A structure longitudinal steel bar precision positioning control system and method

Abstract

The present application relates to the technical field of steel bar positioning, and particularly relates to a structure longitudinal steel bar precise positioning control system and method, the method comprising the following steps: collecting multi-source sensor data on a 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; performing sensor scanning on a target installation area to generate actual position information of a reference steel bar; 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, performing virtual assembly simulation and checking engineering constraints to calculate an optimal compromise placement point; according to the optimal compromise placement point, driving an automated hoisting device to place the steel bar to be installed in place, and performing conformity confirmation and correction after placement. The present application is helpful to improve the precision, efficiency and reliability of steel bar installation.

Description

Technical Field

[0001] This invention relates to the technical field of rebar positioning, specifically to a precise positioning control system and method for longitudinal rebar in a structure. Background Technology

[0002] In modern building construction, especially in large structures such as high-rise buildings or bridges, the precise positioning and installation of longitudinal reinforcing bars is a crucial step in ensuring structural safety. Traditional manual methods are not only inefficient and labor-intensive, but their positioning accuracy is also easily affected by human factors, making it difficult to guarantee a high degree of consistency.

[0003] To address these issues, the industry has introduced a rebar positioning control system based on sensors and automated actuators. This system aims to achieve millimeter-level precision placement and fixing of longitudinal rebars through automation, thereby significantly improving construction quality and efficiency.

[0004] However, the actual environment makes the entire positioning task more complex, which in turn affects the accuracy of positioning. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings by proposing a precise positioning control system and method for longitudinal steel bars in a structure.

[0006] The present invention adopts the following technical solution:

[0007] A method for precise positioning and control of longitudinal reinforcement in a structure, comprising the following steps:

[0008] Multi-source sensor data is collected for the steel bars to be installed, the 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 steel bars to be installed.

[0009] Sensors scan the target installation area to generate the actual location information of the reference reinforcement.

[0010] Based on the real-time 3D morphological information of the reinforcing bars to be installed and the actual position information of the reference reinforcing bars, a virtual assembly simulation is performed to verify engineering constraints in order to calculate the optimal compromise placement point. The steps for calculating the optimal compromise placement point include: during the virtual assembly simulation, a 3D digital scene based on the target installation area is constructed based on the real-time 3D morphological information of the reinforcing bars to be installed and the actual position information of the reference reinforcing bars; a constraint potential field is generated in real-time within the 3D digital scene; the constraint potential field assigns potential energy values ​​to each candidate placement point according to the relative positional relationship between the reinforcing bars to be installed and the reference reinforcing bars and the engineering constraints; and the constraints are updated in real-time according to the dynamic changes of the reference reinforcing bars. Potential field; based on the potential energy distribution of each candidate placement point, guide the steel bar to be installed to move to the candidate placement point with the minimum potential energy value; when the minimum potential energy value is higher than the preset safety threshold, adjust the corresponding structural safety potential energy component weight, assign a high penalty weight to the structural safety potential energy component weight, obtain the adjusted potential energy value, and define the candidate placement point corresponding to the adjusted potential energy value as the optimal compromise placement point; the steps of guiding the steel bar to be installed to move to the candidate placement point with the minimum potential energy value include: determining the potential energy gradient based on the potential energy distribution of each candidate placement point; acquiring the kinematic constraint information and dynamic constraint information of the automated hoisting equipment, and automatically... The kinematic constraints of the automated hoisting equipment include the dimensions of the robotic arm's end effector and joint limits, while the dynamic constraints include maximum speed and maximum acceleration limits. Based on the potential energy gradient, the kinematic and dynamic constraints of the automated hoisting equipment, path planning is performed to generate a collision-free guiding path that satisfies both constraints. The automated hoisting equipment is then driven along this collision-free guiding path to move the rebar to be installed to the candidate placement point with the lowest potential energy value. The steps for generating the collision-free guiding path include: constructing a candidate path set based on the potential energy gradient, and... The selected path set undergoes collision detection and feasibility screening to obtain a valid path set. Under the premise of satisfying the kinematic and dynamic constraints of the automated hoisting equipment, the valid path set is then optimized for path smoothing and joint trajectory. Path smoothing optimization includes continuity adjustment based on curvature constraints, while joint trajectory optimization includes time parameterization based on velocity profiles. During the optimization process, the path node positions and sequences of the optimized valid path set are adjusted in real time, taking into account obstacle distribution and dynamic changes at the construction site, to obtain an adjusted path set. The adjusted path set is then subjected to collision detection and feasibility screening again to obtain a collision-free guiding path.

[0011] Based on the optimal compromise placement point, the automated hoisting equipment is driven to place the steel bars to be installed into position, and the conformity is confirmed and corrected after placement.

