Multi-stage safety obstacle avoidance protection method and system for mobile formwork robot

Abstract

The application provides a multi-stage safety obstacle avoidance protection method and system for a mobile frame robot, and relates to the technical field of robot control. Firstly, motion parameters and frame shape data of the mobile frame robot are collected. Secondly, multi-stage safety zones are dynamically delimited based on the motion parameters and frame shape data. Thirdly, the multi-stage safety zones are continuously scanned to obtain environmental information and detect obstacles. Fourthly, motion control instructions are cooperatively generated according to the positions of the obstacles in the multi-stage safety zones and the current motion states and frame postures. Finally, the motion control instructions are executed to drive the mobile frame robot to perform obstacle avoidance protection. The technical scheme provided by the application not only realizes adaptive safety obstacle avoidance of the mobile frame robot in complex operations, but also improves operation continuity and protection reliability.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to a multi-level safety obstacle avoidance and protection method and system for a mobile scaffold robot. Background Technology

[0002] With the development of prefabricated buildings and on-site construction of large components, mobile formwork robots need to perform high-precision movement and posture adjustment in complex dynamic environments. The formwork structure of such robots usually has the characteristics of being multi-segmented, extensible and flexible. During the movement process, they not only need to avoid collisions with external obstacles, but also need to adapt to the real-time changes in their own shape, which puts forward clear requirements for active and adaptive safety protection.

[0003] Currently, the technical solution adopted is an obstacle avoidance method based on real-time environment modeling and fixed safety thresholds. This method uses sensors such as LiDAR to scan the environment around the robot, construct a real-time point cloud map, and preset a fixed three-dimensional safety envelope around the robot. The system continuously detects whether the environmental point cloud intrudes into the fixed envelope space, and triggers an emergency stop command once an intrusion is detected.

[0004] However, the existing solution has obvious limitations. Since the actual outline of the mobile mold robot changes dynamically with the extension and bending of its mold segments, the fixed safety envelope cannot match its real-time shape. This results in the protected area being either too conservative and restricting the working space, or leaving collision risks in certain postures. At the same time, the solution adopts a single response strategy of stopping upon intrusion, lacks risk classification capabilities, and cannot decelerate or fine-tune the path in potential risk stages, which seriously affects the efficiency and continuity of operation. Summary of the Invention

[0005] This application provides a multi-level safety obstacle avoidance protection method and system for mobile scaffold robots, which solves the problems in the prior art where the safety protection area does not match the dynamic shape of the robot, and the obstacle avoidance response strategy is singular and lacks hierarchical processing capability, resulting in low operation efficiency.

[0006] Firstly, this application provides a multi-level safety obstacle avoidance protection method for a mobile scaffold robot, including:

[0007] Collect motion parameters and mold morphology data of the mobile mold robot;

[0008] Based on the motion parameters and mold frame shape data, taking the outer contour of the mold frame as a reference, a multi-level safety zone around the mobile mold frame robot is dynamically calculated and delineated according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone.

[0009] Continuously scan the multi-level security zone to obtain environmental information and detect obstacles;

[0010] Based on the different levels of safety zones where the obstacle is located, motion control commands containing different control strategies are generated collaboratively. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control.

[0011] The motion control commands are executed to drive the mobile scaffold robot to perform obstacle avoidance and protection.

[0012] Optionally, based on the motion parameters and mold frame morphology data, and using the mold frame outline as a reference, a multi-level safety zone is dynamically calculated and delineated around the mobile mold frame robot according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone, including:

[0013] Based on the motion parameters of the mobile template robot, determine the direction and speed of movement of the mobile template robot at the current moment;

[0014] Using the outer contour of each mold frame segment in the mold frame morphology data as the initial boundary, the boundary of the warning area is calculated based on the moving direction and moving speed;

[0015] Based on the boundary of the warning area, the boundary of the braking area is calculated according to the extension length and relative rotation angle of the formwork segment.

[0016] Optionally, using the structural outer contour of each mold segment in the mold frame morphology data as the initial boundary, and based on the moving direction and moving speed, the boundary of the warning area is calculated, including:

[0017] Based on the direction of movement, a first component along the direction of movement and a second component perpendicular to the direction of movement are defined for multiple points on the outer contour of the structure of each mold frame segment.

[0018] Calculate the first extended distance of each point in the first component direction based on the moving speed;

[0019] Set a fixed second extension distance for each point in the direction of the second component;

[0020] The initial position of each point is obtained by adding the first expansion distance along the corresponding first component and the second expansion distance along the corresponding second component to obtain the expanded position of each point.

[0021] The extended positions of all points belonging to the same formwork segment are connected to form the boundary of the early warning sub-region of the formwork segment;

[0022] By integrating the boundaries of all early warning sub-regions of the formwork segments, the complete boundary of the early warning area is obtained.

[0023] Optionally, based on the boundary of the warning area, the boundary of the braking area is calculated according to the extension length and relative rotation angle of the formwork segment, including:

[0024] Each point on the boundary of the warning area is used as a reference point;

[0025] Obtain the current extension length of the formwork segment corresponding to each reference point;

[0026] Determine the relative rotation angle between the formwork segment corresponding to each reference point and the adjacent formwork segment;

[0027] Calculate the basic extension distance based on the current extension length corresponding to each reference point;

[0028] Calculate the angle adjustment based on the relative rotation angle corresponding to each reference point;

[0029] The target expansion distance is obtained by adding the basic expansion distance to the angle adjustment amount;

[0030] Along the normal direction of the boundary of the warning area at each reference point, move each reference point outward by the target extension distance to obtain the braking boundary point of each reference point;

[0031] Connect all the braking boundary points in sequence to form the boundary of the braking area.

[0032] Optionally, the multi-level security zone is continuously scanned to obtain environmental information and detect obstacles, including:

[0033] Pre-set scanning points are arranged on the boundaries of the warning area and the braking area, as well as on the outer contour of the mobile mold frame robot.

[0034] The driving sensor sequentially measures the distance in the direction indicated by each scanning point to obtain the distance data corresponding to each scanning point;

[0035] The distance data corresponding to all the scanning points is compared with the boundary positions of the warning area and the braking area. If the distance data corresponding to the scanning point is less than the distance to the boundary position corresponding to the scanning point, the scanning point is marked as an abnormal point.

[0036] Multiple anomalies that are adjacent in location and meet the distance condition are combined to form potential obstacle objects;

[0037] Determine the spatial extent occupied by each potential obstacle object in the multi-level safety zone, and label the potential obstacle object as an obstacle.

