Methods for mapping an environment and related devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing portable electronic devices lack the ability to efficiently map environments based on movement and location data, limiting their functionality in various situations and environments.
Innovation Solution
Portable electronic devices equipped with sensors and processors generate paths and maps of environments using movement and location data, employing machine learning techniques to estimate probable configurations and characteristics of the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If portable electronic devices collect and process location and movement data to generate environment maps, then environmental awareness and automation capability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system segments the mapping process into distinct phases: data collection by sensors, path generation by processor, and map generation by processor. This segmentation allows each component to focus on specific tasks, reducing overall system complexity while maintaining environmental awareness capabilities.
Solution Approach 2:
The device performs preliminary actions by continuously collecting location and movement data in the background before actual mapping is needed. This preliminary data accumulation enables faster and more accurate environment mapping when automation tasks are initiated, without requiring complex real-time processing during critical operations.
2Measurement precision
If the device generates detailed paths and maps using machine learning techniques, then mapping accuracy and automation capability improve, but energy consumption and processing time increase
Solution Approach 1:
The system applies partial action by using machine learning techniques selectively for specific mapping challenges rather than continuously. The processor employs machine learning to estimate probable configurations when data is incomplete or ambiguous, while relying on simpler algorithms when sufficient data exists, thereby reducing overall energy consumption while maintaining high mapping accuracy.
Solution Approach 2:
The device performs self-service by automatically processing its own sensor data to generate and update environment maps without external intervention. The processor continuously refines path and map generation using accumulated data, enabling the device to improve its own mapping accuracy over time while optimizing energy usage patterns.
3Ease of operation
If the system passively generates paths and maps without active user input, then ease of operation and automation improve, but reliability and accuracy of environmental representation may worsen
Solution Approach 1:
The system implements feedback mechanisms where the generated maps and paths are continuously validated against new sensor data. The processor compares expected movements with actual sensor readings, identifying discrepancies that indicate potential mapping errors. This feedback loop enables the system to maintain high reliability automatically, correcting errors without user intervention while preserving ease of operation.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated sensor-based systems. Instead of requiring users to manually input environment data, the system uses sensors, processors, and machine learning algorithms to automatically capture and interpret environmental information, thereby improving ease of operation while maintaining reliability through multiple verification layers.
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 the creation of topological maps that enhance user interaction with home automation systems and improve environmental awareness without active user input, facilitating tasks like automated lighting based on room occupancy.
Implementation Method 1
a sensor configured to detect a first location of the portable electronic device within an environment at a first instance of time. The sensor is further configured to detect a second location of the portable electronic device within the environment at a second instance of time
Data Source
AI summary
One or more electronic devices can map an environment based on movement or location data of the one or more electronic devices within the environment. The data can be analyzed, processed, or otherwise relied on to generate a path along which one or more of the electronic devices were carried (e.g., by a user) or located at given time over the duration of time. The path and attributes related to the environment can be analyzed (e.g., using machine learning techniques) to generate a topological or other type of map of the environment.