Robot control method and device based on multi-target path planning and medium

Abstract

The invention discloses a robot control method and device based on multi-target path planning and a medium, and relates to the technical field of robot motion control, and the method comprises the steps: carrying out the discrete division of a robot motion region to form grid units, obtaining the environment perception original information at each grid unit, and carrying out the calculation of the environment perception original information; calculating a multi-source risk index based on the environment perception original information, and generating a median risk quantity and a risk gradient vector field; calculating a risk gradient direction angle of the current position through the risk gradient vector field, calculating a target direction angle according to the current position and the target position, determining an advancing direction angle of the next position according to the risk gradient direction angle, the target direction angle and the median risk quantity of the current position, and forming a path evolution direction sequence; and calculating an advancing direction angle in the path evolution direction sequence and an orientation angle of the current position of the robot to obtain a direction deviation angle and an angle difference, and generating an angular velocity control quantity and a linear velocity control quantity. The dynamic coordination of the path evolution direction is realized.

Description

Technical Field

[0001] This invention relates to the field of robot motion control technology, and in particular to a robot control method, device and medium based on multi-objective path planning. Background Technology

[0002] When mobile robots perform autonomous navigation tasks in semi-structured or unstructured environments, they typically rely on the coordinated operation of core technologies such as multi-sensor perception, environmental characterization, path planning, and motion control. In existing technologies, environmental information is mostly collected through methods such as laser ranging, depth imaging, and ultrasonic detection, and combined with grid maps, semantic maps, or cost maps to construct an environmental model. Based on this, path planning algorithms generally generate a sequence of feasible travel directions according to the target position, environmental spatial structure, and local obstacle avoidance requirements. The motion control module, on the other hand, calculates angular and linear velocity commands based on the robot's orientation, target direction, and local environmental constraints, enabling the robot to perform continuous navigation in dynamic environments. Existing methods have been widely applied in scenarios such as inspection, transportation, and service robots.

[0003] In multi-sensor environments, existing path planning typically relies on single-directional information or separate geometric relationships to construct the direction of travel. This lacks a quantitative balance mechanism between risk avoidance and target approach requirements, making it difficult to maintain the stability of directional decisions when local obstacle distribution changes. Furthermore, current motion control generally adjusts speed based on instantaneous directional deviations, lacking methods to quantify trends in continuous directional changes along the path. This makes it difficult to adjust motion control intensity according to the degree of change in forward and backward directions, easily leading to speed fluctuations and steering discontinuities in areas with significant local path changes, affecting the overall smoothness of the motion control process. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a robot control method based on multi-objective path planning, which solves the problems of lack of uniformity in the spatial expression of multi-source risk indicators and the inability to quantify local directional changes in the path, leading to unstable control.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a robot control method based on multi-objective path planning, which includes, at the start of the robot motion control task, discretizing the robot motion area to form grid units, acquiring raw environmental perception information at each grid unit, calculating multi-source risk indicators based on the raw environmental perception information, and generating median risk quantity and risk gradient vector field.

[0008] When the robot performs path planning tasks, the risk gradient direction angle of the current position is calculated through the risk gradient vector field, and the target direction angle is calculated based on the current position and the target position. Then, based on the risk gradient direction angle, the target direction angle, and the median risk of the current position, the forward direction angle of the next position is determined, forming a path evolution direction sequence.

[0009] When the robot performs motion control, the forward direction angle in the path evolution direction sequence and the orientation angle of the robot's current position are calculated to obtain the direction deviation angle and angle difference, and angular velocity control quantity and linear velocity control quantity are generated.

[0010] In the path evolution direction sequence, the direction change rate is calculated based on the change of adjacent forward direction angles, a direction stabilization factor is generated, and the angular velocity control quantity and linear velocity control quantity are adjusted based on the direction stabilization factor to obtain the final angular velocity control quantity and the final linear velocity control quantity.

[0011] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, wherein: the step of discretizing the robot motion area to form grid units and obtaining the original environmental perception information at each grid unit includes uniformly dividing the robot motion area along the horizontal and vertical directions according to a fixed discrete step length to form grid units;

[0012] At the center coordinates of each grid unit, the ranging values ​​of lidar, laser echo intensity, depth camera, and ultrasonic range are collected to obtain the raw information of environmental perception.