[0038] Optionally, motion control commands containing different control strategies are collaboratively generated based on different levels of safety zones where the obstacle is located. When the obstacle is in a braking zone, the control strategy includes at least braking control and attitude adjustment control, including:

[0039] The current motion state of the mobile template robot is obtained, and the current motion state includes the direction of movement and the speed of movement;

[0040] Obtain the current mold frame posture of the mobile mold frame robot, the current mold frame posture including the extension length and relative rotation angle of each mold frame segment;

[0041] Based on the location of the obstacle in the multi-level safety zone, determine whether the obstacle is located in the warning zone or the braking zone of the multi-level safety zone;

[0042] When the obstacle is located in the braking area, a first control command and a second control command are generated based on the specific location of the obstacle, the direction of movement, and the relative rotation angle of the mold frame segment.

[0043] When the obstacle is located in the warning area, a third control command is generated based on the specific location of the obstacle, the direction of movement, and the speed of movement.

[0044] The first control command, the second control command, or the third control command are combined to form a motion control command.

[0045] Optionally, executing the motion control commands to drive the mobile scaffold robot to perform obstacle avoidance includes:

[0046] The motion control command is parsed to identify the first control command, second control command, or third control command contained in the motion control command;

[0047] Convert the first control command into a braking drive signal;

[0048] The second control command is converted into an attitude adjustment drive signal;

[0049] The third control command is converted into a path correction drive signal;

[0050] Send the braking drive signal to the moving mechanism of the mobile mold frame robot to stop the mobile mold frame robot from moving;

[0051] After the mobile mold frame robot stops moving, the attitude adjustment drive signal is sent to the actuator corresponding to the target mold frame segment to change the extension length or relative rotation angle of the target mold frame segment;

[0052] When the motion control command does not include the first control command and the second control command, but includes the third control command, the path correction drive signal is sent to the moving mechanism of the mobile mold robot to change the moving direction or moving speed of the mobile mold robot.

[0053] Secondly, this application provides a multi-level safety obstacle avoidance system for a mobile scaffold robot, including:

[0054] The data acquisition module is used to collect motion parameters and mold shape data of the mobile mold robot;

[0055] The delineation module is used to dynamically calculate and delineate a multi-level safety zone around the mobile mold robot based on the motion parameters and mold frame shape data, with the mold frame outline as a reference, according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone.

[0056] The scanning module is used to continuously scan the multi-level security zone to obtain environmental information and detect obstacles;

[0057] The generation module is used to collaboratively generate motion control commands containing different control strategies based on the different levels of safety zones where the obstacle is located. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control.

[0058] An execution module is used to execute the motion control commands to drive the mobile scaffold robot to perform obstacle avoidance and protection.

[0059] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement a multi-level safety obstacle avoidance and protection method for a mobile scaffold robot as described in the first aspect above.

[0060] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multi-level safety obstacle avoidance and protection method for a mobile scaffold robot as described in the first aspect.

[0061] This application dynamically delineates multi-level safety zones based on motion parameters and mold frame morphology data, enabling the boundaries of the warning and braking zones to conform in real time to the actual outline and motion trend of the mobile mold frame robot at the current moment. This solves the problem in existing technologies where fixed safety envelopes cannot adapt to changes in the shape of the mold frame, such as extension, bending, etc., ensuring that the protection range always matches the dynamic physical contour of the robot. This avoids the waste of working space caused by an excessively large protection area and eliminates the collision risk caused by an excessively small protection area, thereby improving the accuracy and environmental adaptability of safety protection.

[0062] Furthermore, by collaboratively generating motion control commands containing different control instructions based on the specific location of obstacles in dynamically defined multi-level safety zones, a graded and differentiated obstacle avoidance response is achieved. When an obstacle only enters the warning zone, a pre-response of path or speed adjustment can be triggered; only when an obstacle enters the inner braking zone is emergency braking and mold attitude adjustment triggered. This collaborative control mechanism linked to risk levels overcomes the drawbacks of the existing single strategy of stopping upon intrusion, enabling the robot to continue operating in most potentially risky scenarios through flexible adjustments, and only stopping completely when necessary. Thus, under the premise of ensuring safety, the continuity of operation and overall work efficiency of the mobile mold robot are effectively improved.

[0063] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A flowchart of a multi-level safety obstacle avoidance protection method for a mobile scaffold robot provided in this application is shown;

[0066] Figure 2 This invention provides a schematic diagram of the structure of a multi-level safety obstacle avoidance and protection system for a mobile scaffold robot.

[0067] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0069] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0070] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] Figure 1 This application provides a flowchart of a multi-level safety obstacle avoidance method for a mobile scaffold robot, such as... Figure 1 As shown, the method includes:

[0072] Step 101: Collect motion parameters and mold shape data of the mobile mold robot.

[0073] In this step, the mobile mold robot refers to an automated device that can move autonomously within a predetermined work area and is equipped with a mold structure that can be extended, bent, or folded. It is mainly used for supporting, handling, or assembling large components.

[0074] Motion parameters refer to data that describe the overall movement state of a mobile chassis robot. They mainly include its instantaneous speed and direction of movement in two-dimensional or three-dimensional space. These parameters are used to determine the robot's motion trend and momentum and are usually acquired through encoders and inertial measurement units (IMUs) mounted on the robot's mobile chassis.

[0075] Mold frame morphology data refers to data describing the current physical shape and size of the mold frame structure carried by the mobile mold frame robot. It mainly includes the extension length of each mold frame segment and the relative bending or rotation angle between adjacent mold frame segments. It is used to accurately determine the real-time external contour of the robot and is usually obtained through angle sensors installed at each joint of the mold frame and displacement sensors on each segment.

[0076] In this step, firstly, an optical encoder is installed on the drive motor of the mobile chassis of the mobile mold frame robot. The pulse frequency signal output by the encoder is processed by the pulse counting and conversion algorithm to calculate the real-time rotation speed of the drive wheel. Then, combined with the wheel radius parameter, the current moving speed of the mobile mold frame robot is obtained through kinematic conversion.

[0077] Secondly, by installing an inertial measurement unit on the mobile chassis, which integrates a three-axis gyroscope and a three-axis accelerometer, and using a strapdown inertial navigation solution algorithm, the angular velocity signal output by the gyroscope is integrated to update the attitude. At the same time, the specific force information output by the accelerometer is used to convert in the attitude matrix and fused with the gravity vector through a complementary filtering algorithm, thereby calculating the real-time movement direction of the mobile chassis robot relative to the geographic coordinate system.

[0078] Meanwhile, a magnetostrictive displacement sensor is installed inside each telescopic segment of the mold frame. The magnetostrictive displacement sensor sends a current pulse to the waveguide and measures the time difference between the returned strain pulse and the initial pulse. Through a time-displacement conversion algorithm, the precise linear extension length of the mold frame segment is directly obtained.

[0079] Furthermore, an absolute encoder is installed at the rotary joint connecting adjacent mold frame segments. This absolute encoder reads the absolute position of the shaft angle through photoelectric or magnetoelectric principles. Its internal decoding circuit directly converts the light or magnetic signal into a digital signal. By reading the digital signal and applying the angle calibration mapping algorithm, the real-time relative rotation angle between the two mold frame segments can be obtained.

