# Motion Control Analysis of Tennis Robot Based on Ant Colony Algorithm

Authors: Feng Wang; Yujie Dong; Haobo Gao; Lin Wu
Journal: Journal of Sensors
 (2022)

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## Abstract

Visual recognition and automatic control technology is an important way to realize robot automatic ball picking. Therefore, a tennis robot motion control method based on ant colony algorithm is evaluated. After the camera position was fixed, the motion control system software was designed, and the optimal path was solved by ant colony algorithm. The results show that the two adjacent positioning errors and the current total positioning errors fluctuate around -0.05 mm and -2.29 mm, respectively, and the fluctuation range is less than 3.50 mm. Ant colony algorithm is superior to greedy algorithm in path planning of collecting tennis balls. The number of iterations of ant colony algorithm for optimal path planning of 30 to 50 tennis balls is about 20 to 30 times, and the path length is reduced to reduce the time of collecting tennis balls, which meets the actual work requirements.


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## Body

## 1. Introduction

With the development of society and promotion of all-people exercise, tennis has gradually become a fashion sport to get more people love. Especially after Li Na won the Grand Slam, the domestic tennis fever is set off, and more and more people participate in the sport of tennis [1]. According to statistics, China’s tennis population has broken through 15 million, and tennis is becoming more and more common, for professional matches or professional athletes training, may be responsible for ball caddies [2]. But not all players and tennis fans have such conditions and benefits. With a standard tennis court measuring no less than 670 square meters, collecting the balls scattered on the court is no doubt a lot of physical work. At present, there are also some tennis picking machines on the market, most of which are pure mechanical or semiautomatic ball-picking machines with relatively economic manual operation [3]. Manual operation is both a waste of time and reduces the efficiency of training, and this manual operation is also subject to the danger of being hit by tennis balls [4]. To meet the market demand and solve these problems, it is necessary to develop a safe, efficient, stable, and economical intelligent tennis collecting robot [5]. Machine vision technology is a new technology that has gradually developed in recent years. It can be applied in intelligent manufacturing and many intelligent living fields. Machine vision has become an important way of intelligent development, and its application has become more extensive [6, 7]. In this development background, this topic is proposed to achieve the identification of tennis ball positioning and pick up through machine vision technology and the improvement of existing equipment integration and innovation. This topic is to develop an intelligent tennis collecting robot, which can provide the possibility of convenience and better experience for tennis practitioners to practice alone. At the same time, it can also save physical strength for the practitioners, quickly and conveniently realize the tennis picking up, so that the practitioners can put more energy on physical and mental entertainment and technical improvement.Therefore, the development of a tennis collecting robot with high intelligence and simple operation has better market demand and the possibility of intelligent development. This project integrates advanced visual technology into the research and development of tennis collecting robot, which is also a new attempt and practice. Therefore, this research has great academic research significance and application value. Figure1 shows the design and development of tennis robot.Figure 1
Design and development of tennis robot.


