Pickleball training robot and method and system for perceiving trainer's level

Abstract

The present invention relates to a pickleball training robot and a method and system for perceiving a trainee's level; the robot comprises a moving module, a ball receiving module, a ball serving module, a positioning module, and a control circuit board; the method for perceiving a trainee's level comprises establishing a coordinate system with the midpoint of the trainee's opponent's baseline in a pickleball court; adopting a Kriging proxy model to construct a batting performance model under a complex fixed-point ball path; establishing a training task difficulty quantification model that considers cognitive complexity; calculating the trainee's performance under a determined task difficulty, thereby evaluating the trainee's comprehensive ball return level; through in-depth analysis of the player's task complexity, accurately understanding the player's weak links and providing targeted guidance and suggestions, thereby promoting the improvement of the player's pickleball skills and optimizing the training effect; the present invention realizes complete pickleball launch trajectory control capability, and the pickleball serve time, serve speed and rotation degree, and serve angle can all be adjusted to achieve diversity and personalization of simulated playing.

Description

Technical Field

[0001] The present invention belongs to the technical field of sports robots and relates to a pickleball auxiliary training device. Specifically, it relates to an automated device that can cyclically receive and serve the ball and perform sparring training with a pickleball trainee, as well as a method and system for sensing the trainee's hitting level. Background Art

[0002] Pickleball, a new sport that blends elements of tennis and table tennis, has seen a surge in popularity and participation globally in recent years. While relatively easy to learn, improving pickleball skills requires long-term, diverse practice to enhance the player's ability to predict the ball's trajectory and finely control various body parts.

[0003] Pickleball training involves a human-machine interaction problem involving the player, the machine, and the ball. Applying intelligent training equipment and establishing a scientific human-machine interaction training model will significantly reduce training time and costs. While numerous training robots for table tennis, badminton, tennis, and other sports are currently available on the market, there are very few specialized pickleball training robots. Pickleball serving machines are immature and limited in use. Most are modified from other racket-based serving machines. They can only effectively control the placement of different ball feeds, but their ball delivery positions are fixed and they often require multiple balls to operate. After each training session, they must be collected and refilled. Existing robots lack scientific and intelligent human-machine interaction training methods and can only feed balls to the player according to a pre-set serving trajectory. Developing a new pickleball training robot, endowed with rapid omnidirectional movement, effective ball recovery, and precise and maneuverable serving capabilities, and developing methods for sensing and analyzing the player's batting ability, will enable the training system to adjust to the appropriate training difficulty and optimize training efficiency. This technical equipment can help trainees improve their ball skills, save training costs, provide effective tools and new models for sports enthusiasts, and promote a new intelligent training format that integrates man and machine. It is expected to rely on advanced scientific and technological means to improve training results and promote the intelligent development of ball sports training. Summary of the Invention

[0004] The purpose of the present invention is to provide a pickleball training robot that has the functions of automatically and cyclically sending and receiving pickleballs and timely directional movement, and further establishes a quantitative model of the difficulty of pickleball hitting tasks, dynamically evaluates the trainee's ball return performance, so that the robot can adapt to the trainee's performance and carry out corresponding ball feeding training, providing the trainee with a scientific training tool.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions, which are described as follows in conjunction with the accompanying drawings:

[0006] A pickleball practice robot, comprising a moving module, a ball receiving module, a ball serving module, a positioning module, and a control circuit board;

[0007] The mobile module serves as the chassis of the robot and is set below the ball-serving module, while the ball-receiving module is set above the ball-serving module.

[0008] The positioning module includes a binocular camera, an ultra-wideband positioning base station, and a location tag;

[0009] The ultra-wideband positioning base stations of the positioning module are set up at the four corners of the pickleball court, the location tags of the positioning module are installed on the control circuit board, and the binocular camera of the positioning module is set on the chassis; the control circuit board is installed on the mobile module.

[0010] Furthermore, one of the ultra-wideband positioning base stations set up in the four corners is the main base station, which is connected to the computer and can read the positions of the location tags and the base stations in the four corners respectively, thereby calculating the position coordinates of the mobile module through a formula.

[0011] Furthermore, the binocular camera can capture RGB images and depth images of the scene at high speed, use a conventional neural network to identify the plane coordinates of the pickleball in the RGB image, and combine the depth information of the binocular camera to comprehensively form the spatial position coordinates of the pickleball; predict the landing point of the pickleball through the pickleball aerodynamic model, and send the result to the control circuit board via Wi-Fi; similarly, the position coordinates of the trainee on the court are measured and sent to the control circuit board.

[0012] Furthermore, the mobile module uses a wheel-foot mechanism; a housing is installed above the mobile module, and the housing contains a serving module, which is connected to the ball net of the ball receiving module above through a pipe;

[0013] The mobile module is driven by wheels, connecting parts and a drive system. When the motor wheels rotate, the pickleball training robot can move at a variety of speeds and directions. The wheel-foot mechanism can also flexibly adjust the height and angle of the pickleball serving and receiving.