[0080] Finally, the central controller runs a multi-sensor data fusion program. This program first timestamps all data from the encoder, inertial measurement unit, displacement sensor, and angle sensor. Then, it uses an extended Kalman filter algorithm to align and filter all data to eliminate sensor noise and micro-delay differences. After processing, the normalized moving speed, moving direction, extension length of each segment, and relative rotation angle of each joint are packaged and stored in the shared memory area, thus completing the synchronous acquisition and preprocessing of motion parameters and mold shape data.

[0081] For example, in a construction scenario involving the installation of a large component, the mobile formwork robot A first moves the component within the site; then, the encoder on the drive motor of the robot A's chassis continuously outputs pulse signals, and the control settings use pulse counting to calculate the real-time rotational speed. Combined with the known wheel diameter, the current moving speed of the robot A is calculated to be 0.3 meters per second; simultaneously, the inertial measurement unit mounted on the chassis continuously collects acceleration and angular velocity data, and through strapdown inertial navigation calculation and complementary filtering, its moving direction is determined to be due east.

[0082] Next, the frame of robot A is composed of three hinged segments. The magnetostrictive displacement sensor in the second segment measures the return time difference of the current pulses. The internal processor calculates and outputs that the second segment has been extended by 15 centimeters relative to the reference length. Then, at the joint connecting the first and second segments, the absolute encoder directly reads and outputs that the relative rotation angle between the two is 5 degrees. The raw data of all sensors are transmitted to the industrial control computer via the CAN bus. The data fusion program in the industrial control computer adds a precise timestamp to the data of each channel and applies an extended Kalman filter for noise reduction and synchronization processing. Finally, a set of synchronized data containing the moving speed, moving direction, length of each segment, and angle of each joint is generated and stored.

[0083] Step 102: Based on the motion parameters and mold frame shape data, and taking the mold frame outline as a reference, dynamically calculate and delineate a multi-level safety zone around the mobile mold frame robot according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone.

[0084] Optionally, step 102 may specifically include:

[0085] Step 1021: Determine the moving direction and moving speed of the mobile mold frame robot at the current moment based on the motion parameters of the mobile mold frame robot.

[0086] Step 1022: Using the outer contour of each mold segment in the mold frame morphology data as the initial boundary, calculate the boundary of the warning area based on the moving direction and moving speed.

[0087] Optionally, step 1022 may include the following steps: defining a first component along the movement direction and a second component perpendicular to the movement direction for multiple points on the outer contour of each mold frame segment according to the movement direction; calculating a first extension distance for each point in the direction of the first component according to the movement speed; setting a fixed second extension distance for each point in the direction of the second component; adding the first extension distance along the corresponding first component and the second extension distance along the corresponding second component to the initial position of each point to obtain the extended position of each point; connecting the extended positions of all points belonging to the same mold frame segment to form the boundary of the early warning sub-region of the mold frame segment; integrating the boundaries of the early warning sub-regions of all mold frame segments to obtain the boundary of the complete early warning region.

[0088] Step 1023: Based on the boundary of the warning area, calculate the boundary of the braking area according to the extension length and relative rotation angle of the mold segment.

[0089] Optionally, step 1023 may include the following steps: taking each point on the boundary of the warning area as a reference point; obtaining the current extension length of the formwork segment corresponding to each reference point; determining the relative rotation angle between the formwork segment corresponding to each reference point and adjacent formwork segments; calculating the basic extension distance based on the current extension length corresponding to each reference point; calculating the angle adjustment amount based on the relative rotation angle corresponding to each reference point; adding the basic extension distance and the angle adjustment amount to obtain the target extension distance; moving each reference point outward by the target extension distance along the normal direction at each reference point on the boundary of the warning area to obtain the braking boundary point of each reference point; and sequentially connecting all the braking boundary points to form the boundary of the braking area.

[0090] In this step, the multi-level safety zone refers to one or more nested spatial ranges for different safety protection levels, dynamically calculated using a geometric expansion algorithm based on the real-time outline of the mobile scaffold robot, and used to achieve graded obstacle avoidance response.

[0091] The warning zone refers to a flexible warning space located in the outermost layer of a multi-level safety zone, with a relatively large spatial range. It is used to trigger warnings or deceleration commands when an obstacle approaches but is still some distance away.

[0092] The braking zone refers to a rigid braking space located in the inner layer of the multi-level safety zone, closely adjacent to the warning zone. It is used to trigger emergency stop and attitude adjustment commands when an obstacle is very close. It is obtained by secondary expansion calculation based on the boundary of the warning zone and combined with the morphological parameters of the formwork segment.

[0093] The direction of movement refers to the tendency of the center of mass of the mobile gantry robot to move at the current moment. It is an angle in a two-dimensional plane or a direction vector in three-dimensional space. It is used to determine the main direction of the expansion of the safe area and is obtained by extracting the attitude data calculated by the inertial measurement unit.

[0094] The movement speed refers to the scalar value of the movement rate of the center of mass of the moving model robot at the current moment. It is used to quantify the speed of the robot's movement and affects the size of the forward expansion of the safety area. It is calculated by the encoder pulse signal.

[0095] The initial boundary refers to the original, unexpanded outer surface contour or contour surface occupied in space by all the module segments of the mobile module robot before calculating the safe area, which serves as the geometric basis for the calculation of the safe area.

[0096] A mold frame segment refers to a basic structural unit that constitutes the deformable mold frame body of a mobile mold frame robot and is capable of relative movement. It is used to describe the local shape of the mold frame and is obtained by physically dividing the overall mold frame structure.

[0097] The first component refers to a unit vector defined for each contour point on the outer contour of the mold frame when calculating the boundary of the warning area. It is used to indicate the axial direction of the expansion of the safe area at that point along the direction of motion and is obtained through a vector projection algorithm.

[0098] The second component refers to a unit vector perpendicular to the first component defined for each contour point on the outer contour of the mold frame when calculating the boundary of the warning area. It is used to indicate the axial direction of the lateral expansion of the safe area at that point and is obtained through vector cross product and normalization algorithm.

[0099] The first extension distance refers to the length that a point on the boundary of the warning area needs to extend outward along its first component direction. This length is positively correlated with the current moving speed of the mobile scaffold robot and is used to reflect the forward safety margin required due to motion inertia. It is calculated by inputting the moving speed value into a predefined linear or nonlinear mapping function.

[0100] The second extension distance refers to a fixed length value that points on the boundary of the warning area need to extend outward along the direction of their second component. It is used to provide a static lateral safety margin and is obtained by reading a preset constant from the configuration parameters.

[0101] The boundary of the early warning sub-region refers to the closed boundary formed by connecting all points on the initial outer boundary of a single frame segment, which represents the independent early warning range of that segment. It is obtained by connecting the expanded positions of all points belonging to the same segment.

