Multi-Sensor Floor Plan Construction for Autonomous Robot Navigation
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Solution Overview
Problem
Autonomous robots face challenges in effectively mapping and navigating complex environments due to limitations in sensor data integration and processing, leading to inefficiencies in path planning and task execution.
Innovation Solution
The use of a plurality of sensors, including imaging and movement sensors, to capture and process data for constructing a top-view model of the environment, allowing for real-time navigation and task management, with a processor transmitting this data to a smartphone application for user input and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to capture environmental data, then the quality and detail of the environmental model improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent combines data from multiple sensors (imaging sensors, movement sensors, and illumination sensors) into a unified environmental model. The processor integrates these diverse data sources to construct a comprehensive top-view model, achieving high measurement precision through data fusion while managing complexity through systematic integration architecture.
Solution Approach 2:
The environmental model serves multiple functions: it enables path planning, obstacle avoidance, navigation, and task execution. This multi-functionality justifies the complexity of using multiple sensors, as the single integrated model supports diverse robotic operations without requiring separate processing systems for each function.
2Speed
If real-time data processing is implemented for navigation, then the robot's responsiveness and path planning efficiency improve, but the energy consumption and computational load increase
Solution Approach 1:
The robot constructs an environmental model in advance during exploration phases, storing this pre-processed information for later use. This preliminary action reduces the computational load during actual navigation tasks, as the robot can reference the pre-built model rather than processing raw sensor data in real-time, thereby reducing energy consumption while maintaining fast response speeds.
Solution Approach 2:
The system processes and updates only the relevant portions of the environmental model that are necessary for current navigation tasks, rather than continuously processing all sensor data. This selective local processing reduces computational load and energy consumption while maintaining the speed needed for effective path planning and obstacle avoidance.
3Extent of automation
If the robot autonomously constructs and uses environmental models for navigation, then the level of automation improves, but the difficulty of detecting and measuring environmental features increases
Solution Approach 1:
The patent introduces an active illumination source as an intermediary that projects structured light patterns onto environmental surfaces. This intermediary element facilitates the detection of environmental features by creating visible reference patterns that the imaging sensor can easily detect and measure, thereby reducing the difficulty of feature detection while maintaining high autonomous navigation capability.
4Measurement precision
If active illumination is used to enhance sensor detection, then the measurement precision of environmental features improves, but the energy consumption increases
Solution Approach 1:
The active illumination source operates periodically rather than continuously, activating only when needed for specific detection tasks or when environmental lighting conditions are insufficient. This periodic operation maintains measurement precision when required while significantly reducing overall energy consumption compared to continuous illumination.
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 autonomous navigation and task execution by providing a detailed environmental model for path planning and task management, enhancing the robot's ability to avoid obstacles and optimize cleaning tasks.
Implementation Method 1
an active source of illumination is positioned adjacent to the imaging sensor such that reflections of illumination light illuminating a path of the robot fall within a field of view of the imaging sensor
Data Source
AI summary
A method for perceiving a model of an environment, including: capturing a plurality of data while the robot moves within the environment, wherein: the plurality of data comprises at least a first data and a second data captured by a first sensor of a first sensor type and a second sensor of a second sensor type, respectively; the first sensor type is an imaging sensor; the second senor type captures movement data; an active source of illumination is positioned adjacent to the imaging sensor such that reflections of illumination light illuminating a path of the robot fall within a field of view of the imaging sensor; perceiving the model of the environment based on at least a portion of the plurality of data; storing the model of the environment in a memory; and transmitting the model of the environment to an application of a smartphone.