Intelligent light control system for a robotic pet and method of controlling the same

Abstract

The application relates to the field of robots, in particular to an intelligent light control system of a robot pet and a control method thereof, which comprises an environment detection device, a processing device and a plurality of light emitting devices arranged on the robot pet; the environment detection device comprises a radar sensor, an illumination sensor, a visual sensor and a voice recognition sensor; the processing device comprises an image processing module, a voice processing module and a control module; the image processing module acquires environment information collected by the visual sensor and carries out pretreatment to obtain image information; the voice processing module pretreats collected environment sound information to acquire a sound instruction; the control module processes obstacle distance, speed, illumination intensity, images and the sound instruction to generate a control instruction, and executes corresponding control on the light emitting module; and the light emitting devices are ADB modules, DLP modules and contour lamp modules respectively. The application can switch high / low beams, has high recognition degree, high safety in night walking and good user experience.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to an intelligent lighting control system and control method for a robotic pet. Background Technology

[0002] Over the past decade, intelligent robot systems have developed rapidly, with some even becoming commercially available. Typical examples include Sony's AIBO entertainment robot pet and Honda's ASIMO humanoid robot, just a few examples. These robots have always existed as robotic assistants, including robot pets designed to accompany their owners. Existing robot pets can guide users when walking at night, such as by turning on light sources to illuminate the path. However, current robot pets are only equipped with a single-function lamp and lack a specialized lighting system. Existing systems have the following drawbacks:

[0003] (1) Poor lighting adaptability, unable to automatically switch between high and low beams according to the environment in front, which can easily cause glare interference to pedestrians or vehicles at night, posing a safety hazard;

[0004] (2) Lack of environmental interaction and lack of marking function for dangerous areas on the road surface. When the robot walks at night, it cannot warn users or surrounding targets of dangers such as steps and potholes.

[0005] (3) The outline recognition is low and there is no clear outline display at night. It is difficult for people or vehicles around to quickly judge the robot's size and movement trajectory, which makes it easy to collide.

[0006] (4) The interaction method is simple. The adjustment of lighting parameters mostly relies on remote control or physical buttons. There is a lack of convenient voice interaction methods, resulting in low user operation efficiency and poor user experience. Summary of the Invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0008] Therefore, the present invention provides an intelligent lighting control system for robotic pets, which can switch between high and low beams, has high recognition, improves the safety of walking at night, and provides a good user experience.

[0009] According to an embodiment of the present invention, an intelligent lighting control system for a robotic pet includes: an environmental detection device, a processing device, and a plurality of light-emitting devices disposed on the robotic pet.

[0010] The environmental monitoring device includes:

[0011] The radar sensor is configured to collect the distance and speed of obstacles ahead;

[0012] The illuminance sensor is configured to collect information on the light intensity of the surrounding environment;

[0013] A visual sensor is configured to collect information about the surrounding environment;

[0014] A voice recognition sensor is configured to collect the user's voice commands.

[0015] The processing device includes:

[0016] An image processing module is connected to the vision sensor and is configured to acquire environmental information collected by the vision sensor, perform preprocessing, and acquire image information.

[0017] A voice processing module is connected to the voice recognition sensor, and the voice processing module is configured to preprocess the collected environmental sound information to obtain voice commands;

[0018] The control module is connected to the radar sensor, the illuminance sensor, the image processing module, the voice processing module, and the light-emitting module. The control module is configured to process the distance, speed, illuminance, image information, and sound commands of the obstacle and generate control commands. The control module performs corresponding control on the corresponding light-emitting module according to the control commands.

[0019] The light-emitting devices are an ADB module, a DLP module, and a contour light module.

[0020] The beneficial effects of this invention are:

[0021] 1. By linking the ADB module with the environmental detection device, intelligent switching between high and low beams is achieved, avoiding glare interference to pedestrians or vehicles at night, thus ensuring high safety;

[0022] 2. By linking the DLP module with the environmental detection device, visual markings are projected onto the road surface to provide nighttime warnings;

[0023] 3. Set up an outline light module, which forms a complete outline when lit at night, improving the accuracy of surrounding targets in judging the size and movement trajectory of the robot pet;

[0024] 4. The voice interaction module enables voice control of lighting parameters, providing a good user experience.