[0102] The extension length refers to the amount of length change of a mold segment relative to its fully contracted state. It is used to characterize the degree of deformation of the segment in the length direction and is obtained by reading the measurement value of the displacement sensor and subtracting the reference length.

[0103] The relative angle refers to the angle formed by the central axes of the two adjacent mold frame segments at the hinge joint at the current moment. It is used to characterize the degree of bending of the mold frame and is obtained by reading the angle measurement value of the absolute encoder.

[0104] A reference point is a specific location on the boundary of the warning area selected when calculating the boundary of the braking area. It is used as the starting reference position for calculating the braking boundary and is obtained by traversing all discrete points on the boundary of the warning area.

[0105] The basic extension distance refers to the initial extension amount calculated for a reference point when calculating the boundary of the braking area, which is related to the extension length of the mold segment to which it belongs. It is used to reflect the additional risk buffering requirements brought about by the segment extension. It is calculated by inputting the extension length value into a scaling function.

[0106] The angle adjustment amount refers to the extended increment calculated for a reference point when calculating the boundary of the braking zone, used to compensate for the additional risks caused by the relative rotation angle between the segment to which it belongs and the adjacent segment. This value is positive on the convex side of the bend and is obtained by multiplying the sine of the relative rotation angle by an adjustment coefficient.

[0107] The target extension distance refers to the total distance that a reference point needs to move outward along the normal direction when calculating the boundary of the braking area. It is obtained by adding the base extension distance and the angle adjustment amount and is used to determine the position of the braking boundary point corresponding to the reference point.

[0108] In this step, the movement direction and movement speed data are first extracted from the generated data packet. The movement direction data is a unit direction vector in three-dimensional space. The vector normalization algorithm ensures that its length is 1 so that the direction projection calculation can be performed later. The movement speed data is a scalar value that can be read directly. These two parameters together define the robot's current motion state.

[0109] Next, from the mold frame morphology data, a set of discrete points representing the outer contour of each mold frame segment in three-dimensional space is obtained. These discrete point sets collectively constitute the initial boundary. First, two orthogonal expansion direction vectors are calculated for each point on the initial boundary. For any point, using a vector projection algorithm, the contour out-normal vector at that point is decomposed into the movement direction and its perpendicular direction, resulting in two unit vectors: a first component along the movement direction and a second component perpendicular to the movement direction. Next, the expansion amount of each point along the first component direction is calculated. The current movement speed is read and input into a predefined linear mapping function. This linear mapping function outputs a first expansion distance proportional to the speed; the greater the speed, the larger this distance. Simultaneously, a fixed constant value is read from the safety configuration as the second expansion distance of each point along the second component direction. Then, the position of each point on the initial boundary is... The offset calculation involves adding the first extended distance multiplied by the first component vector and the second extended distance multiplied by the second component vector to the original 3D coordinates of each point. This vector addition operation yields the new extended coordinates of the point. Then, the new extended coordinates of all points belonging to the same frame segment are processed using a piecewise linear connection or polygon generation algorithm to connect all the extended points on the same segment sequentially, forming a closed polygonal loop enclosing the segment. This polygonal loop is the boundary of the warning sub-region of the frame segment. Finally, the boundaries of all warning sub-regions of the frame segments are integrated. By calculating the union of these independent polygonal loops and using the polygonal Boolean union algorithm in computational geometry, all warning sub-region boundaries are merged into a continuous, possibly complex, closed region. The outer boundary of this final closed region is the boundary of the complete warning region.

[0110] Furthermore, firstly, the calculated boundary of the complete early warning area is discretized into a series of dense points, which are defined as reference points. Secondly, each reference point is associated with its corresponding formwork segment, and the original formwork segment corresponding to each reference point on the initial boundary is determined through spatial location mapping query. Then, the current extension length of the associated formwork segment and the relative rotation angle between the segment and adjacent segments are read from the formwork morphology data. Next, the basic extension distance of each reference point is calculated. The extension length value of the associated formwork segment is input into a linear proportional function, which outputs a basic extension distance that is proportional to the extension length. The longer the extension, the larger the basic extension distance. At the same time, the angle adjustment amount of each reference point is calculated. The associated relative rotation angle value is read, and the sine value of the angle in radians is calculated. Then, this sine value is multiplied by a preset positive coefficient for the convex side or a negative coefficient for the concave side. The angle adjustment amount is obtained. On the curved, convex side, the relative angle makes it easier for the mold to touch the outside, so the angle adjustment amount is positive to increase the expansion. Then, an addition operation is performed, adding the calculated basic expansion distance to the angle adjustment amount to obtain the final target expansion distance of the reference point. Subsequently, the normal offset of the point is performed. For each reference point on the boundary of the warning area, the unit vector of the outward normal direction of the boundary at that reference point is calculated. Then, the coordinates of the reference point are added to the target expansion distance multiplied by the unit outward normal vector at that point. A new three-dimensional coordinate point is obtained through vector addition. This new point is the braking boundary point corresponding to the reference point. Finally, all braking boundary points are connected. According to the original order of the reference points on the boundary of the warning area, curve fitting or polyline connection algorithm is used to connect all braking boundary points in sequence to form a closed boundary located inside the warning area and with a similar shape. This boundary is the boundary of the braking area.

[0111] For example, following the specific implementation of the previous step, firstly, the control module of the mobile mold frame robot A has acquired the current motion parameters: a moving speed of 0.3 meters per second, a moving direction of due east, and mold frame shape data showing that the extension length of the second segment increases by 15 centimeters, with a relative rotation angle of 5 degrees between the first and second segments. Secondly, based on the due east moving direction, the first component along the due east direction and the second component perpendicular to it are calculated for the hundreds of discrete points constituting the outer contour of the mold frame. The speed value of 0.3 meters per second is substituted into the mapping function to calculate the first forward extension distance of 0.5 meters, and the fixed second lateral extension distance of 0.3 meters is read from the configuration. Then, each outer contour point extends 0.5 meters along its respective first component direction and 0.3 meters along its second component direction. After all the extended points are connected, a larger teardrop-shaped warning area boundary is formed, extending due eastward in the current posture of robot A.

[0112] Then, based on the boundary of this warning area, dense reference points are taken. For reference points located outside the extended second segment, a proportional basic extension distance, such as 0.2 meters, is calculated based on the 15-centimeter extension length. Simultaneously, based on a relative rotation angle of 5 degrees, a positive additional angle adjustment, such as 0.05 meters, is obtained by calculating the sine value and multiplying it by a positive coefficient. The two are added together to obtain the target extension distance of 0.25 meters. Finally, along the outer normal direction of the warning area boundary at these reference points, it is moved outward by another 0.25 meters to obtain new braking boundary points. For reference points located on the concave side or other non-significant deformation areas, the target extension distance may only be 0.1 meters. Finally, all braking boundary points are connected to form a braking area boundary that fits more closely with the bent and protruding part of the mold frame inside the warning area.