[0014] The serving module launches the pickleball in front of the trainee's coordinates and requires the trainee to return the ball with the robot's location as the target; after the trainee hits the ball, the binocular camera captures and calculates the landing point of the pickleball, and the control circuit board controls the motor wheel in the mobile module to rotate, so that the mobile module moves to the landing point of the pickleball to the receiving module to receive the ball. At the same time, the control circuit board controls the serving module to launch the next pickleball.

[0015] Furthermore, the serving module is mainly composed of a support frame and a pickle ball propulsion mechanism; the support frame is located on one side of the serving module and is connected to the chassis by bolts at the bottom; the pickle ball propulsion mechanism is composed of a support plate, a pickle ball propulsion device, a ball shooting barrel and a ball storage barrel, etc. The support plate is fixed on the support frame, the ball shooting barrel and the ball storage barrel form a three-way pipe and are fixed by the support plate, the pickle ball propulsion device extends into the ball shooting barrel from one side opening of the three-way pipe, and the other side opening faces the gap between the two friction wheels, the upper side opening is the ball storage barrel and is connected to the ball dropping hole of the ball receiving module.

[0016] Furthermore, the ball receiving module is mainly composed of a ball receiving hole, a ball receiving net, and a bracket. The ball receiving hole is integrally connected to the ball receiving net, with a mesh hole the size of a pickle ball left in the middle. The lower end of the mesh hole is connected to the ball shooting tube and the ball storage tube to form a three-way pipe; the ball receiving net is made of low-elasticity mesh knitted fabric and is fixed to the ball receiving frame by four adjustable brackets; the shape of the ball net is high on all sides and low in the middle, with a ball landing hole with an aperture slightly larger than the diameter of a standard pickle ball in the middle, which is connected to the ball storage tube of the serving module; the bracket is installed under the ball net, the upper end supports and connects the fixed parts of the ball net, and the lower end is fixed to the chassis.

[0017] Furthermore, the control circuit board adjusts the serving speed and rotation by controlling the rotation speed of the left and right friction wheel motors in the serving module; after the trainee hits the pickleball from the robot, the binocular camera provides the pickleball coordinates to the control circuit board, and the ultra-wideband positioning system base station provides the robot position coordinates to the control circuit board, and the control circuit board controls the rotation of the motor wheel in the moving module to move the robot to the landing point of the pickleball; after the ball receiving net in the ball receiving module intercepts the pickleball hit by the trainee, the pickleball falls into the ball receiving hole and rolls into the ball storage barrel; each time a pickleball is launched from the shooting barrel, the ball storage barrel will be refilled with a pickleball to the shooting barrel.

[0018] An adaptive adjustment strategy for the training difficulty of a pickleball training robot:

[0019] First, a coordinate system was established using the midpoint of the baseline of the opponent's opponent on the pickleball court. The variables of the training system were defined, and basic data for quantitative analysis of task difficulty and task performance was obtained. Second, a Kriging surrogate model was used to construct a batting performance model for complex fixed-point ball paths, thereby expanding the batting performance data for partial ball paths to batting performance for all ball paths. Third, a quantitative model for training task difficulty was established that took cognitive complexity into account. Task difficulty and task complexity were scored separately to determine single-task difficulty indicators and multi-task difficulty indicators. Fourth, the trainee's performance under the determined task difficulty was calculated to assess their overall return level. Through in-depth analysis of the player's task complexity, accurate insights into the player's weaknesses were obtained and targeted guidance and suggestions were provided to promote the improvement of the player's pickleball skills and optimize training results.

[0020] Furthermore, a coordinate system is established based on the midpoint of the baseline of the opponent on the pickleball court, and the training system variables are defined, including:

[0021] Let m be the number of trainings, each training contains N rounds, where i represents a round; let the task variable be in the i-th round of the m-th training This includes the speed at which the robot shoots the ball and the distance between the landing point and the player Players in fixed-point task variables The quality of the return ball is reflected in the accuracy of the return ball The ball return accuracy is calculated as:

[0022]

[0023] Where, is the deviation between the actual landing point of the player's return ball and the target point; A is the set error resolution.

