Tennis Training Robot Feedback Control for Spin and Ball Placement
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Solution Overview
Problem
Existing tennis training devices are limited in their ability to simulate trick shots with rotation and predict ball placements, unable to evaluate athlete levels effectively, and lack targeted ball serving capabilities.
Innovation Solution
An intelligent learning and adjustment system for a tennis training robot, incorporating an image recognition system, algorithm model, back-end processing platform, and optimization model, to capture and process ball data, predict placements, and optimize algorithms based on athlete performance data for interactive feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed-force and fixed-direction serving mechanism is used, then the device structure is simple, but the ball serving modes are limited and cannot simulate trick shots with rotation
Solution Approach 1:
The patent applies dynamics by transforming the static, fixed-force serving mechanism into a dynamic system that can adjust ball serving parameters in real-time. The intelligent control system modifies ball speed, direction, and rotation based on captured athlete performance data, enabling the device to simulate various trick shots and adapt to different training scenarios without requiring complex mechanical reconfiguration.
Solution Approach 2:
The patent implements parameter changes by using an intelligent control system that dynamically adjusts multiple ball serving parameters including speed, direction, and rotation. The system captures athlete performance data and uses this information to modify serving parameters, transforming a fixed-parameter device into a variable-parameter system capable of simulating diverse ball trajectories and spin patterns.
2Measurement precision
If no image recognition and prediction system is implemented, then the device is simple, but the device cannot predict placements of incoming balls or evaluate athlete levels
Solution Approach 1:
The patent replaces mechanical measurement methods with an image recognition system using cameras and computer vision technology. Instead of mechanical sensors to track ball placement, the system uses optical capture and algorithmic processing to predict ball trajectories and placements, significantly improving measurement precision while avoiding complex mechanical sensing arrangements.
Solution Approach 2:
The patent introduces an intermediary intelligent control system that acts as a mediator between the physical ball-serving mechanism and the digital prediction/evaluation functions. This intermediary layer processes image data, predicts ball placements, evaluates athlete performance, and translates these insights into adjusted serving parameters, bridging the gap between simple mechanical serving and sophisticated analysis.
3Ease of operation
If no intelligent feedback system is implemented, then the device is simple, but the device cannot provide targeted ball serving or evaluate athlete performance
Solution Approach 1:
The patent implements a comprehensive feedback system where the intelligent control system captures athlete performance data during training, analyzes this data using prediction algorithms, and uses the insights to adjust subsequent ball serving parameters. This closed-loop feedback enables targeted training by continuously adapting the serving mechanism to match athlete skill levels and training objectives, transforming a simple serving device into an intelligent training partner.
Data Source
AI summary
Disclosed is an intelligent learning and adjustment system for a tennis training robot, including an image recognition system, an algorithm model, a back-end processing platform, and an optimization model. Preprocessing of incoming ball data is performed, various necessary data, such as speeds and directions of flying tennis balls, spinning and placements, are collected, various data sets are processed by using various machine learning algorithms, effective predictions and decisions are generated to facilitate the prediction of the placement and difficulty level of the incoming ball, so that a capability and level of a sparring athlete can be evaluated, the tennis training robot accordingly makes prediction and recognition, and carries out interactive feedback actions in a timely manner. The entire training process involves continuously updating of weights and bias values to make the predictions increasingly accurate, and the tennis training robot can provide an interactive intelligent training method.