Mobile robot with improved accuracy and generating three-dimensional depth map
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Solution Overview
Problem
Conventional cleaning robots require multiple sensors to perform obstacle avoidance, positioning, and object recognition, leading to high computation loading, power consumption, and reduced recognition correctness.
Innovation Solution
A mobile robot utilizing a single optical sensor with different light sources (laser diode and light emitting diode) to capture image frames, processing these frames with a processor embedded with a machine learning algorithm to perform obstacle avoidance, visual simultaneous localization and mapping, and object recognition, while reducing computation and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to perform obstacle avoidance, positioning, and object recognition, then detection accuracy is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent applies multi-functionality by enabling a single optical sensor to perform multiple detection functions (obstacle avoidance, positioning, and object recognition) that traditionally required multiple specialized sensors. The sensor alternates between capturing images under laser diode illumination for obstacle detection and under LED illumination for visual simultaneous localization and mapping (VSLAM) and object recognition, thereby eliminating the need for separate sensor systems while maintaining comprehensive detection capabilities
Solution Approach 2:
The patent merges the functions of multiple sensors into a single optical sensor system. By combining obstacle avoidance detection, positioning (VSLAM), and object recognition capabilities into one sensor that captures images under different lighting conditions, the system reduces the total number of components while integrating previously separate detection functions into a unified system
2Measurement precision
If multiple sensors are used to perform multiple detecting functions, then detection coverage is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic action by alternating the optical sensor between two operational modes: capturing images under laser diode illumination for obstacle avoidance detection, and capturing images under LED illumination for VSLAM and object recognition. This time-division multiplexing approach allows the single sensor to perform multiple detection functions sequentially, maintaining comprehensive detection coverage while significantly reducing power consumption compared to having multiple sensors operating simultaneously
3Adaptability or versatility
If multiple sensors are used to perform different detecting functions, then functional versatility is improved, but computation loading increases
Solution Approach 1:
The patent applies multi-functionality by enabling a single optical sensor to perform multiple detection functions (obstacle avoidance, positioning, and object recognition) that traditionally required multiple specialized sensors. The sensor alternates between capturing images under laser diode illumination for obstacle detection and under LED illumination for visual simultaneous localization and mapping (VSLAM) and object recognition, thereby eliminating the need for separate sensor systems while maintaining comprehensive detection capabilities
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 solution enables multiple detecting functions with a single optical sensor, reducing computational load and power consumption while improving recognition correctness and efficiency.
Implementation Method 1
a laser diode is emitting light
Implementation Method 2
a light emitting diode is emitting light
Implementation Method 3
image frames captured by the same optical sensor
Data Source
AI summary
There is provided a mobile robot that performs the de-flickering and different auto exposures in a pixel array in the range estimation to be adaptable to different operating scenarios, and constructs a three-dimensional depth map to lower the cost.