Environment sensing system of human-shaped accompanying robot and precision motor control method

Abstract

The invention relates to the technical field of robots, in particular to an environment sensing system of a human-shaped accompanying robot and a precision motor control method, and the system comprises a data collection module, a data processing module, a space analysis module, an environment sensing evaluation module, a task planning module and a control instruction module. The data acquisition module acquires environment data through various sensors, the data processing module processes data from different sensors and generates a comprehensive environment information graph, the space analysis module performs space analysis on the environment information graph and identifies feasible paths, obstacles and potential dangerous areas, and the environment perception evaluation module performs environment perception evaluation according to a space analysis result. The task planning module generates an action plan of the robot according to an adaptability score, and the control instruction module generates a control instruction according to the task plan; the adaptive capacity and execution efficiency of the robot in a dynamic and complex environment are improved, and the method has important application value.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to an environment perception system and a precision motor control method for a humanoid companion robot. Background Art

[0002] With the rapid development of artificial intelligence and robotics, humanoid companion robots, as an important type of service robots, have been widely used in many fields such as home, medical care, and education. Humanoid companion robots usually need to interact with humans and the surrounding environment in complex and dynamic environments. This requires robots to have a high degree of environmental perception, be able to obtain and analyze surrounding environmental information in real time and accurately, and identify obstacles, paths, spatial structures, and potential dangerous areas.

[0003] However, with the diversification of robot application environments, how to efficiently process data from different sensors and how to perform path planning and task execution in complex environments have become major challenges facing humanoid companion robots. In existing technologies, many robot systems rely on traditional single sensors or simplified environmental perception algorithms, which often make it difficult to accurately respond to complex situations in dynamic environments. Even in multi-sensor fusion systems, there are still problems such as insufficient data processing, insufficient environmental adaptability assessment, and low task planning and execution efficiency, which make it impossible for robots to achieve accurate and stable navigation and task execution in dynamic and changing environments. Summary of the invention

[0004] The present invention provides an environment perception system and a precision motor control method for a humanoid companion robot.

[0005] An environmental perception system for a humanoid companion robot includes a data acquisition module, a data processing module, a space analysis module, an environmental perception evaluation module, a task planning module and a control instruction module, wherein;

[0006] The data acquisition module is used to collect environmental data through a variety of sensors during the operation of the robot, and the sensors include depth cameras, laser radars and ultrasonic sensors;

[0007] The data processing module is used to receive the environmental data provided by the data acquisition module, and uses multimodal data fusion technology to process the data from different sensors and generate a comprehensive environmental information map;

[0008] The spatial analysis module is used to perform spatial analysis on the environmental information map to identify feasible paths and potential danger areas in the robot's current environment;

[0009] The environmental perception evaluation module is used to calculate and evaluate the adaptability of the robot's current environment based on environmental data and spatial analysis results, including spatial complexity and dynamic environmental factors, to obtain an environmental adaptability score;

[0010] The task planning module generates an action plan for the robot based on the environmental adaptability score provided by the environmental perception assessment module;

[0011] The control instruction module is used to control the robot to perform specific actions, including movement, turning and speed, according to the action plan generated by the task planning module.

[0012] Optionally, the data acquisition module includes:

[0013] During the operation of the robot, the image data of the surrounding environment is collected through the depth camera installed on the robot;

[0014] During the operation of the robot, the laser transmitter on the lidar emits a laser beam to the surrounding environment. After encountering an obstacle, the laser beam is reflected back to the receiver. The lidar calculates the distance of each measurement point based on the time difference of the laser return. Using this process, the lidar generates a 3D point cloud map of the robot's surrounding environment. Each point in the point cloud map represents a distance information, reflecting the position and shape of obstacles in the environment.

[0015] During the operation of the robot, high-frequency sound waves are emitted through ultrasonic sensors, and the distance to obstacles is calculated based on the time difference of the sound wave echoes;

[0016] The collected environmental data (data collected by depth cameras, lidar and ultrasonic sensors) are transmitted to the data processing module through a unified protocol.

