Chapter 1 Overall Scheme Design and Demonstration After investigation, research and analysis, comprehensive consideration of various tennis court environment, tennis picking efficiency and other factors, finally determine the roller drive as the main body of the product receiving device, combined with Pixy visual recognition system and Arduino single-chip microcomputer control system to design an intelligent tennis picking robot, the design makes the tennis pick more intelligent and efficient, more stable performance and lower cost, can completely replace the human to pick up tennis. The overall design scheme consists of five systems: vision system, drive system, ball receiving system, ball storage system and control system. In terms of tennis recognition, we use Pixy vision sensor as the eyes of the robot. It uses color to recognize objects and selectively processes useful information to quickly and accurately complete the recognition of tennis targets. In terms of the storage device, we designed a roller with a length of about 33cm to collect the ball, which can pick up multiple tennis balls at a time during the traveling process, thus greatly improving the efficiency of tennis ball picking, at the same time, the ball storage bin is set at a certain angle, which can also efficiently complete the ball unloading work. In terms of robot motion control, we use ARM10 as the upper implementation of machine path optimization and other functions. Arduino microcontroller as the lower computer processor, programming motor speed and steering control. In the aspect of path optimization, after comparing the three preliminary algorithms on Matlab software, the algorithm based on the fusion of improved ant colony algorithm and genetic algorithm is used to plan the optimal path of picking. This algorithm not only overcomes the problem that ant colony algorithm is easy to fall into local optimum, but also improves the efficiency of the algorithm by using the crossover and mutation operation of genetic algorithm. The design idea of the system is: (1) After the vision sensor intelligently recognizes the tennis target, the tennis position information is transmitted to the upper computer; (2) After the upper computer obtains the position information, it will automatically judge the distance between the tennis ball and the intelligent tennis picking robot, find out an optimal path, and pass the information to the lower machine, the lower machine combines the algorithm to automatically optimize the path to find the first goal; (3) After determining the target, the single-chip microcomputer controls the motor speed and steering, and drives the intelligent tennis picking robot to pick up the tennis ball; (4) in order to avoid the intelligent tennis pick-up robot in the process of moving accidents, in the car around the installation of infrared photoelectric sensor, write the corresponding program to achieve obstacle avoidance function Kan Chapter 2 Mechanical System Design Scheme The tennis ball enters the ball storage system under the joint action of the squeezing force and friction force between the roller and the bottom plate. By adjusting the relative position of the roller and the ground and the distance between the roller and the bottom plate, it is used to collect different kinds of balls. 2.1 ball collection system In the ball receiving device, the material of the roller is double-sided felt with a thickness of 5mm, and the double-sided felt has strong elasticity and high friction coefficient. The roller is parallel to the bottom plate and the distance between the two is slightly less than the diameter d of the tennis ball (the diameter of the tennis ball is between 65mm and 68mm, when the pressure of 18 pounds or 8.165kg is applied to the ball, the inward elastic deformation should be between 5.59mm and 7.37mm, and the range of elastic deformation after compression should be between 8mm and 10.8mm). The distance between the roller and the bottom plate is fixed, which is slightly smaller than the diameter of the tennis ball, and the tennis ball is smoothly transferred to the ball storage device under the action of the squeezing force and the friction force. In order to ensure the efficiency of picking up tennis balls, in the process of picking up tennis balls, the tennis balls must be purely rolled, and the tennis balls and rollers do not slip, and the tennis balls and the bottom plates do not slip. 2.2 drive system The drive system is based on the control of the single-chip microcomputer. The four wheels of the chassis are driven by the motor. When moving forward, the four wheels rotate at a constant speed, reverse when moving backward, turn left when the two wheels on the left are reversed, and turn right when turning right. 