## 2. Literature Review

After consulting a large number of relevant literature and market research, from the development of tennis picking robots, tennis picking institutions can be roughly divided into three categories.(1)
Simple tennis ball pickerBall-picking basket and ball-picking Jane are the two most common ball-picking devices on the market. Because it is convenient and cheap to use and carry, it is a popular auxiliary ball-picking tool among most tennis trainers. This kind of ball-picking device mainly relies on artificial will pick up the ball basket or collection of Jane’s goal port under pressure at the tennis ball, so that it enters the Jane; this kind of ball-picking device has the advantages of simple organization, low cost, but needs more people to participate in, and pick up tennis discontinuous, and its efficiency is very low.(2)
Sophisticated tennis ball pickerWith the deepening of research and development of tennis ball-picking mechanism, the designed ball-picking mechanism is relatively complex and its functions are gradually improved, which overcomes some shortcomings of simple ball-picking device, but its intelligence degree is still not very high. In the aspect of picking up the ball, this topic uses the principle of rolling simple picking up the ball, carries on the modeling analysis to pick up the tennis ball process, carries on the design optimization to the mechanism, and through the prototype debugging finds that the experimental test data is basically consistent with the theoretical value. However, the overall size and weight of the device are too large. Although the body part of the car realizes the transformation from human drive to electric, it still needs human to control the direction and speed.Some ball-picking institutions use electric drive, belt drive, or chain plate drive to pick up the ball for the wheel shaft drive belt baffle push tennis, and tennis collection is realized. The front end of the ball is added to collect the player and guide the tennis ball into the ball-picking device, and the degree of automation of the way of picking up the ball is obviously improved. However, this scheme reduces the continuity of ball picking and has a complex structure. With the use of multiple motors, its control becomes more complex, with an increase in cost and a decrease in efficiency. In addition, the control of walking mechanism is not flexible enough and has poor adaptability.(3)
A ball-picking robot with a high degree of intelligenceWith the development of artificial intelligence and visual recognition technology, under the general trend of multidisciplinary integration, many researchers have applied advanced visual technology to the ball-picking robot and put forward different ball-picking schemes. They have made breakthroughs in the research of tennis picking robot, and its intelligence degree has been improving, but only a small number of research teams have successfully developed a fully autonomous intelligent ball-picking robot.In Li et al.’s project, the vision system uses ultrasonic ranging technology to find the landing tennis ball and adds infrared ranging sensors to the ultrasonic ranging system to guide the robot. Through the control system, the robot moves to the specified position and realizes the action of picking up the ball [8]. Ganser et al. proposed an embedded intelligent ball-picking car, and the scheme uses color and shape recognition algorithm, combined with PID control algorithm, respectively, using motor drive car body movement and steering gear control car ball-picking action, to achieve the function of picking up tennis balls [9, 10]. However, this scheme is only a theoretical solution, and the concrete realization remains to be practiced. According to the single-view imaging theory, the researchers made a panoramic camera with omnidirectional reflector [11]. Ba’s designed ball-picking mechanism uses the friction force of the ball and the outer wall to pick up the ball and creatively combines the ball-picking device with the self-developed serving device. He designed a tennis car integrating ball picking and serving, realizing that the car drives the rolling machine to rotate to pick up the ball. However, manual movement of the car body and identification of the tennis balls are required in the process of picking up the ball, so the degree of automation of picking up the ball is not high [12]. The automatic tennis collecting robot designed by Tu and Chen uses infrared sensor to locate the tennis ball and grasp and recover the ball through the manipulator claw. This way of picking up the ball is more flexible and theoretical. It can pick up the net and the tennis ball near the corner, but the positioning of the tennis ball is higher. In addition, only one ball can be picked up each time, and each ball collection needs to be raised and released through alignment clamp, so the efficiency of ball picking is relatively low [13]. The ball-picking robot designed and developed by Shu et al. adopts a drum baffle type ball-picking mechanism. During the robot’s progress, the impeller independently driven by the motor drives the baffle to rotate, and the tennis ball is involved and raised to the highest position and then falls into the collecting device by its own gravity. This way of picking up the ball is relatively efficient. However, due to the rigid contact between the baffle and the tennis ball, there is impact at the moment of contact between the baffle and the tennis ball in the process of picking up the ball [14]. Luo et al. use RFID radio frequency identification technology combined with LANDMARC algorithm, through the communication between the RFID tag installed on the sphere and the reference beacon arranged in the environment, to achieve the positioning of the sphere and the navigation of the ball-picking robot. This positioning scheme requires additional rf tags for tennis balls and the surrounding working environment, which is difficult to be applied in practice [15]. Li uses a global camera to collect the image information of the golf ball and the ball-picking robot in the field of vision and transmits it to the server in real time. He uses the image processing technology to identify the special artificial marks on the golf ball and the robot and realizes the autonomous positioning and navigation of the golf ball collecting robot [16]. The intelligent tennis ball-picking robot designed by Lv et al. uses binocular vision to realize the identification and positioning of tennis balls. Through BM (BlockMatching) algorithm, stereo matching is carried out on the imaging results of binocular vision system, and parallax matrix is obtained. The parallax matrix is mapped to the coordinate system of the ball-picking robot to complete the 3D reconstruction and relative positioning of the identified tennis balls in the robot coordinate system. In addition, the 2d plane positioning system is combined to determine its own orientation, and the autonomous ball picking under the visual guidance of the ball-picking robot is realized [17]. Visual recognition and automatic control technology is an important way to realize robot automatic ball picking. Many literatures have proposed the combination of multimodules such as visual recognition, motion control, and path planning to pick up the ball to realize intelligent tennis picking, but there is no specific implementation process, and many of these modules need to be further studied and optimized. Therefore, this topic can be studied and optimized in the module of visual recognition and target positioning, motion control, and path planning [18, 19].