[0113] This step incorporates the robot's real-time movement speed, direction, and shape changes into safety space calculations, constructing a dual-layered, dynamically matched protection zone. The warning zone expands asymmetrically based on movement trends, quantifying the risk of motion inertia. The braking zone further correlates with the extension and bending shape of the mold frame, precisely identifying high-risk areas. This provides a dynamic and accurate spatial benchmark for subsequent graded and precise obstacle avoidance responses, from warning to braking.

[0114] Step 103: Continuously scan the multi-level security zone to obtain environmental information and detect obstacles.

[0115] Optionally, step 103 may specifically include:

[0116] Step 1031: Arrange preset scanning points on the boundary of the warning area, the boundary of the braking area, and the outer contour of the mobile mold frame robot.

[0117] Step 1032: Drive the sensor to measure the distance in the direction indicated by each scanning point in sequence to obtain the distance data corresponding to each scanning point.

[0118] Step 1033: Compare the distance data corresponding to all the scan points with the boundary positions of the warning area and the braking area. If the distance data corresponding to the scan point is less than the distance to the boundary position corresponding to the scan point, then mark the scan point as an abnormal point.

[0119] Step 1034: Combine multiple anomalies that are adjacent in location and meet the distance condition to form a potential obstacle object.

[0120] Step 1035: Determine the spatial extent occupied by each potential obstacle object in the multi-level safety zone, and mark the potential obstacle object as an obstacle.

[0121] In this step, the preset scanning points refer to a series of virtual or physical pointing positions that are pre-set and uniformly placed on the boundary of the warning area, the boundary of the braking area, and the outer surface of the mobile module robot frame for active environmental detection. These points are used to guide the sensors to measure in a specific direction and are calculated through an adaptive point placement algorithm.

[0122] Distance data refers to the physical length of the straight line from each scanning point to the nearest reflecting object surface, measured by the sensor after it emits a detection signal in its predetermined direction and receives the echo. It is used to determine whether there is an object in front and how far away it is. It is obtained by calculating the signal flight time and converting it.

[0123] Anomalies refer to scan points whose measured distance data value is less than the distance to the preset boundary of the security zone associated with the scan point during the scanning detection process. They are used to initially identify the locations where the security zone may be invaded. They are obtained by comparing the distance data with the preset boundary distance in real time and applying conditional judgment logic.

[0124] Potential obstacle objects refer to a set of point clouds of objects in three-dimensional space that are physically coherent and consist of multiple anomalies whose distance data features meet preset association conditions. They are used to identify suspected obstacles with actual size and shape from discrete anomalies and are obtained by grouping anomalies using a spatial clustering algorithm.

[0125] In this step, the equidistant sampling algorithm is first used to calculate and generate a series of three-dimensional coordinate points on the spatial curves of the calculated warning area boundary and braking area boundary, as well as on the grid of the outer contour surface of the mold frame reconstructed from the mold frame morphology data, according to the preset fixed spatial interval, so as to obtain the preset set of scanning points and their corresponding detection direction vectors.

[0126] Secondly, the LiDAR sensor is driven. Through the sensor control protocol, the spatial coordinates and direction vector of each scanning point are converted into specific motor rotation angles and laser emission timing control commands. The LiDAR sensor emits laser pulses in each specified direction in sequence, and uses a time-to-digital converter to measure the time interval between the echo signal and the emitted signal. By calculating the signal flight time and performing calculations with the speed of light, the straight-line distance from each virtual scanning point to the nearest object in the environment is obtained, thereby acquiring the distance data corresponding to each scanning point.

[0127] Next, real-time numerical comparison processing is performed. For each scanning point, the spatial ray projection algorithm is used to calculate the intersection point with the defined three-dimensional model of the warning or braking area from its coordinate point along the detection direction vector. The theoretical boundary position distance in that direction is obtained. The measured distance data is compared with this theoretical value. If the measured value is less than the theoretical value, it is determined that there is an intrusion, and the scanning point is marked as an abnormal point by setting a status flag.

[0128] Then, a density clustering algorithm based on Euclidean distance is applied to process the outliers. This density clustering algorithm calculates the spatial distance between all pairs of outliers, and groups outliers with a distance less than the preset neighborhood radius into the same cluster. By traversing all outliers and recursively merging neighboring points, multiple spatially dense outliers are finally combined into an independent point cloud set, and each set is defined as a potential obstacle object.

[0129] Finally, a spatial range analysis is performed on each potential obstacle. By traversing the coordinates of all points in the point cloud set, the maximum and minimum values ​​in the X, Y, and Z axes are determined using an extreme value search algorithm. This allows the calculation of an axis-aligned boundary cuboid that can completely enclose the point cloud. Then, an intersection detection algorithm in computational geometry is used to determine the inclusion or intersection relationship between this boundary cuboid and the 3D models of the warning and braking areas. Based on the highest level of intrusion, the potential obstacle is finally marked as an obstacle with a defined threat level.

[0130] For example, following the specific implementation of the previous step, the control module of the mobile mold robot A firstly calculates and generates hundreds of preset scanning points at 0.2-meter intervals on the dynamically generated teardrop-shaped warning area boundary, the internal braking area boundary, and the mold surface; secondly, the rotating lidar installed on the top of the robot sequentially emits lasers in the direction of each scanning point. For example, for a warning boundary scanning point pointing slightly eastward, the radar measures the distance to the object in front as 1.8 meters, and through ray projection calculation, the theoretical distance from this direction to the warning area model boundary is obtained as 2.1 meters. Since the measured distance is less than the theoretical distance, this point is marked as an abnormal point.

[0131] Next, in adjacent directions, the radar also detected multiple anomalies. A density clustering algorithm based on Euclidean distance was run to group multiple anomalies within a spatial distance of 0.3 meters into a point cloud set containing about 15 points, which is a potential obstacle object. Then, the minimum axis-aligned boundary cuboid of the point cloud set was calculated, and the intersection detection algorithm was used to determine that the cuboid has intersected with the braking area model. Finally, this potential obstacle object was officially marked as an obstacle that has invaded the braking area, and its spatial range was recorded.

[0132] This step involves implementing structured active perception in multi-level dynamic safety zones and on the robot's surface. Real-time detection results are then accurately compared with dynamic safety boundaries and subjected to spatial clustering analysis. This transforms the complex environment into obstacle information with clear intrusion levels and geometric ranges, providing precise and reliable decision input for subsequent graded responses.

[0133] Step 104: Based on the different levels of safety zones where the obstacle is located, collaboratively generate motion control commands containing different control strategies. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control.

[0134] Optionally, step 104 may specifically include:

[0135] Step 1041: Obtain the current motion state of the mobile template robot, which includes the direction of movement and the speed of movement.

[0136] Step 1042: Obtain the current mold frame posture of the mobile mold frame robot. The current mold frame posture includes the extension length and relative rotation angle of each mold frame segment.