[0013] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, the calculation of multi-source risk index and generation of median risk quantity and risk gradient vector field includes performing a ratio calculation on the lidar ranging value and the laser echo intensity value at the center coordinates of the grid cell to obtain the lidar risk quantity.

[0014] The depth camera range and ultrasonic range at the center coordinates of the grid cell are inversely transformed to obtain the depth camera risk quantity and the ultrasonic risk quantity.

[0015] The median risk value is obtained by performing median selection on the risk values ​​of lidar, depth camera, and ultrasound.

[0016] After forming the spatial distribution of the median risk at the center coordinates of each grid cell, the changes in the median risk in the horizontal and vertical coordinates are calculated using the finite difference method to obtain the risk gradient vector field.

[0017] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, the step of calculating the risk gradient direction angle of the current position through the risk gradient vector field and calculating the target direction angle based on the current position and the target position includes obtaining the robot's initial position in the path planning task and the robot's current position at the current moment through the robot positioning device, and obtaining the robot's target position in the path planning task through the task instruction.

[0018] Based on the grid cell where the current position is located, read the horizontal and vertical components of the current position from the risk gradient vector field;

[0019] The risk gradient direction angle at the current position is obtained by performing an arctangent operation on the ratio of the vertical component to the horizontal component.

[0020] The target direction angle is obtained by performing an arctangent operation on the coordinate difference between the target position coordinates and the current position coordinates.

[0021] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, the step of determining the forward direction angle of the next position based on the risk gradient direction angle, the target direction angle, and the median risk of the current position to form a path evolution direction sequence includes obtaining the direction difference based on the angle difference between the risk gradient direction angle and the target direction angle.

[0022] By combining the directional difference with the median risk of the grid cell where the current position is located, the path evolution feasibility of the current position is obtained.

[0023] The median risk of the grid cell where the robot's initial position is located at the start of the path planning task is compared with the median risk of the grid cell where the target position is located. The larger value is taken as the risk benchmark value for the path planning task. The path evolution feasibility threshold is obtained by inversely transforming the risk benchmark value.

[0024] The forward direction angle for the next position is determined by comparing the path evolution feasibility quantity at the current position with the path evolution feasibility threshold.

[0025] When the feasibility of path evolution is greater than the feasibility threshold of path evolution, the target direction angle is used as the forward direction angle of the next position.

[0026] When the feasibility of path evolution is less than or equal to the feasibility threshold of path evolution, the opposite direction of the risk gradient direction angle is taken as the forward direction angle of the next position.

[0027] By repeatedly calculating the feasibility of path evolution, comparing the feasibility threshold of path evolution, and determining the forward direction angle of the next position at each newly arrived position, a sequence of path evolution directions composed of forward direction angles is formed.

[0028] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, the step of calculating the forward direction angle in the path evolution direction sequence and the orientation angle of the robot's current position to obtain the direction deviation angle and angle difference includes obtaining the orientation angle of the robot's current position through a robot positioning device, and performing an arctangent operation on the angle difference between the forward direction angle corresponding to the current position and the orientation angle of the current position in the path evolution direction sequence to obtain the direction deviation angle.

[0029] The angle difference is obtained by performing an arctangent operation on the angle difference between the direction deviation angle and the risk gradient direction angle.

[0030] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, the generation of angular velocity control quantity and linear velocity control quantity includes taking the absolute value of the angle difference as the robot's angular velocity control quantity, and obtaining the robot's linear velocity control quantity by inversely transforming the median risk quantity of the grid cell where the current position is located.

[0031] After the robot reaches each new position in the path evolution direction sequence, the calculation of the direction deviation angle, the calculation of the angle difference, the calculation of the angular velocity control quantity, and the calculation of the linear velocity control quantity are repeated.

[0032] As a preferred embodiment of the robot control method based on multi-objective path planning described in this invention, wherein: the adjustment of angular velocity control quantity and linear velocity control quantity based on the direction stabilization factor to obtain the final angular velocity control quantity and the final linear velocity control quantity includes, in the path evolution direction sequence, performing arctangent operation through the angle difference between adjacent forward direction angles to obtain the direction change rate;

[0033] An orientation stabilization factor is generated by inversely transforming the rate of change of orientation.

[0034] The directional stabilization factor is applied to the angular velocity control quantity and the linear velocity control quantity respectively to obtain the final angular velocity control quantity and the final linear velocity control quantity.