[0025] According to one embodiment of the present invention, the ADB module is used to illuminate the road in front of the robotic pet and switch between high and low beam modes;

[0026] The DLP module is used to project optical patterns;

[0027] The outline light module is used to display the position and appearance of the robotic pet;

[0028] Both the ADB module and the DLP module are located at the front end of the robot pet's torso.

[0029] The outline light module includes several LED lights, which are evenly distributed on the robot pet's torso and limbs.

[0030] According to one embodiment of the present invention, the control module includes a processor, wherein the processor is an ARM Cortex-A53 chip.

[0031] According to an embodiment of the present invention, a method for intelligent lighting control of a robotic pet is provided. The method employs the intelligent lighting control system for robotic pets described above and includes the following steps:

[0032] S1: Start the robot pet and enter the initialization process; control the environmental detection device to automatically enter the detection state.

[0033] S2: The radar sensor collects the distance and speed of obstacles in front, the illuminance sensor collects the light intensity information of the surrounding environment, the vision sensor collects the surrounding environment information, and the voice recognition sensor collects the user's voice command information, and transmits all the information to the processing device.

[0034] S3: The processing device processes the received information and generates control commands to control the corresponding light-emitting modules to perform corresponding controls, including:

[0035] Based on the light intensity information, a threshold algorithm is used to generate corresponding control commands to control the contour light module;

[0036] Based on the distance and speed of obstacles and combined with light intensity information, a decision algorithm based on set rules generates corresponding high / low beam switching commands to control the ADB module.

[0037] It processes environmental information and generates DLP projection instructions to control the DLP module;

[0038] Process voice command information, output corresponding control commands and provide voice feedback;

[0039] S4: The system repeats steps S2 to S4 every 500ms and records system information, including information on light source mode, projection pattern and flickering status, for information diagnosis in case of subsequent faults.

[0040] According to one embodiment of the present invention, generating corresponding control commands based on light intensity information using a threshold algorithm to control the contour light module to perform corresponding actions includes:

[0041] If the light intensity is >15 Lux, enter daytime mode, the control module turns on the contour light module and turns off the ADB module and DLP module.

[0042] According to one embodiment of the present invention, based on the distance and speed of obstacles and combined with light intensity information, a corresponding high / low beam switching command is generated through a set rule-based decision algorithm to control the ADB module to perform the corresponding actions, including:

[0043] If the light intensity is ≤15Lux, or the distance to the obstacle is ≤20m, or the relative speed is >30km / h, the control module issues a low beam mode control command to control the ADB module to switch to low beam mode, and at the same time controls the outline light module to flash red.

[0044] If the light intensity is ≤15Lux, the distance to the obstacle is >20m, and the relative speed is ≤30km / h, the control module issues a high beam mode control command to control the ADB module to switch to high beam mode.

[0045] According to one embodiment of the present invention, processing environmental information and generating DLP projection instructions, the control of the DLP module specifically includes:

[0046] The image processing module acquires environmental information, performs target recognition through a deep learning algorithm, and obtains image information, which includes the target object and the target object region. The target object and the target object region are then sent to the control module.

[0047] Establish the robot pet's body coordinate system XOY, where the robot pet's forward direction is the X-axis, the robot pet's left and right direction is the Y-axis, and the direction perpendicular to the ground is the Z-axis;

[0048] The control module calculates the three-dimensional coordinates of the target area in the robot pet's body coordinate system. X b , Y b , Z b );

[0049] Convert the robot pet's body coordinate system to the world coordinate system, calculate the actual position of the target area on the road surface, and calculate the projection range of the marking pattern based on the actual position of the target area on the road surface.

[0050] The control module sends the projection range of the marker pattern to the DLP module, which then projects the pattern based on that range.