[0024] Furthermore, a batting performance model under complex fixed-point ball paths is constructed, specifically as follows:

[0025] In the entire m training, there are N different combinations of task variables; let represents the set of task variables in the mth training session. The set of player return quality actually measured during the human-computer training is:

[0026]

[0027] Using the Kriging prediction model, the player's response to the task variable x is predicted based on the test results of all historical task variables. m The ball mass Y(x m ):

[0028] Y(x m )=Kriging{y(x m )}

[0029] The predicted value of ball return performance for a task variable is:

[0030] Furthermore, a training task difficulty quantification model that considers cognitive complexity is established, specifically including:

[0031] For the task variable x m Difficulty of hitting the ball C s (x m ) is expressed as follows, where ε is the task difficulty correction factor;

[0032]

[0033] Set batting combination In the example, let the joint probability distribution of ball path i be X i (d,v), the variation between this ball path and the previous ball path is

[0034]

[0035] The cognitive load of a particular shot for

[0036]

[0037] In the mth training cycle, let the cognitive complexity of the i-th shot be We believe that the cognitive complexity of each shot is related to the degree of difference between the previous shots in the same training session. Therefore, we use the exponentially weighted moving average method to calculate the cognitive complexity of the shot:

[0038]

[0039] Among them, β is the weighting coefficient;

[0040] The difficulty index of the task in the i-th round of the m-th training Indicates the difficulty of hitting the ball and cognitive complexity The product of:

[0041]

[0042] The total task difficulty index TCI of the mth training m is the average task difficulty of the entire training process:

[0043]

[0044] Furthermore, the comprehensive level of the trainees' ball return is evaluated, including the following:

[0045] In the i-th round of the m-th training, the task difficulty index The player's return accuracy is The comprehensive return score P of the mth training is m (TCI m )for:

[0046]

[0047] A pickleball training robot's horizontal perception system for a trainee, comprising:

[0048] The basic data acquisition module is used to establish a coordinate system based on the midpoint of the baseline of the opponent on the pickleball court, define the variables of the training system, and obtain basic data for quantitative analysis of task difficulty and task performance;

[0049] Construct a batting performance model module, which uses the Kriging proxy model to construct a batting performance model under complex fixed-point ball paths, thereby expanding the batting performance data of some ball paths to the batting performance under all ball paths;

[0050] Establish a training task difficulty quantification model module, which is used to establish a training task difficulty quantification model that takes cognitive complexity into account. Scoring is performed for task difficulty and task complexity respectively, thereby determining single task difficulty indicators and multi-task difficulty indicators.

[0051] The calculation and evaluation module is used to calculate the trainee's performance under a certain task difficulty, thereby evaluating the trainee's overall level of ball return. Through in-depth analysis of the player's task complexity, it accurately understands the player's weaknesses and provides targeted guidance and suggestions, promoting the improvement of the player's pickleball skills and optimizing the training effect.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The wheeled, footed, and integrated pickleball training robot of the present invention achieves complete control over the pickleball launch trajectory through a clever mechanical structure layout. The pickleball serve time, serve speed, degree of rotation, and serve angle can all be adjusted, achieving diversity and personalization in simulated play.

[0054] 2. The method for quantifying the complexity of pickleball playing robot training tasks described in the present invention comprehensively considers the impact of differences in ball paths such as the distance between the incoming ball and the trainee and the incoming ball speed. It can calculate and analyze the difficulty of training tasks in real time based on training site data, solve the problem that traditional robots lack perception and analysis capabilities, quantify the trainee's comprehensive hitting ability, and provide scientific decision-making for the design of targeted training programs. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to better describe and explain the technical solution of the invention and clearly show the content and structure of the invention, the present invention is further described below with reference to the accompanying drawings:

[0056] Figure 1 This is an axonometric drawing of the overall appearance of the pickleball training robot according to the present invention;

[0057] Figure 2 This is an axonometric diagram of the combination of the mobile module and the serving module of the pickleball training robot according to the present invention;

[0058] Figure 3 This is an axonometric diagram of the mobile module of the pickleball training robot according to the present invention;

[0059] Figure 4 This is an axonometric diagram of the ball collecting module of the pickleball training robot according to the present invention;

[0060] Figure 5 Axonometric view of the serving module of the pickleball training robot according to the present invention;

[0061] Figure 6 This is a left view of the serving module of the pickleball training robot according to the present invention;

[0062] Figure 7 This is a front view of the serving module of the pickleball training robot according to the present invention;

[0063] Figure 8 This is a detailed view of the light strip of the pickleball training robot described in the present invention;

[0064] Figure 9 This is a schematic diagram of the ball collecting function of the pickleball training robot according to the present invention;

[0065] Figure 10 This is a schematic diagram of the working scene of the pickleball training robot according to the present invention;

[0066] Figure 11 This is a flow chart of the control method for the pickleball training robot according to the present invention;

[0067] Figure 12 A schematic diagram of the human-computer interaction method and variables according to the present invention;

[0068] Figure 13 This is a flow chart of the trainee level perception method described in the present invention.