[0035] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the robot control method based on multi-objective path planning as described in the first aspect of the present invention.

[0036] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the robot control method based on multi-objective path planning as described in the first aspect of the present invention.

[0037] The beneficial effects of this invention are as follows: by utilizing the quantitative relationship between the risk gradient direction angle and the target direction angle to determine the forward direction angle of the next position, dynamic coordination between risk avoidance and target approach is achieved in the path evolution direction; by generating a direction stabilization factor based on the direction change rate of adjacent forward direction angles, synchronous and smooth adjustment of angular velocity control quantity and linear velocity control quantity is achieved, so that motion control maintains turning continuity and speed stability in the path change region. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a robot control method based on multi-objective path planning.

[0040] Figure 2 The flowchart for generating the median risk and risk gradient vector field.

[0041] Figure 3 A flowchart for forming a path evolution direction sequence.

[0042] Figure 4 A flowchart for generating and adjusting angular velocity control values ​​and linear velocity control values. Detailed Implementation

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0046] Reference Figures 1-4 This is one embodiment of the present invention, which provides a robot control method based on multi-objective path planning, including the following steps:

[0047] S1. At the start of the robot motion control task, the robot motion area is discretized into grid units. The original environmental perception information is obtained at each grid unit. Based on the original environmental perception information, multi-source risk indicators are calculated to generate the median risk quantity and risk gradient vector field.

[0048] Furthermore, at the start of the robot motion control task, the robot motion area is uniformly divided along the horizontal and vertical directions according to a fixed discrete step length, so that the robot motion area is divided into multiple grid cells represented by the center coordinates.

[0049] The value of the distance step length is determined by the robot chassis width, the maximum deviation of the robot's positioning error, and the spatial resolution required for the robot to distinguish the edges of obstacles in the environment. The example value range of the distance step length is five centimeters to twenty centimeters. A distance step length less than half the width of the robot chassis is used to enable the grid unit to distinguish the outline shape of the obstacle. A distance step length greater than the maximum deviation of the robot's positioning error is used to ensure that the grid unit can stably correspond to the actual spatial position without spatial drift, while avoiding an increase in the computational load of path generation due to an excessively small distance step length.

[0050] Furthermore, at the center coordinates of each grid unit, laser radar ranging values, laser echo intensity values, depth camera ranging values, and ultrasonic ranging values ​​are collected to obtain raw environmental perception information.

[0051] At the center coordinates of each grid cell, the LiDAR ranging value and laser echo intensity value are obtained through LiDAR, the depth camera ranging value is obtained through depth camera, and the ultrasonic sensor ranging value is obtained through ultrasonic sensor. This allows the center coordinates of the grid cell to simultaneously possess multi-source sensing quantities from LiDAR, depth camera, and ultrasonic sensor, which are used to characterize the spatial distance and reflection characteristics between the center coordinates of the grid cell and surrounding obstacles.

[0052] Furthermore, multi-source risk indicators include LiDAR risk quantity, depth camera risk quantity, and ultrasonic risk quantity.

[0053] When the lidar ranging value is smaller and the laser echo intensity value is larger, it indicates that the obstacle is closer to the robot. The lidar risk value is obtained by calculating the ratio of the lidar ranging value and the laser echo intensity value at the center coordinates of the grid cell, and is expressed as:

[0054] ;

[0055] in, For lidar risk quantity, This represents the laser echo intensity value. This is the range measured by the lidar. To avoid extremely small positive numbers with a denominator of zero.

[0056] Represents the center coordinates of the grid cell The obstacle proximity is determined based on the LiDAR sensing results. Indicates the coordinates of the laser beam at the center of the grid cell. The magnitude of the echo energy at that location, Represents the coordinates of the lidar sensor and the center of the grid cell. The straight-line distance between the obstacles.

[0057] When both the depth camera ranging value and the ultrasonic ranging value decrease, it indicates that the obstacle is getting closer to the robot. By performing an inverse proportional transformation on the depth camera ranging value and the ultrasonic ranging value at the center coordinates of the grid cell, we obtain the depth camera risk quantity and the ultrasonic risk quantity, expressed as:

[0058] ;

[0059] ;

[0060] in, To measure the risk of depth cameras This is the distance measured by the depth camera. This refers to the risk level of ultrasound. This is the ultrasonic ranging value.