[0051] According to one embodiment of the present invention, the target object is a step, a stone, or a pit.

[0052] According to one embodiment of the present invention, the voice command information includes turning the high beams on or off, switching between high and low beam modes, adjusting the brightness of the lights, and / or changing the optical color.

[0053] According to one embodiment of the present invention, let the confidence level of the deep learning model in recognizing the target be C, where C ranges from [0, 1], and set the trigger threshold to C0, where C ranges from [0.7, 0.9]. When the confidence level C ≥ C0, the DLP module is activated to project the marker.

[0054] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0057] Figure 1 This is a schematic diagram of the system structure of Embodiment 1 of the present invention.

[0058] Figure 2 This is a schematic diagram of the environmental detection device of Embodiment 1 of the present invention installed on the robot dog.

[0059] Figure 3 This is a schematic diagram of the method in Embodiment 2 of the present invention.

[0060] In the diagram: 1. Environmental monitoring device; 11. Radar sensor; 12. Illuminance sensor; 13. Vision sensor; 14. Voice recognition sensor; 2. Processing device; 21. Image processing module; 22. Voice processing module; 23. Control module; 31. ADB module; 32. DLP module; 33. Contour light module. Detailed Implementation

[0061] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0062] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0063] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0064] It should be noted that in this embodiment, the robotic pet refers to a machine device that is based on digital and logic computing devices and can move on its own without external commands.

[0065] Example 1

[0066] This application provides an intelligent lighting control system for a robotic pet, such as... Figure 1 As shown, the system includes: an environmental detection device 1, a processing device 2, and multiple light-emitting devices installed on the robot pet, wherein the light-emitting devices are an ADB module 31, a DLP module 32, and a contour light module 33.

[0067] The environmental detection device 1 includes a radar sensor 11, a light intensity sensor 12, a vision sensor 13, and a voice recognition sensor 14. The radar sensor 11 and vision sensor 13 are both located at the front end of the robot pet's torso, while the light intensity sensor 12 and voice recognition sensor 14 are located on the robot pet's torso. The radar sensor 11 is configured to collect the distance and speed of obstacles in front; the light intensity sensor 12 is configured to collect ambient light intensity information; the vision sensor 13 is configured to collect ambient environmental information; and the voice recognition sensor 14 is configured to collect user voice commands.

[0068] Processing device 2 includes: an image processing module 21, a voice processing module 22, and a control module 23. The image processing module 21 is connected to the vision sensor 13, the voice processing module 22 is connected to the voice recognition sensor 14, and the radar sensor 11, the illuminance sensor 12, the image processing module 21, the voice processing module 22, and the light-emitting module are all connected to the control module 23. The image processing module 21 is configured to acquire environmental information collected by the vision sensor 13 and perform preprocessing to obtain image information; the voice processing module 22 is configured to preprocess the acquired environmental sound information to obtain sound commands; the control module 23 is configured to process the distance, speed, illuminance, image information, and sound commands of obstacles and generate control commands. The control module 23 executes corresponding control on the corresponding light-emitting module according to the control commands. The corresponding control includes:

[0069] Based on the light intensity information, a threshold algorithm is used to generate corresponding control commands to control the contour light module 33.

[0070] Based on the distance and speed of obstacles and combined with light intensity information, a decision algorithm based on set rules generates corresponding high and low beam switching commands to control the ADB module 31.

[0071] Based on image information, DLP projection instructions are generated to control the DLP module 32;

[0072] Based on voice commands, it outputs corresponding control commands and provides voice feedback.

[0073] In this embodiment, both the ADB module 31 and the DLP module 32 are located at the front end of the robot pet's torso; the contour light module 33 includes several LEDs, which are evenly distributed on the robot pet's torso and limbs. The ADB module 31 is used to illuminate the road in front of the robot pet and switch between high and low beam modes; the DLP module 32 is used to project optical patterns; and the contour light module 33 is used to display the robot pet's position and shape.