[0069] Description of labels:

[0070] Net bag 1; moving module 2; ball receiving module 3; ball serving module 4; camera group 5; strip light 6; pickle ball 7;

[0071] Left motor wheel 2-1; right motor wheel 2-2; wheel assembly 2-3; left engine assembly (left) 2-4-1; left engine assembly (right) 2-4-2; right engine assembly (left) 2-5-1; right engine assembly (right) 2-5-2;

[0072] Ball receiving net 3-1; ball receiving hole 3-2, bracket 3-3;

[0073] Chassis 4-1; right friction wheel 4-2; left friction wheel 4-3; right motor 4-4; left motor 4-5; ball storage cylinder 4-6; push rod 4-7; friction wheel brackets (two) 4-8; ball launching hole 4-9;

[0074] Left camera 5-1; Right camera 5-2;

[0075] Left light strip 6-1; upper light strip 6-2; right light strip 6-3; lower light strip 6-4;

[0076] Positioning base station 7-1; positioning tag 7-2. DETAILED DESCRIPTION

[0077] In order to make the objectives, technical solutions and advantages of the embodiments of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings.

[0078] In order to make the drawings concise and easy to understand, the figure numbers in the patent of this invention adopt the secondary numbering rule. Not all parts of the robot are marked, but only the relevant parts of the important contents such as the structure and working principle described in this article are marked in detail.

[0079] In the invention described herein, unless otherwise specified or limited, terms such as "mounted," "disposed," and "connected" should be interpreted broadly. For example, these terms may include fixed connections, removable connections, and integral connections; they may be mechanical or electrical connections; they may be direct connections between two components, indirect connections achieved through an intermediary, or even connections within components. Those skilled in the art will understand the specific meanings of these terms in the context of the invention.

[0080] like Figure 1 The figure shows an axonometric view of the overall appearance of the pickleball training robot described in the present invention. The dual-wheeled, integrated pickleball training robot adopts a modular design, with the ball receiving module 3 and the moving module 2 located above and below the robot, respectively. The net bag 1 is located in the middle of the robot, and the ball serving module 4 is located inside the housing.

[0081] The net bag 1 is made of low-elasticity mesh knitted fabric to buffer the speed of the hit pickleball and make the ball roll into the ball receiving hole to prepare for the next serve.

[0082] The mobile module 2 and the control circuit board control the rotation speed of the two motor wheels respectively to realize the directional movement of the robot. At the same time, it can control the four motor groups 2-4 and 2-5 to adjust the height and angle of the wheel groups, maintaining the balance of the robot while realizing the ball receiving and sending functions at different heights and angles.

[0083] The ball receiving module 3 collects the pickleballs through the ball receiving net 3-1 supported by the bracket 3-3, and sends the balls to the ball serving module 4 through the ball storage cylinder 4-6 below the ball receiving hole 3-2.

[0084] The serving module 4 can push the pickle balls collected by the receiving module 3 to the right and left friction wheels 4-2 and 4-3 through the push rod 4-7. The right and left motors 4-4 and 4-5 control the rotation of the friction wheels to make the pickle balls shoot out from the ball outlet 4-9 with adjustable time, speed, rotation and direction.

[0085] The positioning module includes left and right binocular cameras 5-1 and 5-2, which can measure the spatial coordinates of the pickleball, the robot, and the trainer.

[0086] The strip light strip 6 is rectangular and fixed around the net bag. It can display different colors in real time according to the trainee's hitting effect in the form of clockwise flow of left light strip 6-1, upper light strip 6-2, right light strip 6-3, and lower light strip 6-4, giving simple feedback on the hitting effect to the hitter.

[0087] like Figure 2 The figure shows the axonometric view of the combination of the mobile module 2 and the ball serving module 4 according to the present invention. Figure 3 and Figure 7 This is a view showing two modules presented separately. The serving module 4 is installed on the chassis of the mobile module 2 through the chassis 4-1. When the serving module adjusts the shooting angle, the chassis 4-1 supports the serving module to adjust the angle together.

[0088] like Figure 3 The figure shows an axonometric view of the mobile module of the present invention, wherein the metal chassis 4-1 and the four motor wheel sets 2-4 and 2-5 are symmetrically distributed, and the control circuit board is bonded to the middle of the lower end of the chassis.

[0089] like Figure 4 The figure shows an axonometric view of the ball receiving module of the present invention. The bracket 3-3 is fixed to the four corners of the chassis 4-1 to support the net bag 1; the ball receiving net 3-1 and the ball receiving hole 3-2 are integrated into one. The ball receiving net 3-1 is concave with the ball receiving hole 3-2 as the center, so that the pickle ball can roll smoothly into the ball receiving hole 3-2.

