Accurate positioning control system and method for structural longitudinal steel bars

Abstract

The invention relates to the technical field of reinforcing steel bar positioning, in particular to a structural longitudinal reinforcing steel bar accurate positioning control system and method.The method comprises the following steps that multi-source sensor data collection is conducted on a reinforcing steel bar to be installed, the multi-source sensor data are fused, and the quality of the multi-source sensor data is evaluated; generating real-time three-dimensional form information of the to-be-mounted reinforcing steel bar; performing sensor scanning on the target mounting area to generate actual position information of the reference steel bar; based on the real-time three-dimensional form information of the to-be-installed reinforcing steel bars and the actual position information of the reference reinforcing steel bars, virtual assembly simulation is carried out, and engineering constraints are verified, so that an optimal compromise placement point is calculated; and according to the optimal compromise placement point, the automatic hoisting equipment is driven to place the to-be-mounted reinforcing steel bar in place, and conformity confirmation and correction after placement are carried out. And the precision, efficiency and reliability of reinforcing steel bar installation can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel bar positioning, and in particular to a structural longitudinal steel bar precise positioning control system and method. BACKGROUND

[0002] In modern construction, especially in large structures such as high-rise buildings or bridges, the precise positioning and installation of longitudinal steel bars is a key link to ensure the safety of the structure. Traditional manual operation is not only inefficient and labor-intensive, but also the positioning accuracy is easily affected by human factors, making it difficult to ensure the consistency of height.

[0003] In order to solve these problems, the industry has introduced a steel bar positioning control system based on sensors and automatic actuators, aiming to achieve millimeter-level precise placement and fixation of longitudinal steel bars through automated means, thereby significantly improving construction quality and efficiency.

[0004] However, due to the influence of the actual environment, the entire positioning task becomes complex, thereby affecting the accuracy of positioning. SUMMARY

[0005] The purpose of the present application is to address the above-mentioned deficiencies by providing a structural longitudinal steel bar precise positioning control system and method.

[0006] The present application adopts the following technical solutions: A structural longitudinal steel bar precise positioning control method, the method comprising the following steps: Collecting multi-source sensor data on the steel bar to be installed, fusing the multi-source sensor data and evaluating the quality of the multi-source sensor data to generate real-time three-dimensional shape information of the steel bar to be installed; Scanning the target installation area with a sensor to generate actual position information of the reference steel bar; The method comprises the following steps: based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar, a virtual assembly simulation is performed and engineering constraints are verified to calculate an optimal compromise placement point; wherein the step of calculating the optimal compromise placement point comprises: during the virtual assembly simulation, based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar, a three-dimensional digital scene based on a target installation area is constructed, a constraint potential field is generated in the three-dimensional digital scene in real time, and the constraint potential field assigns a potential energy value to each candidate placement point according to the relative position relationship between the steel bar to be installed and the reference steel bar and the engineering constraint conditions; the constraint potential field is updated in real time according to the dynamic changes of the reference steel bar; the steel bar to be installed is guided to move to the candidate placement point with the minimum potential energy value according to the distribution of the potential energy values of the candidate placement points; when the minimum potential energy value is higher than a preset safety threshold, the weight of the potential energy component of the structural safety is adjusted, a high penalty weight is assigned to the potential energy component of the structural safety, an adjusted potential energy value is obtained, and the candidate placement point corresponding to the adjusted potential energy value is defined as the optimal compromise placement point; wherein the step of guiding the steel bar to be installed to move to the candidate placement point with the minimum potential energy value comprises: determining a potential energy gradient according to the distribution of the potential energy values of the candidate placement points; obtaining kinematic constraint information and dynamic constraint information of the automated hoisting equipment, the kinematic constraint information of the automated hoisting equipment comprising a mechanical arm end effector size and joint limit, and the dynamic constraint information of the automated hoisting equipment comprising a maximum speed limit and a maximum acceleration limit; based on the potential energy gradient, the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment, path planning is performed to generate a collision-free guide path that satisfies the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment; the automated hoisting equipment is driven along the collision-free guide path to move the steel bar to be installed to the candidate placement point with the minimum potential energy value; wherein the step of generating the collision-free guide path comprises: based on the potential energy gradient, a candidate path set is constructed, and collision detection and feasibility screening are performed on the candidate path set to obtain an effective path set; under the premise of satisfying the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment, the effective path set is subjected to path smoothing optimization and joint trajectory optimization, wherein the path smoothing optimization comprises continuity adjustment based on curvature constraint, and the joint trajectory optimization comprises time parameterization based on velocity profile; during the optimization process, the path node positions and sequences of the optimized effective path set are adjusted in real time to obtain an adjusted path set in combination with the obstacle distribution information and dynamic change information of the construction site; the adjusted path set is subjected to collision detection and feasibility screening again to obtain the collision-free guide path. According to the optimal compromise placement point, the automated hoisting equipment is driven to place the steel bar to be installed in place, and conformity confirmation and correction after placement are performed.

[0007] By the technical scheme, multi-source information can be effectively integrated, precise three-dimensional modeling and environment perception of the steel bars are realized, and through virtual assembly and engineering constraint checking, the best placement point considering design requirements and actual site conditions is intelligently calculated, so that the disadvantages of low precision and poor efficiency of traditional methods are overcome, and the problems that an automatic system is difficult to adapt to benchmark deviation and ensure structural safety in a complex site environment are solved, and the precision, efficiency and reliability of steel bar installation are significantly improved.

[0008] Further, the step of generating the constraint potential field in real time comprises: Real-time monitoring of environmental noise, when detecting transient or local environmental noise, starting the noise suppression module; The noise suppression module adjusts the sensor data acquisition parameters according to the environmental noise, and filters the sensor data affected by the environmental noise to generate processed sensor data; According to the type and intensity of the environmental noise, dynamically adjust the weight of the noise compensation potential energy component affected in the constraint potential field calculation, and based on the processed sensor data and the adjusted weight of the noise compensation potential energy component, generate the constraint potential field in the three-dimensional digital scene in real time.

[0009] Further, the transient environmental noise is an impact noise event in which the amplitude of the acoustic signal changes within a preset time window and the change rate exceeds a preset rate threshold, and the peak amplitude after the change exceeds a preset multiple of the average amplitude of the background noise.

[0010] Further, the step of obtaining the engineering constraint condition between the steel bar to be installed and the reference steel bar comprises: Real-time acquisition of construction environment factor information, the construction environment factor information including temperature information and humidity information; Real-time acquisition of structural material state information, the structural material state information including concrete strength information; According to the construction environment factor information and the structural material state information, dynamically adjust the engineering constraint condition between the steel bar to be installed and the reference steel bar, the engineering constraint condition between the steel bar to be installed and the reference steel bar including the minimum protective layer thickness and the steel bar lap length.

[0011] Further, the step of assigning potential energy values to each candidate placement point comprises: Determine the spatial coordinates of each candidate placement point in the three-dimensional digital scene; Based on the relative position relationship between the steel bar to be installed and the reference steel bar, calculate the geometric adaptation degree potential energy component of each candidate placement point; Based on the engineering constraint condition between the steel bar to be installed and the reference steel bar, calculate the constraint compliance potential energy component of each candidate placement point; The geometric fitness potential component of each candidate placement point and the constraint compliance potential component of each candidate placement point are weighted and summed according to the preset adaptive constraint weight to obtain the comprehensive potential value of each candidate placement point, and the comprehensive potential value of each candidate placement point is taken as the result of the potential field assigning potential values to each candidate placement point.

[0012] Further, after obtaining the collision-free guide path, the smoothness and efficiency of the collision-free guide path are evaluated, and the generation parameters of the collision-free guide path are adjusted according to the evaluation result.

[0013] The application also discloses a structure longitudinal steel bar precise positioning control system applied to the structure longitudinal steel bar precise positioning control method. The processing module collects multi-source sensor data of the steel bar to be installed, fuses the multi-source sensor data and evaluates the quality of the multi-source sensor data to generate real-time three-dimensional shape information of the steel bar to be installed. The position information acquisition module is used for performing sensor scanning on the target installation area to generate actual position information of the reference steel bar. The placement point calculation module performs virtual assembly simulation and checks engineering constraints based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar to calculate the best compromise placement point. The placement control module drives the automated hoisting equipment to place the steel bar to be installed to the position according to the best compromise placement point and performs compliance confirmation and correction after placement.

