Robot Floor Plan Construction Using Multi-Sensor Data Integration
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Solution Overview
Problem
Autonomous robots face challenges in efficiently mapping and navigating complex environments due to limitations in sensor integration and data processing, leading to suboptimal path planning and task execution.
Innovation Solution
A robot configuration with a combination of imaging sensors, movement sensors, and a processor that captures and processes data to create a top-view model of the environment, allowing for real-time navigation, object recognition, and user input integration to adjust cleaning tasks and schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor types are integrated to improve environmental perception accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (imaging sensors, movement sensors, and other sensors) into an integrated sensor system that captures data from different modalities simultaneously. This merging approach enables the robot to perceive environmental features, motion, and other parameters through a unified sensing architecture, improving measurement precision while managing complexity through integration.
Solution Approach 2:
The sensor system is designed to perform multiple functions: imaging sensors capture visual environmental data, movement sensors track robot and object motion, and the processor integrates these diverse data streams to achieve comprehensive environmental perception. This multi-functional design allows a single integrated system to address various sensing requirements without proportionally increasing complexity.
2Productivity
If real-time data processing is implemented to improve navigation speed, then productivity is improved, but use of energy increases
Solution Approach 1:
The processor performs real-time data processing selectively, focusing computational resources on critical navigation decisions and environmental features that require immediate response. Rather than continuously processing all sensor data at maximum intensity, the system applies partial processing to maintain navigation speed while managing energy consumption through prioritized computation.
3Measurement precision
If detailed environmental modeling is created to improve path planning accuracy, then measurement precision is improved, but loss of time in data processing increases
Solution Approach 1:
The environmental model is constructed by segmenting the processing task: the processor first identifies key environmental features and structures from sensor data, then progressively builds the top-view model by integrating data from multiple sources. This segmented approach allows detailed modeling to proceed in manageable stages, improving path planning accuracy while reducing overall processing time through systematic data integration.
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 efficient navigation and task execution by providing a detailed environmental model for autonomous operation, improving cleaning efficiency and user interaction through accurate path planning and task management.
Implementation Method 1
upon incidence of illumination light with an object in a path of the robot reflections of the illumination light fall within a field of view of the first sensor
Data Source
AI summary
A robot configured to perceive a model of an environment, including: a chassis; a set of wheels; a plurality of sensors; a processor; and memory storing instructions that when executed by the processor effectuates operations including: capturing a plurality of data while the robot moves within the environment; perceiving the model of the environment based on at least a portion of the plurality of data, the model being a top view of the environment; storing the model of the environment in a memory accessible to the processor; and transmitting the model of the environment and a status of the robot to an application of a smartphone previously paired with the robot.