Charging robot for automatically identifying new energy automobile based on Internet of Things and charging method

Abstract

The invention discloses an automatic identification new energy automobile charging robot based on the Internet of Things and a charging method, and the charging robot comprises a robot body. The robot body is provided with a 3D vision module, a calling module, an automatic driving module, a mechanical arm charging plug matching module, an online payment settlement module and an automatic warehouse returning and electricity supplementing module, and the calling module is connected with the 3D vision module and the automatic driving module. The mechanical arm charging plug matching module is connected with the 3D vision module and the online payment and settlement module. According to the invention, through three-dimensional processing and analysis of the data, accurate positioning of the image can be obtained, so that the recognition accuracy and adaptability of the 3D visual system are higher, and the charging efficiency is higher.

Description

Technical Field

[0001] The invention relates to an automatic identification new energy vehicle charging robot and a charging method based on the Internet of Things, belonging to the technical field of robot automation. Background Art

[0002] The new energy vehicle charging robot is an automated system specially designed to provide automated charging services for electric vehicles or other new energy vehicles. Its main function is to autonomously identify the charging needs of the vehicle through advanced technology and software systems, automatically go to the location of the vehicle, and perform charging operations without human intervention. The emergence of new energy vehicle charging robots aims to solve the convenience, efficiency and cost problems existing in traditional charging methods, provide electric vehicle users with more convenient and intelligent charging solutions, and promote the popularization and development of electric vehicles. With the rapid development and popularization of new energy vehicles, the demand for charging facilities is also growing. However, the current public charging piles have problems such as insufficient number, unreasonable layout and slow charging speed, which has brought many inconveniences to car owners. In order to solve these problems, new energy vehicle charging robots came into being and became an innovative charging solution.

[0003] During the position recognition process of existing new energy vehicle charging robots, the recognition accuracy and adaptability of the 3D vision system are not strong, resulting in inaccurate position recognition and thus low charging efficiency. Summary of the invention

[0004] The purpose of the present invention is to solve the deficiencies of the above-mentioned prior art and to provide an automatic identification new energy vehicle charging robot based on the Internet of Things. The charging robot can obtain accurate positioning of the image through three-dimensional processing and analysis of the data, so that the recognition accuracy and adaptability of the 3D vision system are higher, thereby improving the charging efficiency.

[0005] Another object of the present invention is to provide a charging method, which is implemented based on the above-mentioned automatic identification new energy vehicle charging robot.

[0006] The purpose of the present invention can be achieved by adopting the following technical solutions:

[0007] An automatic identification new energy vehicle charging robot based on the Internet of Things comprises a robot body, on which a 3D vision module, a summoning module, an automatic driving module, a mechanical arm charging plug matching module, an online payment settlement module and an automatic return to warehouse charging module are arranged, wherein the summoning module is respectively connected to the 3D vision module and the automatic driving module, and the mechanical arm charging plug matching module is respectively connected to the 3D vision module and the online payment settlement module;

[0008] The 3D vision module is used to use a 3D vision camera and image processing technology to identify the location and charging port of the vehicle and provide accurate positioning information for the robotic arm;

[0009] The calling module is used to receive a calling instruction from a terminal device to control the operation of the 3D vision module;

[0010] The autonomous driving module is used to receive the summoning instruction from the summoning module, perform intelligent path planning based on the summoning instruction, calculate the best route, and enable the robot to move and position autonomously;

[0011] The robot arm charging plug matching module is used to match the charging head according to the positioning information provided by the 3D vision module, and then insert the charging plug into the charging port of the vehicle;

[0012] The online payment and settlement module is used to obtain the charging completion information of the robot arm charging plug matching module and perform online payment and settlement;

[0013] The automatic return to the charging warehouse module is used to automatically return to the charging warehouse for recharging when the robot is low on power.