[0012] This technical solution effectively integrates multi-source information to achieve accurate 3D modeling and environmental perception of steel bars. Through virtual assembly and engineering constraint verification, it intelligently calculates the optimal placement point that takes into account both design requirements and actual site conditions. This overcomes the shortcomings of traditional methods, such as low accuracy and poor efficiency, and solves the problem that automated systems are unable to adapt to benchmark deviations and ensure structural safety in complex site environments. It significantly improves the accuracy, efficiency, and reliability of steel bar installation.

[0013] Furthermore, the steps for generating the constraint potential field in real time include:

[0014] Real-time monitoring of ambient noise; when transient or localized ambient noise is detected, the noise suppression module is activated.

[0015] The noise suppression module adjusts the sensor data acquisition parameters according to the ambient noise and filters the sensor data affected by the ambient noise to generate processed sensor data.

[0016] Based on the type and intensity of environmental noise, the weights of the potential energy components affected by noise compensation in the calculation of the constraint potential field are dynamically adjusted. Based on the processed sensor data and the adjusted weights of the potential energy components of noise compensation, the constraint potential field is generated in real time in the three-dimensional digital scene.

[0017] Furthermore, transient environmental noise is an impact-type noise event in which the amplitude of the acoustic signal changes 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.

[0018] Furthermore, the steps for obtaining the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars include:

[0019] Real-time acquisition of construction environmental factor information, including temperature and humidity information;

[0020] Real-time acquisition of structural material status information, including concrete strength information;

[0021] Based on information on construction environment factors and structural material status, the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars are dynamically adjusted. The engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars include the minimum protective layer thickness and the lap length of the reinforcing bars.

[0022] Furthermore, the step of assigning potential energy values ​​to each candidate placement point includes:

[0023] Determine the spatial coordinates of each candidate placement point in the 3D digital scene;

[0024] Based on the relative positional relationship between the reinforcing bars to be installed and the reference reinforcing bars, the geometric fit potential energy components of each candidate placement point are calculated.

[0025] Based on the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars, calculate the constraint compliance potential energy components of each candidate placement point.

[0026] The geometric fit potential energy component and the constraint compliance potential energy component of each candidate placement point are weighted and summed according to the preset fit constraint weights to obtain the comprehensive potential energy value of each candidate placement point. The comprehensive potential energy value of each candidate placement point is then used as the result of the constraint potential field assigning potential energy values ​​to each candidate placement point.

[0027] Furthermore, after obtaining the collision-free guidance path, the smoothness and efficiency of the collision-free guidance path are evaluated, and the generation parameters of the collision-free guidance path are adjusted based on the evaluation results.

[0028] This application also discloses a precise positioning control system for longitudinal steel bars in a structure, applied to the aforementioned precise positioning control method for longitudinal steel bars in a structure. The system includes:

[0029] The processing module acquires multi-source sensor data for the steel bars 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 morphological information of the steel bars to be installed.

[0030] The location information acquisition module is used to perform sensor scanning on the target installation area to generate the actual location information of the reference reinforcement.

[0031] The placement point calculation module performs virtual assembly simulation and verifies engineering constraints based on the real-time three-dimensional shape information of the steel bars to be installed and the actual position information of the reference steel bars, in order to calculate the optimal compromise placement point.

[0032] The placement control module drives the automated hoisting equipment to place the steel bars to be installed into position according to the optimal compromise placement point, and performs conformity confirmation and correction after placement.

[0033] This technical solution provides a system entity for implementing the above method. Through modular design, the responsibilities of each functional unit are clearly defined, enabling the entire system to work efficiently and collaboratively, achieving precise positioning and control of the longitudinal reinforcement of the structure, and providing reliable hardware and software support for practical engineering applications.

[0034] This application significantly improves the accuracy, efficiency, and reliability of rebar installation, effectively overcoming the shortcomings of existing technologies such as low efficiency and limited accuracy of manual operation, as well as the poor adaptability of automated systems in complex dynamic construction environments. It provides a safer and more efficient rebar positioning solution for high-rise buildings and large-scale structure construction.

[0035] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0036] Figure 1 This is a flowchart of a method for precise positioning and control of longitudinal steel bars in a structure according to the present invention.

[0037] Figure 2 This is a structural schematic diagram of a precise positioning control system for longitudinal reinforcing bars according to the present invention. Detailed Implementation

[0038] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0039] This embodiment provides a precise positioning control system and method for longitudinal steel reinforcement in structures, combined with... Figure 1 and Figure 2 As shown.

[0040] refer to Figure 1 A method for precise positioning and control of longitudinal reinforcement in a structure, comprising the following steps:

[0041] Multi-source sensor data is collected for the steel bars to be installed, the 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 steel bars to be installed.

[0042] Sensors scan the target installation area to generate the actual location information of the reference reinforcement.