## 3. Research Method

### 3.1. Camera Calibration

The purpose of camera calibration is to find the corresponding relationship between the pixel coordinates in the image and the world coordinates. Camera calibration is an essential step to obtain accurate position information of object in 3d space from 2d image taken by camera.The camera imaging model mainly involves four coordinate systems:World coordinate system: an artificial frame of reference that facilitates representation of the position of a target object in the real world.Camera coordinate system: the Z axis is parallel to the optical axis of the camera.Image frameX‐Y: a coordinate system based on a photo taken by a camera with the focal point of the camera’s optical axis and the imaging plane as the origin [20].The pixel coordinate system takes the upper left corner of the image as the origin, and the coordinates and coordinates are the number of columns and rows of the pixel, respectively.The transformations between partial coordinate systems are as follows:Any coordinateX,Y in the image coordinate system has the following conversion relation with its corresponding pixel coordinate u,v, as shown in the following equation:
(1)u=xdx+u0,v=ydy+v0.dx and dy, respectively, represent the width of one pixel of the axis and the axis direction, u0,v0 is called the main point of the image plane, equivalent to the discretization of the axis x and the axis y, and dx, dy, and u0,v0 are the internal parameters of the camera. Write Equation (1) in matrix form, as in the following equation:
(2)uv1=1/dx0u001/dxv0001xy1.Conversion of world coordinates to camera coordinates can be achieved by rigid body transformation.The mathematical expression of rigid body changes between them is shown in the following equation:(3)XcYcZc=r00r01r02r10r11r12r20r21r22XwYwZw+TxTyTz.Namely, inXc=RX+T,Xc represents the camera coordinate system, X represents the world coordinate system, R represents rotation,T represents translation, and R,T are the external parameter of the camera.


### 3.2. Software Design of Motion Control System

The software design of motion control system mainly includes controlling motor speed and writing control algorithm suitable for motion as well as optimizing path to improve the efficiency of picking up tennis balls [21, 22].In this study, PID algorithm is used to control the motor speed. PID controller measures the deviation between the actual value and the expected value of the controlled variable and corrects the system accordingly, so as to achieve the purpose of regulating the system. The control system schematic diagram is shown in Figure2.Figure 2
PID control system schematic diagram.In PID control system,rt is the target value set by the system, yt is the actual value of the output and feedback of the system, et is the deviation, and it is the difference value betweenrtandyt. PID control can be expressed as the following equation:
(4)ut=Kpet+1Ti∫0tetdt+TDdetdt.Kp, Ti, and TD are proportional coefficient, integral time, differential time, respectively. These three parts jointly affect the system. Formula (4) is a continuous PID algorithm calculation formula, most of the practical application of discrete digital controller, and digital controller algorithm is usually divided into position and incremental PID control algorithm [23].Position PID is a kind of sampling control. It calculates the control quantity of the motor according to the deviation value at the moment. Therefore, Equation (4) must be discretized and approximated by using and replacing integral and difference to replace differential to get the following equation:
(5)uk&=Kpet+1Ti∫0tetdt+TDdetdt≈Kpek+KpTTi∑j=0kej+KpTDTek−ek−1.ek and uk are, respectively, the deviation value and the control quantity at the time of the first sampling. The incremental PID is to calculate the increment of control quantity of two adjacent samples Δuk=uk−uk−1. The incremental PID is used to control the motor speed. The incremental PID control system is shown in Figure 3 below.Figure 3
Schematic diagram of incremental PID control system.As can be seen from Equation (5), the location-based algorithm accumulates errors, occupies a large storage unit, and is difficult to write programs, so it is improved. The expression can be deduced from Equation (5) as the following equation.
(6)uk−1=Kpek−1+KpTTi∑j=0k−1ej+KpTDTek−1−ek−2.Then, the incremental PID control calculation formula is shown in Equation (7).
(7)Δuk=Kpek−ek−1+KpTTiek+KpTDTek−2ek−1+ek−2.Compared with the position algorithm, the incremental algorithm does not need to do cumulative calculation, calculation error has less influence on the calculation of the control quantity, the cumulative error is small, the computer output controls quantity increment, and misoperation has less influence.