[0137] Step 1043: Based on the location of the obstacle in the multi-level safety zone, determine whether the obstacle is located in the warning zone or the braking zone of the multi-level safety zone.

[0138] Step 1044: When the obstacle is located in the braking area, a first control command and a second control command are generated based on the specific location of the obstacle, the direction of movement, and the relative rotation angle of the mold frame segment.

[0139] Step 1045: When the obstacle is located in the warning area, a third control command is generated based on the specific location of the obstacle, the direction of movement, and the speed of movement.

[0140] Step 1046: Combine the first control command, the second control command, or the third control command to form a motion control command.

[0141] In this step, the current motion state refers to the real-time overall kinematic parameters of the mobile scaffold robot within the current control cycle, mainly including its direction vector and velocity scalar, which are used to evaluate the robot's current momentum and trend. This is obtained by collecting and updating motion parameter data in real time.

[0142] The current mold frame posture refers to the specific spatial shape and joint angle set of the mold frame structure carried by the mobile mold frame robot at the current moment. It mainly includes the real-time extension length of each mold frame segment and the real-time relative rotation angle between adjacent segments. It is used to determine the physical contour and adjustable degrees of freedom of the robot and is obtained by collecting and updating the mold frame morphology data in real time.

[0143] Motion control commands refer to a set of standardized, executable command sequences that are ultimately output and used to directly drive the robot's mobile mechanisms and mold frame actuators. These commands are used to coordinate and control the robot's movement and deformation to achieve obstacle avoidance.

[0144] The first control command refers to the specific control command generated when the obstacle is located in the braking area, which is used to command the mobile mechanism of the mobile mold robot to perform emergency braking and stop the robot as a whole. It is generated through braking decision logic based on the obstacle position, movement direction and mold posture.

[0145] The second control command refers to the specific control command generated when the obstacle is located in the braking area, which is used to instruct the actuators of one or more specific mold frame segments of the moving mold frame robot to adjust their extension length or relative rotation angle. It is generated by an attitude avoidance planning algorithm based on the obstacle position and the relative rotation angle of the mold frame joints.

[0146] The third control command refers to the specific control command generated when an obstacle is located in the warning area, which is used to instruct the mobile mechanism of the mobile scaffold robot to modify its movement direction or reduce its movement speed. It is generated by a path replanning or speed adjustment algorithm based on the obstacle's position, movement direction, and movement speed.

[0147] In this step, the current motion state of the mobile scaffold robot is obtained by first reading the unit vector of the movement direction and the scalar value of the movement speed under the latest timestamp from the continuously maintained shared data area through the memory access interface.

[0148] Secondly, through the same data interface, the latest extension length and relative rotation angle values ​​of all mold frame segments are read in real time from the shared data area to obtain the current mold frame posture of the mobile mold frame robot;

[0149] Next, the safety zone level is determined, the information of the identified obstacles is queried, and the intrusion relationship between their spatial range and the multi-level safety zone model is obtained. If the spatial range of the obstacle intersects with the braking zone model, the obstacle is determined to be located in the braking zone; otherwise, if it only intersects with the warning zone model, the obstacle is determined to be located in the warning zone.

[0150] Then, when the determination result is that the obstacle is located in the braking area, the emergency coordination control strategy is triggered. First, the braking decision logic is run, and the centroid position of the obstacle is vectored with the current position of the robot to obtain a direction vector from the robot to the obstacle. By calculating the dot product of this vector and the current movement direction, and combining it with the pre-made distance-velocity braking curve, the required braking deceleration is calculated, thereby generating the first control command for controlling the chassis drive to perform emergency braking. Subsequently, the attitude avoidance planning algorithm is run. Taking the current mold frame attitude and the specific contact area of ​​the obstacle as input, by traversing the relative rotation angles of each mold frame segment, the high-risk segment that is most likely to collide with the obstacle is identified. At the same time, the attitude avoidance planning algorithm calculates a joint angle adjustment amount or segment contraction amount that can make the segment move away from the direction of the obstacle, and ensures that the adjusted new attitude meets the structural stability constraints, thereby generating the second control command for controlling the specific mold frame joint motor.

[0151] Then, when the judgment result is that the obstacle is located in the warning area, the pre-adjustment control strategy is triggered and the speed adjustment algorithm is run. The current motion state and the predicted future motion position of the obstacle are used as inputs. For example, by calculating the component of the relative velocity of the obstacle in the direction of robot movement, and combining it with the preset safe workshop time distance, the target speed that the robot should adjust to is calculated, thereby generating a third control command for controlling the deceleration of the drive motor.

[0152] Finally, the control commands are combined and encapsulated. Based on the judgment result, the first and second control commands are packaged together, or only the third control command is packaged. Priority encoding and time synchronization mechanism are used to combine the selected commands into a data packet with a unified timestamp and command sequence number, forming the final co-motion control command that can be issued and executed.

[0153] For example, following the specific implementation of the previous step, firstly, the control module of the mobile mold robot A has detected an obstacle B marked as having intruded into the braking area; secondly, it immediately reads from memory the current motion state, the direction of movement being due east, the speed being 0.3 m / s, and the current mold posture, the second segment being extended, with a relative rotation angle of 5 degrees between the first and second segments; then, since obstacle B is determined to be located in the braking area, emergency coordination control is initiated, calculating that obstacle B is located on the right front side of the robot, with a small angle to the direction of movement; then, the braking decision logic calculates the maximum braking force to be applied based on this relative position and the current speed, generating a first control command to immediately stop the movement; simultaneously, the posture avoidance planning algorithm identifies that the obstacle is close to the second segment on the protruding side, calculates that rotating the joint of the second segment counterclockwise by 3 degrees relative to the first segment can increase the clearance, and generates a corresponding second control command;

[0154] Subsequently, the first control command for emergency braking and the second control command for rotating the second segment joint by 3 degrees are encapsulated in the order of braking first and then adjustment, with the same timestamp, to form the final motion control command data packet. If the obstacle is only located in the warning area, a third control command for slightly adjusting the heading to the left by 10 degrees may be generated and encapsulated for output.

[0155] This step integrates obstacle hierarchy information, the robot's real-time motion state, and its posture, and dynamically triggers differentiated collaborative control strategies based on different risk levels. This achieves a decision-making closed loop from accurate risk perception to adaptive response actions, thereby improving the overall adaptability and reliability of the mobile scaffold robot in safely avoiding obstacles in dynamic and complex environments.

[0156] Step 105: Execute the motion control command to drive the mobile scaffold robot to perform obstacle avoidance and protection.

[0157] Optionally, step 105 may specifically include:

[0158] Step 1051: parse the motion control command and identify the first control command, second control command, or third control command contained in the motion control command.

[0159] Step 1052: Convert the first control command into a braking drive signal.

[0160] Step 1053: Convert the second control command into an attitude adjustment drive signal.

[0161] Step 1054: Convert the third control command into a path correction drive signal.