[0074] Specifically, such as Figure 2 As shown, taking a robot dog as an example, the radar sensor 11 is placed at the robot dog's mouth, the illuminance sensor 12 is placed above the robot dog's body, the vision sensor 13 is placed at the robot dog's nose, the voice recognition sensor 14 is placed at the robot dog's chest, the ADB module 31 and the DLP module 32 are respectively placed at the robot dog's eyes, and LED lights are respectively placed at the robot dog's ears, face, body, tail, legs, joints, and foot corners.

[0075] In this embodiment, the control module 23 includes a processor, which is an ARM Cortex-A53 chip, equipped with 4GB of memory, and runs a Linux operating system.

[0076] The illumination intensity range of the light sensor 12 is 0.01-100,000 Lux, and the maximum detection range of the radar sensor 11 is 30 meters. The radar sensor 11 is either a lidar sensor 11 or a millimeter-wave radar sensor 11.

[0077] Example 2

[0078] This application provides an intelligent lighting control method for a robotic pet. The method employs the intelligent lighting control system for robotic pets described above. Figure 3 As shown, the method includes the following steps:

[0079] S1: Start the robot pet and enter initialization. The control environment detection device 1 will automatically enter the detection state. It should be noted that after entering initialization, the outline light module 33 is set to green constant light and the ADB module 31 is set to low beam mode.

[0080] S2: The radar sensor 11 collects the distance and speed of obstacles in front, the illuminance sensor 12 collects the illuminance information of the surrounding environment, the vision sensor 13 collects the surrounding environment information, and the voice recognition sensor 14 collects the user's voice command information, and transmits all the information to the processing device 2.

[0081] S3: Processing device 2 processes the received information and generates control commands to control the corresponding light-emitting module to perform corresponding controls, including:

[0082] Based on the light intensity information, a threshold algorithm is used to generate corresponding control commands to control the contour light module 33.

[0083] Based on the distance and speed of obstacles and combined with light intensity information, a decision algorithm based on set rules generates corresponding high / low beam switching commands to control the ADB module 31.

[0084] It processes environmental information and generates DLP projection instructions to control the DLP module 32;

[0085] It processes voice command information, outputs corresponding control commands, and provides voice feedback.

[0086] S4: The system repeats steps S2 to S4 every 500ms and records system information, including information on the light source mode, projection pattern and flickering status, for information diagnosis in case of subsequent faults.

[0087] In this embodiment, based on the light intensity information, a threshold algorithm is used to generate corresponding control commands to control the contour light module 33 to perform corresponding actions, including:

[0088] If the light intensity is >15 Lux, enter daytime mode, control module 23 turns on contour light module 33, and turns off ADB module 31 and DLP module 32.

[0089] In this embodiment, based on the distance and speed of the obstacle and combined with light intensity information, a decision algorithm based on set rules generates corresponding high / low beam switching commands, controlling the ADB module 31 to perform corresponding actions, including:

[0090] If the light intensity is ≤15Lux, or the distance to the obstacle is ≤20m, or the relative speed is >30km / h, the control module 23 issues a low beam mode control command to control the ADB module 31 to switch to low beam mode, and at the same time controls the outline light module 33 to flash red at a frequency of 1HZ.

[0091] If the light intensity is ≤15Lux, the distance to the obstacle is >20m, and the relative speed is ≤30km / h, the control module 23 issues a high beam mode control command to control the ADB module 31 to switch to high beam mode.

[0092] In this embodiment, processing environmental information and generating DLP projection instructions, and controlling the DLP module 32 specifically include:

[0093] The image processing module 21 acquires environmental information, performs target recognition through a deep learning algorithm, and obtains image information, which includes the target object and the target object region. The target object and the target object region are then sent to the control module 23.

[0094] Establish the robot pet's body coordinate system XOY, where the robot pet's forward direction is the X-axis, the robot pet's left and right direction is the Y-axis, and the direction perpendicular to the ground is the Z-axis.