[0017] Optionally, the data processing module includes:

[0018] The data processing module receives the environmental data collected by the data collection module and performs preprocessing on the environmental data, including denoising and image correction;

[0019] The data processing module uses multimodal data fusion technology to align data from different sensors based on timestamps or synchronization signals, so that the data from each sensor is fused in the same coordinate system to generate a multidimensional environmental data set;

[0020] The data processing module extracts environmental information from the multi-dimensional environmental data set through a feature extraction algorithm to generate a comprehensive environmental information map.

[0021] Optionally, the spatial analysis module performs spatial analysis on the environment information map, analyzes the obstacle positions, free areas and wall information in the environment information map, and extracts feasible paths in the current environment of the robot.

[0022] Optionally, the spatial analysis module performs spatial analysis on the environmental information map to identify potential danger areas.

[0023] Optionally, the environmental perception assessment module includes:

[0024] The environmental perception assessment module evaluates the spatial complexity of the current environment by calculating the distribution and layout of obstacles in the current environment;

[0025] Dynamic environmental factor assessment: Based on the dynamic obstacle information in the environment (such as humans, animals, moving objects, etc.), assess the changes in the dynamic environment;

[0026] The environmental perception evaluation module comprehensively calculates the adaptability score of the robot's current environment based on the evaluation results of spatial complexity and dynamic environmental factors.

[0027] Optionally, the task planning module includes:

[0028] The mission planning module receives the environmental adaptability score from the environmental perception assessment module;

[0029] Based on the environmental adaptability score, the task planning module uses an optimization algorithm to generate the robot's action plan, including path selection, motion strategy determination, and task priority allocation;

[0030] After generating the action plan, the task planning module breaks down the action plan into specific execution paths and task assignments, and outputs the robot's action instructions.

[0031] Optionally, the control instruction module includes:

[0032] The control instruction module receives the action plan and action instructions generated by the task planning module.

[0033] According to the action instructions in the action plan, the control instruction module generates robot control commands to control the movement of each execution unit of the robot.

[0034] A precision motor control method for a humanoid companion robot performs motor control according to the perception result of an environmental perception system, comprising the following steps:

[0035] S1: Receive the perception results of the environmental perception system, including environmental information map, environmental adaptability score, path planning, obstacle location, robot action plan and robot control command;

[0036] S2: Generate the control target of the motor based on the received environmental perception results, including the robot's movement speed, steering angle, acceleration, and actions to be performed.

[0037] S3: Generate specific motor control instructions according to the motor control target, the instructions include motion motor control instructions, steering motor control instructions, speed and acceleration motor control instructions.

[0038] S4: According to the generated motor control instructions, the motor drive system performs specific motion tasks to control the movement of the robot.

[0039] Beneficial effects of the present invention:

[0040] The present invention obtains rich environmental information through the data acquisition module, and uses multimodal data fusion technology to generate high-precision environmental information maps, ensuring that the robot can perceive changes in the surrounding environment in real time and accurately. Through the collaborative work of the depth camera, lidar and ultrasonic sensor, the system can fully obtain environmental data, including information such as the shape, distance, and spatial layout of obstacles. The spatial analysis module further identifies feasible paths and potential danger areas in the environment through precise spatial analysis, thereby providing the robot with a more comprehensive environmental adaptation assessment. This multi-sensor fusion and precise spatial analysis significantly improve the robot's adaptability in dynamic environments, enabling it to successfully perform tasks in complex and changing environments.

[0041] In the present invention, the task planning module is based on the environmental adaptability score provided by the environmental perception evaluation module, and the robot can dynamically generate and optimize its action plan according to real-time environmental changes. The system can not only handle routine tasks in a static environment, but also respond quickly in a dynamic environment. For example, when a new obstacle is detected or a path change occurs, the task planning module will adjust the task execution plan in real time to ensure that the robot always takes the best path. The control instruction module generates precise motor control instructions based on this task plan to control the robot to perform specific actions, including movement, steering, acceleration and deceleration. In this way, the robot not only has high flexibility and adaptability when performing tasks, but also can ensure that the target tasks are completed efficiently and accurately, reducing errors and delays in the task execution process.