[0090] like Figure 5 、 Figure 6 and Figure 7The figures show the axonometric, left-hand, and front views of the ball receiving module according to the present invention. Left and right cameras 5-1 and 5-2 are symmetrically fixed to chassis 4-1. A ball-isolating hole 4-9 faces the gap between the two friction wheels. After being pushed out of the three-way nozzle, the pickle ball is ejected due to the friction generated by the right and left friction wheels 4-2 and 4-3. A pickle ball 7 in the ball storage drum 4-6 enters the ball-isolating hole 4-9. Upon receiving a release time signal, the push rod 4-7 pushes the pickle ball from the drum into the gap between the left and right friction wheels 4-2 and 4-3. The pickle ball is ejected through the friction and compression of the two friction wheels. The friction and relative speed of the two friction wheels determine the ejection speed and degree of rotation of the pickle ball. The initial velocity of the pickle ball is adjusted by varying the rotational speed of the friction wheels. If the linear velocities of the two friction wheels are consistent, the ejected ball remains unrotated. If the linear velocities differ, the ejected ball rotates. The pitch angle of the ejection mechanism is adjusted by controlling the angle of chassis 4-1, thereby adjusting the altitude of the ejected pickle ball. Based on this, the robot can achieve accurate serve.

[0091] like Figure 8 The figure shows a detail of the strip light strip of the present invention, in which the light strip 6 is rectangular and fixed around the net bag 1 .

[0092] like Figure 9 The figure shows the ball-collecting function of the pickleball training robot according to the present invention, i.e., the working principle of the ball-collecting module. The pickleball 7 flies from the opposite side of the court, lands and jumps, touches the net bag 1, decelerates and falls into the ball-collecting net 3-1; the ball-collecting net 3-1 is made of a low-elasticity mesh knitted elastic material, so the pickleball is difficult to bounce out of the net and easily enters the ball-collecting hole 3-2. Figure 9 By adjusting the shape of the wheel set 2-3, the chassis 4-1 is driven to pitch, and the pitch angle or ball receiving area of ​​the net bag 1 and the ball receiving module 4 can be adjusted.

[0093] Figure 10 The figure shows a working scene of the pickleball training robot. A positioning base station 7-1 is arranged at each of the four corners of the pickleball court, and a positioning tag 7-2 is installed on the control circuit board of the pickleball training robot to collect the real-time position coordinates of the pickleball training robot and the trainer.

[0094] The positioning principle of the robot is that four base stations are fixed and a location tag is installed on the robot's mobile module. Then the system can calculate the coordinates of the robot through the distance between the location tag and the four base stations.

[0095] It is an existing technology that an ultra-wideband positioning base station provides the robot position coordinates to the control circuit board.

[0096] One of the four base stations is a main base station connected to a computer, which can read the positions of the tags and the four base stations respectively, and thus calculate the position coordinates of the mobile module through a formula. This is existing technology.

[0097] The left and right cameras 5-1 and 5-2 combine to form a binocular camera, which can quickly capture RGB and depth images of the scene. A conventional neural network is used to identify the plane coordinates of the pickleball within the RGB image. Combined with the depth information provided by the binocular camera, this information is used to determine the spatial coordinates of the pickleball. Similarly, the coordinates of the trainee are obtained. The pickleball's landing point is predicted using the following pickleball aerodynamic model, and the result is transmitted to the pickleball robot via Wi-Fi.

[0098] Binocular cameras are trained through neural networks to identify the spatial position of pickleballs, which is an existing technology;

[0099] Pickleball aerodynamic models are used to predict where a pickleball will land, which is an existing technology;

[0100] The binocular camera sends the predicted landing point of the pickleball to the control circuit board via Wi-Fi, which is an existing technology.

[0101] The tracking algorithm proposed in the linear Kalman filter model that combines prediction information is used for prediction. However, when the number of prediction frames is too large, the accuracy is low. In this case, the prediction is mainly used to assist tracking and improve tracking accuracy. Considering that the pickle ball is mainly affected by gravity and air resistance during movement, a simplified model of the pickle ball position P(t) and time t can be established to fit the pickle ball's motion trajectory:

[0102]

[0103] When calculating, we use the number of frames instead of time. For each three-dimensional coordinate P(t) calculated during tracking, we can establish an equation. At least three points can be used to fit the trajectory of the pickleball. Each time the three-dimensional coordinates of the pickleball are calculated, the model will be updated, and the target landing position P will be predicted based on the updated model. drop and speed v drop After successfully obtaining the coordinate position of the pickle ball in three-dimensional space, these coordinate information can be used to track the motion trajectory of the pickle ball and provide support for the control of the pickle ball training robot.

[0104] During a round of sparring, the pickleball robot's serving module first launches the ball one meter in front of the player's coordinates. The player is then instructed to return the ball towards the robot's location. After the player returns the ball, the binocular camera captures and calculates the pickleball's landing point. Under control of the control circuit board, the robot's mobile module moves to the landing point, where the receiving module collects the ball. Simultaneously, the serving module launches the next pickleball.

[0105] The binocular camera captures and calculates the landing point of the pickle ball, which is the "linear Kalman filter model predicts the landing point of the pickle ball."

[0106] The control circuit board controls the rotation of the motor wheel in the mobile module 2, so that the mobile module moves to the point where the pickle ball lands, and the ball receiving module collects the ball. At the same time, the control circuit board controls the ball serving module to launch the next pickle ball. This is the existing technology.