2.3 technical performance index The technical performance indicators of the storage device are as follows: Overall size 480 × 480 × 300 mm3 Drive mode Motor drive Drive power DC24 V Walking direction forward, backward, turn left, turn right Travel speed 2.7 m/s Turning radius 35mm The collection efficiency is half a tennis court. The collection time is about 1 minute and 10 seconds. Storage bin size 400 × 210 × 230 mm3 Collecting ball roller diameter: 60mm Length: 33cm Power: 15W Angular velocity: 2000 r/min Vehicle mass 20kg Power 150W Payload 25kg (approximately equal to 125 tennis balls) Kan Chapter 3 Electronic Control System Design The intelligent tennis robot obtains the information of the ground environment through the front camera, and realizes the recognition of the tennis ball after being processed by the main CPU. The intelligent tennis picking robot realizes obstacle avoidance function through multi-sensor data fusion method, and the detection obstacle avoidance distance can be adjusted according to actual needs. The six parts of the ball picking robot system are: image acquisition device, obstacle avoidance device, power drive device, ball picking counting device and information processing device. The cooperative work of multiple devices greatly improves the ball picking efficiency of the intelligent tennis ball picking robot system. 3.1 Control Center Module The control center module of the intelligent tennis pick-up robot mainly includes two parts. The first part mainly includes: main CPU, power supply, memory and Pixy camera. The second part is the first part of the auxiliary devices, mainly: CPU, infrared sensors, steering gear and DC motor. The two parts communicate through the serial port. 3.2 visual recognition system The Pixy sensor is equipped with a camera and an independent image processor, using a color recognition method, and can selectively process useful information. The tracking target acquisition device of the intelligent tennis picking robot uses the Pixy vision sensor to lock the tennis position by identifying the shape and color of the tennis ball, and then feeds back the position coordinates to the upper computer. 3.3 control system (1) Drive system The 4 wheels use maxon brand RE25 model, the conveyor belt uses maxon brand RE24 model, and the motor drive control module uses L298N chip to control the steering and speed of the motor. The motion state of the robot is realized by the speed difference between the inner and outer wheels, and the motor has three states of forward rotation, reverse rotation and stop. (2) Obstacle avoidance system If there is an obstacle that hinders the infrared light transceiver path, the infrared photoelectric sensor will transmit the signal to the single-chip microcomputer. The single-chip microcomputer realizes the function of forward, backward and steering of the robot by controlling the steering and speed of the motor, so as to achieve the purpose of avoiding obstacles. The obstacle avoidance function of the robot is realized by infrared photoelectric sensor and corresponding program. The photoelectric sensor uses the principle of reflection of the object to the infrared beam, and judges whether the object exists according to the strength of the received reflected beam. In the process of driving, the robot uses four photoelectric sensors to identify the surrounding environment and objects, which makes the judgment of obstacles more accurate and more effective for obstacle avoidance. Kan Chapter 4 Path Planning Intelligent tennis robot is a kind of autonomous mobile robot, which is different from the general mobile robot in that it can use visual sensors to search for multiple targets. For the intelligent tennis robot, to achieve the task of picking up multiple spheres, the most critical problem is whether it can plan a reasonable path to complete the task of picking up the ball. Ant colony algorithm has parallelism and positive feedback that other intelligent algorithms do not have, and has certain advantages in solving complex optimization problems, especially suitable for multi-objective path planning problems of ball-picking robots. Introduction of 4.1 Ant Colony Algorithm Ant colony algorithm refers to the coordination of their respective action directions through perceptual pheromones between ant groups, and in a certain period of time, when the pheromone concentration on one of the paths is high enough, the ant colony will show the consistency of the group, so as to find the optimal or near-optimal path in the current state. 4.1.1 The implementation steps of the ant colony algorithm. In the tennis court where the intelligent tennis robot works, it is necessary to move from the starting point to the end point in the shortest time, and all the tennis balls must be passed. That is, the given constraint is that the algorithm can make the ants find all the food by searching. So, the process of the ant colony searching for food sources is as follows: (1) In the specified environment, there are m ants, n food sources, and the location coordinates of the food source are known. The ant chooses the path to take according to the pheromone concentration on each path and the distance of the next target relative to the ant's current position. (2) At the initial moment, the ant colony will randomly select the path to walk, and then leave the pheromone. When all the ants have reached the end, the global pheromone is updated and the ant colony is placed back at the starting point. (3) In a period of time, the pheromone concentration on each path accumulates or volatilizes. The ant colony chooses the path according to the pheromone concentration on the path. After repeating, the ant colony will finally get two results: find a path from the