[0042] In the present invention, the environmental perception assessment module can accurately calculate and derive an environmental adaptability score through a comprehensive assessment of spatial complexity and dynamic environmental factors. The score takes into account the complexity of the spatial layout in the environment, as well as the influence of dynamic factors such as pedestrians and moving obstacles, thereby providing the robot with real-time and comprehensive environmental adaptability feedback, helping it to dynamically adjust task planning and path execution, and significantly improving the robot's adaptability and execution efficiency in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or 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 in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of the process flow of an environment perception system according to an embodiment of the present invention;

[0045] Figure 2 Schematic diagram of the process flow of the precision motor control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0047] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0048] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0049] like Figure 1 As shown, an environmental perception system of a humanoid companion robot includes a data acquisition module, a data processing module, a space analysis module, an environmental perception evaluation module, a task planning module and a control instruction module, wherein;

[0050] The data acquisition module is used to collect environmental data through a variety of sensors during the operation of the robot, including depth cameras, lidars, and ultrasonic sensors;

[0051] The data processing module is used to receive the environmental data provided by the data acquisition module, and uses multimodal data fusion technology to process the data from different sensors and generate a comprehensive environmental information map;

[0052] The spatial analysis module is used to perform spatial analysis on the environmental information map and identify feasible paths and potential dangerous areas in the robot's current environment;

[0053] The environmental perception evaluation module is used to calculate and evaluate the adaptability of the robot's current environment based on environmental data and spatial analysis results. The evaluation includes spatial complexity and dynamic environmental factors, and obtains an environmental adaptability score.

[0054] The task planning module generates the robot's action plan based on the environmental adaptability score provided by the environmental perception assessment module;

[0055] The control instruction module is used to control the robot to perform specific actions, including movement, turning and speed, according to the action plan generated by the task planning module.

[0056] The data acquisition module includes:

[0057] During the operation of the robot, the image data of the surrounding environment is collected through the depth camera installed on the robot;

[0058] Specifically, the depth camera uses stereo vision principles or structured light technology to measure the depth information in the scene, thereby generating image data that reflects the depth structure of the environment;

[0059] During the operation of the robot, the laser transmitter on the lidar emits a laser beam to the surrounding environment. After encountering an obstacle, the laser beam is reflected back to the receiver. The lidar calculates the distance of each measurement point based on the time difference of the laser return. Using this process, the lidar generates a 3D point cloud map of the robot's surrounding environment. Each point in the point cloud map represents a distance information, reflecting the position and shape of obstacles in the environment.

[0060] During the operation of the robot, high-frequency sound waves are emitted through ultrasonic sensors, and the distance to obstacles is calculated based on the time difference of the sound wave echoes;

[0061] The collected environmental data (data collected by depth cameras, lidar and ultrasonic sensors) are transmitted to the data processing module through a unified protocol.

[0062] The data processing module includes:

[0063] The data processing module receives the environmental data collected by the data acquisition module and pre-processes the environmental data, including denoising and image correction. The specific steps include:

[0064] Denoising: Use median filtering to denoise sensor data to remove noise that may exist in the data, such as salt and pepper noise in image data or measurement errors in lidar and ultrasonic sensors;

[0065] Image correction: Perform distortion correction on the two-dimensional image data from the depth camera to remove image distortion caused by factors such as lens distortion and sensor error;

[0066] The data processing module uses multimodal data fusion technology to align the data from different sensors based on timestamps or synchronization signals, so that the data from each sensor can be fused in the same coordinate system to generate a multidimensional environmental data set. The specific steps include:

[0067] Time alignment: First, ensure that the data from different sensors are aligned at the same time point based on the timestamps of each sensor. If there is a time deviation in the sensor data, linear interpolation is used to align the data in time.