[0014] Through the technical scheme, a system entity for implementing the above method is provided, the responsibilities of each functional unit are clearly defined through modular design, the whole system can work efficiently and cooperatively, the precise positioning and control of the structure longitudinal steel bar are realized, and reliable hardware and software support is provided for actual engineering application.

[0015] The application significantly improves the precision, efficiency and reliability of steel bar installation, effectively overcomes the deficiencies of low efficiency, limited precision and poor adaptability of the automatic system in a complex dynamic construction environment in the prior art, and provides a safer and more efficient steel bar positioning solution for high-rise building and large structure construction.

[0016] To enable a further understanding of the features and technical contents of the application, reference can be made to the following detailed description and drawings of the application, however, the drawings provided are only used for reference and illustration, and are not used to limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A method flowchart of the structure longitudinal steel bar precise positioning control method of the application; Figure 2A structural longitudinal steel bar precise positioning control system structural schematic diagram of the present application. DETAILED DESCRIPTION

[0018] The following is to illustrate the embodiments of the present application by specific examples, and those skilled in the art can understand the advantages and effects of the present application from the disclosure. The present application can be implemented or applied by other different specific embodiments, and the details in the specification can be modified and changed based on different views and applications without departing from the spirit of the present application. In addition, the drawings of the present application are only simple schematic illustrations, not actual size drawings, and the prior declaration is made. The following embodiments will further illustrate the related technical content of the present application in detail, but the disclosed content is not used to limit the protection scope of the present application.

[0019] The present embodiment provides a structural longitudinal steel bar precise positioning control system and method, which combines Figure 1 and Figure 2 as shown.

[0020] Referring to Figure 1 , a structural longitudinal steel bar precise positioning control method, the method comprising the following steps: Collecting multi-source sensor data on the steel bar to be installed, fusing the multi-source sensor data and evaluating the quality of the multi-source sensor data to generate real-time three-dimensional shape information of the steel bar to be installed; Scanning the target installation area with a sensor to generate actual position information of the reference steel bar; The method comprises the following steps: based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar, a virtual assembly simulation is performed and engineering constraints are verified to calculate an optimal compromise placement point; wherein the step of calculating the optimal compromise placement point comprises: during the virtual assembly simulation, based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar, a three-dimensional digital scene based on a target installation area is constructed, a constraint potential field is generated in the three-dimensional digital scene in real time, and the constraint potential field assigns a potential energy value to each candidate placement point according to the relative position relationship between the steel bar to be installed and the reference steel bar and the engineering constraint conditions; the constraint potential field is updated in real time according to the dynamic changes of the reference steel bar; the steel bar to be installed is guided to move to the candidate placement point with the minimum potential energy value according to the potential energy value distribution of each candidate placement point; when the minimum potential energy value is higher than a preset safety threshold, the weight of the potential energy component of the structural safety is adjusted, a high penalty weight is assigned to the potential energy component of the structural safety, an adjusted potential energy value is obtained, and the candidate placement point corresponding to the adjusted potential energy value is defined as the optimal compromise placement point; wherein the step of guiding the steel bar to be installed to move to the candidate placement point with the minimum potential energy value comprises: determining a potential energy gradient according to the potential energy value distribution of each candidate placement point; obtaining kinematic constraint information and dynamic constraint information of the automated hoisting equipment, the kinematic constraint information of the automated hoisting equipment comprising a mechanical arm end effector size and joint limit, and the dynamic constraint information of the automated hoisting equipment comprising a maximum speed limit and a maximum acceleration limit; based on the potential energy gradient, the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment, path planning is performed to generate a collision-free guide path that satisfies the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment; the steel bar to be installed is moved to the candidate placement point with the minimum potential energy value along the collision-free guide path by driving the automated hoisting equipment; wherein the step of generating the collision-free guide path comprises: constructing a candidate path set based on the potential energy gradient, performing collision detection and feasibility screening on the candidate path set to obtain an effective path set; under the premise of satisfying the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment, the effective path set is subjected to path smoothing optimization and joint trajectory optimization, wherein the path smoothing optimization comprises continuity adjustment based on curvature constraint, and the joint trajectory optimization comprises time parameterization based on velocity profile; during the optimization process, the path node position and sequence of the optimized effective path set are adjusted in real time to obtain an adjusted path set in combination with the obstacle distribution information and dynamic change information of the construction site; the adjusted path set is subjected to collision detection and feasibility screening again to obtain the collision-free guide path; According to the optimal compromise placement point, the automated hoisting equipment is driven to place the steel bar to be installed in place, and conformity confirmation and correction after placement are performed.

[0021] The steel bar to be installed refers to a longitudinal steel bar that is about to be hoisted and placed in place, and the shape information thereof needs to be accurately obtained.

[0022] "Multi-source sensor data collection" refers to the use of multiple types of sensors (such as lidar, vision sensors, inertial measurement units, etc.) to collect data on the reinforcing steel to be installed, providing more comprehensive and robust information.

[0023] "Multi-source sensor data fusion" refers to the integration of data from different sensors to eliminate errors and limitations that may exist in a single sensor, improving the overall accuracy and reliability of the data.

[0024] "Real-time three-dimensional shape information" refers to the precise three-dimensional geometric shape and attitude data of the reinforcing steel to be installed at the current time.

[0025] "Target installation area" refers to the structural area where the reinforcing steel to be installed needs to be placed, usually including the reference reinforcing steel that has been reserved.

[0026] "Reference reinforcing steel" refers to reinforcing steel that has been fixed or reserved in the target installation area, serving as a reference for newly installed reinforcing steel.

[0027] "Virtual assembly simulation" refers to constructing a digital model in a computer to simulate the assembly process between the reinforcing steel to be installed and the reference reinforcing steel, allowing for pre-judgment and optimization before actual operation.

[0028] "Engineering constraints" refer to structural design and construction specification requirements that must be met during reinforcing steel installation, such as minimum protective layer thickness, reinforcing steel lap length, etc.

[0029] "Best compromise placement point" refers to the ideal placement position that takes into account design requirements and actual site conditions to the greatest extent, calculated by optimization algorithms under the premise of meeting all engineering constraints.

[0030] "Automated hoisting equipment" refers to automated mechanical equipment used to grab, move, and place reinforcing steel, such as cranes equipped with robotic arms or specialized robots.

[0031] "Compliance confirmation and correction" refers to checking the final position of the reinforcing steel after it is placed in place and making necessary adjustments based on the inspection results to ensure that it meets the accuracy requirements.

[0032] The structural longitudinal reinforcing steel precise positioning control method proposed in this application is based on fine data collection, intelligent decision-making calculation, and automated precise execution, overcoming the limitations of traditional methods in complex construction environments.

[0033] Specifically, when collecting multi-source sensor data of the steel bar to be installed, multiple methods can be adopted. For example, a laser scanner can be used to collect three-dimensional point cloud data of the steel bar to be installed, and a high-resolution camera can be used to obtain the surface texture image of the steel bar to be installed. Another method is to use multiple ultrasonic sensor arrays to non-contact measure the steel bar to be installed, and assist with an inertial measurement unit to obtain the attitude information of the steel bar to be installed. These sensor data are then fused, for example, by Kalman filtering or extended Kalman filtering algorithm, to align and integrate the data of different sensors in time and space, and to evaluate the quality of the fused data, for example, by calculating the uncertainty of the data or the deviation from the preset model to judge the reliability, and finally generate real-time three-dimensional shape information of the steel bar to be installed.

[0034] For sensor scanning of the target installation area to generate the actual position information of the reference steel bar, the following methods can be adopted. For example, a fixed laser scanning system can be deployed to periodically scan the target installation area to obtain the three-dimensional coordinates of all reference steel bars in the area. Alternatively, a mobile robot can be used to carry a vision sensor to autonomously patrol the target installation area, and identify and locate the reference steel bars through image recognition and three-dimensional reconstruction technology. After processing these scanning data, the actual position information of the reference steel bar can be obtained.

