Tennis Training Robot With Dual-Camera Ball Placement Control
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Solution Overview
Problem
Existing tennis training devices lack the ability to simulate trick shots with rotation, have fixed serving frequencies, and cannot determine ball placement for realistic battle simulation, limiting their training effectiveness.
Innovation Solution
A tennis training robot that predicts ball trajectory and placement using dual-camera image recognition, adjusts movement based on player data, and simulates battle training through a control system with a gyroscope and Hall effect sensor, allowing for intelligent and adaptive training modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed serving mechanism is used, then the device structure is simple, but the training versatility is limited
Solution Approach 1:
The patent implements dynamic ball serving capabilities through independently controllable upper and lower ball serving wheels that can adjust ball release position, speed, and rotation. The robot can dynamically change serving directions (forehand, backhand, serve) and ball trajectories based on real-time detection of athlete position and ball flight, transforming a static device into an adaptive training system.
Solution Approach 2:
The robot is designed to perform multiple training functions including forehand practice, backhand practice, serve practice, and trick shot simulation. The dual-camera system and control algorithm enable the same device to adapt to different training scenarios and athlete skill levels, providing universal training capabilities across various tennis techniques.
2Manufacturing precision
If a single ball serving wheel is used, then the device structure is simple, but the ball placement precision is insufficient
Solution Approach 1:
The ball serving mechanism is segmented into an upper ball serving wheel and a lower ball serving wheel, each independently controlled. The upper wheel handles ball release position and initial trajectory, while the lower wheel controls ball rotation and spin. This segmentation allows precise independent adjustment of each parameter to achieve accurate ball placement.
Solution Approach 2:
The dual-camera image recognition system provides real-time feedback on ball position, speed, and trajectory. The control algorithm uses this feedback to continuously adjust the ball serving wheels' rotation speed and position, enabling closed-loop control that maintains high placement precision despite variations in ball size, weight, or atmospheric conditions.
3Adaptability or versatility
If fixed serving frequency is used, then the device operation is simple, but the training realism is limited
Solution Approach 1:
The robot performs self-service through autonomous ball retrieval and reloading. The ball entry area automatically receives balls from a supply source and feeds them to the serving mechanism without manual intervention. The system autonomously adjusts serving frequency, speed, and trajectory based on training scenarios, eliminating the need for manual ball handling and frequency control.
Solution Approach 2:
The robot dynamically changes serving parameters including frequency, speed, and trajectory based on real-time detection of athlete response and training objectives. The control algorithm adjusts these parameters automatically to create realistic training scenarios, such as varying serve speeds to simulate different opponent strengths or adjusting ball placement to match actual match conditions.
4Extent of automation
If basic serving function is implemented, then the device complexity is low, but the intelligent training capability is insufficient
Solution Approach 1:
The patent replaces manual mechanical control with an automated control system based on dual-camera image recognition and algorithmic decision-making. Instead of manual adjustment of serving parameters, the system uses computational vision to detect ball flight, athlete position, and training scenarios, then automatically adjusts robot behavior through software control algorithms.
Solution Approach 2:
The robot simulates human tennis behavior by copying and analyzing ball flight patterns, athlete movements, and match scenarios through the dual-camera system. The control algorithm learns from detected patterns to generate realistic training scenarios, such as simulating trick shots, varying serve speeds, and adapting to different athlete responses, thereby achieving intelligent training capability.
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 precise control of ball speed and placement, simulates realistic battle scenarios, and provides personalized training plans, enhancing training effectiveness and player engagement.
Implementation Method 1
the control system includes a main controller, a gyroscope, a Hall effect sensor and a dual-camera image recognition system
Implementation Method 2
the control system includes a main controller, a gyroscope, a Hall effect sensor and a dual-camera image recognition system
Data Source
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AI summary
Disclosed is a tennis training robot and a training method, the tennis training robot includes a housing (100) and driving wheels (200) mounted at a bottom of the housing, the driving wheels are driven by a driving motor (300), a ball entry area is formed at an upper part of the housing, a ball serving area is formed at a lower part of the housing, a ball entry opening (1) is formed in the ball entry area, the ball serving area is provided with a ball rotating disk (2), a track (4), an upper ball serving wheel (5) and a lower ball serving wheel (6), the ball rotating disk is arranged below the ball entry opening, the track is arranged below the ball rotating disk; a ball serving opening is formed on the housing in a ball release direction of the upper ball serving wheel and the lower ball serving wheel.