[0068] Synchronous signal alignment: By using a synchronous signal or trigger signal, ensure that each sensor starts collecting data at the same time point to eliminate the time error that may occur when different sensors collect data.

[0069] Coordinate alignment: The data of each sensor is converted into a coordinate system according to the known sensor position and posture relationship to ensure that all data are fused in a unified three-dimensional coordinate system. The coordinate transformation is expressed as;

[0070] X new =R·X old +T;

[0071] Among them, X old is the coordinate of the sensor data in the original coordinate system, X new is the coordinate in the new coordinate system after transformation, R is the rotation matrix, and T is the translation vector;

[0072] Data fusion: The weighted fusion technology is used to fuse the data of each sensor to generate a multi-dimensional environmental data set. The data set contains visual, spatial and obstacle information, which can reflect the overall picture of the robot's environment. The weighted fusion formula is expressed as:

[0073]

[0074] Among them, D fused is the fused data, D i represents the data from the i-th sensor, wi is the weight of the sensor, weight w i Adjustments are made based on factors such as sensor data reliability and accuracy;

[0075] The data processing module extracts environmental information from the multidimensional environmental data set through a feature extraction algorithm and generates a comprehensive environmental information map. The specific steps include:

[0076] Feature extraction: Through the Canny edge detection algorithm, important features in the environment (such as the outline of obstacles, walls in space, door and window positions, etc.) are extracted from the depth map and point cloud data. The formula for Canny edge detection is expressed as:

[0077]

[0078] Among them, I(x,y) represents the pixel value of the image at position (x,y), and Respectively represent the gradient of the image in the x and y directions, and are used to detect the edges of the image;

[0079] Environmental classification: The K-means clustering algorithm is used to classify the extracted features and divide the environmental information into categories such as static obstacles, dynamic obstacles, paths, and open areas. The formula of the K-means clustering algorithm is:

[0080]

[0081] Among them, r ik Represents data point x i The degree of membership to the kth cluster, μ k is the center of the kth cluster, K is the number of clusters, and n is the number of data points;

[0082] Image synthesis: Data from different sensors are synthesized to generate a comprehensive environmental information map that includes all environmental features. This map can accurately reflect the structure of the robot's environment, obstacle locations, feasible paths and other information in space.

[0083] The spatial analysis module performs spatial analysis on the environment information map, analyzes the obstacle positions, free areas, and wall information in the environment information map, and extracts feasible paths in the robot's current environment, including:

[0084] Obstacle location recognition: Based on image segmentation technology, the obstacle area in the environmental information map is extracted. Obstacles are usually marked as areas with high density and impassable. Obstacle areas are identified using a semantic segmentation network based on deep learning.

[0085] Path candidate area generation: Based on the information of the obstacle area, the path candidate area is generated using image morphological operations (dilation and erosion). The morphological operation is performed using the following formula:

[0086] P path =Dilation(I map )\Erosion(I obstacle );

[0087] Among them, I map For environmental information graphics, I obstacle is the obstacle area, P path For the path candidate area, the dilation operation will generate a wider path candidate area, and the erosion operation will reduce the impact of obstacles;

[0088] Feasible path screening: Analyze the generated path candidate areas, remove infeasible paths, and obtain the final feasible path.

[0089] Path constraints: Based on the robot's kinematic model, add constraints such as robot size and turning radius to select paths that meet the robot's motion capabilities. If the path width is smaller than the robot size, or the path turning radius is larger than the robot's turning radius, it will be considered an infeasible path.

[0090] Path evaluation: Evaluate all candidate paths and calculate the feasibility score of each path. The scoring criteria include the length, curvature, obstacle distance, etc. of the path. The calculation formula for the path score is:

[0091]

[0092] Where L is the total length of the path, N is the number of sample points on the path, and d i is the distance w from the path point to the nearest obstacle i is the weight of the path point. By optimizing the path score, the route with longer obstacle distance and shorter path is preferred.