[0035] When simulating virtual assembly based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar, and verifying engineering constraints to calculate the optimal compromise placement point, a high-precision digital twin environment can be constructed. For example, the three-dimensional model of the steel bar to be installed and the actual position data of the reference steel bar are imported into a virtual simulation software to simulate the process of approaching and overlapping each other. During this process, the system will real-time verify the preset engineering constraints, for example, check whether the minimum distance between the steel bar to be installed and the reference steel bar meets the specification requirements, whether the overlapping length is sufficient, etc. If there is a situation that does not meet the constraints, the system will use optimization algorithms, such as potential field method or genetic algorithm, to find an optimal placement position, i.e. the optimal compromise placement point, under the premise of meeting all constraints.

[0036] Finally, according to the calculated optimal compromise placement point, the automated hoisting equipment is driven to place the steel bar to be installed in place, and the conformity after placement is confirmed and corrected. For example, the automated hoisting equipment can be a mechanical arm equipped with high-precision servo motors and force sensors, which accurately grasps and moves the steel bar to be installed according to the coordinates and attitude information of the optimal compromise placement point. After placement, sensors such as laser range finder or vision sensor can be used again to measure the final position of the steel bar to be installed, and compared with the optimal compromise placement point for conformity confirmation. If there is a deviation, the system will drive the automated hoisting equipment to fine-tune according to the deviation amount until the accuracy requirement is met.

[0037] The structure longitudinal steel precise positioning control method proposed in the application is based on the overall working principle of constructing a closed-loop intelligent control system, which can effectively deal with the complexity and uncertainty of the construction site. The application introduces multi-source sensor data acquisition and fusion to ensure real-time and high-precision state perception of the steel bars to be installed and the reference steel bars. Even in harsh environments such as dust and light changes, reliable three-dimensional shape and position information can be obtained through data fusion and quality evaluation.

[0038] On this basis, the introduction of virtual assembly simulation and engineering constraint checking enables the system to predict and avoid potential structural safety hazards before actual lifting. By simulating the assembly process in a digital scene and checking key engineering constraints such as minimum protective layer thickness and steel bar overlap length in real time, the system no longer simply pursues absolute position accuracy, but intelligently balances between "absolute design position" and "relative overlap requirements". When potential conflicts are found, the system can find an optimal solution that meets structural safety specifications and adapts to actual site deviations by calculating the "best compromise placement point".

[0039] Finally, the automated lifting equipment is precisely placed according to the "best compromise placement point", and through compliance confirmation and correction after placement, a feedback loop is formed to ensure the final installation quality of the steel bars. This method closely integrates perception, decision-making and execution, making the entire steel positioning process from passive adaptation to active optimization, significantly improving the intelligent level of construction and structural safety.

[0040] Specifically, constructing a three-dimensional digital scene based on the target installation area refers to using the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar to establish a virtual three-dimensional space model in the computer that corresponds to the actual construction environment. The three-dimensional digital scene can accurately reflect the geometric shapes, sizes of the steel bar to be installed and the reference steel bar, and their relative positions in space. Among them, the real-time generation of the constraint potential field can be understood as calculating a potential energy value for each possible installation position of the steel bar to be installed (i.e. candidate placement point) in the above-mentioned three-dimensional digital scene. The potential energy value comprehensively reflects the "good or bad" degree of the placement point in terms of geometric fitness, engineering constraint compliance, and structural safety, etc. The lower the potential energy value, the more ideal the placement point. The generation of the constraint potential field is dynamic and can be calculated and updated in real time according to the relative position relationship between the steel bar to be installed and the reference steel bar and the pre-set engineering constraint conditions (such as minimum protective layer thickness, steel bar overlap length, etc.). In actual application, the constraint potential field is updated in real time according to the dynamic changes of the reference steel bar, the purpose of which is to ensure the real-time and accuracy of the placement point calculation. For example, during the construction process, the reference steel bar may be slightly displaced or deformed due to external disturbances (such as wind, vibration or adjacent construction activities), or thermal expansion and contraction due to temperature changes. These dynamic changes will be captured in real time by sensors and immediately fed back to the three-dimensional digital scene, triggering the recalculation and update of the constraint potential field to reflect the latest actual situation. In addition, according to the potential energy value distribution of each candidate placement point, the steel bar to be installed is guided to move to the candidate placement point with the minimum potential energy value, the purpose of which is to find one or a group of placement points with the lowest potential energy value in the constraint potential field through the optimization algorithm, and these points represent the most ideal placement position under the current constraint conditions. The automated hoisting equipment will move accurately according to this guide information. Further, when the minimum potential energy value is higher than the pre-set safety threshold, the potential energy component weight of the structural safety is adjusted, the potential energy component weight of the structural safety is given a high penalty weight, the adjusted potential energy value is obtained, and the candidate placement point corresponding to the adjusted potential energy value is defined as the best compromise placement point. This is a key optimization mechanism, the purpose of which is to ensure that structural safety is always the highest priority. The pre-set safety threshold represents the acceptable minimum safety standard. If the calculated best placement point (i.e. the point with the minimum potential energy value) still fails to meet this safety standard, the system will actively increase the weight of the structural safety related potential energy component, making its influence on the total potential energy value greater, thereby "punishing" those placement points that perform poorly in terms of structural safety. In this way, even in the case where the ideal placement point is not available, the system can forcibly guide the automated hoisting equipment to select a "compromise" placement point that performs best in terms of structural safety, even if the point may be slightly inferior in terms of geometric fitness.

[0041] The scheme of the present application effectively solves the problem of how to calculate the best compromise placement point that meets both geometric fitness and structural safety in a complex dynamic construction environment by introducing the concept of constraint potential field and combining real-time updating mechanism and dynamic weight adjustment strategy. Specifically, the constraint potential field quantifies the relative position relationship between the to-be-installed reinforcement and the reference reinforcement and the engineering constraint conditions as potential energy values, so that the advantages and disadvantages of the placement point can be intuitively reflected. Real-time updating of the constraint potential field ensures that the system can adapt to the dynamic changes of the reference reinforcement and avoid placement deviation caused by data lag. More importantly, when the minimum potential energy value calculated initially fails to reach the preset safety threshold, the system can forcibly prioritize structural safety by giving a high penalty weight to the potential energy component of structural safety, even if this means making some "compromise" in geometric fitness. The final selected best compromise placement point will inevitably meet the structural safety requirements. It is precisely due to this dynamic adaptation and safety priority mechanism that the present application can provide a more robust and safe reinforcement precise positioning control method in a complex and variable environment.

[0042] In some preferred embodiments, assume that during the hoisting process of a reinforcement cage of a high-rise building, a reinforcement to be installed needs to be placed at a predetermined position. Through multi-source sensor data collection, real-time three-dimensional morphological information of the reinforcement to be installed is obtained, and at the same time, the target installation area is scanned by sensors to obtain the actual position information of the reference reinforcement. The system constructs a three-dimensional digital scene based on these information and generates a constraint potential field in real time. In the initial stage, the constraint potential field gives each candidate placement point a potential energy value according to the geometric fitness (for example, the degree of alignment between the reinforcement to be installed and the reference reinforcement) and engineering constraints (for example, minimum cover thickness, reinforcement lap length). The system guides the automated hoisting equipment to move towards the candidate placement point with the minimum potential energy value. However, during the hoisting process, due to the slight wind on the construction site or the slight settlement of the foundation, the reference reinforcement has a slight dynamic displacement. The system monitors this change in real time and immediately updates the constraint potential field. At this time, the candidate placement point with the minimum potential energy value may no longer be the best choice due to the displacement of the reference reinforcement, and even may cause the minimum cover thickness to not meet the requirements, thereby increasing its potential energy value (especially the potential energy component related to structural safety). If the minimum potential energy value calculated at this time is still higher than the preset safety threshold (for example, indicating that the minimum cover thickness is seriously insufficient), the system will immediately start the safety protection mechanism. It will adjust the weight of the potential energy component related to structural safety and give it a high penalty weight. This means that even if a placement point looks perfect in geometry, if it has defects in structural safety, its total potential energy value will increase sharply due to the high penalty weight. In this way, the system will recalculate and guide the automated hoisting equipment to move to a new, adjusted candidate placement point with the minimum potential energy value. This new placement point may not be absolutely "perfect" in geometry, but it is fully guaranteed in structural safety, and thus is defined as the best compromise placement point. The automated hoisting equipment will be placed according to the new best compromise placement point, ensuring the quality of reinforcement installation and structural safety.