### 3.3. Ant Colony Algorithm to Solve the Optimal Path

For the randomly distributed tennis ball collection problem, this paper adopts ant colony algorithm to solve the multiobjective path planning problem. The algorithm is the ant colony algorithm proposed by MarcoDorigo inspired by the ant foraging process [24].The algorithm mimics the way ants guide other ants by releasing secretions called pheromones during foraging. Because pheromones are volatile, the concentration of pheromones along the path of the ant colony decreases over time, and the shorter the distance, the more pheromones are retained [25]. Ants will forage along the path with high pheromone concentration, and the path with high pheromone concentration will accumulate more ants, showing a positive feedback. Two key steps in ant colony algorithm are the probability selection of target and pheromone updating method.
(1)
Probabilistic Choice. In the initial state, the pheromone quantity on each path is the same. Set the initial τij0=C0, C0 as a constant, and the ant kk=1,2,⋯,m makes a judgment according to the visibility of the target point and the pheromone quantity on the path in the process of finding the path. The probability t that the ant k located at the tennis ball moves to the tennis ball j as in the following equation:(8)Pijkt=τijtα×ηijtβ∑k∈allowedτiktα×ηiktβ,ifj∈allowedk,0others.τijt is the pheromone intensity on the path of t time connection i and j. ηikt=1/dik, and it is the visibility of the ants at any moment. allowedk is a collection of all tennis target points that have not yet passed. α,β are two constants and are the weighted values of pheromone intensity and visibility, respectively.
(2)
Pheromone Update. After searching all the tennis balls, update the pheromone on the path according to Equation (9).(9)τijt=1−ρτij+∑k=1mΔτijk.m is the number of ants, ρ is pheromone volatilization coefficient, which represents the loss degree of information on the path, and Δτijk is the pheromone left by the first ant on the path in this cycle and is in the initial stage of search. The beginning of the search Δτijk=0.
(10)Δτijk=QLK,ifthekthanttraversesitoj,0,others.Δτijkt represents the influence value of the first ant on the amount of information on the path i,j in this search. Q denotes pheromone intensity, which affects the search speed of the algorithm: LK denotes the path length acquired by the kth ant in this search. Figure 4 shows the flow chart of ant colony algorithm. In order to verify the effectiveness and feasibility of ant colony algorithm for multiobjective path planning, Matlab was used for programming simulation, and the simulation results were compared with greedy algorithm.Figure 4
Flow chart of ant colony algorithm.


## 3.1. Camera Calibration

The purpose of camera calibration is to find the corresponding relationship between the pixel coordinates in the image and the world coordinates. Camera calibration is an essential step to obtain accurate position information of object in 3d space from 2d image taken by camera.The camera imaging model mainly involves four coordinate systems:World coordinate system: an artificial frame of reference that facilitates representation of the position of a target object in the real world.Camera coordinate system: the Z axis is parallel to the optical axis of the camera.Image frameX‐Y: a coordinate system based on a photo taken by a camera with the focal point of the camera’s optical axis and the imaging plane as the origin [20].The pixel coordinate system takes the upper left corner of the image as the origin, and the coordinates and coordinates are the number of columns and rows of the pixel, respectively.The transformations between partial coordinate systems are as follows:Any coordinateX,Y in the image coordinate system has the following conversion relation with its corresponding pixel coordinate u,v, as shown in the following equation:
(1)u=xdx+u0,v=ydy+v0.dx and dy, respectively, represent the width of one pixel of the axis and the axis direction, u0,v0 is called the main point of the image plane, equivalent to the discretization of the axis x and the axis y, and dx, dy, and u0,v0 are the internal parameters of the camera. Write Equation (1) in matrix form, as in the following equation:
(2)uv1=1/dx0u001/dxv0001xy1.Conversion of world coordinates to camera coordinates can be achieved by rigid body transformation.The mathematical expression of rigid body changes between them is shown in the following equation:(3)XcYcZc=r00r01r02r10r11r12r20r21r22XwYwZw+TxTyTz.Namely, inXc=RX+T,Xc represents the camera coordinate system, X represents the world coordinate system, R represents rotation,T represents translation, and R,T are the external parameter of the camera.


