Robot Control Using Camera-Based Foot Posture Recognition
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Solution Overview
Problem
Conventional robot control methods using mobile phone applications or buttons on the robot are cumbersome and inefficient, leading to a poor user experience.
Innovation Solution
A method that uses a camera-mounted robot to detect and recognize foot postures through trained neural network models, allowing for intelligent control based on recognized postures without the need for additional hardware or complex interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If control is based on mobile phone application or buttons on robot machine body, then control functions are available, but operation convenience deteriorates and user experience becomes poor
Solution Approach 1:
The robot performs self-control through foot posture recognition. The system automatically detects the user's foot posture via the camera and autonomously determines control actions without requiring the user to manually operate buttons or a mobile phone application, making the system serve itself in the control process.
Solution Approach 2:
The patent replaces mechanical control interfaces (physical buttons on the robot and mobile phone application interfaces) with a vision-based recognition system. The camera captures foot posture images, and neural network models process these images to generate control commands, substituting mechanical interaction with optical and computational processes.
2Productivity
If control is based on mobile phone application or buttons on robot machine body, then control functions are available, but control speed deteriorates and efficiency becomes low
Solution Approach 1:
The system performs preliminary detection of foot posture continuously through the camera. The neural network models are pre-trained and ready to immediately recognize postures and generate control commands, eliminating the time delay associated with manual operation. The foot posture detection and control command generation happen automatically and rapidly as preliminary actions before actual control execution.
3Ease of operation
If camera is used for foot posture recognition, then control convenience is improved, but computational complexity increases
Solution Approach 1:
The computational task is segmented into two separate neural network models: a first model dedicated to detecting whether foot posture images are present, and a second model dedicated to recognizing specific foot postures. This segmentation divides the complex computational workload into manageable stages, processing images in sequence rather than requiring a single complex model to handle all tasks simultaneously.
Solution Approach 2:
The first neural network model performs preliminary detection to determine whether a foot posture image exists in the captured scene. Only when the first model confirms the presence of a foot posture does the system activate the second model for detailed recognition. This preliminary filtering action avoids unnecessary computational processing by the second model when no foot posture is present, reducing overall computational complexity.
Data Source
AI summary
Provided are a robot control method and apparatus, and a storage medium. The robot control method includes: detecting whether a first scene image captured by a camera of a robot includes a foot; obtaining, in response to detecting the foot in multiple consecutive frames of first scene images, multiple frames of second scene images captured by the camera; and recognizing a foot posture based on the multiple frames of second scene images, and controlling the robot based on a control manner corresponding to the recognized foot posture. In a scene image captured by the camera mounted at the robot, when the foot is captured within a field of view, the foot posture is recognized using a video captured by the camera. Intelligent control of the robot is realized based on the recognized foot posture.