[0095] Control module 23 calculates the three-dimensional coordinates of the target area in the robot pet's body coordinate system. X b , Y b , Z b Specifically, it includes:

[0096] The intrinsic parameter matrix of the visual sensor was obtained using Zhang's calibration method. K This describes the mapping relationship between pixel coordinates and the coordinates of the visual sensor 13, and the calculation formula is as follows:

[0097]

[0098] in, , The focal length (in pixels) of the vision sensor is 13. , Principal point coordinates (image center pixels, unit: pixels).

[0099] Let the translation vector of the vision sensor 13 relative to the coordinate system of the robotic pet be... T c→b = [ t x , t y , t z ] T (Unit: m), the translation vector of the vision sensor 13 relative to the robot pet's body coordinate system is obtained by measuring the installation position of the vision sensor 13 (Example: t x =0.15m, t y =0m, t z =0.2m, meaning the visual sensor 13 is located 15cm in front of the center of the body and 20cm at a height.

[0100] Assume the orientation angle of the vision sensor 13 relative to the robot pet's body is the pitch angle. α Roll angle β, yaw angle c (Example:) α =-15°, β=0°, c =0°, meaning the pitch angle of vision sensor 13 is 15°), which is converted into a rotation matrix through attitude angle. R c→b (ZYX Euler angle rotation sequence).

[0101] The robot pet's attitude angle, specifically: the robot pet's pitch angle relative to the world coordinate system. i Roll angle f (θ= when driving horizontally) f =0°), heading angle ψ (Forward direction deflection), used to calculate the rotation matrix of the robot pet's body relative to the world coordinate system. R b→w .

[0102] Target recognition is performed using deep learning algorithms to obtain the pixel bounding box of the target area, and the top left corner is recorded. u 1, v 1) Bottom right corner ( u 2, v 2) That is, the pixel range of the target area in the image: u ∈[u 1, u 2],v∈[ v 1, v 2).

[0103] The depth value of any pixel in the image is known. d (That is, the point in the visual sensor 13 coordinate system) Z c coordinate: Z c = d ), calculate the three-dimensional coordinates of the target area in the camera coordinate system ( X c , Y c , Z c )

[0104]

[0105] Based on the three-dimensional coordinates of the target area in the camera coordinate system ( X c , Y c , Z c Through homogeneous transformation, the three-dimensional coordinates of the target area in the robot pet's body coordinate system are calculated. X b , Y b , Z b Specifically, this includes: using rotation matrices R c→b Translation vector T c→b To achieve the transformation, homogeneous coordinates are used to calculate the three-dimensional coordinates of the target area in the robot pet's body coordinate system. X b , Y b , Z b ):

[0106]

[0107] Wherein, rotation matrix (ZYX Euler Angle Unfold):

[0108] The rotation matrices (angle → radians) for each axis are as follows:

[0109]

[0110]

[0111]

[0112] Convert the robot pet's body coordinate system to the world coordinate system, calculate the actual position of the target area on the road surface, and calculate the projection range of the marking pattern based on the actual position of the target area on the road surface.

[0113] Specifically, the target area is projected onto the ground at the location of the "real road surface" ( Z=0 The coordinates need to be converted to the world coordinate system, including: the ground projection within the computer pet's body coordinate system ( , ), Let the target area on the road surface be... =0 The projection formula (ignoring the robot pet's pitch / roll and simplifying it to horizontal movement) is:

[0114]

[0115] .

[0116] The transformation from the robot pet's physical form to the world coordinate system involves first rotating around the Z-axis (heading angle ψ), then translating (cumulative displacement). D X,ΔY ):

[0117] 。

[0118] Traverse all pixels within the target area and transform them to obtain the ground-projected world coordinates of each pixel. )( i=1,2,...,N ), take the extreme value:

[0119]

[0120] 。

[0121] Assuming an outward projection distance Δ = 0.05~0.1m (default 0.08m), the projection range must cover the outer edge of the target area by Δ. The calculation formula is as follows:

[0122]

[0123] .

[0124] The control module 23 sends the projection range of the marker pattern to the DLP module 32, and the DLP module 32 projects the pattern based on the projection range.