## 3.2. Software Design of Motion Control System

The software design of motion control system mainly includes controlling motor speed and writing control algorithm suitable for motion as well as optimizing path to improve the efficiency of picking up tennis balls [21, 22].In this study, PID algorithm is used to control the motor speed. PID controller measures the deviation between the actual value and the expected value of the controlled variable and corrects the system accordingly, so as to achieve the purpose of regulating the system. The control system schematic diagram is shown in Figure2.Figure 2
PID control system schematic diagram.In PID control system,rt is the target value set by the system, yt is the actual value of the output and feedback of the system, et is the deviation, and it is the difference value betweenrtandyt. PID control can be expressed as the following equation:
(4)ut=Kpet+1Ti∫0tetdt+TDdetdt.Kp, Ti, and TD are proportional coefficient, integral time, differential time, respectively. These three parts jointly affect the system. Formula (4) is a continuous PID algorithm calculation formula, most of the practical application of discrete digital controller, and digital controller algorithm is usually divided into position and incremental PID control algorithm [23].Position PID is a kind of sampling control. It calculates the control quantity of the motor according to the deviation value at the moment. Therefore, Equation (4) must be discretized and approximated by using and replacing integral and difference to replace differential to get the following equation:
(5)uk&=Kpet+1Ti∫0tetdt+TDdetdt≈Kpek+KpTTi∑j=0kej+KpTDTek−ek−1.ek and uk are, respectively, the deviation value and the control quantity at the time of the first sampling. The incremental PID is to calculate the increment of control quantity of two adjacent samples Δuk=uk−uk−1. The incremental PID is used to control the motor speed. The incremental PID control system is shown in Figure 3 below.Figure 3
Schematic diagram of incremental PID control system.As can be seen from Equation (5), the location-based algorithm accumulates errors, occupies a large storage unit, and is difficult to write programs, so it is improved. The expression can be deduced from Equation (5) as the following equation.
(6)uk−1=Kpek−1+KpTTi∑j=0k−1ej+KpTDTek−1−ek−2.Then, the incremental PID control calculation formula is shown in Equation (7).
(7)Δuk=Kpek−ek−1+KpTTiek+KpTDTek−2ek−1+ek−2.Compared with the position algorithm, the incremental algorithm does not need to do cumulative calculation, calculation error has less influence on the calculation of the control quantity, the cumulative error is small, the computer output controls quantity increment, and misoperation has less influence.


## 3.3. Ant Colony Algorithm to Solve the Optimal Path

For the randomly distributed tennis ball collection problem, this paper adopts ant colony algorithm to solve the multiobjective path planning problem. The algorithm is the ant colony algorithm proposed by MarcoDorigo inspired by the ant foraging process [24].The algorithm mimics the way ants guide other ants by releasing secretions called pheromones during foraging. Because pheromones are volatile, the concentration of pheromones along the path of the ant colony decreases over time, and the shorter the distance, the more pheromones are retained [25]. Ants will forage along the path with high pheromone concentration, and the path with high pheromone concentration will accumulate more ants, showing a positive feedback. Two key steps in ant colony algorithm are the probability selection of target and pheromone updating method.
(1)
Probabilistic Choice. In the initial state, the pheromone quantity on each path is the same. Set the initial τij0=C0, C0 as a constant, and the ant kk=1,2,⋯,m makes a judgment according to the visibility of the target point and the pheromone quantity on the path in the process of finding the path. The probability t that the ant k located at the tennis ball moves to the tennis ball j as in the following equation:(8)Pijkt=τijtα×ηijtβ∑k∈allowedτiktα×ηiktβ,ifj∈allowedk,0others.τijt is the pheromone intensity on the path of t time connection i and j. ηikt=1/dik, and it is the visibility of the ants at any moment. allowedk is a collection of all tennis target points that have not yet passed. α,β are two constants and are the weighted values of pheromone intensity and visibility, respectively.
(2)
Pheromone Update. After searching all the tennis balls, update the pheromone on the path according to Equation (9).(9)τijt=1−ρτij+∑k=1mΔτijk.m is the number of ants, ρ is pheromone volatilization coefficient, which represents the loss degree of information on the path, and Δτijk is the pheromone left by the first ant on the path in this cycle and is in the initial stage of search. The beginning of the search Δτijk=0.
(10)Δτijk=QLK,ifthekthanttraversesitoj,0,others.Δτijkt represents the influence value of the first ant on the amount of information on the path i,j in this search. Q denotes pheromone intensity, which affects the search speed of the algorithm: LK denotes the path length acquired by the kth ant in this search. Figure 4 shows the flow chart of ant colony algorithm. In order to verify the effectiveness and feasibility of ant colony algorithm for multiobjective path planning, Matlab was used for programming simulation, and the simulation results were compared with greedy algorithm.Figure 4
Flow chart of ant colony algorithm.