[0061] Represents the center coordinates of the grid cell The obstacle proximity is determined based on the distance measured by the depth camera. Represents the coordinates of the depth camera and the center of the grid unit. The straight-line distance between the obstacles. Represents the center coordinates of the grid cell The obstacle proximity is determined based on ultrasonic ranging results. Indicates the coordinates of the ultrasonic sensor and the center of the grid cell. The straight-line distance between the obstacles.

[0062] Furthermore, after obtaining the LiDAR risk quantity, depth camera risk quantity, and ultrasonic risk quantity at the center coordinates of each grid cell, the LiDAR risk quantity, depth camera risk quantity, and ultrasonic risk quantity are used as dimensionless risk indicators to represent the degree of obstacle proximity at the center coordinates of the grid cell. They are obtained by performing proportional or inverse proportional calculations on the LiDAR ranging value, depth camera ranging value, ultrasonic ranging value, and laser echo intensity value.

[0063] By comparing the numerical values ​​of the risk quantities of LiDAR, depth camera, and ultrasound, the risk quantity with the middle value among the three is selected as the median risk quantity.

[0064] After establishing the spatial distribution of the median risk at the center coordinates of all grid cells, the changes in the median risk in the horizontal and vertical coordinates are calculated using the finite difference method, yielding the risk gradient vector field, expressed as:

[0065] ;

[0066] in, This represents the median risk level. For the risk gradient vector field, This represents the change in median risk per unit displacement in the horizontal direction. This represents the change in median risk per unit displacement in the vertical direction.

[0067] S2. When the robot is performing path planning tasks, the risk gradient direction angle of the current position is calculated through the risk gradient vector field, and the target direction angle is calculated based on the current position and the target position. Then, based on the risk gradient direction angle, the target direction angle, and the median risk of the current position, the forward direction angle of the next position is determined, forming a path evolution direction sequence.

[0068] Furthermore, when the robot begins to execute the path planning task, the robot's initial position in the path planning task and its current position at the current moment are obtained through the robot positioning device, and the target position of the robot in the path planning task is obtained through the task instructions.

[0069] During the path evolution process, for each grid cell where the current position is located, the horizontal and vertical components are read from the risk gradient vector field.

[0070] The horizontal component represents the change in median risk per unit displacement in the horizontal direction, while the vertical component represents the change in median risk per unit displacement in the vertical direction.

[0071] The risk gradient direction angle at the current position is obtained by performing an arctangent operation on the ratio of the vertical component to the horizontal component, and is expressed as:

[0072] ;

[0073] in, The risk gradient direction angle at the current location. The vertical component. The horizontal component, This is the arctangent operation.

[0074] Furthermore, based on the relative positions of the current position and the target position in the planar coordinate system, the target direction angle from the current position to the target position is calculated.

[0075] The target direction angle is obtained by performing an arctangent operation on the coordinate difference between the target position coordinates and the current position coordinates, and is represented as:

[0076] ;

[0077] in, The target direction angle from the current position to the target position. , These are the planar coordinates of the current position. , The coordinates of the target location are in planar coordinates.

[0078] Furthermore, the angle difference between the risk gradient direction angle and the target direction angle is defined as the direction difference, which is used to characterize the degree of deflection of the current position between obstacle avoidance requirements and target approach requirements.

[0079] The larger the value of the directional difference, the closer the current position is to the area of ​​increased risk, requiring stronger obstacle avoidance maneuvers; the smaller the value of the directional difference, the more suitable the current position is to move towards the target direction.

[0080] Combining the directional difference with the median risk of the current grid cell, we obtain the path evolution feasibility of the current location, expressed as:

[0081] ;

[0082] in, This represents the feasibility of path evolution at the current location. For direction difference, This represents the median risk level of the grid cell where the current location is located.

[0083] The path evolution feasibility measure is used to characterize the feasibility of evolving from the current position along the target direction. The path evolution feasibility measure decreases as the direction difference increases and as the median risk increases, so that the path evolution feasibility measure reflects both the risk level and the direction deviation.