[0014] Furthermore, the 3D vision module includes:

[0015] The 3D sensor unit is used to obtain a stereoscopic image of the environment based on the internal data of the 3D vision camera and generate point cloud data based on the laser scanner;

[0016] Image processing and data fusion unit, used to filter, segment and reconstruct the point cloud data from the 3D sensor unit, obtain clear target object shape and position information, and fuse data from different sensors;

[0017] The target detection and recognition unit is used to detect and recognize the vehicle and the charging port in the three-dimensional scene and analyze the characteristics of the target;

[0018] The positioning and attitude estimation unit is used to analyze sensor data and map information, determine the position and attitude of the target object, and calculate the motion path of the robot arm based on the data fusion results and target information;

[0019] The first control unit is used to convert the calculation result of the motion path of the robotic arm into a control command of the robotic arm.

[0020] Furthermore, filtering, segmenting and reconstructing the point cloud data from the 3D sensor unit to obtain clear target object shape and position information and fusing the data from different sensors specifically includes:

[0021] The point cloud data from the 3D sensor unit is de-noised, and a three-dimensional Gaussian filtering algorithm is applied to smooth the data and eliminate the point cloud data of non-target objects. The formula of the three-dimensional Gaussian filtering algorithm is as follows:

[0022]

[0023] Among them, (x, y, z) represents the offset of the pixel or voxel in the respective direction, σ x σ y σ z is the standard deviation of the Gaussian function in all directions, and H(x,y,z) is the value of the three-dimensional Gaussian filter kernel at position (x,y,z);

[0024] The points belonging to the target object in the preprocessed point cloud data are separated using a density clustering algorithm. The formula of the density clustering algorithm is as follows:

[0025]

[0026] Among them, (Q, P) is the distance between points Q and P, N ∈ P is the ∈ neighborhood of point P, |N ∈ P| represents the set N ∈ The size of P,∈ is a pre-set distance threshold, and MP is the minimum number of points contained in the neighborhood of a point∈;

[0027] All unvisited points are marked as noise points or boundary points;

[0028] Extract features from the segmented point cloud of the target object;

[0029] Convolutional neural network deep learning technology is used to fuse data from different sensors.

[0030] Furthermore, the calling module includes:

[0031] A communication unit, used to establish a communication connection with a terminal device to receive a calling instruction from the terminal device;

[0032] A processing unit, used for parsing the calling instruction received by the terminal device;

[0033] A second control unit, used to convert the parsed call command into a control signal, and electrically connect to the 3D vision module to control the operation of the 3D vision module;

[0034] The electrical connection interface unit is used to provide an interface for physical or electrical connection with the 3D vision module.

[0035] Furthermore, the autonomous driving module includes:

[0036] A communication interface unit, used for receiving a calling instruction from a calling module;

[0037] A summon instruction parsing unit, used to parse the summon instruction received from the summon module, including identifying the type of instruction and converting the summon instruction into an internal instruction format that can be understood by the autonomous driving module;

[0038] The path planning unit is used to convert the target position into coordinates on the map and calculate the best moving path using the path planning algorithm;

[0039] The motion control unit is used to generate motion control commands according to the results of path planning, and control the parameters of the robot's wheel speed and steering angle to achieve autonomous movement of the robot.

[0040] Furthermore, the target position is converted into coordinates on a map, and the best moving path is calculated using a path planning algorithm, which specifically includes:

[0041] Add the starting point q to the tree as the root node;

[0042] Randomly sample a point r from the state space;

[0043] Find the node n closest to r in the tree;

[0044] Expand one step from n to r to get a new node nr;

[0045] If nr is a valid node outside the obstacle and obeys the motion constraints, add it to the tree and establish a connection from n to nr;

[0046] Check whether nr is close to the target point g. If the termination condition is met, the final path is generated as the optimal moving path.

[0047] Furthermore, the robot arm charging plug matching module includes:

[0048] An information receiving unit, used to receive the positioning information of the vehicle charging port provided by the 3D vision module, and perform plug matching and insertion operations;

[0049] The plug matching unit is used to determine the insertion path and direction of the charging plug on the robot arm according to the charging port position and direction information provided by the 3D vision module;

[0050] Motion control unit, used to control the movement of the robotic arm.