[0043] Specifically, determining the potential energy gradient according to the potential energy value distribution of each candidate placement point means that by analyzing the potential energy value corresponding to each candidate placement point in the three-dimensional digital scene, the direction and size of the fastest potential energy value change are calculated, thereby forming a "force field" or "trend" guiding the movement of the reinforcement to be installed. The potential energy gradient can be regarded as the driving force direction guiding the reinforcement to be installed to move to the area with the minimum potential energy value.

[0044] The kinematic constraint information and the dynamic constraint information of the automated hoisting device are obtained, aiming to ensure that the planned path is actually executable by the automated hoisting device. The kinematic constraint information of the automated hoisting device can be understood as the geometric limitations and movement ranges of the device in space. For example, the end effector size of the mechanical arm refers to the physical size of the hoisting device in grabbing the component of the steel bar to be installed, which affects its passability in a narrow space; the joint limit refers to the maximum and minimum rotation or extension angle of each joint of the mechanical arm, which limits the posture and reachable space of the mechanical arm. The dynamic constraint information of the automated hoisting device relates to the speed and acceleration limitations of the device during movement. For example, the maximum speed limitation refers to the maximum allowed speed of each component or the overall movement of the device, and the maximum acceleration limitation refers to the maximum acceleration that the device can withstand when starting, stopping or changing direction, which directly affects the time efficiency and smoothness of path planning.

[0045] In practical applications, path planning based on potential energy gradient, kinematic constraint information and dynamic constraint information of the automated hoisting device means using these information to calculate an optimal path from the current position of the steel bar to be installed to the candidate placement point with the minimum potential energy value through an algorithm. This path not only follows the guiding direction of the potential energy gradient, but also strictly meets the kinematic and dynamic constraints of the automated hoisting device, such as ensuring that the mechanical arm does not exceed its joint limit and the movement speed and acceleration do not exceed its maximum limit. At the same time, path planning also needs to consider avoiding collision with other structures or obstacles in the three-dimensional digital scene, thereby generating a collision-free guiding path. The collision-free guiding path is the instruction sequence for the automated hoisting device to actually execute the movement operation.

[0046] Therefore, moving the steel bar to be installed to the candidate placement point with the minimum potential energy value along the collision-free guiding path of the automated hoisting device means converting the planned collision-free guiding path into control instructions that can be recognized and executed by the automated hoisting device, such as a series of joint angles, speeds or end effector pose instructions, so that the automated hoisting device can accurately move the steel bar to be installed along the path until it reaches the predetermined optimal compromise placement point.

[0047] The scheme of the present application effectively solves the problems of infeasible path, low efficiency or collision risk that may occur when guiding only according to the potential energy value distribution by introducing potential energy gradient, kinematic constraint information and dynamic constraint information of the automated hoisting equipment, and performing path planning. Specifically, the potential energy gradient provides a clear directional guide for the movement of the steel bar to be installed, ensuring that it approaches the area with the minimum potential energy value. At the same time, by obtaining and integrating the kinematic constraint information and dynamic constraint information of the automated hoisting equipment, such as the size of the end effector of the robotic arm, joint limit, maximum speed limit and maximum acceleration limit, the path planning algorithm can generate a path that fully complies with the actual operating capacity of the equipment. It is precisely because these physical constraints and potential obstacle information are fully considered in the path planning process that a collision-free guiding path can be generated, thereby avoiding interference between the equipment and the environment or the reference steel bar during movement, ensuring the safety and smoothness of the hoisting operation. Finally, the automated hoisting equipment drives along this collision-free guiding path, accurately, efficiently and safely moving the steel bar to be installed to the optimal compromise placement point.

[0048] In some preferred embodiments, during the process of guiding the steel bar to be installed to move to the candidate placement point with the minimum potential energy value, first, the system will use numerical differentiation or gradient descent algorithm to calculate the potential energy gradient in real time according to the potential energy value distribution of each candidate placement point calculated in advance in the three-dimensional digital scene. The gradient indicates the direction in which the potential energy decreases most quickly. For example, if the potential energy value decreases sharply in a certain direction, the gradient value of that direction will be larger, indicating that the direction is the preferred direction of movement.

[0049] At the same time, the kinematic constraint information and dynamic constraint information of the automated hoisting equipment will be preloaded into the system. For example, for a multi-joint robotic arm, its kinematic constraint information may include the rotation angle range of each joint (joint limit), the length of each link of the robotic arm, and the geometry and size of the end effector. The dynamic constraint information may include the maximum rotational speed of each joint, the maximum acceleration, and the maximum linear and angular speed of the entire robotic arm. These information is usually obtained through the CAD model of the equipment, the manufacturer's specification or on-site calibration.

[0050] Based on this information, the path planning module can use, for example, the rapid random tree algorithm, the probabilistic roadmap algorithm or the A* search algorithm, in combination with the collision detection module, to search for a collision-free path in the three-dimensional digital scene from the current position of the steel bar to be installed to the target placement point. During the path search process, the algorithm will continuously check whether the generated path segment meets the kinematic and dynamic constraints of the automated hoisting equipment, for example, ensuring that each point on the path is located within the reachable workspace of the robotic arm, and the speed and acceleration change between adjacent path points does not exceed the limit of the equipment. If a potential collision or constraint violation is detected, the algorithm will re-plan the path.

[0051] Once the collision-free guiding path satisfying all constraints is generated, the path is converted into a series of discrete path points or joint trajectories. These trajectory data are then sent to the controller of the automated hoisting device. According to these instructions, the controller drives each joint of the robotic arm to move precisely along the planned trajectory, so that the steel reinforcement to be installed can be smoothly and safely moved to the candidate placement point with the minimum potential energy value, completing accurate positioning.

[0052] Specifically, constructing the candidate path set based on potential energy gradient means that the system uses path search algorithms such as rapid random tree algorithm, probabilistic roadmap algorithm or A* algorithm to explore and generate a series of potential paths from the current position to the candidate placement point with the minimum potential energy value in the three-dimensional digital scene according to the potential energy distribution between the steel reinforcement to be installed and the reference steel reinforcement. These paths constitute the preliminary candidate path set. Collision detection and feasibility screening are performed on the candidate path set, which aims to eliminate those paths that collide with known obstacles in the environment, and those paths that do not meet the kinematic constraint information (such as the size of the end effector of the robotic arm, joint limit) and dynamic constraint information (such as maximum speed limit, maximum acceleration limit) of the automated hoisting device, so as to obtain a preliminary feasible and effective path set.

[0053] Further, path smoothing optimization and joint trajectory optimization are performed on the effective path set, which aims to improve the execution efficiency of the path and the motion stability of the device. Among them, path smoothing optimization includes continuity adjustment based on curvature constraint, which can specifically use methods such as spline interpolation and Bezier curve fitting to make the path more geometrically smooth, avoid sharp turns or sharp path segments, and thus reduce impact and vibration during device motion, improve placement accuracy. Joint trajectory optimization includes time parameterization based on velocity profile, which can specifically plan appropriate velocity, acceleration and jerk curves for each joint of the robotic arm according to the dynamic characteristics of the automated hoisting device, to ensure that all joints move in coordination and avoid unnecessary pauses or jitter, thereby improving the efficiency and smoothness of hoisting operations.

[0054] Further, in the optimization process, the distribution information and dynamic change information of the obstacles in the construction site are combined to adjust the position and order of the path nodes of the optimized effective path set in real time to obtain an adjusted path set. This means that the system can continuously monitor the construction site environment, such as by acquiring real-time obstacle data through additional sensors (such as lidar, visual sensors). When new obstacles are detected or the positions of existing obstacles change, the system can quickly respond and dynamically modify the nodes of the planned path, such as by local path re-planning or obstacle avoidance algorithms, so that the adjusted path can bypass or avoid these dynamic obstacles. Collision detection and feasibility screening are performed again on the adjusted path set, which aims to ensure that the path after real-time adjustment is still collision-free and feasible to cope with the complexity and uncertainty of the construction site, and finally obtain a safe and reliable collision-free guidance path.