[0043] Based on the real-time 3D morphological information of the reinforcing bars to be installed and the actual position information of the reference reinforcing bars, a virtual assembly simulation is performed to verify engineering constraints in order to calculate the optimal compromise placement point. The steps for calculating the optimal compromise placement point include: during the virtual assembly simulation, a 3D digital scene based on the target installation area is constructed based on the real-time 3D morphological information of the reinforcing bars to be installed and the actual position information of the reference reinforcing bars; a constraint potential field is generated in real-time within the 3D digital scene; the constraint potential field assigns potential energy values ​​to each candidate placement point according to the relative positional relationship between the reinforcing bars to be installed and the reference reinforcing bars and the engineering constraints; and the constraints are updated in real-time according to the dynamic changes of the reference reinforcing bars. Potential field; based on the potential energy distribution of each candidate placement point, guide the steel bar to be installed to move to the candidate placement point with the minimum potential energy value; when the minimum potential energy value is higher than the preset safety threshold, adjust the corresponding structural safety potential energy component weight, assign a high penalty weight to the structural safety potential energy component weight, obtain the adjusted potential energy value, and define the candidate placement point corresponding to the adjusted potential energy value as the optimal compromise placement point; the steps of guiding the steel bar to be installed to move to the candidate placement point with the minimum potential energy value include: determining the potential energy gradient based on the potential energy distribution of each candidate placement point; acquiring the kinematic constraint information and dynamic constraint information of the automated hoisting equipment, and automatically... The kinematic constraints of the automated hoisting equipment include the dimensions of the robotic arm's end effector and joint limits, while the dynamic constraints include maximum speed and maximum acceleration limits. Based on the potential energy gradient, the kinematic and dynamic constraints of the automated hoisting equipment, path planning is performed to generate a collision-free guiding path that satisfies both constraints. The automated hoisting equipment is then driven along this collision-free guiding path to move the rebar to be installed to the candidate placement point with the lowest potential energy value. The steps for generating the collision-free guiding path include: constructing a candidate path set based on the potential energy gradient, and... The selected path set undergoes collision detection and feasibility screening to obtain a valid path set. Under the premise of satisfying the kinematic and dynamic constraints of the automated hoisting equipment, the valid path set is then optimized for path smoothing and joint trajectory. Path smoothing optimization includes continuity adjustment based on curvature constraints, while joint trajectory optimization includes time parameterization based on velocity profiles. During the optimization process, the path node positions and sequences of the optimized valid path set are adjusted in real time, taking into account obstacle distribution and dynamic changes at the construction site, to obtain an adjusted path set. The adjusted path set is then subjected to collision detection and feasibility screening again to obtain a collision-free guiding path.

[0044] Based on the optimal compromise placement point, the automated hoisting equipment is driven to place the steel bars to be installed into position, and the conformity is confirmed and corrected after placement.

[0045] "Reinforcing bars to be installed" refers to longitudinal reinforcing bars that are about to be hoisted and placed in place, and their shape information needs to be accurately obtained.

[0046] "Multi-source sensor data acquisition" refers to using various types of sensors (such as lidar, vision sensors, inertial measurement units, etc.) to acquire data on the steel bars to be installed, in order to provide more comprehensive and robust information.

[0047] "Multi-source sensor data fusion" refers to integrating and processing data from different sensors to eliminate the errors and limitations that may exist in a single sensor, thereby improving the overall accuracy and reliability of the data.

[0048] "Real-time three-dimensional morphological information" refers to the precise three-dimensional geometric shape and orientation data of the steel bars to be installed at the current moment.

[0049] The “target installation area” refers to the structural area where the reinforcing bars to be installed will eventually be placed, which usually includes the pre-reserved reference reinforcing bars.

[0050] "Reference reinforcement" refers to the reinforcement that has been fixed or reserved in the target installation area, which serves as a reference for newly installed reinforcement.

[0051] "Virtual assembly simulation" refers to building a digital model in a computer to simulate the assembly process between the steel bars to be installed and the reference steel bars, so as to make predictions and optimizations before actual operation.

[0052] "Engineering constraints" refer to the structural design and construction specifications that must be met during the installation of reinforcing bars, such as the minimum protective layer thickness and the lap length of reinforcing bars.

[0053] The "optimal compromise placement point" refers to the ideal placement location calculated by an optimization algorithm, which maximizes the balance between design requirements and actual site conditions, while satisfying all engineering constraints.

[0054] "Automated hoisting equipment" refers to automated mechanical equipment used to grab, move, and place steel bars, such as cranes equipped with robotic arms or specialized robots.

[0055] "Conformity verification and correction" refers to checking the final position of the reinforcing bars after they have been placed in place, and making necessary fine adjustments based on the inspection results to ensure that they meet the accuracy requirements.

[0056] The core of the precise positioning and control method for longitudinal steel bars in this application lies in overcoming the limitations of traditional methods in complex construction environments through refined data acquisition, intelligent decision calculation, and automated precise execution.