[0084] Furthermore, the median risk of the grid cell where the robot's initial position is located at the start of the path planning task is compared with the median risk of the grid cell where the target position is located. The larger value is taken as the risk benchmark value for the path planning task. By performing an inverse proportional transformation on the risk benchmark value, the path evolution feasibility threshold is obtained, expressed as:

[0085] ;

[0086] in, The threshold for path evolution feasibility. This represents the median risk of the grid cell containing the initial position. This represents the median risk level of the grid cell containing the target location. The larger of the median risk value between the grid cell where the initial position is located and the grid cell where the target position is located is selected.

[0087] It should be noted that the forward direction angle for the next position is determined by comparing the path evolution feasibility quantity at the current position with the path evolution feasibility threshold.

[0088] When the feasibility of path evolution is greater than the feasibility threshold of path evolution, the target direction angle is used as the forward direction angle of the next position, so that the path extends toward the target position; when the feasibility of path evolution is less than or equal to the feasibility threshold of path evolution, the opposite direction of the risk gradient direction angle is used as the forward direction angle of the next position, so that the path extends toward the direction of reduced risk.

[0089] By repeatedly calculating the feasibility of path evolution, comparing the feasibility threshold of path evolution, and determining the forward direction angle of the next position at each newly reached location, a path evolution direction sequence consisting of multiple forward direction angles is gradually formed.

[0090] S3. When the robot performs motion control, the forward direction angle in the path evolution direction sequence and the orientation angle of the robot's current position are calculated to obtain the direction deviation angle and angle difference, and angular velocity control quantity and linear velocity control quantity are generated.

[0091] Furthermore, when the robot performs motion control, the robot positioning device obtains the orientation angle of the robot's current position, and performs an arctangent operation on the angle difference between the forward direction angle corresponding to the current position and the orientation angle of the current position in the path evolution direction sequence to obtain the direction deviation angle, expressed as:

[0092] ;

[0093] in, This is the direction deviation angle. The orientation angle of the robot's current position. This is the forward direction angle corresponding to the current position in the path evolution direction sequence.

[0094] The angle difference is obtained by performing an arctangent operation on the angle difference between the orientation deviation angle and the risk gradient orientation angle, and is expressed as:

[0095] ;

[0096] in, This is the angle difference.

[0097] The directional deviation angle is used to represent the directional deviation towards the target, and the risk gradient directional angle is used to represent the obstacle avoidance direction. Converting the angle difference between the directional deviation angle and the risk gradient directional angle into an angle difference allows the robot's angular velocity to simultaneously reflect both the directional deviation towards the target and the risk gradient of obstacle avoidance requirements.

[0098] The absolute value of the angle difference is used as the robot's angular velocity control quantity, so that the robot's angular velocity increases as the absolute value of the angle difference increases. This gives the robot a strong turning ability when the angle difference is large and a small turning amplitude when the angle difference is small.

[0099] The robot's angular velocity control quantity is a dimensionless control quantity, which is converted into actual angular velocity execution commands by the robot's motion control device.

[0100] Furthermore, by performing an inverse proportional transformation on the median risk of the grid cell where the current position is located, the linear velocity control quantity of the robot is obtained, expressed as:

[0101] ;

[0102] in, This refers to the linear velocity control quantity for the robot.

[0103] Since the robot's linear speed control quantity is obtained by inversely transforming the median risk quantity of the grid cell where the current position is located, the robot can simultaneously reflect the environmental risk level in its linear speed control, allowing the robot to reduce its forward speed in high-risk areas and increase its forward speed in low-risk areas.

[0104] The linear velocity control quantity of a robot is a dimensionless control quantity, which is converted into a specific linear velocity execution command by the robot motion control device.

[0105] Furthermore, angular velocity control and linear velocity control are input to the robot motion control device, so that the robot moves towards the target position step by step according to the sequence of path evolution directions.

[0106] After the robot reaches each new position in the path evolution direction sequence, the calculation of the direction deviation angle, the angle difference, the angular velocity control amount, and the linear velocity control amount are repeatedly executed, so that the robot can continuously perform motion control according to the environmental risk distribution and the path evolution direction throughout the entire path following process.

[0107] S4. In the path evolution direction sequence, calculate the direction change rate based on the change of adjacent forward direction angles, generate the direction stabilization factor, and adjust the angular velocity control quantity and linear velocity control quantity based on the direction stabilization factor to obtain the final angular velocity control quantity and the final linear velocity control quantity.