[0093] Path selection: Based on the results of path evaluation, the best feasible path is selected as the target path for the robot to move.

[0094] The spatial analysis module performs spatial analysis on the environmental information map to identify potential danger areas. The specific steps are as follows:

[0095] Dangerous area identification: Based on the density, distance and other information of obstacles in the environment information map, potential dangerous areas are identified. Dangerous areas usually refer to areas with high obstacle density that the robot cannot avoid.

[0096] Density calculation: By calculating the density of obstacles in each area, high-density areas are identified. Density ρ obstacleCalculated as:

[0097]

[0098] Among them, N obstacle represents the number of obstacles in the area, and A represents the area of ​​the area;

[0099] Delineation of potential danger areas: If the obstacle density in a certain area exceeds the preset threshold, the area is considered a potential danger area. This can be determined using the following formula:

[0100]

[0101] Among them, dangerous area is the potential dangerous area, ρ threshold is the density threshold, and the area beyond this threshold is marked as a potential danger area;

[0102] The spatial analysis module provides feasible paths and risk avoidance suggestions for the subsequent task planning module based on the path selection and potential danger area identification results, ensuring that the robot can operate safely in complex environments.

[0103] The environment perception assessment module includes:

[0104] The environmental perception evaluation module evaluates the spatial complexity of the current environment by calculating the distribution and layout of obstacles in the current environment. The spatial complexity reflects the openness of the environment and the robot's ability to pass through. Based on the number and layout of obstacles in the environment and the passability of the path, the spatial complexity score is calculated using the following formula:

[0105]

[0106] Among them, A free is the area of ​​barrier-free area, A total The area of ​​the entire environment. The higher the spatial complexity score, the more open the robot's current environment is, the fewer obstacles there are, and the easier it is to navigate.

[0107] Dynamic environmental factor assessment: Based on the dynamic obstacle information in the environment (such as humans, animals, moving objects, etc.), the dynamic environment changes are assessed. By analyzing the speed, direction, distance and other parameters of the dynamic objects, combined with prediction algorithms such as Kalman filtering or particle filtering, the possible obstacle positions at future moments are estimated. The dynamic environmental factor assessment can be performed using the following formula:

[0108]

[0109] Among them, v i is the velocity of the ith dynamic obstacle, t predTo predict the time interval, M is the number of dynamic obstacles. This formula is used to calculate the future dynamic obstacles that may affect the robot's action area;

[0110] The environmental perception evaluation module calculates the adaptability score of the robot's current environment based on the evaluation results of spatial complexity and dynamic environmental factors. The adaptability score is an indicator for comprehensively evaluating the robot's environmental adaptability. The adaptability score S adapt Calculated as:

[0111] S adapt =w 1 ·C space +w 2 ·D dynamic ;

[0112] Among them, w 1 ,w 2 are the weight coefficients of spatial complexity and dynamic environmental factors respectively;

[0113] The weight coefficient is set according to the needs of the actual application scenario. The higher the adaptability score, the better the robot can adapt to the current environment; when the adaptability score is low, the robot may need to adjust the path planning or take other obstacle avoidance measures;

[0114] The environmental perception evaluation module outputs the robot's environmental fitness evaluation results based on the adaptability score and passes it to the task planning module to further optimize the robot's path planning and action decisions in the environment.