starting point to the ending point and pass through all food sources; Reaching the upper limit of search time or iteration number, falling into local optimum and not searching for the optimal path. 4.1.2 The characteristics and shortcomings of the ant colony algorithm. Through the above analysis of ant colony algorithm, some characteristics and shortcomings of ant colony algorithm can be summarized. characteristics of ant colony algorithm: (1) Ant colony algorithm has the ability of self-organization. (2) Ant colony algorithm is a parallel search algorithm. (3) ant colony algorithm is a positive feedback guided algorithm. the lack of ant colony algorithm: The application of basic ant colony algorithm in path planning mainly relies on the combination of pheromone concentration guidance and heuristic search to complete the path search, it is precisely because of these characteristics that the algorithm has a large amount of calculation. If you want to use the advantages of the ant colony algorithm without being held back by its advantages, you need to improve and upgrade the basic algorithm. 4.2 genetic algorithm Genetic algorithm is a search algorithm with randomness, which can dynamically adjust the direction of search, and has good parallelism and global search ability. Mainly used to solve optimization problems, the search process of genetic algorithms is enlightening, thanks to its evolutionary properties, so that the search process will appear some reliable solutions. However, if the genetic algorithm is not properly set, it may cause the algorithm to fall into local optimization. 4.2.1 The implementation steps of the genetic algorithm. The six main components of genetic algorithm are: the setting of algorithm parameters, the method of generating the initial population, the coding method, the genetic operation, the evaluation function, and the termination condition of the algorithm. If you want to use genetic algorithms to solve real-world problems effectively, you need to deal with each part of it. 4.2.2 The characteristics and shortcomings of genetic algorithms. Genetic algorithm is a general algorithm to solve search optimization problems. In genetic algorithms, parallel search based on chromosome groups (in this work, the path information searched in the path planning process), genetic operations such as path selection, crossover and mutation with randomness. These operations are unique to genetic algorithms. Characteristics of Genetic Algorithms (1) Genetic algorithm as an optimization algorithm, in the early stage of the search run, the genetic algorithm is randomly generated from a certain number of solutions to start the search. It is because of this feature that genetic algorithm has the advantage of global optimization, while most other optimization algorithms are easy to fall into local optimization. (2) The parallel search ability of genetic algorithm enables it to operate on multiple obtained solutions at the same time, which further reduces the probability of falling into local optimum. Genetic algorithm also has the ability of self-adaptation, self-organization and learning. In the process of search evolution, randomness makes it have global search ability, while crossover and mutation operations make it avoid falling into local optimum, reflecting its adaptability. In the seemingly random search process, the solution obtained by the group is consistent, which reflects the self-organizing ability of the genetic algorithm. The continuous accumulation of effective paths and the emergence of subsequent close to optimal paths are due to their own learning ability. The main shortcomings of genetic algorithms are: (1) The constraints in the real problem are difficult to express comprehensively in the genetic algorithm, which leads to the increase of search time. (2) The initial genetic algorithm basically relies on random search, resulting in a large number of redundant iterations, so its search efficiency is lower than other algorithms. (3) Although the genetic algorithm has parallelism and self-organizing ability, if the parameters are not set properly, the algorithm search is easy to converge to a local solution prematurely. Working Environment and Modeling of 4.3 Ball Picking Robot The working environment of the ball-picking robot is a tennis court. After obtaining the environmental information through the top visual sensor in the field, the information is directly processed. After all the environmental data learning is completed, the environment is divided by grid method, and the grid size can be adjusted according to actual needs and processing capacity. According to the standards of the International Tennis Federation, the size of the standard tennis court is 36.57m * 18.28m. In this work, there are 1520 grid spaces after modeling, and the size of a single grid is 0.5m * 0.5m. In the plane space, the coordinate origin is O, the vertical axis is y, and the horizontal axis is x. Establish a system Cartesian coordinate system. The maximum values of the grid space in the horizontal and vertical directions are x and y, respectively. To make it easier to understand intuitively, now let x = y = 10, with each grid occupying one width. In order to facilitate the calculation of the location coordinates of each grid in the program, each grid of the grid environment in this paper has its own unique serial number, and the grid serial number can be converted to the coordinate information of the location of the grid. Multi-objective path planning 4.4 on improved ant colony algorithm Ant colony algorithm can find all the target depends on the node transfer strategy and pheromone update strategy. For multi-objective path planning, in the process of searching the path of the algorithm, the parameter factors of different values can be used according to the characteristics of different running stages, so as to improve the efficiency and accuracy of the algorithm's path search. 