[0162] Step 1055: Send the braking drive signal to the moving mechanism of the mobile mold frame robot to stop the mobile mold frame robot from moving.

[0163] Step 1056: After the mobile mold frame robot stops moving, the attitude adjustment drive signal is sent to the actuator corresponding to the target mold frame segment to change the extension length or relative rotation angle of the target mold frame segment.

[0164] Step 1057: When the motion control command does not include the first control command and the second control command, but includes the third control command, send the path correction drive signal to the moving mechanism of the mobile mold robot to change the moving direction or moving speed of the mobile mold robot.

[0165] In this step, the braking drive signal refers to the underlying control signal that directly controls the moving mechanism to achieve emergency deceleration until it stops. It is obtained by converting the deceleration parameter in the first control command into a control command that the drive motor can recognize.

[0166] The attitude adjustment drive signal refers to the underlying control signal that directly controls the telescopic or rotation mechanism of a specified mold frame segment to change the attitude of the body. It is obtained by converting the joint angle or telescopic parameters in the second control command into control commands that can be recognized by the actuator.

[0167] Path correction drive signals refer to the underlying control signals that directly control the moving mechanism to adjust its direction of travel or speed. They are obtained by converting the heading or speed parameters in the third control command into control commands that the drive system can recognize.

[0168] In this step, the central controller first runs an instruction decoding program to parse the received motion control instruction data packets. According to the predefined communication protocol frame structure, it extracts the instruction type field and parameter data field from the data packets. By comparing the instruction type field with the instruction codebook inside the settings, it accurately identifies whether the specific instruction category contained in the data packet is the first control instruction, the second control instruction, or the third control instruction, and extracts the corresponding control parameters.

[0169] Secondly, when the first control command is identified, the braking signal conversion program will be triggered to read the target deceleration parameter contained in the first control command. The digital parameter will be linearly converted into an analog signal with a specific voltage value through a digital-to-analog converter according to a preset mapping relationship. The analog signal obtained after conversion is the braking drive signal.

[0170] Then, when the second control command is identified, the attitude signal conversion program will be triggered to read the target frame segment identifier, target extension length change or target relative rotation angle contained in the second control command. That is, for motor-driven joints, the angle value is converted into a PWM waveform signal with a corresponding duty cycle through pulse width modulation technology. For hydraulically driven segments, the extension amount is converted into an analog current signal of a certain size through the current output card. The analog current signal obtained after conversion is the attitude adjustment drive signal.

[0171] Then, when the third control command is identified, the path correction signal conversion program will be triggered to read the target heading angle correction or target speed value contained in the third control command. The motion controller will use the inverse kinematics algorithm to decompose the target heading angle and speed into the target speeds of the left and right drive wheels respectively. Then, through the speed loop control algorithm, the control voltage or torque command required by the drive motor will be calculated and formatted into a path correction drive signal that the drive unit can receive.

[0172] Specifically, the generated braking drive signal is sent to the mobile mechanism drive controller of the mobile mold robot via the fieldbus. After receiving the signal, the drive controller immediately interrupts the current constant speed or acceleration command and controls the drive motor to output reverse braking torque according to the deceleration curve specified by the signal, so that the mobile chassis of the robot decelerates rapidly until it stops moving completely.

[0173] After confirming that the robot has stopped moving through the internal sensors of the mobile mechanism, the attitude adjustment step is executed. That is, through a dedicated control circuit, the generated attitude adjustment drive signal with the target segment identifier is sent to the corresponding mold frame segment actuator. For example, the PWM signal is sent to the designated joint servo driver, or the current signal is sent to the designated hydraulic proportional valve. The actuator drives the motor or cylinder to move according to the received signal, thereby accurately adjusting the extension length or relative rotation angle of the target mold frame segment to the position specified by the instruction.

[0174] Finally, after analysis and confirmation that the motion control command only contains the third control command and not the first and second control commands, it is determined that the current state is in the early warning and obstacle avoidance mode. Then, the generated path correction drive signal is sent to the drive controller of the mobile mechanism through the fieldbus. The drive controller smoothly adjusts the output of each drive wheel according to the new speed or differential command, thereby changing the overall movement direction or movement speed of the mobile module robot without completely stopping, thus achieving active obstacle avoidance.

[0175] For example, following the specific implementation of the previous step, firstly, when the central controller of the mobile frame robot A receives the encapsulated motion control command, the command decoding program parses it to find that it contains a first control command for deceleration of -2m / s² and a second control command for controlling the second segment joint to rotate counterclockwise by 3 degrees; secondly, the first control command is processed, and the braking signal conversion program converts the digital quantity -2m / s² into a -5V analog voltage signal through a digital-to-analog converter, which is sent as a braking drive signal to the chassis drive controller. The drive controller immediately controls the drive motor to implement electric braking, and robot A comes to a complete stop after 1.5 seconds;

[0176] Next, after confirming that the robot has come to a complete stop, the second control command is processed. The attitude signal conversion program converts the 3-degree rotation into a corresponding PWM waveform signal as an attitude adjustment drive signal. This signal is sent to the servo driver controlling the second segment joint via the CAN bus. The servo driver drives the joint motor to rotate precisely, causing the second segment of the mold frame to rotate 3 degrees counterclockwise relative to the first segment, increasing the gap with obstacle B. Then, if the initial command was only a third control command to fine-tune the heading by 10 degrees to the left, the path correction signal conversion program will convert it into a speed difference command between the left and right wheels and send it directly to the chassis drive controller. Robot A will then smoothly turn to the left while moving.

[0177] This step ensures that adaptive obstacle avoidance responses for different risk levels are reliably and orderly physically executed by accurately converting high-level collaborative instructions into low-level drive signals and controlling the mechanical unit's actions according to the deterministic logic of prioritizing braking, adjusting attitude, or making online corrections. This completes the final closed loop from intelligent decision-making to physical protection.

[0178] Figure 2 This application provides a structural schematic diagram of a multi-level safety obstacle avoidance and protection system for a mobile scaffold robot, as shown below. Figure 2 As shown, the system includes:

[0179] The data acquisition module 21 is used to acquire motion parameters and mold shape data of the mobile mold robot;

[0180] The delineation module 22 is used to dynamically calculate and delineate a multi-level safety zone around the mobile mold robot based on the motion parameters and mold frame shape data, with the mold frame outline as a reference, according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone.

[0181] Scanning module 23 is used to continuously scan the multi-level security area to obtain environmental information and detect obstacles;

[0182] The generation module 24 is used to collaboratively generate motion control commands containing different control strategies based on the different levels of safety zones where the obstacle is located. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control.

[0183] The execution module 25 is used to execute the motion control commands to drive the mobile template robot to perform obstacle avoidance and protection.