[0051] Furthermore, the online payment settlement module includes:

[0052] The payment interface unit is used to provide an interface for communicating with the payment platform and the banking system;

[0053] The settlement unit is used to process the settlement operation after the payment is completed;

[0054] Notification and confirmation unit, used to send a notification of successful payment to the user;

[0055] The system integration unit is used to obtain the charging completion information of the robot arm charging plug matching module.

[0056] Furthermore, the automatic return to warehouse power replenishment module includes:

[0057] A power detection unit, used to monitor the current power level of the robot;

[0058] A position sensing unit, used to detect the current position of the robot through sensors;

[0059] The path planning unit is used to plan the optimal path back to the charging station based on the current location and the location of the charging station.

[0060] Another object of the present invention can be achieved by adopting the following technical solutions:

[0061] A charging method is implemented based on the above-mentioned automatic identification new energy vehicle charging robot, and the method includes:

[0062] The 3D vision module uses 3D cameras and image processing technology to identify the vehicle's location and charging port, providing precise positioning information for the robotic arm;

[0063] The calling module receives the calling instruction sent by the terminal device;

[0064] The autonomous driving module receives the summoning instruction from the summoning module, performs intelligent path planning based on the summoning instruction, calculates the best route, and the robot moves and positions autonomously:

[0065] The robot arm charging plug matching module matches the charging head according to the positioning information provided by the 3D vision module, and then inserts the charging plug into the charging port of the vehicle;

[0066] The online payment and settlement module obtains the charging completion information of the robot arm charging plug matching module and performs online payment and settlement;

[0067] When the robot is low on power, the automatic return to the charging station module enables the robot to automatically return to the charging station for recharging.

[0068] The present invention has the following beneficial effects compared with the prior art:

[0069] The present invention uses a 3D camera and image processing technology to identify the position and charging port of the vehicle, provide accurate positioning information for the robotic arm, receive a call command issued by a terminal device, perform intelligent path planning based on the call command, calculate the best route, enable the robot to move and position autonomously, match the charging head according to the positioning information provided by the 3D vision module, and then insert the charging plug into the charging port of the vehicle. Through three-dimensional processing and analysis of the data, accurate positioning of the image can be obtained, thereby making the positioning information more accurate, and making the recognition accuracy and adaptability of the 3D vision system higher, thereby making the charging efficiency higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0071] Figure 1 This is a structural block diagram of an automatic identification new energy vehicle charging robot based on the Internet of Things according to an embodiment of the present invention.

[0072] Figure 2 This is a structural block diagram of a 3D vision module in an Internet of Things-based automatic identification new energy vehicle charging robot according to an embodiment of the present invention.

[0073] Figure 3 This is a structural block diagram of a summoning module in an automatic identification new energy vehicle charging robot based on the Internet of Things according to an embodiment of the present invention.

[0074] Figure 4 This is a structural block diagram of the automatic driving module in the automatic identification new energy vehicle charging robot based on the Internet of Things in an embodiment of the present invention.

[0075] Figure 5 This is a structural block diagram of a matching module for a robotic arm charging plug in an automatic identification new energy vehicle charging robot based on the Internet of Things according to an embodiment of the present invention.

[0076] Figure 6 This is a structural block diagram of an online payment settlement module in an automatic identification new energy vehicle charging robot based on the Internet of Things in an embodiment of the present invention.

[0077] Figure 7 This is a structural block diagram of the automatic return to storage and power replenishment module in the automatic identification new energy vehicle charging robot based on the Internet of Things in an embodiment of the present invention.

[0078] Figure 8This is a flow chart of a charging method for automatically identifying a new energy vehicle charging robot based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] In order to better understand the present invention, a more detailed description will be made of various aspects of the present invention with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0080] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by ordinary technicians in the field. In addition, in the present invention, the order in which the processing of each step is described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0081] It should also be understood that expressions such as "comprises", "including", "having", "includes" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention". And, the term "exemplary" is intended to refer to an example or illustration.