[0125] In this embodiment, the voice command information includes turning the high beam on or off, switching between high and low beam modes, adjusting the brightness of the light and / or changing the optical color. The voice processing module 22 processes the voice command information and matches it with the model to obtain the matching degree M. When the matching degree M>0.8, it controls the corresponding light-emitting module to execute the corresponding command.

[0126] In this embodiment, the target object is a step, a stone, or a pit. The confidence level of the deep learning model in recognizing the target object is set to C, and the value range of C is [0, 1]. The trigger threshold is set to C0, and the value range is [0.7, 0.9]. When the confidence level C≥C0, the DLP module 32 is activated to project the mark.

[0127] In this embodiment, when the target object is a pedestrian or a vehicle, the confidence level of the deep learning model in recognizing the target object is set to Z, with the value range of [0, 1]. The trigger threshold is set to Z1, with the value range of [0.6, 0.8]. When the confidence level Z≥Z1, the ADB module 31 is controlled to switch to low beam mode.

[0128] In summary, the beneficial effects of the intelligent lighting control system and control method for the robotic pet of the present invention are:

[0129] 1. By linking the ADB module 31 with the environmental detection device 1, intelligent switching between high and low beams is achieved, avoiding glare interference to pedestrians or vehicles at night, thus ensuring high safety;

[0130] 2. By linking the DLP module 32 with the environmental detection device 1, visual markings are projected onto the road surface to provide nighttime warnings;

[0131] 3. Set up outline light module 33, which forms a complete outline when lit at night, improving the accuracy of surrounding targets in judging the size and movement trajectory of the robot pet;

[0132] 4. The voice interaction module enables voice control of lighting parameters, providing a good user experience.

[0133] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. An intelligent lighting control system for a robotic pet, characterized in that, The system includes: an environmental detection device (1), a processing device (2), and multiple light-emitting devices installed on the robotic pet; The environmental monitoring device (1) includes: Radar sensor (11) is configured to acquire the distance and speed of obstacles ahead; An illuminance sensor (12) is configured to collect information on the illuminance of the surrounding environment; A visual sensor (13) is configured to collect information about the surrounding environment; The voice recognition sensor (14) is configured to collect the user's voice command information; The processing device (2) includes: An image processing module (21) is connected to the vision sensor (13). The image processing module (21) is configured to acquire environmental information collected by the vision sensor (13) and perform preprocessing to obtain image information. A voice processing module (22) is connected to the voice recognition sensor (14). The voice processing module (22) is configured to preprocess the collected environmental sound information to obtain voice commands. The control module (23) is connected to the radar sensor (11), the illuminance sensor (12), the image processing module (21), the voice processing module (22), and the light-emitting device. The control module (23) is configured to process the distance, speed, illuminance, image information, and sound commands of the obstacle and generate control commands. The control module (23) performs corresponding control on the corresponding light-emitting device according to the control commands. The light-emitting devices are an ADB module (31), a DLP module (32), and a contour light module (33). The intelligent lighting control method for robotic pets includes the following steps: S1: Start the robot pet and enter the initialization process, and control the environmental detection device (1) to automatically enter the detection state; S2: The radar sensor (11) collects the distance and speed of the obstacles in front, the illuminance sensor (12) collects the illuminance information of the surrounding environment, the vision sensor (13) collects the surrounding environment information, the voice recognition sensor (14) collects the user's voice command information, and transmits all the information to the processing device (2). S3: The processing device (2) processes the received information and generates control commands to control the corresponding light-emitting device to perform corresponding controls, including: Based on the light intensity information, a threshold algorithm is used to generate corresponding control commands to control the contour light module (33); Based on the distance and speed of the obstacle and combined with the light intensity information, the decision algorithm based on the set rules generates the corresponding high and low beam switching command to control the ADB module (31). Process environmental information and generate DLP projection instructions to control the DLP module (32); Process voice command information, output corresponding control commands and provide voice feedback; S4: The system repeats steps S2 to S4 every 500ms and records system information, including information on light source mode, projection pattern and flickering status, for information diagnosis in subsequent fault situations. The specific functions of processing environmental information and generating DLP projection instructions, and controlling the DLP module (32), include: The image processing module (21) acquires environmental information, performs target recognition through deep learning algorithm, and obtains image information, which includes the target object and the target object region. The target object and the target object region are then sent to the control module (23). Establish the robot pet's body coordinate system XOY, where the robot pet's forward direction is the X-axis, the robot pet's left and right direction is the Y-axis, and the direction perpendicular to the ground is the Z-axis; The control module (23) calculates the three-dimensional coordinates of the target area in the robot pet's body coordinate system. X b , Y b , Z b ), The robot pet's body coordinate system is converted to the world coordinate system. The actual position of the target area on the road surface is calculated. Based on the actual position of the target area on the road surface, the projection range of the marking pattern is calculated. The specific process includes: The ground projection coordinates within the computer pet's body coordinate system ( , ), Let the target area on the road surface be... =0 The projection formula is: ； The transformation from the robot pet's physical form to the world coordinate system involves first rotating it around the Z-axis by a heading angle ψ, then translating it, and finally accumulating the displacement. Δ X,ΔY ): ； Traverse all pixels within the target area and transform them to obtain the ground-projected world coordinates of each pixel. )( i=1,2,...,N ), take the extreme value: ， ； Assuming an outward expansion distance Δ = 0.05~0.1m, and the projection range needs to cover the outer edge of the target area by Δ, the calculation formula is as follows: ； The control module (23) sends the projection range of the marker pattern to the DLP module (32), and the DLP module (32) projects the marker pattern based on the projection range.