[0115] The mission planning module includes:

[0116] The mission planning module receives the environmental adaptability score from the environmental perception assessment module as input, and the environmental adaptability score includes the evaluation results of spatial complexity and dynamic environmental factors;

[0117] If the environmental adaptability score is lower than the preset threshold, the mission planning module will further analyze the environmental adaptability to determine whether it is necessary to re-evaluate the path or adopt other strategies, such as temporarily stopping movement or adjusting the action plan;

[0118] Based on the environmental adaptability score, the task planning module uses an optimization algorithm to generate the robot's action plan, including path selection, motion strategy determination, and task priority allocation;

[0119] Path planning: The task planning module plans the path according to the feasibility of the path in the environmental adaptability score, and selects a path with fewer obstacles and open space;

[0120] Motion strategy optimization: The task planning module determines the motion strategy based on the evaluation results, including control parameters such as speed, acceleration, and steering. The optimization strategy can be optimized through technologies such as model predictive control (MPC) to ensure that the robot can perform tasks safely and efficiently in complex environments. The specific control formula is:

[0121]

[0122] Where u(t) is the optimal control input, x i (t) is the state of the robot at time t, x des is the expected state, λ is the weight of the control cost, and N is the time sequence. Through this optimization, the robot can perform tasks efficiently while ensuring safety;

[0123] Task priority assignment: Based on the environmental adaptability score and task requirements, the task planning module is responsible for assigning task priorities to the robot. Specifically, if the environmental adaptability score is low, the task planning module may prioritize obstacle avoidance or re-evaluation of path planning, while when the environmental adaptability score is high, more complex tasks (such as user interaction, object grasping, etc.) may be prioritized;

[0124] Task scheduling algorithm: The task planning module uses a priority-based scheduling algorithm to schedule tasks according to their urgency and environmental adaptability scores. The scheduling algorithm priority assignment formula is:

[0125]

[0126] Among them, P task is the priority of the task, S adapt Score the environmental adaptability, w task is the importance weight of the task, α is the time weighting coefficient, and time remaining is the remaining time of the task. This formula can ensure that urgent tasks are executed first;

[0127] After generating the action plan, the task planning module decomposes the action plan into specific execution paths and task assignments, and outputs the robot's action instructions. The specific steps include:

[0128] Path allocation: Based on the selected feasible paths, the task planning module allocates the paths to the robots for execution. The path allocation takes into account the current state of the robot (position, speed, motion mode, etc.) and adjusts the path selection according to the environmental adaptability score.

[0129] Task execution instruction generation: Based on the generated task priority and path planning, the task planning module generates specific execution instructions, including moving direction, speed, steering angle, etc. The generated control instructions will be passed to the control instruction module to perform specific actions.

[0130] The control instruction module includes:

[0131] The control instruction module receives the action plan and action instructions generated by the mission planning module. The specific steps are as follows:

[0132] (1) Task execution plan input: The control instruction module receives the action plan from the task planning module, which includes the robot's motion path, speed, steering angle, and other related task instructions. The action plan determines the robot's moving direction, speed, and motion strategy based on the environmental adaptability score and task priority.

[0133] (2) Mission plan analysis: The control instruction module analyzes the action plan and extracts key action instructions, including movement instructions, steering instructions, acceleration / deceleration instructions, etc. The analysis process ensures that the robot's behavior can accurately match the plan generated by the mission planning module.

[0134] (2.1) Motion instruction parsing: Extract the specific motion target from the action plan, including target position, speed, acceleration and other parameters, and calculate the transition path of the robot from the current state to the target state.

[0135] (2.2) Steering instruction analysis: Based on the steering strategy in the action plan, the steering angle of the robot in the path is calculated and control instructions are provided for the steering operation.

[0136] According to the action instructions in the action plan, the control instruction module generates robot control commands to control the various execution units of the robot to move. The specific steps are as follows:

[0137] Motion control command generation: Based on the target position, speed, acceleration and other information specified in the task plan, the control instruction module generates motion control commands using the kinematic model. The kinematic model considers the robot's motion limitations (such as maximum speed, acceleration, etc.) and calculates the appropriate control input. The kinematic model expression is:

[0138] u=f(x,x des ,v max ,a max );

[0139] Among them, u is the control command, x is the current position of the robot, and x des is the target position, v max and a max are the maximum speed and maximum acceleration respectively. The control command u ensures that the robot reaches the target position with the appropriate speed and acceleration;

[0140] Steering control command generation: Based on the target direction and current orientation, the control instruction module generates a steering control command to adjust the robot orientation to match the path requirements. The steering control command calculation formula is:

[0141]

[0142] Among them, θ cmd is the steering angle controlled, (x current ,y current ) is the current robot coordinate, (x des ,y des ) is the target position coordinate. The steering angle calculation ensures that the robot can move towards the target direction;

[0143] Speed ​​control command generation: According to the requirements of path planning and environmental adaptability score, the control command module also needs to adjust the robot's travel speed. Especially in complex or dynamic environments, the speed needs to be adjusted to avoid collisions with obstacles. The speed control can be adjusted using the following formula:

[0144]

[0145] Among them, v cmd To control the speed, v desired is the target speed, d safe is the safe distance to the nearest obstacle, t safe is the safety time interval. This formula ensures that the robot moves at an appropriate speed to avoid the risk of collision;

[0146] The control instruction module drives the driving unit (such as the motor) of the robot for precise control according to the generated motion control command, steering control command and speed control command.

[0147] like Figure 2 As shown, a precision motor control method for a humanoid companion robot performs motor control according to the perception result of an environmental perception system, including the following steps:

[0148] S1: Receive the perception results of the environmental perception system, including environmental information map, environmental adaptability score, path planning, obstacle location, robot action plan and robot control command;

[0149] S2: Generate motor control targets based on the received environmental perception results (such as path planning, obstacle locations, current robot status, robot control commands, etc.), including the robot's moving speed, steering angle, acceleration, and actions to be performed. Specifically, the motor control targets include:

[0150] S21, speed control target: Calculate the speed control target required during the robot's movement according to the task execution plan provided by the task planning module.

[0151] S22, steering control target: According to the steering instruction of the task planning module, calculate the steering angle and ensure that the motor can drive the robot to make precise steering according to the calculation results.

[0152] S23, acceleration and deceleration control target: Based on the environmental adaptability score and dynamic environmental changes, calculate the acceleration or deceleration control target to ensure the stability and safety of the robot when performing tasks.

[0153] S3: Generate specific motor control instructions according to the motor control target, the instructions include motion motor control instructions, steering motor control instructions, speed and acceleration motor control instructions, where;

[0154] Motion motor control instructions: Generate motor control signals to control the robot's mobile motors (such as wheels, leg motors, etc.) to ensure that the robot moves according to the predetermined path, speed and acceleration.

[0155] Steering motor control command: Generates motor control signals to control the robot's steering motor (such as servo, wheel rotation system, etc.) and adjust the robot's steering angle.

[0156] Speed ​​and acceleration motor control instructions: Generate corresponding motor control signals according to the speed control target and acceleration control target to adjust the speed or acceleration of the robot to adapt to the dynamically changing environment.

[0157] S4: According to the generated motor control instructions, the motor drive system performs specific motion tasks to control the movement of the robot. The specific steps include:

[0158] The motor drive system precisely controls speed, acceleration, and steering to ensure the robot moves stably along the mission planning path.

[0159] Adjust motor control instructions based on real-time feedback: During the execution of the robot, the motor control system continuously receives real-time data from sensors, such as obstacle detection and speed feedback, and adjusts motor control instructions based on real-time feedback to ensure that the robot can accurately respond to environmental changes and avoid collisions with obstacles.

[0160] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0161] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An environmental perception system for a humanoid companion robot, characterized in that: It includes data acquisition module, data processing module, space analysis module, environment perception and evaluation module, task planning module and control instruction module, among which; The data acquisition module is used to collect environmental data through a variety of sensors during the operation of the robot, and the sensors include depth cameras, laser radars and ultrasonic sensors; The data processing module is used to receive the environmental data provided by the data acquisition module, and uses multimodal data fusion technology to process the data from different sensors and generate a comprehensive environmental information map; The spatial analysis module is used to perform spatial analysis on the environmental information map to identify feasible paths and potential danger areas in the robot's current environment; The environmental perception evaluation module is used to calculate and evaluate the adaptability of the robot's current environment based on environmental data and spatial analysis results, including spatial complexity and dynamic environmental factors, to obtain an environmental adaptability score; The task planning module generates an action plan for the robot based on the environmental adaptability score provided by the environmental perception assessment module; The control instruction module is used to control the robot to perform specific actions, including movement, turning and speed, according to the action plan generated by the task planning module.