[0082] Unless otherwise defined, all terms (including engineering terms and scientific and technological terms) used in this document have the same meaning as commonly understood by ordinary technical personnel in the field to which the present invention belongs. It should also be understood that unless otherwise clearly stated in the present invention, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0083] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0084] Example:

[0085] like Figure 1 As shown, this embodiment provides an automatic identification new energy vehicle charging robot based on the Internet of Things, the charging robot includes a robot body, and the robot body is provided with a 3D vision module 101, a summoning module 102, an automatic driving module 103, a robotic arm charging plug matching module 104, an online payment settlement module 105 and an automatic return to warehouse power replenishment module 106, the summoning module 102 is electrically connected to the 3D vision module 101 and the automatic driving module 103, respectively, and the robotic arm charging plug matching module 104 is electrically connected to the 3D vision module 101 and the online payment settlement module 105, respectively.

[0086] Furthermore, the 3D vision module 101 is used to use a 3D vision camera and image processing technology to identify the position and charging port of the vehicle and provide accurate positioning information for the robot arm, such as Figure 2 As shown, it includes:

[0087] The 3D sensor unit 1011 is used to obtain a stereoscopic image in the environment according to the internal data of the 3D vision camera and generate point cloud data based on a laser scanner.

[0088] The image processing and data fusion unit 1012 is used to filter, segment and reconstruct the point cloud data from the 3D sensor unit, obtain clear target object shape and position information, and fuse data from different sensors to improve the accuracy and stability of target recognition and positioning.

[0089] The target detection and recognition unit 1013 is used to detect and recognize the targets of the vehicle and the charging port in the three-dimensional scene and analyze the characteristics of the targets.

[0090] The positioning and posture estimation unit 1014 is used to analyze the sensor data and map information, determine the position and posture of the target object, and calculate the motion path of the robot arm based on the data fusion result and the target information.

[0091] The first control unit 1015 is used to convert the calculation result of the motion path of the robot arm into a control command of the robot arm.

[0092] Furthermore, the point cloud data from the 3D sensor unit is filtered, segmented and reconstructed to obtain clear target object shape and position information, and the data from different sensors are fused, including:

[0093] (1) Data preprocessing: Remove noise from the point cloud data from the 3D sensor unit, apply a three-dimensional Gaussian filter algorithm to smooth the data and eliminate the point cloud data of non-target objects. The formula of the three-dimensional Gaussian filter algorithm is as follows:

[0094]

[0095] Among them, (x, y, z) represents the offset of the pixel or voxel in the respective direction, σ x σ y σ z is the standard deviation of the Gaussian function in all directions, and H(x,y,z) is the value of the three-dimensional Gaussian filter kernel at position (x,y,z);

[0096] (2) Target segmentation: The points belonging to the target object in the preprocessed point cloud data are separated using a density clustering algorithm. The formula of the density clustering algorithm is as follows:

[0097]

[0098] Among them, (Q, P) is the distance between points Q and P, N ∈ P is the ∈ neighborhood of point P, |N ∈ P| represents the set N ∈ The size of P,∈ is a pre-set distance threshold, and MP is the minimum number of points contained in the neighborhood of a point∈;

[0099] All unvisited points are marked as noise points or boundary points.

[0100] (3) Feature extraction and description: Extract features from the segmented point cloud of the target object.

[0101] (4) Data fusion: Use convolutional neural network deep learning technology to fuse data from different sensors.

[0102] Furthermore, convolutional neural network deep learning technology is used to fuse data from different sensors, including:

[0103] (4-1) Collect data from different sensors, including image data, point cloud data, and IMU data.

[0104] (4-2) Preprocess the data of each sensor.

[0105] (4-3) Feature fusion based on the extracted features: perform weighted summation on the feature vectors extracted by each sensor, where the formula for feature fusion is as follows:

[0106]

[0107] Among them, P(c) is the probability of category (c) after integration, P j (c) is the probability of category (c) given by sensor P, w j is the weight of sensor j.

[0108] Furthermore, the calling module 102 is used to receive a calling instruction from a terminal device to control the operation of the 3D vision module, such as Figure 3 As shown, it includes:

[0109] The communication unit 1021 is used to establish a communication connection with a terminal device to receive a summoning instruction from the terminal device, wherein the terminal device may be a mobile device such as a mobile phone or a tablet computer, and the user may send instructions through an APP installed on the terminal device.