4.4.1 Improved ant colony algorithm and rules In the search process, the basic ant colony algorithm relies on random selection strategy to construct the path, which makes the path search speed slow and easy to make the algorithm fall into local optimum. In order to solve these problems, this work has made improvements and optimizations, which can not only speed up the search speed, but also effectively avoid the algorithm from falling into local optimum. 4.4.2 Improved ant colony algorithm Through the in-depth analysis of the multi-objective path planning process of the ant colony algorithm, it is found that different search stages have different requirements for algorithm parameters, and this paper proposes an improved ant colony algorithm that adaptively changes the parameters of the algorithm. In the search process of this algorithm, several ants search for multiple targets at the same time. By adaptively adjusting the pheromone heuristic coefficient, distance heuristic coefficient, pheromone volatilization coefficient, pheromone quantity Q and population size m, the search speed can be accelerated and the global search ability can be improved. 4.5 Fusion Algorithm Based on Improved Ant Colony Algorithm and Genetic Algorithm 4.5.1 Fusion algorithm description In view of the characteristics of multi-target sphere picking in this paper, the ant colony algorithm is easy to fall into the problem of local optimization, and the improved ant colony algorithm proposed in this paper, although the defects of the ant colony algorithm have been greatly improved, but the existing problems are still not completely eliminated. After the improved ant colony algorithm and genetic algorithm are integrated, the problems of ant colony algorithm can be further improved. Moreover, drawing on the crossover and mutation operations of genetic algorithms can increase the escape ability of the algorithm and improve the efficiency of the algorithm. 4.5.2 Fusion mode Different fusion methods will determine the performance orientation of the algorithm after fusion, so how to choose the fusion method is a key issue in this paper. The fusion mode of genetic algorithm and improved ant colony algorithm can be divided into before and after stage fusion and multi-stage cross fusion according to the timing of fusion. Although the front and back stage fusion surface draws on the advantages of the two algorithms, it shortens the search time of the algorithm to a certain extent, but in addition to shortening a little time, the fusion algorithm does not have much advantage over the improved ant colony algorithm proposed in this paper. The multi-stage cross-fusion method proposed by the work, in addition to drawing on the ability of genetic algorithm to quickly establish initial path information in the early stage, in the whole search process of the algorithm, the genetic algorithm and the improved ant colony algorithm always blend and influence each other. The main operations related to genetic algorithms are as follows: (1) After the local pheromone update, the current valid path and the current worst path are marked in the genetic algorithm.High probability of crossover and mutation of late choice paths. (2) Also after the global pheromone update, the current optimal or near-optimal path obtained is marked, and the worst is marked as an alternative path for the intersection. (3) Specify the timing of genetic manipulation according to the size of the problem, and when the timing is ripe, start genetic manipulation. (4) After a period of time and does not reach the upper limit of the number of iterations, after many times of comparison of the current global optimal path records, if the change is found to be extremely small or no change, it is determined that the current algorithm has fallen into local optimization or algorithm search stagnation. At this time, the crossover and mutation operation of the genetic algorithm is used again to make the algorithm escape from the current state and continue to search for the path. Through the fusion algorithm composed of these algorithms cooperating with each other, the search time of the algorithm can be shortened, the diversity of solutions can be increased, and the global search ability of the algorithm can be improved. 