[0108] Furthermore, during the motion control process of the robot according to the path evolution direction sequence, by calculating the rate of change of direction between two consecutive forward direction angles, the robot can dynamically stabilize and adjust the motion control quantity according to the local change trend of the path evolution direction sequence.

[0109] The rate of change of direction is obtained by performing an arctangent operation on the angle difference between two adjacent forward direction angles, and is expressed as:

[0110] ;

[0111] in, The rate of change of direction, The current forward direction angle in the path evolution direction sequence. This refers to the previous forward direction angle in the path evolution direction sequence. This refers to the index in the path evolution direction sequence. It is an integer greater than or equal to two.

[0112] By performing an inverse proportional transformation on the rate of change of direction, the direction stabilization factor is calculated, which decreases as the rate of change of direction increases. This is expressed as:

[0113] ;

[0114] in, This is the directional stabilization factor.

[0115] The orientation stabilization factor is used to suppress swaying caused by excessive changes in the continuous forward direction angle, enabling the robot to reduce the turning amplitude and maintain path stability in areas of drastic local changes in the path.

[0116] Furthermore, the directional stabilization factor is applied to the angular velocity control quantity and the linear velocity control quantity respectively, resulting in the final angular velocity control quantity and the final linear velocity control quantity, expressed as:

[0117] ;

[0118] ;

[0119] in, For the final angular velocity control quantity, This is the final linear velocity control variable.

[0120] The directional stabilization factor causes the final angular velocity control quantity and the final linear velocity control quantity to decay synchronously when the path evolution direction sequence changes rapidly, suppressing unnecessary swaying or over-adjustment.

[0121] The final angular velocity control value and the final linear velocity control value are input to the robot motion control device, so that the robot can maintain a stable trajectory while following the path evolution direction sequence, and achieve stable convergent motion control from the initial position to the target position.

[0122] This embodiment also provides a computer device applicable to robot control methods based on multi-objective path planning, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the robot control method based on multi-objective path planning as proposed in the above embodiment.

[0123] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0124] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the robot control method based on multi-objective path planning as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0125] In summary, this invention achieves dynamic coordination between risk avoidance and target approach by: determining the forward direction angle of the next position using the quantitative relationship between the risk gradient direction angle and the target direction angle; and generating a direction stabilization factor based on the direction change rate of adjacent forward direction angles to achieve synchronous and smooth adjustment of angular velocity control and linear velocity control, so that motion control maintains turning continuity and speed stability in the path change region.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A robot control method based on multi-objective path planning, characterized in that: include, At the start of the robot motion control task, the robot motion area is discretized into grid units. Raw environmental perception information is acquired at each grid unit. Based on the raw environmental perception information, multi-source risk indicators are calculated to generate median risk and risk gradient vector field. When the robot performs path planning tasks, the risk gradient direction angle of the current position is calculated through the risk gradient vector field, and the target direction angle is calculated based on the current position and the target position. Then, based on the risk gradient direction angle, the target direction angle, and the median risk of the current position, the forward direction angle of the next position is determined, forming a path evolution direction sequence. When the robot performs motion control, the forward direction angle in the path evolution direction sequence and the orientation angle of the robot's current position are calculated to obtain the direction deviation angle and angle difference, and angular velocity control quantity and linear velocity control quantity are generated. In the path evolution direction sequence, the direction change rate is calculated based on the change of adjacent forward direction angles, a direction stabilization factor is generated, and the angular velocity control quantity and linear velocity control quantity are adjusted based on the direction stabilization factor to obtain the final angular velocity control quantity and the final linear velocity control quantity.

2. The robot control method based on multi-objective path planning as described in claim 1, characterized in that: The step of discretizing the robot's motion area to form grid units and obtaining the original environmental perception information at each grid unit includes uniformly dividing the robot's motion area along the horizontal and vertical directions according to a fixed discrete step length to form grid units. At the center coordinates of each grid unit, the ranging values ​​of lidar, laser echo intensity, depth camera, and ultrasonic range are collected to obtain the raw information of environmental perception.