2. The intelligent lighting control system for the robotic pet as described in claim 1, characterized in that, The ADB module (31) is used to illuminate the road in front of the robot pet and switch between high and low beam modes; The DLP module (32) is used to project optical patterns; The outline light module (33) is used to display the position and shape of the robot pet; Both the ADB module (31) and the DLP module (32) are located at the front end of the robot pet's torso. The outline light module (33) includes several LED lights, which are evenly distributed on the robot pet's torso and limbs.

3. The intelligent lighting control system for the robotic pet as described in claim 1, characterized in that, The control module (23) includes a processor, the chip of which is ARM Cortex-A53.

4. The intelligent lighting control system for the robotic pet as described in claim 1, characterized in that, Based on the light intensity information, a threshold algorithm is used to generate corresponding control commands to control the contour light module (33) to perform corresponding actions, including: If the light intensity is >15 Lux, enter daytime mode, control module (23) turns on contour light module (33), and turns off ADB module (31) and DLP module (32).

5. The intelligent lighting control system for the robotic pet as described in claim 1, characterized in that, Based on the distance and speed of obstacles and combined with light intensity information, a decision algorithm based on set rules generates corresponding high / low beam switching commands, controlling the ADB module (31) to perform corresponding actions, including: If the light intensity is ≤15Lux, or the distance to the obstacle is ≤20m, or the relative speed is >30km / h, the control module (23) issues a low beam mode control command to control the ADB module (31) to switch to low beam mode, and at the same time controls the outline light module (33) to flash red. If the light intensity is ≤15Lux, the distance to the obstacle is >20m, and the relative speed is ≤30km / h, the control module (23) issues a high beam mode control command to control the ADB module (31) to switch to high beam mode.

6. The intelligent lighting control system for the robotic pet as described in claim 1, characterized in that, The target object is a step, a rock, or a pit.

7. The intelligent lighting control system for the robotic pet as described in claim 1, characterized in that, Voice commands include turning high beams on or off, switching between high and low beam modes, adjusting headlight brightness, and / or changing optical colors.

8. The intelligent lighting control method for a robotic pet as described in claim 1, characterized in that, Let the confidence level of the deep learning model in recognizing the target be C, where C ranges from [0, 1]. Set the trigger threshold to C0, where C ranges from [0.7, 0.9]. When the confidence level C ≥ C0, start the DLP module (32) to project the label.

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