[0110] The processing unit 1022 is used to parse the calling instruction received by the terminal device.

[0111] The second control unit 1023 is used to convert the parsed calling instruction into a control signal and electrically connect to the 3D vision module to control the operation of the 3D vision module.

[0112] The electrical connection interface unit 1024 is used to provide an interface for physical or electrical connection with the 3D vision module.

[0113] Furthermore, the automatic driving module 103 is used to receive the summoning instruction of the summoning module, perform intelligent path planning based on the summoning instruction, calculate the best route, and enable the robot to move and position autonomously, such as Figure 4 As shown, it includes:

[0114] The communication interface unit 1031 is electrically connected to the summoning module 102 and is used to receive a summoning instruction from the summoning module.

[0115] The summon instruction parsing unit 1032 is used to parse the summon instruction received from the summon module, including identifying the type of instruction and converting the summon instruction into an internal instruction format that can be understood by the autonomous driving module.

[0116] The path planning unit 1033 is used to convert the target position into coordinates on the map and calculate the best moving path using a path planning algorithm.

[0117] The motion control unit 1034 is used to generate motion control commands according to the results of path planning, and control the parameters of the wheel speed and steering angle of the robot to achieve autonomous movement of the robot.

[0118] Furthermore, the target location is converted into coordinates on the map, and the best moving path is calculated using a path planning algorithm, which specifically includes:

[0119] (1) Add the starting point q to the tree as the root node.

[0120] (2) Randomly sample a point r from the state space;

[0121] (3) Find the node n closest to r in the tree;

[0122] (4) Expand one step from n to r to obtain a new node nr;

[0123] (5) If nr is a valid node outside the obstacle and obeys the motion constraints, add it to the tree and establish a connection from n to nr;

[0124] (6) Check whether nr is close to the target point g. If the termination condition is met, the final path is generated as the optimal moving path.

[0125] Path output and execution: Output the specific route and path point sequence of the optimal path as input to the motion control unit 1034.

[0126] Furthermore, the robot arm charging plug matching module 104 is used to match the charging head according to the positioning information provided by the 3D vision module, and then insert the charging plug into the charging port of the vehicle, such as Figure 5 As shown, it includes:

[0127] The information receiving unit 1041 is used to receive the positioning information of the vehicle charging port provided by the 3D vision module 101, and perform the matching and insertion operations of the plug.

[0128] The plug matching unit 1042 is used to determine the insertion path and direction of the charging plug on the robotic arm according to the charging port position and direction information provided by the 3D vision module 101.

[0129] The motion control unit 1043 is used to control the motion of the robot arm.

[0130] Furthermore, the online payment settlement module 105 is used to obtain the charging completion information of the robot arm charging plug matching module and perform online payment and settlement, such as Figure 6 As shown, it includes:

[0131] The payment interface unit 1051 is used to provide an interface for communicating with the payment platform and the banking system.

[0132] The settlement unit 1052 is used to process the settlement operation after the payment is completed.

[0133] The notification and confirmation unit 1053 is used to send a notification of successful payment to the user.

[0134] The system integration unit 1054 is electrically connected to the robot arm charging plug matching module 104 and is used to obtain charging completion information of the robot arm charging plug matching module 104 .

[0135] Furthermore, the automatic return to the charging station module 106 is used to automatically return to the charging station for charging when the robot is low on power. Figure 7 As shown, it includes:

[0136] The power detection unit 1061 is used to monitor the current power level of the robot.

[0137] The position sensing unit 1062 is used to detect the current position of the robot through sensors.

[0138] The path planning unit 1063 is used to plan the optimal path to return to the charging station according to the current position and the position of the charging station.

[0139] like Figures 1 to 8 As shown, this embodiment also provides a charging method, which is implemented based on the above-mentioned automatic identification new energy vehicle charging robot and includes the following steps:

[0140] S801, 3D vision module uses 3D camera and image processing technology to identify the vehicle's position and charging port, providing precise positioning information for the robotic arm.

[0141] S802: The calling module receives a calling instruction sent by the terminal device.