[0055] The scheme of the present application first constructs a candidate path set based on the potential energy gradient, and performs preliminary collision detection and feasibility screening to ensure the basic availability of the path. Further, by performing path smoothing optimization and joint trajectory optimization on the effective path set, especially by introducing continuity adjustment based on curvature constraint and time parameterization based on velocity profile, the generated guidance path is not only collision-free, but also has good smoothness and executability, avoiding sudden stop, sudden turn or vibration of the device during movement, thereby improving the stability and precision of hoisting. Further, in the optimization process, the distribution information and dynamic change information of the obstacles in the construction site are combined to adjust the position and order of the path nodes, so that the system can dynamically adapt to changes in the construction environment. Even if new obstacles appear or existing obstacles move, the path can be adjusted in time to ensure the safety of the hoisting process, effectively solving the problem of insufficient adaptability of traditional path planning in dynamic complex environment.

[0056] In some preferred embodiments, assume that at a certain construction site, an automated hoisting device is moving a steel bar to be installed to a predetermined position. First, the system generates a series of potential candidate paths according to the potential energy gradient between the current steel bar to be installed and the reference steel bar. These paths are preliminarily subjected to collision detection, and obviously infeasible paths are removed to obtain a set of valid paths. Subsequently, in order to ensure the smoothness and efficiency of the hoisting process, the system will fine-tune these valid paths. For example, by applying algorithms such as Bezier curve or spline interpolation, the path is smoothed and optimized to ensure that the end effector of the robotic arm changes in speed and acceleration smoothly during movement, avoiding sharp acceleration or deceleration or direction changes, which can be achieved by adjusting the curvature constraint of the path. At the same time, the motion trajectory of each joint of the robotic arm is optimized, for example, by time parameterization technology, a suitable speed profile is allocated to each joint to ensure the coordinated motion of all joints and achieve the best motion efficiency and accuracy. During the hoisting process, if temporary obstacles (such as moving construction vehicles or personnel) enter the preset path range, the system will obtain the information of these dynamic obstacles in real time and immediately adjust the optimized path node position and order to generate a new collision-free guide path. For example, the system may plan a detour around the obstacle, or temporarily pause if necessary and continue after the obstacle is removed. Finally, the system will again perform collision detection and feasibility screening on the adjusted path to ensure that it is still safe and feasible in the new environment, and use it as the final collision-free guide path to guide the automated hoisting device to complete the precise placement of the steel bar.

[0057] The application further proposes a step of generating a constraint potential field in real time, which includes: Real-time monitoring of environmental noise, when transient or local environmental noise is detected, starting the noise suppression module; The noise suppression module adjusts the sensor data acquisition parameters according to the environmental noise, and filters the sensor data affected by the environmental noise to generate processed sensor data; According to the type and intensity of the environmental noise, dynamically adjust the weight of the noise compensation potential energy component affected in the constraint potential field calculation, and based on the processed sensor data and the adjusted weight of the noise compensation potential energy component, generate a constraint potential field in a three-dimensional digital scene in real time.

[0058] Specifically, real-time monitoring of environmental noise refers to continuously obtaining noise signals in the environment through acoustic sensors, vibration sensors or other environmental monitoring devices deployed at the construction site. Among them, environmental noise can be understood as any non-target signal that may interfere with sensor data collection, such as the operating noise of construction machinery, the sound produced by human activity, wind-induced vibration, etc. When detecting transient or local environmental noise, such as when the acoustic signal amplitude changes dramatically within a pre-set time window and exceeds a pre-set threshold, or when the vibration intensity in a local area abnormally rises, the system will automatically activate the noise suppression module.

[0059] The noise suppression module is configured to intelligently adjust sensor data acquisition parameters according to the characteristics of the monitored environmental noise. For example, for optical sensors, the exposure time or gain can be adjusted; for acoustic sensors, the sampling frequency or sensitivity can be adjusted. At the same time, the noise suppression module also filters the sensor data affected by environmental noise. This filtering process can use various techniques, such as Kalman filtering, wavelet denoising, median filtering or adaptive filtering, etc., the purpose of which is to effectively separate and remove noise components from the original sensor data, thereby generating more pure and accurate processed sensor data.

[0060] In addition, according to the type and intensity of environmental noise, the weight of the noise compensation potential energy component affected in the potential field calculation is dynamically adjusted. For example, when high-intensity impact noise is detected, the weight of the potential energy component related to noise compensation may be significantly increased to ensure that the impact of noise on potential field calculation is fully considered and compensated. This noise compensation potential energy component aims to quantify the potential impact of noise on reinforcement positioning accuracy and incorporate it into potential field calculation. Based on these processed sensor data and adjusted noise compensation potential energy component weights, a more accurate and robust constraint potential field is generated in real time in the three-dimensional digital scene.

[0061] The scheme of the present application effectively solves the problem that traditional methods are easily disturbed by noise when generating a constraint potential field in a complex construction environment by introducing a real-time monitoring and suppression mechanism for environmental noise. Specifically, when environmental noise is monitored in real time and reaches a certain level, the noise suppression module is activated, which adjusts sensor data acquisition parameters and filters affected data to ensure that sensor data input into potential field calculation has higher purity and accuracy. At the same time, the weight of the noise compensation potential energy component is dynamically adjusted according to the type and intensity of the noise, so that the constraint potential field can more accurately reflect the actual engineering constraints and reinforcement position relationship, and remain effective even in a noisy environment. It is precisely due to the effective management and compensation of noise that the generated constraint potential field is more stable and reliable, providing a solid foundation for subsequent best compromise placement point calculation.

[0062] In some preferred embodiments, assume that an automated hoisting device is performing precise positioning of steel bars at a steel bar installation site of a high-rise building. There can be various environmental noises at the site, for example, persistent low-frequency vibrations caused by a nearby working concrete pump truck, or an instantaneous impact sound caused by a sudden falling of heavy objects.

[0063] The method of the present application first monitors these environmental noises in real time through a plurality of acoustic sensors and vibration sensors deployed at the site. When an acoustic sensor detects an instantaneous impact sound, whose amplitude rises rapidly from 2 times to 10 times the average amplitude of background noise within 0.1 seconds, the system immediately identifies it as an instantaneous environmental noise and starts the noise suppression module.

[0064] The noise suppression module adjusts the scanning frequency and data integration time of the laser radar sensor (used to obtain real-time three-dimensional shape information of the steel bar to be installed) according to the characteristics of the impact sound, such as its frequency range and duration, and performs wavelet transform-based denoising processing on the point cloud data returned by the laser radar to remove point cloud jitter and outliers caused by impact vibration. At the same time, for persistent low-frequency vibrations, the noise suppression module uses adaptive Kalman filtering to process the data of the reference steel bar position sensor (such as high-precision GNSS or total station) to eliminate the measurement drift caused by vibration.

[0065] On this basis, the system dynamically adjusts the potential energy component weight of noise compensation in the constraint potential field calculation according to the intensity and type of the detected impact sound and vibration. For example, for impact sound, the corresponding noise compensation weight will be significantly increased to impose a higher "penalty" on potential positioning errors in the potential field. In this way, even under noise interference, the generated constraint potential field can still accurately guide the steel bar to be installed to move to the candidate placement point with the smallest potential energy value, thereby ensuring the precise positioning and installation quality of the steel bar.

[0066] In the above process of generating a constraint potential field in real time, the instantaneous environmental noise mentioned is defined as follows: An impact-type noise event that changes the amplitude of the acoustic signal within a preset time window and the rate of change exceeds a preset rate threshold, and the peak amplitude after the change exceeds a preset multiple of the average amplitude of the background noise.