2. The environment perception system of a humanoid companion robot according to claim 1, characterized in that: The data acquisition module comprises: During the operation of the robot, the image data of the surrounding environment is collected through the depth camera installed on the robot; During the operation of the robot, the laser transmitter on the laser radar emits a laser beam to the surrounding environment. After the laser beam encounters an obstacle, it is reflected back to the receiver. The laser radar calculates the distance of each measurement point based on the time difference of the laser return. Using this process, the laser radar generates a 3D point cloud map of the robot's surrounding environment. During the operation of the robot, high-frequency sound waves are emitted through ultrasonic sensors, and the distance to obstacles is calculated based on the time difference of the sound wave echoes; The collected environmental data is transmitted to the data processing module through a unified protocol.

3. The environment perception system of a humanoid companion robot according to claim 2, characterized in that: The data processing module comprises: The data processing module receives the environmental data collected by the data collection module and performs preprocessing on the environmental data, including denoising and image correction; The data processing module uses multimodal data fusion technology to align data from different sensors based on timestamps or synchronization signals, so that the data from each sensor is fused in the same coordinate system to generate a multidimensional environmental data set; The data processing module extracts environmental information from the multi-dimensional environmental data set through a feature extraction algorithm to generate a comprehensive environmental information map.

4. The environment perception system of a humanoid companion robot according to claim 3, characterized in that: The spatial analysis module performs spatial analysis on the environment information map, analyzes the obstacle positions, free areas and wall information in the environment information map, and extracts feasible paths in the current environment of the robot.

5. The environment perception system of a humanoid companion robot according to claim 4, characterized in that: The spatial analysis module performs spatial analysis on the environmental information map to identify potential danger areas.

6. The environment perception system of a humanoid companion robot according to claim 5, characterized in that: The environmental perception assessment module includes: The environmental perception assessment module evaluates the spatial complexity of the current environment by calculating the distribution and layout of obstacles in the current environment; The environmental perception assessment module assesses the changes in the dynamic environment based on the dynamic obstacle information in the environment; The environmental perception evaluation module comprehensively calculates the adaptability score of the robot's current environment based on the evaluation results of spatial complexity and dynamic environmental factors.

7. The environment perception system of a humanoid companion robot according to claim 6, characterized in that: The mission planning module includes: The mission planning module receives the environmental adaptability score from the environmental perception assessment module; Based on the environmental adaptability score, the task planning module uses an optimization algorithm to generate the robot's action plan, including path selection, motion strategy determination, and task priority allocation; After generating the action plan, the task planning module breaks down the action plan into specific execution paths and task assignments, and outputs the robot's action instructions.

8. The environment perception system of a humanoid companion robot according to claim 7, characterized in that: The control instruction module comprises: The control instruction module receives the action plan and action instructions generated by the task planning module; According to the action instructions in the action plan, the control instruction module generates robot control commands to control the movement of each execution unit of the robot.

9. A precision motor control method for a humanoid companion robot, characterized in that: According to the perception result of the environment perception system according to any one of claims 1 to 8, motor control is performed, comprising the following steps: S1: Receive the perception results of the environmental perception system, including environmental information map, environmental adaptability score, path planning, obstacle location, robot action plan and robot control command; S2: Generate motor control targets based on the received environmental perception results, including the robot's moving speed, steering angle, acceleration, and actions to be performed; S3: Generate specific motor control instructions according to the motor control target, the instructions include motion motor control instructions, steering motor control instructions, speed and acceleration motor control instructions; S4: According to the generated motor control instructions, the motor drive system performs specific motion tasks to control the movement of the robot.

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