[0142] S803, the automatic driving module receives the summoning instruction from the summoning module, performs intelligent path planning based on the summoning instruction, calculates the best route, and the robot moves and positions autonomously.

[0143] S804. The robot arm charging plug matching module matches the charging head according to the positioning information provided by the 3D vision module, and then inserts the charging plug into the charging port of the vehicle.

[0144] S805. The online payment and settlement module obtains the charging completion information of the robot arm charging plug matching module, and performs online payment and settlement.

[0145] S806. When the robot is low on power, the automatic return-to-charging module enables the robot to automatically return to the charging compartment for recharging.

[0146] In summary, the present invention uses a 3D camera and image processing technology to identify the position and charging port of the vehicle, provide accurate positioning information for the robotic arm, receive a call command from a terminal device, perform intelligent path planning based on the call command, calculate the optimal route, enable the robot to move and position autonomously, match the charging head according to the positioning information provided by the 3D vision module, and then insert the charging plug into the charging port of the vehicle. Through three-dimensional processing and analysis of the data, the image can be accurately positioned, thereby making the positioning information more accurate, and making the recognition accuracy and adaptability of the 3D vision system higher, thereby making the charging efficiency higher.

[0147] It should be noted that, in the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0148] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic identification new energy vehicle charging robot based on the Internet of Things, characterized in that: It includes a robot body, on which a 3D vision module, a summoning module, an automatic driving module, a robot arm charging plug matching module, an online payment settlement module and an automatic return to warehouse power replenishment module are arranged, the summoning module is respectively connected to the 3D vision module and the automatic driving module, and the robot arm charging plug matching module is respectively connected to the 3D vision module and the online payment settlement module; The 3D vision module is used to use a 3D vision camera and image processing technology to identify the location and charging port of the vehicle and provide accurate positioning information for the robotic arm; The calling module is used to receive a calling instruction from a terminal device to control the operation of the 3D vision module; The autonomous driving module is used to receive the summoning instruction from the summoning module, perform intelligent path planning based on the summoning instruction, calculate the best route, and enable the robot to move and position autonomously; The robot arm charging plug matching module is used to match the charging head according to the positioning information provided by the 3D vision module, and then insert the charging plug into the charging port of the vehicle; The online payment and settlement module is used to obtain the charging completion information of the robot arm charging plug matching module and perform online payment and settlement; The automatic return to the charging warehouse module is used to automatically return to the charging warehouse for recharging when the robot is low on power.

2. The automatic identification new energy vehicle charging robot according to claim 1 is characterized in that: The 3D vision module includes: The 3D sensor unit is used to obtain a stereoscopic image of the environment based on the internal data of the 3D vision camera and generate point cloud data based on the laser scanner; Image processing and data fusion unit, used to filter, segment and reconstruct the point cloud data from the 3D sensor unit, obtain clear target object shape and position information, and fuse data from different sensors; The target detection and recognition unit is used to detect and recognize the vehicle and the charging port in the three-dimensional scene and analyze the characteristics of the target; The positioning and attitude estimation unit is used to analyze sensor data and map information, determine the position and attitude of the target object, and calculate the motion path of the robot arm based on the data fusion results and target information; The first control unit is used to convert the calculation result of the motion path of the robotic arm into a control command of the robotic arm.

3. The automatic identification new energy vehicle charging robot according to claim 2 is characterized in that: The point cloud data from the 3D sensor unit is filtered, segmented and reconstructed to obtain clear target object shape and position information, and the data from different sensors are fused, specifically including: The point cloud data from the 3D sensor unit is de-noised, and a three-dimensional Gaussian filtering algorithm is applied to smooth the data and eliminate the point cloud data of non-target objects. The formula of the three-dimensional Gaussian filtering algorithm is as follows: Among them, (x, y, z) represents the offset of the pixel or voxel in the respective direction, σ x σ y σ z is the standard deviation of the Gaussian function in all directions, and H(x,y,z) is the value of the three-dimensional Gaussian filter kernel at position (x,y,z); The points belonging to the target object in the preprocessed point cloud data are separated using a density clustering algorithm. The formula of the density clustering algorithm is as follows: Among them, (Q, P) is the distance between points Q and P, N ∈ P is the ∈ neighborhood of point P, |N ∈ P| represents the set N ∈ The size of P,∈ is a pre-set distance threshold, and MP is the minimum number of points contained in the neighborhood of a point∈; All unvisited points are marked as noise points or boundary points; Extract features from the segmented point cloud of the target object; Convolutional neural network deep learning technology is used to fuse data from different sensors.