[0184] Figure 2 The aforementioned multi-level safety obstacle avoidance system for a mobile scaffold robot can perform... Figure 1The implementation principle and technical effects of the multi-level safety obstacle avoidance protection method for a mobile mold frame robot described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the multi-level safety obstacle avoidance protection system for a mobile mold frame robot in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0185] In one possible design, Figure 2 The multi-level safety obstacle avoidance system for a mobile scaffold robot shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0186] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0187] The processing component 32 is used for the above Figure 1 The embodiment describes a multi-level safety obstacle avoidance and protection method for a mobile scaffold robot.

[0188] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0189] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component 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 read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0190] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0191] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0192] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0193] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0194] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a multi-level safety obstacle avoidance and protection method for a mobile scaffold robot.

[0195] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0197] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0198] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-level safety obstacle avoidance and protection method for a mobile scaffolding robot, characterized in that, include: Collect motion parameters and mold morphology data of the mobile mold robot; Based on the motion parameters and mold frame morphology data, and using the mold frame outline as a reference, a multi-level safety zone is dynamically calculated and delineated around the mobile mold frame robot according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone. The process includes: determining the direction of movement and speed of the mobile mold frame robot at the current moment based on the motion parameters of the mobile mold frame robot; using the structural outline of each mold frame segment in the mold frame morphology data as the initial boundary, and calculating the boundary of the warning zone according to the direction of movement and speed; and using the boundary of the warning zone as a basis, calculating the boundary of the braking zone according to the extension length and relative rotation angle of the mold frame segments. The system continuously scans the multi-level safety zones to obtain environmental information and detect obstacles, including: arranging preset scanning points on the boundaries of the warning zone and the braking zone, as well as on the outer contour of the mobile module robot; driving sensors to sequentially measure the distance in the direction indicated by each scanning point to obtain distance data corresponding to each scanning point; comparing the distance data corresponding to all scanning points with the boundary positions of the warning zone and the braking zone; if the distance data corresponding to a scanning point is less than the distance to the boundary position corresponding to the scanning point, then marking the scanning point as an anomaly; combining multiple anomaly points that are adjacent in position and meet the distance condition to form potential obstacles; determining the spatial range occupied by each potential obstacle in the multi-level safety zones, and marking the potential obstacle as an obstacle; Based on the different levels of safety zones where the obstacle is located, motion control commands containing different control strategies are generated collaboratively. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control. The motion control commands are executed to drive the mobile scaffold robot to perform obstacle avoidance and protection.

2. The method according to claim 1, characterized in that, Using the outer contour of each mold segment in the mold frame morphology data as the initial boundary, and based on the moving direction and moving speed, the boundary of the warning area is calculated, including: Based on the direction of movement, a first component along the direction of movement and a second component perpendicular to the direction of movement are defined for multiple points on the outer contour of the structure of each mold frame segment. Calculate the first extended distance of each point in the first component direction based on the moving speed; Set a fixed second extension distance for each point in the direction of the second component; The initial position of each point is obtained by adding the first expansion distance along the corresponding first component and the second expansion distance along the corresponding second component to obtain the expanded position of each point. The extended positions of all points belonging to the same formwork segment are connected to form the boundary of the early warning sub-region of the formwork segment; By integrating the boundaries of all early warning sub-regions of the formwork segments, the complete boundary of the early warning area is obtained.

3. The method according to claim 1, characterized in that, Based on the boundary of the warning area, and according to the extension length and relative rotation angle of the formwork segment, the boundary of the braking area is calculated, including: Each point on the boundary of the warning area is used as a reference point; Obtain the current extension length of the formwork segment corresponding to each reference point; Determine the relative rotation angle between the formwork segment corresponding to each reference point and the adjacent formwork segment; Calculate the basic extension distance based on the current extension length corresponding to each reference point; Calculate the angle adjustment based on the relative rotation angle corresponding to each reference point; The target expansion distance is obtained by adding the basic expansion distance to the angle adjustment amount; Along the normal direction of the boundary of the warning area at each reference point, move each reference point outward by the target extension distance to obtain the braking boundary point of each reference point; Connect all the braking boundary points in sequence to form the boundary of the braking area.

4. The method according to claim 1, characterized in that, Based on the different safety zones where the obstacle is located, motion control commands containing different control strategies are collaboratively generated. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control, including: The current motion state of the mobile template robot is obtained, and the current motion state includes the direction of movement and the speed of movement; Obtain the current mold frame posture of the mobile mold frame robot, the current mold frame posture including the extension length and relative rotation angle of each mold frame segment; Based on the location of the obstacle in the multi-level safety zone, determine whether the obstacle is located in the warning zone or the braking zone of the multi-level safety zone; When the obstacle is located in the braking area, a first control command and a second control command are generated based on the specific location of the obstacle, the direction of movement, and the relative rotation angle of the mold frame segment. When the obstacle is located in the warning area, a third control command is generated based on the specific location of the obstacle, the direction of movement, and the speed of movement. The first control command, the second control command, or the third control command are combined to form a motion control command.

5. The method according to claim 1, characterized in that, Executing the motion control commands to drive the mobile scaffold robot to perform obstacle avoidance includes: The motion control command is parsed to identify the first control command, second control command, or third control command contained in the motion control command; Convert the first control command into a braking drive signal; The second control command is converted into an attitude adjustment drive signal; The third control command is converted into a path correction drive signal; Send the braking drive signal to the moving mechanism of the mobile mold frame robot to stop the mobile mold frame robot from moving; After the mobile mold frame robot stops moving, the attitude adjustment drive signal is sent to the actuator corresponding to the target mold frame segment to change the extension length or relative rotation angle of the target mold frame segment; When the motion control command does not include the first control command and the second control command, but includes the third control command, the path correction drive signal is sent to the moving mechanism of the mobile mold robot to change the moving direction or moving speed of the mobile mold robot.

6. A multi-level safety obstacle avoidance system for a mobile mold frame robot, applied to the multi-level safety obstacle avoidance method for a mobile mold frame robot according to any one of claims 1-5, characterized in that, include: The data acquisition module is used to collect motion parameters and mold shape data of the mobile mold robot; The delineation module is used to dynamically calculate and delineate a multi-level safety zone around the mobile mold robot based on the motion parameters and mold frame shape data, with the mold frame outline as a reference, according to the direction of movement and speed. The multi-level safety zone includes at least a warning zone and a braking zone. The scanning module is used to continuously scan the multi-level security zone to obtain environmental information and detect obstacles; The generation module is used to collaboratively generate motion control commands containing different control strategies based on the different levels of safety zones where the obstacle is located. When the obstacle is located in the braking zone, the control strategy includes at least braking control and attitude adjustment control. An execution module is used to execute the motion control commands to drive the mobile scaffold robot to perform obstacle avoidance and protection.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a multi-level safety obstacle avoidance and protection method for a mobile scaffold robot as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device stores a computer program, which, when executed by a computer, implements a multi-level safety obstacle avoidance and protection method for a mobile scaffold robot as described in any one of claims 1 to 5.

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