[0067] Specifically, the transient environmental noise can be understood as a sudden, short-term, high-energy noise event. Among them, the "preset time window" refers to a pre-set duration for observing the change of the acoustic signal, which can be set to a time range of milliseconds to seconds to capture rapidly changing noise events. The "change in acoustic signal amplitude" refers to a significant fluctuation in sound pressure or sound intensity in the environment when a noise event occurs. The "change rate exceeding the preset rate threshold" means that the amplitude change is rapid and drastic, rather than slow or gradual, and the rate threshold can be set according to the background noise characteristics of the actual construction environment. The "peak amplitude after the change exceeds the preset multiple of the average amplitude of the background noise" further limits the intensity of the transient environmental noise, ensuring that its peak value is much higher than the normal background noise level, for example, it can be set to 3 times, 5 times or higher than the average amplitude of the background noise. The "impact noise event" refers to noise caused by physical events such as mechanical impact, collision, knocking, etc., which is characterized by concentrated energy, short duration, and wide frequency spectrum.

[0068] The scheme of the present application accurately defines the transient environmental noise, so that the noise suppression module can more accurately identify and distinguish such noise. In the process of generating the constraint potential field in real time, when transient environmental noise meeting the above characteristics is detected, the noise suppression module can be started accordingly, and the sensor data acquisition parameters and filtering processing strategy can be adjusted according to the characteristics. Therefore, the influence of transient noise on sensor data can be effectively filtered out or compensated, ensuring that the processed sensor data has higher accuracy and reliability, and further making the calculation of the constraint potential field more accurate.

[0069] The step of obtaining the engineering constraint condition between the to-be-installed steel bar and the reference steel bar includes: Real-time acquisition of construction environment factor information, the construction environment factor information including temperature information and humidity information; Real-time acquisition of structure material state information, the structure material state information including concrete strength information; According to the construction environment factor information and the structure material state information, dynamically adjusting the engineering constraint condition between the to-be-installed steel bar and the reference steel bar, the engineering constraint condition between the to-be-installed steel bar and the reference steel bar including a minimum protective layer thickness and a steel bar lap length.

[0070] Specifically, real-time acquisition of construction environment factor information, such as temperature information and humidity information, refers to continuous monitoring and collection of environmental parameters of the current construction area through environmental sensors deployed on the construction site. These parameters have important influence on the physical properties of steel bars and the setting process of concrete. At the same time, real-time acquisition of structural material state information, such as concrete strength information, can be obtained through non-destructive testing methods or embedded sensors, to reflect the actual bearing capacity and maturity of the structural material. Among them, concrete strength information is the key basis for evaluating structural safety and determining the thickness of the steel bar protective layer. Based on these real-time acquisition of construction environment factor information and structural material state information, the engineering constraint conditions between the to-be-installed steel bars and the reference steel bars can be dynamically adjusted. For example, when the environmental temperature is too high or the concrete strength has not reached the design requirement, the minimum protective layer thickness or the steel bar lap length can be adjusted accordingly to ensure the safety and durability of the structure. The minimum protective layer thickness refers to the minimum distance between the outer surface of the steel bar and the surface of the concrete, which aims to protect the steel bar from corrosion and provide sufficient fireproof performance. The steel bar lap length refers to the minimum length of overlap when two steel bars are connected, which aims to ensure that the force can be effectively transmitted and avoid stress concentration.

[0071] The scheme of the present application solves the limitation that the engineering constraint conditions in the traditional method may be static preset values and cannot adapt to the dynamic changes of the construction site by real-time acquisition of construction environment factor information and structural material state information, and dynamic adjustment of engineering constraint conditions accordingly. Specifically, when the environmental temperature or humidity changes, the thermal expansion and contraction effect of the steel bar and the setting characteristics of the concrete will be affected, which in turn affects the actual position and stress state of the steel bar. By real-time acquisition of these environmental information, the minimum protective layer thickness and other constraint conditions can be adjusted in time to ensure the protection effect of the steel bar under different environments. At the same time, real-time acquisition of concrete strength information can accurately reflect the actual performance of the structural material, avoiding improper placement of steel bars when the concrete strength is insufficient, thereby ensuring the safety of the overall structure. Therefore, the dynamically adjusted engineering constraint conditions can more accurately reflect the actual construction requirements, so that the subsequent generated constraint potential field is more consistent with the actual situation on site, and the calculated optimal compromise placement point is also more reliable and safe.

[0072] In some preferred embodiments, assume that during the reinforcement installation process of a large construction project, it is necessary to place the to-be-installed reinforcement into the area of the already poured concrete. At the early stage of construction, the environmental temperature is 25°C and the humidity is 60%. At this time, the system determines the minimum cover thickness to be 30mm and the reinforcement lap length to be 500mm according to the preset parameters and real-time monitoring data. However, during the middle stage of construction, the weather suddenly changes and the environmental temperature drops to 10°C and the humidity rises to 85%. At the same time, through non-destructive testing, it is found that the early strength growth rate of the concrete in some areas is lower than expected due to the influence of low temperature. At this time, the method of the present application can obtain these construction environmental factor information (temperature 10°C, humidity 85%) and structural material state information (concrete strength lower than expected) in real time. The system will dynamically adjust the engineering constraint conditions according to these real-time data. For example, in order to cope with the influence of low temperature on the strength of concrete and the protection effect of reinforcement, the system may adjust the minimum cover thickness to 35mm to provide more adequate protection; at the same time, in order to ensure the reliability of force transmission under the condition of slow growth of concrete strength, the reinforcement lap length may be adjusted to 550mm. These adjusted engineering constraint conditions will be used in subsequent constraint potential field generation and optimal compromise placement point calculation, so as to ensure that the placement of the to-be-installed reinforcement can still meet the strict engineering specifications and safety requirements even in the dynamically changing construction environment.

[0073] The step of assigning potential energy values to each candidate placement point includes: determining the spatial coordinates of each candidate placement point in the three-dimensional digital scene; calculating the geometric fitness potential component of each candidate placement point based on the relative positional relationship between the to-be-installed reinforcement and the reference reinforcement; calculating the constraint compliance potential component of each candidate placement point based on the engineering constraint conditions between the to-be-installed reinforcement and the reference reinforcement; weighting and summing the geometric fitness potential component of each candidate placement point and the constraint compliance potential component of each candidate placement point according to the preset fitness constraint weight to obtain the comprehensive potential energy value of each candidate placement point, and taking the comprehensive potential energy value of each candidate placement point as the result of the constraint potential field assigning potential energy values to each candidate placement point.

[0074] Wherein, determining the spatial coordinates of each candidate placement point in the three-dimensional digital scene means defining the three-dimensional position (e.g. X, Y, Z coordinates) and attitude (e.g. pitch, yaw, roll angle) of each potential reinforcement placement position in the constructed digital model. These coordinate points constitute the entire discrete or continuous spatial position set that the to-be-installed reinforcement can occupy.

[0075] Further, a geometric fitness potential component of each candidate placement point is calculated based on the relative position relationship between the to-be-installed reinforcement and the reference reinforcement. The geometric fitness potential component aims to quantify the degree of geometric matching between the to-be-installed reinforcement and the surrounding reference reinforcement at a specific candidate placement point. For example, this component can be calculated according to factors such as the minimum distance between the to-be-installed reinforcement and the reference reinforcement, whether there is a collision or excessive gap, etc. When the geometric fitness is high, i.e. the space utilization is reasonable and there is no interference, the potential value is lower; on the contrary, if there is a collision or unreasonable gap, the potential value is higher to be punished.

[0076] In addition, a constraint compliance potential component of each candidate placement point is calculated based on the engineering constraint conditions between the to-be-installed reinforcement and the reference reinforcement. The engineering constraint conditions are the specifications and standards that must be followed in guiding the placement of reinforcement, such as minimum cover thickness, reinforcement lap length, etc. The constraint compliance potential component is used to assess whether the to-be-installed reinforcement meets all relevant engineering constraints at a specific candidate placement point. If any engineering constraint is violated, this component will give a higher potential value to reflect the degree of non-compliance with the specification. For example, when the calculated cover thickness is less than the specification requirement, the potential value will increase significantly.

[0077] Finally, the geometric fitness potential component of each candidate placement point and the constraint compliance potential component of each candidate placement point are weighted and summed according to the pre-set fitness constraint weight to obtain the comprehensive potential value of each candidate placement point. The fitness constraint weight is an adjustable parameter used to balance the importance of geometric fitness and engineering constraint in the overall evaluation. For example, according to the specific requirements or safety priority of the project, a higher weight can be given to the engineering constraint. The resulting comprehensive potential value is the result of the constraint potential field assigning potential values to each candidate placement point, which comprehensively reflects the overall performance of each candidate placement point in terms of geometric feasibility and engineering compliance.

