors, depth cameras, or other distance measurement devices
Implementation Method 2
Equipping mobile robots with LIDAR sensors
Implementation Method 3
depth cameras, or other distance measurement devices
Data Source
AI summary
A method for identifying a doorway, including: capturing, with a sensor disposed on a robot, sensor data of an environment of the robot as the robot drives along a movement path; identifying, with a processor of the robot, at least one feature from the sensor data indicative of a doorway; identifying, with the processor, the doorway at a location within the environment upon detecting the at least one feature in the sensor data; and generating, with the processor, a map of the environment based on at least the sensor data.