4. The automatic identification new energy vehicle charging robot according to claim 1 is characterized in that: The calling module comprises: A communication unit, used to establish a communication connection with a terminal device to receive a calling instruction from the terminal device; A processing unit, used for parsing the calling instruction received by the terminal device; A second control unit, used to convert the parsed call command into a control signal, and electrically connect to the 3D vision module to control the operation of the 3D vision module; The electrical connection interface unit is used to provide an interface for physical or electrical connection with the 3D vision module.

5. The automatic identification new energy vehicle charging robot according to claim 1 is characterized in that: The autonomous driving module comprises: A communication interface unit, used for receiving a calling instruction from a calling module; A summon instruction parsing unit, used to parse the summon instruction received from the summon module, including identifying the type of instruction and converting the summon instruction into an internal instruction format that can be understood by the autonomous driving module; The path planning unit is used to convert the target position into coordinates on the map and calculate the best moving path using the path planning algorithm; The motion control unit is used to generate motion control commands according to the results of path planning, and control the parameters of the robot's wheel speed and steering angle to achieve autonomous movement of the robot.

6. The automatic identification new energy vehicle charging robot according to claim 5 is characterized in that: The target position is converted into coordinates on the map, and the best moving path is calculated using a path planning algorithm, specifically including: Add the starting point q to the tree as the root node; Randomly sample a point r from the state space; Find the node n closest to r in the tree; Expand one step from n to r to get a new node nr; If nr is a valid node outside the obstacle and obeys the motion constraints, add it to the tree and establish a connection from n to nr; Check whether nr is close to the target point g. If the termination condition is met, the final path is generated as the optimal moving path.

7. The automatic identification new energy vehicle charging robot according to claim 1 is characterized in that: The robot arm charging plug matching module includes: An information receiving unit, used to receive the positioning information of the vehicle charging port provided by the 3D vision module, and perform plug matching and insertion operations; The plug matching unit is used to determine the insertion path and direction of the charging plug on the robot arm according to the charging port position and direction information provided by the 3D vision module; Motion control unit, used to control the movement of the robotic arm.

8. The automatic identification new energy vehicle charging robot according to claim 1 is characterized in that: The online payment settlement module includes: The payment interface unit is used to provide an interface for communicating with the payment platform and the banking system; The settlement unit is used to process the settlement operation after the payment is completed; Notification and confirmation unit, used to send a notification of successful payment to the user; The system integration unit is used to obtain the charging completion information of the robot arm charging plug matching module.

9. The automatic identification new energy vehicle charging robot according to claim 1 is characterized in that: The automatic return to warehouse power replenishment module includes: A power detection unit, used to monitor the current power level of the robot; A position sensing unit, used to detect the current position of the robot through sensors; The path planning unit is used to plan the optimal path back to the charging station based on the current location and the location of the charging station.

10. A charging method, implemented based on the automatic identification new energy vehicle charging robot according to any one of claims 1 to 9, characterized in that: The method comprises: The 3D vision module uses 3D cameras and image processing technology to identify the vehicle's location and charging port, providing precise positioning information for the robotic arm; The calling module receives the calling instruction sent by the terminal device; The autonomous driving module receives the summoning instruction from the summoning module, performs intelligent path planning based on the summoning instruction, calculates the best route, and the robot moves and positions autonomously: The robot arm charging plug matching module matches the charging head according to the positioning information provided by the 3D vision module, and then inserts the charging plug into the charging port of the vehicle; The online payment and settlement module obtains the charging completion information of the robot arm charging plug matching module and performs online payment and settlement; When the robot is low on power, the automatic return to the charging station module enables the robot to automatically return to the charging station for recharging.

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