[0078] The scheme of the present application can comprehensively and quantitatively evaluate the feasibility of each candidate placement point by refining the calculation of potential values into geometric fitness potential components and constraint compliance potential components, and performing weighted summation. Among them, the geometric fitness potential component directly reflects the degree of spatial matching between the to-be-installed reinforcement and the reference reinforcement, ensuring physical placeability; while the constraint compliance potential component converts engineering specifications and safety requirements into quantitative penalty terms, ensuring the compliance of the placement scheme. Through this componentization and weighted fusion, the final comprehensive potential value can more accurately reflect the installation difficulty, risk and compliance with engineering requirements of the to-be-installed reinforcement at a specific position, thereby providing a more reliable basis for the subsequent selection of the best compromise placement point.

[0079] By the technical solution, the limitation of insufficient comprehensive or accurate potential value evaluation in the traditional method can be overcome. Specifically, by introducing the geometric fitness potential component and the constraint compliance potential component, and performing weighted fusion, the calculated potential value can more accurately and comprehensively reflect the actual placement conditions and engineering requirements of the to-be-installed reinforcement in the target installation area. This not only improves the authenticity and reliability of virtual assembly simulation, but also significantly improves the accuracy and efficiency of the calculation of the best compromise placement point, thereby effectively reducing the rework rate and potential risks of on-site construction, ensuring the accurate positioning and installation quality of the longitudinal reinforcement of the structure.

[0080] In some preferred embodiments, assuming that in a certain reinforced concrete structure construction, a to-be-installed reinforcement needs to be placed between existing reference reinforcements. First, the system generates a plurality of candidate placement points in the three-dimensional digital scene and determines their spatial coordinates. For each candidate placement point, the system calculates its geometric fitness potential component, for example, by analyzing the minimum gap, overlapping area, etc. between the to-be-installed reinforcement and the surrounding reference reinforcements, if the gap is too small or there is overlap, a higher potential value is given. At the same time, the system dynamically obtains and adjusts the minimum protective layer thickness and reinforcement lap length, etc. engineering constraint conditions according to the current temperature information, humidity information, etc. construction environment factors and concrete strength information, etc. structure material state information. Based on these engineering constraint conditions, the constraint compliance potential component is calculated, for example, if a certain candidate placement point will cause the protective layer thickness to be lower than the specification requirement, a significant penalty potential will be applied. Finally, according to the pre-set fitness constraint weight (for example, the geometric fitness weight is 0.6 and the constraint compliance weight is 0.4), the two components are weighted and summed to obtain the comprehensive potential value of the candidate placement point. By repeating this process for all candidate placement points, a potential value distribution map is formed, where the point with the lowest potential value is the best compromise placement point, indicating that this position is the most geometrically fit and most compliant to engineering constraints.

[0081] The application further proposes that after obtaining the collision-free guide path, the smoothness and efficiency of the collision-free guide path are evaluated, and the generation parameters of the collision-free guide path are adjusted according to the evaluation results.

[0082] Specifically, after generating the collision-free guide path, it is necessary to evaluate its quality. "Evaluating the smoothness and efficiency of the collision-free guide path" refers to quantitatively analyzing the generated path to determine its feasibility and optimization degree in actual operation. Among them, the smoothness can be evaluated according to indicators such as the rate of change of curvature, acceleration or jerk of each point on the path, for example, by calculating the rate of change of curvature of each point on the path, if the change is violent, the smoothness is low. Efficiency can be evaluated according to indicators such as total length, estimated completion time, energy consumption, or required kinematics / dynamics resources of the path, for example, the shorter the path, the less the completion time, the higher the efficiency. These evaluations can be made through pre-set mathematical models or simulation tools.

[0083] "Adjusting the generation parameters of the collision-free guide path according to the evaluation results" refers to dynamically adjusting the internal parameters of the algorithm or model used to generate the collision-free guide path according to the evaluation results of the smoothness and efficiency. These generation parameters can include but are not limited to: step size, iteration number, optimization target weight (such as smoothness weight, path length weight) in path planning algorithm, parameters of smoothing algorithm (such as order of spline curve, number of control points), and tolerance of kinematics and dynamics constraints of automated hoisting equipment, etc. For example, if the evaluation result shows that the path smoothness is insufficient, the weight of the smoothness optimization in the path generation algorithm can be increased, or the continuity adjustment parameter based on curvature constraint in path smoothing optimization can be adjusted; if the efficiency is low, the related parameters in path length optimization or time parameterization can be adjusted.

[0084] The scheme of the present application introduces an evaluation and adjustment mechanism after generating the collision-free guide path, so that the path planning process can form a closed-loop feedback system. Specifically, after generating the collision-free guide path, its smoothness and efficiency are quantitatively evaluated to obtain objective feedback on the quality of the path. Based on the evaluation results, the system can intelligently identify the shortcomings of the current path planning parameters and adjust these parameters accordingly. For example, when the path smoothness is poor, the optimization weight related to smoothness or the smoothing algorithm parameter can be increased; when the path efficiency is low, the optimization parameters related to path length or time can be adjusted. This iterative optimization process enables the subsequently generated path to better meet the demand for smooth and efficient operation in actual construction, thereby ensuring that the automated hoisting equipment can move the steel reinforcement to be installed to the target position in a more optimized manner.

[0085] In some preferred embodiments, assuming that in a steel bar hoisting task, the automated hoisting device moves according to the initially generated collision-free guide path, but the system monitors that there is a large acceleration change of the end effector of the robotic arm at certain path points, which indicates that the smoothness of the path is insufficient. At this time, the system will calculate that the smoothness score of the current path is lower than the safety threshold according to the preset smoothness evaluation model. Based on this evaluation result, the system will automatically adjust the smoothness optimization weight in the path generation algorithm, for example, increase it from 0.5 to 0.8, and may also adjust the continuity adjustment parameter based on the curvature constraint in the path smoothness optimization. Subsequently, the system will regenerate a collision-free guide path using these adjusted parameters. The acceleration change of the new path will be significantly reduced after evaluation, and the smoothness score will be improved, so that the automated hoisting device can complete the precise placement of the steel bar in a more stable and efficient manner.

[0086] Reference Figure 2 The present application proposes a structural longitudinal steel bar precise positioning control system, which is designed to implement the above-mentioned structural longitudinal steel bar precise positioning control method. The system comprises: A processing module is configured to collect multi-source sensor data of the steel bar to be installed, fuse the multi-source sensor data, and evaluate the quality of the multi-source sensor data, so as to generate real-time three-dimensional shape information of the steel bar to be installed. A position information acquisition module is configured to perform sensor scanning on the target installation area to generate actual position information of the reference steel bar. A placement point calculation module is configured to perform virtual assembly simulation and check engineering constraints based on the real-time three-dimensional shape information of the steel bar to be installed and the actual position information of the reference steel bar, so as to calculate an optimal compromise placement point. A placement control module is configured to drive the automated hoisting device to place the steel bar to be installed in place according to the optimal compromise placement point, and perform compliance confirmation and correction after placement.

[0087] The processing module is configured to collect multi-source sensor data of the steel bar to be installed, fuse the multi-source sensor data, and evaluate the quality of the multi-source sensor data, so as to generate real-time three-dimensional shape information of the steel bar to be installed. Specifically, the multi-source sensor can include but is not limited to laser radar, visual sensor, inertial measurement unit, etc., for obtaining information such as geometric size, attitude, surface feature, etc. of the steel bar to be installed. Data fusion can use algorithms such as Kalman filter, extended Kalman filter or particle filter, etc. to improve the accuracy and robustness of the data. Data quality evaluation can be based on indicators such as signal-to-noise ratio, consistency, integrity, etc. of the sensor data to ensure that the generated three-dimensional shape information has high reliability.

