Mobile Robot Heading Guidance Using Optical Wall Alignment
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Solution Overview
Problem
Mobile robots lack an efficient method to automatically maintain a desired heading relative to their environment, such as walls, which is essential for routine tasks, as they often rely on manual alignment and lack sensors to adapt to changes in their surroundings.
Innovation Solution
A robotic device equipped with a camera and light emitters that project a collimated light pattern, allowing the processor to analyze image distortion and adjust the device's heading by emitting and capturing light patterns, using machine learning algorithms to refine the heading adjustment process, and generating a map of the environment to ensure accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If mobile robots rely on manual alignment without sensors, then device complexity is reduced, but heading accuracy and adaptability to environmental changes deteriorate
Solution Approach 1:
The patent introduces light emitters and image sensors as intermediary elements between the robot and the environment. The light emitters project patterns onto environmental surfaces, and the image sensors capture the reflected light to determine heading accuracy. This intermediary optical system enables automatic heading maintenance without requiring complex mechanical alignment mechanisms.
Solution Approach 2:
The patent replaces manual mechanical alignment with an optical-based sensing system. Instead of relying on physical adjustment mechanisms or human operators to align the robot's heading, the system uses light projection and image capture to automatically detect and correct heading deviations, substituting mechanical operations with optical field interactions.
2Ease of operation
If mobile robots use manual alignment methods, then ease of operation is maintained, but productivity and task execution efficiency deteriorate
Solution Approach 1:
The robot performs self-alignment through autonomous optical sensing. The light emitters and image sensors enable the robot to automatically detect its heading relative to environmental features and adjust its orientation without human intervention. This self-service capability eliminates the need for manual alignment operations while maintaining ease of operation.
Solution Approach 2:
The system establishes a reference light pattern on the environment before task execution begins. This preliminary action of projecting the light pattern and capturing the initial image allows the robot to pre-determine its starting heading accuracy, enabling efficient task execution from the outset without requiring continuous manual adjustments.
3Device complexity
If mobile robots lack environmental sensing capabilities, then device complexity is reduced, but adaptability to environmental changes deteriorates
Solution Approach 1:
The light emitter and image sensor system serves multiple functions: it determines heading accuracy, detects environmental features, and adapts to changes in the environment. By using the same optical components for various sensing tasks, the system achieves environmental adaptability without proportionally increasing device complexity.
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
The system enables the robotic device to autonomously maintain a desired heading relative to environmental features, improving its navigation and task execution by using sensor data and machine learning to adapt to changes in the environment, enhancing its ability to perform routine tasks efficiently.
Implementation Method 1
A robotic device equipped with a camera and light emitters that project a collimated light pattern, allowing the processor to analyze image distortion
Data Source
AI summary
A robotic device including a medium storing instructions that when executed by a processor effectuates operations including: capturing images and sensor data of an environment as the robotic device drives back and forth in straight lines; generating or updating a map of the environment based on at least one of the one or more images and the sensor data; recognizing one or more rooms in the map based on at least one of the one or more images and the sensor data; determining at least one of a position and an orientation of the robotic device relative to the environment based on at least one of the one or more images and the sensor data; and actuating the robotic device to adjust a heading of the robotic device based on the at least one of the position and the orientation of the robotic device relative to the environment.