4.5.3 Crossover and mutation treatment (1) Cross-processing In the search process of the algorithm, when the cross condition is met, the cross operation is performed on the obtained path. The number of crossings in the process of each crossing can be set independently, or the crossing can be performed according to the default parameters. When selecting a path to cross, the cross operation is performed between the optimal path obtained by this search and a randomly selected path or the current worst path. If the two paths have the same tennis target point, a same tennis target point is randomly selected to start the cross processing; if the two paths do not have the same tennis target point, the cross processing is not performed. If, after the cross processing, a path shorter than the current optimal path is generated, the cross operation is successful, and the current optimal path is replaced with the shortest path and the pheromones of the path are updated; otherwise, the search continues or the next cross operation is performed. (2) Variation treatment When the search process of the algorithm meets the mutation condition, the mutation processing is performed on the obtained path. When selecting the path to be mutated, there is no special requirement for the path to be mutated. In the process of each mutation, the number of paths to be mutated can be set independently, or mutated according to default parameters. 4.6 mode selection The tennis ball picking robot can be divided into the following three modes in the work project: (1) Cruise mode In the cruise mode, the robot will not enter the court to pick up the outfield ball outside the court according to the positioning system during the tennis movement, and will not hinder the tennis movement in the court. (2) Half-time mode The half-court mode is applied to the rest process of athletes after playing. The robot picks up half of the court tennis through programming, and then completes the process of picking up the ball in the other half. (3) Priority mode The priority mode is to set the tennis ball in the court to a higher priority, first collect the tennis ball in the court, clear the sports field for the players, and then carry out the outfield picking process similar to the cruise mode. Kan Chapter 5 Overall Functional Testing This work through the mechanical structure, control system and the overall path planning design of the intelligent tennis pick-up robot work process, and through the program control to enable its autonomous planning path and can complete the process of autonomous pick-up. Next, the working efficiency of the intelligent tennis picking robot is tested systematically and the error in the process of picking up the ball is analyzed. 5.1 System Testing The overall function of the pick-up robot was tested in the tennis court. The test includes the accuracy, real-time and robustness of the tennis recognition process, the test of the picking efficiency of the tennis picking mechanism, and the test of the stability, fluency and rapidity of the motion system. The overall height of the intelligent tennis pick-up device is 300cm, the width is 480cm, and the length is 480cm. The upper computer uses ARM10 to complete the tennis recognition function, and the lower computer uses Arduino microcontroller to control the pick-up mechanism and sports system. Tennis pick up process, each pick up up up to 5 tennis, pick up efficiency and belt drive speed, tennis ball density. The speed of the transmission belt is 2 m/s, and the distribution of tennis balls is particularly dense, up to 150 tennis balls can be picked up per minute, and the ball picking efficiency is high. The speed of the motion system is 2.7 m/s, and the motion is stable and smooth. In summary, the robot involved in this product can accurately identify tennis balls in real time, and the ball collection system can efficiently pick up tennis balls during the movement to achieve the desired function. 5.2 error analysis At present, tennis courts are composed of several tennis courts. Although this intelligent tennis picking robot can only collect tennis balls in this court through positioning, if tennis balls of the same type and color from other courts enter by mistake, the robot will regard it as a target tennis ball and will also plan and pick it up. There are errors in the picking process, but for a single tennis court, there is no need to consider the error. Chapter 6 Summary In view of the problem of picking up the ball in the process of tennis, the robot can intelligently identify the tennis target, automatically optimize the path and complete the tennis pick-up. The design process mainly includes the following aspects: (1) Design the mechanical structure of the robot, mainly including the design of the ball receiving device and the driving device, to ensure that the robot completes the ball receiving work (2) Design the electronic control system of the robot, including visual recognition, control system, obstacle avoidance system and remote monitoring terminal. (3) The improved ant colony algorithm and genetic algorithm are combined to optimize the path of tennis pick-up and improve the efficiency of picking up the ball. Compared to the current market molding products only pure mechanical hand-held tennis pick-up device, this design has a great market value advantage. It can increase the sports experience of tennis lovers, increase the fun of tennis lovers, reduce additional physical consumption, fill the gap of ball picking devices on the market, and have certain social benefits.