## 4. Interpretation of Result

### 4.1. Tennis Ball Positioning Experiment

In order to verify the effect of this algorithm on tennis ball positioning, experimental verification is conducted. Due to the limited experimental conditions, the real three-dimensional coordinates of the tennis center cannot be measured accurately during the experiment, so the positioning error cannot be calculated accurately. Therefore, in this experiment, the movement of the binocular camera is precisely controlled by virtue of the precision movement of the six-axis mechanical arm, and the positioning error is calculated by using its precise relative displacement to verify the positioning effect of the tennis ball, as shown in Figures5 and 6.Figure 5
Analysis diagram of two adjacent positioning errors.Figure 6
Current total positioning error analysis diagram.It can be seen from Figures5 and 6 that the two adjacent positioning errors and the current total positioning errors fluctuate around -0.05 mm and -2.29 mm, respectively, and their fluctuation ranges are less than 3.50 mm, indicating that the positioning method can accurately position tennis balls and meet the positioning accuracy requirements of tennis balls.


### 4.2. Comparing Algorithm

In order to better compare the advantages of ant colony algorithm and greedy algorithm, a simulation experiment was conducted on the path planning of 30, 35, 40, 45, and 50 tennis balls, respectively. Five experiments were conducted for each problem scale to calculate the average value as the experimental results for comparison, and the results are shown in Figure7.Figure 7
Comparison of algorithm data.It can be seen from the experimental results that the ant colony algorithm is superior to the greedy algorithm in the path planning of collecting tennis balls. The number of iterations of the ant colony algorithm for the optimal path planning of 30 to 50 tennis balls is about 20 to 30 times. The path length is reduced to reduce the collection time of tennis balls to meet the actual work requirements.


## 4.1. Tennis Ball Positioning Experiment

In order to verify the effect of this algorithm on tennis ball positioning, experimental verification is conducted. Due to the limited experimental conditions, the real three-dimensional coordinates of the tennis center cannot be measured accurately during the experiment, so the positioning error cannot be calculated accurately. Therefore, in this experiment, the movement of the binocular camera is precisely controlled by virtue of the precision movement of the six-axis mechanical arm, and the positioning error is calculated by using its precise relative displacement to verify the positioning effect of the tennis ball, as shown in Figures5 and 6.Figure 5
Analysis diagram of two adjacent positioning errors.Figure 6
Current total positioning error analysis diagram.It can be seen from Figures5 and 6 that the two adjacent positioning errors and the current total positioning errors fluctuate around -0.05 mm and -2.29 mm, respectively, and their fluctuation ranges are less than 3.50 mm, indicating that the positioning method can accurately position tennis balls and meet the positioning accuracy requirements of tennis balls.


## 4.2. Comparing Algorithm

In order to better compare the advantages of ant colony algorithm and greedy algorithm, a simulation experiment was conducted on the path planning of 30, 35, 40, 45, and 50 tennis balls, respectively. Five experiments were conducted for each problem scale to calculate the average value as the experimental results for comparison, and the results are shown in Figure7.Figure 7
Comparison of algorithm data.It can be seen from the experimental results that the ant colony algorithm is superior to the greedy algorithm in the path planning of collecting tennis balls. The number of iterations of the ant colony algorithm for the optimal path planning of 30 to 50 tennis balls is about 20 to 30 times. The path length is reduced to reduce the collection time of tennis balls to meet the actual work requirements.


## 5. Conclusion

At present, there are few intelligent tennis collecting robots on the market, and most of the existing equipment is simple auxiliary ball-picking device, which cannot achieve efficient and autonomous collection of tennis balls. For athletes and amateurs in training, picking up loose balls on the court can be a grueling, manual labor that can cost a lot to hire caddies. In view of the difficulty in picking up balls, this topic studies the key technology of tennis collecting robot and tries to develop an intelligent tennis collecting robot based on vision to realize the autonomous collection of tennis balls instead of manual work. The main work and research results of this topic are as follows:(1)
Tennis Positioning. A stereo matching algorithm is proposed, which takes the tennis ball recognition region as the basic element and the tennis ball center as the point to be matched. The matching of tennis target area is completed, and the coordinate of three-dimensional space is calculated by parallax, and the high-precision positioning of tennis ball is realized. The algorithm has short matching operation time and high accuracy(2)
Motion Control. The motion model of the two-wheel differential drive is designed to complete the hardware and software design of the control system and the path planning of the ball picking. Matlab programming is used to complete the simulation experiment of ball-picking path planning in the simulation of actual working environment, and the feasibility of the algorithm is verified



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