3. The robot control method based on multi-objective path planning as described in claim 2, characterized in that: The calculation of multi-source risk indicators and the generation of median risk quantity and risk gradient vector field include: performing a ratio calculation on the lidar ranging value and the laser echo intensity value at the center coordinates of the grid cell to obtain the lidar risk quantity. The depth camera range and ultrasonic range at the center coordinates of the grid cell are inversely transformed to obtain the depth camera risk quantity and the ultrasonic risk quantity. The median risk value is obtained by performing median selection on the risk values ​​of lidar, depth camera, and ultrasound. After forming the spatial distribution of the median risk at the center coordinates of each grid cell, the changes in the median risk in the horizontal and vertical coordinates are calculated using the finite difference method to obtain the risk gradient vector field.

4. The robot control method based on multi-objective path planning as described in claim 3, characterized in that: The step of calculating the risk gradient direction angle of the current position through the risk gradient vector field and calculating the target direction angle based on the current position and the target position includes obtaining the robot's initial position in the path planning task and the robot's current position at the current moment through the robot positioning device, and obtaining the robot's target position in the path planning task through the task instruction. Based on the grid cell where the current position is located, read the horizontal and vertical components of the current position from the risk gradient vector field; The risk gradient direction angle at the current position is obtained by performing an arctangent operation on the ratio of the vertical component to the horizontal component. The target direction angle is obtained by performing an arctangent operation on the coordinate difference between the target position coordinates and the current position coordinates.

5. The robot control method based on multi-objective path planning as described in claim 4, characterized in that: The step of determining the forward direction angle of the next position based on the risk gradient direction angle, the target direction angle, and the median risk of the current position to form a path evolution direction sequence includes obtaining the direction difference based on the angle difference between the risk gradient direction angle and the target direction angle. By combining the directional difference with the median risk of the grid cell where the current position is located, the path evolution feasibility of the current position is obtained. The median risk of the grid cell where the robot's initial position is located at the start of the path planning task is compared with the median risk of the grid cell where the target position is located. The larger value is taken as the risk benchmark value for the path planning task. The path evolution feasibility threshold is obtained by inversely transforming the risk benchmark value. The forward direction angle for the next position is determined by comparing the path evolution feasibility quantity at the current position with the path evolution feasibility threshold. When the feasibility of path evolution is greater than the feasibility threshold of path evolution, the target direction angle is used as the forward direction angle of the next position. When the feasibility of path evolution is less than or equal to the feasibility threshold of path evolution, the opposite direction of the risk gradient direction angle is taken as the forward direction angle of the next position. By repeatedly calculating the feasibility of path evolution, comparing the feasibility threshold of path evolution, and determining the forward direction angle of the next position at each newly arrived position, a sequence of path evolution directions composed of forward direction angles is formed.

6. The robot control method based on multi-objective path planning as described in claim 5, characterized in that: The step of calculating the forward direction angle in the path evolution direction sequence and the orientation angle of the robot's current position to obtain the direction deviation angle and angle difference includes obtaining the orientation angle of the robot's current position through the robot positioning device, and performing an arctangent operation on the angle difference between the forward direction angle corresponding to the current position and the orientation angle of the current position in the path evolution direction sequence to obtain the direction deviation angle. The angle difference is obtained by performing an arctangent operation on the angle difference between the direction deviation angle and the risk gradient direction angle.

7. The robot control method based on multi-objective path planning as described in claim 6, characterized in that: The generation of angular velocity control quantity and linear velocity control quantity includes taking the absolute value of the angle difference as the robot's angular velocity control quantity, and obtaining the robot's linear velocity control quantity by inversely transforming the median risk quantity of the grid cell where the current position is located. After the robot reaches each new position in the path evolution direction sequence, the calculation of the direction deviation angle, the calculation of the angle difference, the calculation of the angular velocity control quantity, and the calculation of the linear velocity control quantity are repeated.

8. The robot control method based on multi-objective path planning as described in claim 7, characterized in that: The method of adjusting the angular velocity control quantity and linear velocity control quantity based on the direction stabilization factor to obtain the final angular velocity control quantity and the final linear velocity control quantity includes, in the path evolution direction sequence, performing arctangent operation by the angle difference between adjacent forward direction angles to obtain the direction change rate. An orientation stabilization factor is generated by inversely transforming the rate of change of orientation. The directional stabilization factor is applied to the angular velocity control quantity and the linear velocity control quantity respectively to obtain the final angular velocity control quantity and the final linear velocity control quantity.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the robot control method based on multi-objective path planning as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the robot control method based on multi-objective path planning as described in any one of claims 1 to 8.

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