[0088] The position information acquisition module is configured to perform sensor scanning on the target installation area to generate actual position information of the reference reinforcement. This module can adopt similar or different sensor technologies as the processing module, such as three-dimensional laser scanners or high-precision RTK-GNSS systems, to accurately obtain the spatial coordinates, directions, and geometric shapes of the installed reference reinforcement within the target installation area. Through comprehensive scanning of the target area, a digital model of the reference reinforcement can be constructed, providing accurate reference for subsequent virtual assembly.

[0089] The placement point calculation module is configured to calculate the optimal compromise placement point based on the real-time three-dimensional shape information of the reinforcement to be installed and the actual position information of the reference reinforcement, perform virtual assembly simulation and check engineering constraints. This module receives the output of the processing module and the position information acquisition module, simulates the relative position relationship between the reinforcement to be installed and the reference reinforcement in the digital environment, and considers engineering constraint conditions such as minimum protective layer thickness and reinforcement lap length. Through iterative calculation and optimization algorithms, such as potential field method or optimization search algorithm, the placement position that meets all constraint conditions and has the best comprehensive performance, i.e. the optimal compromise placement point, is found.

[0090] The placement control module is configured to drive the automated hoisting equipment to place the reinforcement to be installed in place according to the optimal compromise placement point, and perform compliance confirmation and correction after placement. This module takes the calculated optimal compromise placement point as an instruction and sends it to the automated hoisting equipment, such as an intelligent crane equipped with a high-precision mechanical arm or a cable-driven system. During the placement process, the module can monitor the actual position of the reinforcement to be installed in real time and compare it with the target position. If there is a deviation, fine-tuning correction is performed to ensure that the reinforcement is accurately placed in the predetermined position.

[0091] The above disclosed content is only the preferred feasible embodiment of the present application, and does not limit the protection scope of the present application, so any equivalent technical changes made according to the content of the present application specification and drawings are included in the protection scope of the present application, and in addition, the elements can be updated as technology develops.

Claims

1. A method for accurate positioning control of structural longitudinal reinforcement, characterized in that, The method comprises the following steps: Multi-source sensor data acquisition is performed on the to-be-installed steel bar, multi-source sensor data is fused, and the quality of the multi-source sensor data is evaluated to generate real-time three-dimensional morphological information of the to-be-installed steel bar; Sensor scanning is performed on the target installation area to generate actual position information of the reference steel bar; Based on the real-time three-dimensional morphological information of the to-be-installed steel bar and the actual position information of the reference steel bar, virtual assembly simulation is performed and engineering constraints are checked to calculate an optimal compromise placement point; wherein the step of calculating the optimal compromise placement point comprises: during the virtual assembly simulation, a three-dimensional digital scene based on the target installation area is constructed based on the real-time three-dimensional morphological information of the to-be-installed steel bar and the actual position information of the reference steel bar, a constraint potential field is generated in real time in the three-dimensional digital scene, and the constraint potential field assigns a potential energy value to each candidate placement point according to the relative positional relationship between the to-be-installed steel bar and the reference steel bar and the engineering constraint conditions; the constraint potential field is updated in real time according to the dynamic changes of the reference steel bar; the to-be-installed steel bar is guided to move to the candidate placement point with the minimum potential energy value according to the distribution of the potential energy values of the candidate placement points; when the minimum potential energy value is higher than a preset safety threshold, the potential energy component weight of the structural safety is adjusted, a high penalty weight is given to the potential energy component weight of the structural safety, an adjusted potential energy value is obtained, and the candidate placement point corresponding to the adjusted potential energy value is defined as the optimal compromise placement point; wherein the step of guiding the to-be-installed steel bar to move to the candidate placement point with the minimum potential energy value comprises: determining a potential energy gradient according to the distribution of the potential energy values of the candidate placement points; obtaining kinematic constraint information and dynamic constraint information of the automated hoisting equipment, the kinematic constraint information of the automated hoisting equipment comprising a mechanical arm end effector size and a joint limit, and the dynamic constraint information of the automated hoisting equipment comprising a maximum speed limit and a maximum acceleration limit; based on the potential energy gradient, the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment, path planning is performed to generate a collision-free guide path that satisfies the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment; the to-be-installed steel bar is moved to the candidate placement point with the minimum potential energy value along the collision-free guide path by driving the automated hoisting equipment; wherein the step of generating the collision-free guide path comprises: constructing a candidate path set based on the potential energy gradient, performing collision detection and feasibility screening on the candidate path set to obtain an effective path set; under the premise of satisfying the kinematic constraint information and the dynamic constraint information of the automated hoisting equipment, the effective path set is subjected to path smoothing optimization and joint trajectory optimization, wherein the path smoothing optimization comprises continuity adjustment based on curvature constraint, and the joint trajectory optimization comprises time parameterization based on a velocity profile; during the optimization process, the path node positions and sequences of the optimized effective path set are adjusted in real time to obtain an adjusted path set in combination with obstacle distribution information and dynamic change information of the construction site; the adjusted path set is subjected to collision detection and feasibility screening again to obtain the collision-free guide path; The to-be-installed steel bar is placed in place by driving the automated hoisting equipment according to the optimal compromise placement point, and conformity confirmation and correction after placement are performed.

2. The method of claim 1, wherein the method further comprises: The step of generating the constraint potential field in real time comprises: Real-time monitoring of environmental noise, when detecting transient or local environmental noise, starting the noise suppression module; The noise suppression module adjusts the sensor data acquisition parameters according to the environmental noise, and filters the sensor data affected by the environmental noise to generate processed sensor data; According to the type and intensity of the environmental noise, dynamically adjust the weight of the noise compensation potential energy component affected in the constraint potential field calculation, and based on the processed sensor data and the adjusted weight of the noise compensation potential energy component, generate the constraint potential field in the three-dimensional digital scene in real time.

3. The method of claim 2, wherein the method further comprises: The transient environmental noise is an impact noise event in which the amplitude of the acoustic signal changes within a preset time window and the change rate exceeds a preset rate threshold, and the peak amplitude after the change exceeds a preset multiple of the average amplitude of the background noise.

4. The method of claim 1, wherein the method further comprises: The step of obtaining the engineering constraint condition between the to-be-installed steel bar and the reference steel bar comprises: Real-time acquisition of construction environment factor information, including temperature information and humidity information; Real-time acquisition of structural material state information, including concrete strength information; According to the construction environment factor information and the structural material state information, dynamically adjust the engineering constraint condition between the to-be-installed steel bar and the reference steel bar, including the minimum protective layer thickness and the steel bar lap length.

5. The method of claim 1, wherein the method further comprises: The step of assigning potential energy values to each candidate placement point comprises: Determine the spatial coordinates of each candidate placement point in the three-dimensional digital scene; Based on the relative position relationship between the to-be-installed steel bar and the reference steel bar, calculate the geometric fitness potential energy component of each candidate placement point; Based on the engineering constraint condition between the to-be-installed steel bar and the reference steel bar, calculate the constraint compliance potential energy component of each candidate placement point; According to the preset adaptation constraint weight, the geometric fitness potential energy component of each candidate placement point and the constraint compliance potential energy component of each candidate placement point are weighted and summed to obtain the comprehensive potential energy value of each candidate placement point, and the comprehensive potential energy value of each candidate placement point is taken as the result of the constraint potential field assigning potential energy values to each candidate placement point.

6. The method of claim 1, wherein the method further comprises: After obtaining the collision-free guide path, the smoothness and efficiency of the collision-free guide path are evaluated, and the generation parameters of the collision-free guide path are adjusted according to the evaluation results.

7. A structure longitudinal steel bar accurate positioning control system applied to the structure longitudinal steel bar accurate positioning control method of claim 1, characterized in that, The system comprises: The processing module collects multi-source sensor data of the to-be-installed steel bar, fuses the multi-source sensor data and evaluates the quality of the multi-source sensor data to generate real-time three-dimensional shape information of the to-be-installed steel bar; The position information acquisition module is used for sensor scanning of the target installation area to generate actual position information of the reference steel bar; The placement point calculation module calculates the best compromise placement point based on the real-time three-dimensional shape information of the to-be-installed steel bar and the actual position information of the reference steel bar, performs virtual assembly simulation and checks the engineering constraint; The placement control module drives the automated hoisting equipment to place the to-be-installed steel bar in place according to the best compromise placement point, and performs placement compliance confirmation and correction.

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