[0107] Figure 11 This is a flow chart of the control method for the pickleball training robot described in the present invention. The control circuit board adjusts the serving speed and rotation by controlling the rotational speed of the left and right friction wheel motors in the serving module 4. The control circuit board also controls the telescopic position of the electric push rod 4-7. After the trainee hits the pickleball from the robot, the binocular camera provides the control circuit board with the pickleball coordinates, and the UWB provides the control circuit board with the robot's position coordinates. The control circuit board then controls the rotation of the motor wheels 2-4 and 2-5 in the moving module 2, moving the robot to the location where the pickleball landed. Finally, the receiving net 3-1 in the ball collecting module 3 intercepts the pickleball hit by the trainee, and the pickleball falls into the ball receiving hole 3-2 and rolls into the ball storage barrel 4-6. Each time a pickleball is launched from the shooting barrel, the ball storage barrel 4-6 is refilled with a pickleball.

[0108] like Figure 13 As shown, the trainer's level perception method is specifically described as follows:

[0109] 1. Establish a coordinate system based on the midpoint of the baseline of the opponent on the pickleball court and define the training system variables

[0110] During the training process, the robot's serving pattern, the relative position of the robot and the player, and the player's return performance are constantly changing. Let m be the number of training sessions, and each training session contains N rounds, where i represents a certain round. As the number of training sessions accumulates, the player's overall level will gradually improve. In many racket-based ball games, including pickleball, winning mainly depends on speed and accuracy. The robot's ball speed and the distance between the landing point and the trainer are the most important task variables. The trainer's return speed and accuracy under the task variables are the representation of the player's level. For example Figure 12 As shown, in the i-th round of the m-th training, the task variable is This includes the speed at which the robot shoots the ball and the distance between the landing point and the player Players in fixed-point task variables The quality of the ball return is mainly reflected in the accuracy of the ball return. The ball return accuracy is calculated as:

[0111]

[0112] Where, is the deviation between the actual landing point of the player's return ball and the target point; A is the set error resolution.

[0113] 2. Modeling Complex Ball Paths and Batting Performance Through Fixed-Point Batting Training

[0114] In the entire m training, there are N different combinations of task variables. represents the set of task variables in the mth training session. The set of player return quality actually measured during the human-computer training is:

[0115]

[0116] Due to the variety of combinations of task variables, it is impossible to traverse all combinations during the human-computer interaction process. Therefore, this paper proposes to use the Kriging model with high computational efficiency and strong nonlinear modeling capabilities to scientifically predict the player's response to all task variables x based on the test results of some task variables. m The quality of the ball return:

[0117] Y(x m )=Kriging{y(x m )}

[0118] The predicted value of ball return performance for a task variable is:

[0119] 3. Calculation method of training task difficulty considering cognitive complexity The difficulty of dynamic batting training needs to consider not only the difficulty of hitting a certain ball path for the trainee, but also the cognitive complexity of the batting combination for the trainee. m The performance of returning the ball is different, and the difficulty of the same task is not consistent for trainees of different levels. m The difficulty of hitting the ball is negatively correlated with the trainee's ball return performance. Therefore, for the task variable x m The batting difficulty can be expressed as follows, where ε is the task difficulty correction coefficient.

[0120]

[0121] In batting combination In the example, let the joint probability distribution of ball path i be X i (d,v), the variation between this ball path and the previous ball path is

[0122]

[0123] The cognitive load of a particular shot for

[0124]

[0125] In the mth training cycle, let the cognitive complexity of the i-th shot be We believe that the cognitive complexity of each shot is related to the degree of difference between the previous shots in the same training session. Therefore, we use the exponentially weighted moving average method to calculate the cognitive complexity of the shot:

[0126]

[0127] Here, β is the weighting coefficient, which controls the rate of weighted descent; smaller values ​​result in faster descent. This formula uses the minimum quantization error as its cornerstone and combines the influence of the previous cognitive complexity on the current calculation to control the influence and range of the weighting coefficient.

[0128] The difficulty index of the task in the i-th round of the m-th training It is expressed as the product of the difficulty of hitting the ball and the cognitive complexity:

[0129]

[0130] Therefore, the total task difficulty index TCI of the mth training m is the average task difficulty of the entire training process:

[0131]

[0132] 4. Evaluation of the comprehensive level of players' ball return

[0133] The core of the autonomous perception and interaction of the training robot is to effectively evaluate the comprehensive level of the players and formulate corresponding training plans so that the difficulty index of the training task is consistent with the player's level. The player's return accuracy is The comprehensive return score P of the mth training is m (TCI m )for:

[0134]

[0135] like Figure 13 As shown, the specific numerical examples of the trainer's level perception method are as follows:

[0136] Perform the mth (m=5) batting practice, which consists of a set of ball paths x5:

[0137] 1) In this training session, the deviation d between the actual return ball landing point and the target point error (x5) = {10, 20, 30, 40, 50}, substitute Where A = 10 cm, and the ball return accuracy is y(x5) = {1, 0.5, 0.33, 0.25, 0.2};

[0138] 2) Build a Kriging model of player performance based on historical batting performance Substitute x5 prediction

[0139]

[0140] 3) Bring in arrive ε is 0.8, and we get C s (x5)={0.82, 1.31, 2.50, 3.48, 4.21};

[0141] The variation between the paths is C i (x5) = {1.00, 0.47, 0.35, 0.61, 0.35}, substituting We get E(x5) = {0, 0.0007, 0.025, 0.0001, 0.0018}, and substitute Formula, set β = 0.5, assign initial value Get C d (x5)={13.32, 25.67, 55.14, 110.28, 220.64};

[0142] 4) According to In, get

[0143] 5) Bring in The final result is 71.07.

[0144] The present invention provides a pickleball training robot's level perception system for a trainee, comprising:

[0145] The basic data acquisition module is used to establish a coordinate system based on the midpoint of the baseline of the opponent on the pickleball court, define the variables of the training system, and obtain basic data for quantitative analysis of task difficulty and task performance;

[0146] Construct a batting performance model module, which uses the Kriging proxy model to construct a batting performance model under complex fixed-point ball paths, thereby expanding the batting performance data of some ball paths to the batting performance under all ball paths;

[0147] Establish a training task difficulty quantification model module, which is used to establish a training task difficulty quantification model that takes cognitive complexity into account. Scoring is performed for task difficulty and task complexity respectively, thereby determining single task difficulty indicators and multi-task difficulty indicators.

[0148] The calculation and evaluation module is used to calculate the trainee's performance under a certain task difficulty, thereby evaluating the trainee's overall ball return level; through in-depth analysis of the player's task complexity, it accurately understands the player's weaknesses and provides targeted guidance and suggestions.

[0149] Promote the improvement of players' pickleball skills and optimize training results.

[0150] The foregoing description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by any person skilled in the art within the technical scope disclosed herein and within the spirit and principles of the present invention shall be covered by the scope of protection of the present invention. Furthermore, any matters not described in detail in this specification constitute prior art known to those skilled in the art.

Claims

1. A method for a pickleball training robot to sense a player's level, characterized by: First, a coordinate system was established using the midpoint of the baseline of the opponent on the pickleball court to define the variables of the training system and obtain basic data for quantitative analysis of task difficulty and task performance. Secondly, the Kriging proxy model is used to construct a batting performance model under complex fixed-point ball paths, thereby expanding the batting performance data of some ball paths to the batting performance under all ball paths; Third, a training task difficulty quantification model that considers cognitive complexity was established, scoring task difficulty and task complexity separately to determine single-task difficulty indicators and multi-task difficulty indicators. Fourth, calculate the trainee's performance under a certain task difficulty, so as to evaluate the trainee's overall level of ball return; through in-depth analysis of the player's task complexity, accurately understand the player's weak links and provide targeted guidance and suggestions, so as to promote the improvement of the player's pickleball skills and optimize the training effect.

2. The method for sensing a player's level using a pickleball training robot according to claim 1, wherein: Establish a coordinate system based on the midpoint of the baseline of the opponent on the pickleball court and define the training system variables, including: Let m be the number of trainings, each training contains N rounds, where i represents a round; let the task variable be in the i-th round of the m-th training This includes the speed at which the robot shoots the ball and the distance between the landing point and the player Players in fixed-point task variables The quality of the return ball is reflected in the accuracy of the return ball The ball return accuracy is calculated as: Where, is the deviation between the actual landing point of the player's return ball and the target point; A is the set error resolution.

3. The method for sensing the level of a player using a pickleball training robot according to claim 2, wherein: Construct a batting performance model for complex fixed-point ball paths. The specific contents are as follows: In the entire m training, there are N different combinations of task variables; let represents the set of task variables in the mth training session. The set of player return quality actually measured during the human-computer training is: Using the Kriging prediction model, the player's response to the task variable x is predicted based on the test results of all historical task variables. m The ball mass Y(x m ): Y(x m )=Kriging{y(x m )} The predicted value of ball return performance for a task variable is:

4. The method for sensing the level of a player using a pickleball training robot according to claim 3, wherein: Establish a training task difficulty quantification model that takes cognitive complexity into account, specifically including: For the task variable x m Difficulty of hitting the ball C s (x m ) is expressed as follows, where ε is the task difficulty correction factor; Set batting combination In the example, let the joint probability distribution of ball path i be X i (d,v), the variation between this ball path and the previous ball path is The cognitive load of a particular shot for In the mth training cycle, let the cognitive complexity of the i-th shot be We believe that the cognitive differences between each shot are related to the differences between the previous shots in the same training session; therefore, we use the exponentially weighted moving average method to calculate the cognitive complexity of the shot: Among them, β is the weighting coefficient; The difficulty index of the task in the i-th round of the m-th training Indicates the difficulty of hitting the ball and cognitive complexity The product of: The total task difficulty index TCI of the mth training m is the average task difficulty of the entire training process:

5. The method for sensing the level of a player using a pickleball training robot according to claim 4, wherein: Evaluate the comprehensive level of the trainee's ball return, including the following: In the i-th round of the m-th training, the task difficulty index The player's return accuracy is The comprehensive return score P of the mth training is m (TCI m )for:

6. The method for sensing a player's level using a pickleball training robot according to claim 1, wherein: The pickleball training robot includes a moving module, a ball receiving module, a ball serving module, a positioning module, and a control circuit board; The mobile module serves as the chassis of the robot and is set below the ball-serving module, while the ball-receiving module is set above the ball-serving module. The positioning module includes a binocular camera, an ultra-wideband positioning base station, and a location tag; The ultra-wideband positioning base stations of the positioning module are set up at the four corners of the pickleball court, the location tags of the positioning module are installed on the control circuit board, and the binocular camera of the positioning module is set on the chassis; the control circuit board is installed on the mobile module.

7. The method for sensing a player's level using a pickleball training robot according to claim 6, wherein: The mobile module uses a wheel-foot mechanism; a housing is installed above the mobile module, and the housing contains a serving module, which is connected to the ball net of the ball receiving module above through a pipe; The mobile module is driven by wheels, connecting parts and a drive system. When the motor wheels rotate, the pickleball training robot can move at a variety of speeds and directions. The wheel-foot mechanism can also flexibly adjust the height and angle of the pickleball serving and receiving. The serving module launches the pickleball in front of the trainee's coordinates and requires the trainee to return the ball with the robot's location as the target; after the trainee hits the ball, the binocular camera captures and calculates the landing point of the pickleball, and the control circuit board controls the motor wheel in the mobile module to rotate, so that the mobile module moves to the landing point of the pickleball to the receiving module to receive the ball. At the same time, the control circuit board controls the serving module to launch the next pickleball.

8. The method for sensing a player's level using a pickleball training robot according to claim 6, wherein: The serving module is mainly composed of a support frame and a pickle ball propulsion mechanism; the support frame is located on one side of the serving module and is connected to the chassis by bolts at the bottom; the pickle ball propulsion mechanism is composed of a support plate, a pickle ball propulsion device, a ball shooting barrel and a ball storage barrel, etc. The support plate is fixed on the support frame, and the ball shooting barrel and the ball storage barrel form a three-way pipe and are fixed by the support plate. The pickle ball propulsion device extends into the ball shooting barrel from one side opening of the three-way pipe, and the other side opening faces the gap between the two friction wheels. The upper side opening is the ball storage barrel and is connected to the ball dropping hole of the ball receiving module.

9. The method for sensing a player's level using a pickleball training robot according to claim 6, wherein: The ball receiving module is mainly composed of a ball receiving hole, a ball receiving net, and a bracket. The ball receiving hole is integrally connected to the ball receiving net, with a mesh hole the size of a pickle ball left in the middle. The lower end of the mesh hole is connected to the ball shooting tube and the ball storage tube to form a three-way pipe; the ball receiving net is made of low-elasticity mesh knitted fabric and is fixed to the ball receiving frame by four adjustable brackets; the shape of the ball net is high on all sides and low in the middle, with a ball landing hole with an aperture slightly larger than the diameter of a standard pickle ball in the middle, which is connected to the ball storage tube of the serving module; the bracket is installed under the ball net, the upper end supports and connects the fixed parts of the ball net, and the lower end is fixed to the chassis.

10. A pickleball training robot's level perception system for a trainer, characterized by: include: The basic data acquisition module is used to establish a coordinate system based on the midpoint of the baseline of the opponent on the pickleball court, define the variables of the training system, and obtain basic data for quantitative analysis of task difficulty and task performance; Construct a batting performance model module, which uses the Kriging proxy model to construct a batting performance model under complex fixed-point ball paths, thereby expanding the batting performance data of some ball paths to the batting performance under all ball paths; Establish a training task difficulty quantification model module, which is used to establish a training task difficulty quantification model that takes cognitive complexity into account. Scoring is performed for task difficulty and task complexity respectively, thereby determining single task difficulty indicators and multi-task difficulty indicators. The calculation and evaluation module is used to calculate the trainee's performance under a certain task difficulty, thereby evaluating the trainee's overall level of ball return. Through in-depth analysis of the player's task complexity, it accurately understands the player's weaknesses and provides targeted guidance and suggestions, promoting the improvement of the player's pickleball skills and optimizing the training effect.

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