[0057] Specifically, various methods can be employed when acquiring multi-source sensor data for the reinforcing bars to be installed. For example, a laser scanner can be used to acquire 3D point cloud data of the reinforcing bars, while a high-resolution camera is used to obtain surface texture images. Another method is to use multiple ultrasonic sensor arrays to perform non-contact measurements on the reinforcing bars, supplemented by an inertial measurement unit to acquire their attitude information. These sensor data are then fused, for example, using Kalman filtering or extended Kalman filtering algorithms to align and integrate the data from different sensors in time and space. The quality of the fused data is then assessed, for example, by calculating the uncertainty of the data or its deviation from a preset model to determine its reliability, ultimately generating real-time 3D morphological information of the reinforcing bars to be installed.

[0058] To generate the actual location information of the reference reinforcement bars through sensor scanning of the target installation area, the following methods can be used. For example, a fixed laser scanning system can be deployed to periodically scan the target installation area and obtain the three-dimensional coordinates of all reference reinforcement bars within the area. Alternatively, a mobile robot equipped with vision sensors can be used to autonomously inspect the target installation area, identifying and locating the reference reinforcement bars through image recognition and 3D reconstruction technology. After processing, the actual location information of the reference reinforcement bars can be obtained.

[0059] A high-precision digital twin environment can be constructed to calculate the optimal compromise placement point by performing virtual assembly simulation and verifying engineering constraints based on the real-time 3D morphological information of the reinforcing bars to be installed and the actual position information of the reference reinforcing bars. For example, the 3D model of the reinforcing bars to be installed and the actual position data of the reference reinforcing bars can be imported into virtual simulation software to simulate their approach and overlap. During this process, the system will verify the preset engineering constraints in real time, such as checking whether the minimum clear distance between the reinforcing bars to be installed and the reference reinforcing bars meets the code requirements and whether the lap length is sufficient. If there are cases where the constraints are not met, the system will use optimization algorithms, such as those based on the potential field method or genetic algorithm, to find an optimal placement position, i.e., the optimal compromise placement point, while satisfying all constraints.

[0060] Finally, based on the calculated optimal compromise placement point, the automated hoisting equipment is driven to place the rebar to be installed into position, and post-placement conformity verification and correction are performed. For example, the automated hoisting equipment could be a robotic arm equipped with a high-precision servo motor and force sensors. It precisely grasps and moves the rebar to be installed based on the coordinates and posture information of the optimal compromise placement point. After placement, sensors (such as laser rangefinders or vision sensors) can be used again to measure the final position of the rebar to be installed and compare it with the optimal compromise placement point for conformity verification. If a deviation exists, the system will drive the automated hoisting equipment to make fine adjustments based on the deviation until the accuracy requirements are met.

[0061] The precise positioning control method for longitudinal steel bars in this application works by constructing a closed-loop intelligent control system, which can effectively cope with the complexity and uncertainty of the construction site. This application ensures real-time, high-precision status perception of the steel bars to be installed and the reference steel bars by introducing multi-source sensor data acquisition and fusion. Even in harsh environments such as dust and changes in lighting, reliable three-dimensional morphology and position information can be obtained through data fusion and quality assessment.

[0062] Building upon this foundation, the introduction of virtual assembly simulation and engineering constraint verification enables the system to anticipate and mitigate potential structural safety hazards before actual hoisting. By simulating the assembly process in a digital scenario and verifying key engineering constraints such as minimum protective layer thickness and rebar lap length in real time, the system no longer simply pursues absolute positional precision but intelligently balances "absolute design position" and "relative lap requirements." When potential conflicts are detected, the system can calculate the "optimal compromise placement point," finding an optimal solution that satisfies structural safety specifications while adapting to actual on-site deviations.

[0063] Ultimately, the automated hoisting equipment precisely places the rebar according to this "optimal compromise placement point," and through post-placement conformity verification and correction, a feedback loop is formed to ensure the final installation quality of the rebar. This method tightly integrates perception, decision-making, and execution, transforming the entire rebar positioning process from passive adaptation to proactive optimization, significantly improving the level of construction intelligence and structural safety.

[0064] Specifically, constructing a 3D digital scene based on the target installation area refers to creating a virtual 3D spatial model in a computer that corresponds to the actual construction environment, using the real-time 3D morphological information of the rebar to be installed and the actual position information of the reference rebar. This 3D digital scene can accurately reflect the geometry, size, and relative position of the rebar to be installed and the reference rebar in space. Real-time generation of the constraint potential field can be understood as calculating a potential energy value for each possible placement location of the rebar to be installed (i.e., candidate placement point) in the aforementioned 3D digital scene. This potential energy value comprehensively reflects the "quality" of the placement point in terms of geometric fit, compliance with engineering constraints, and structural safety. The lower the potential energy value, the more ideal the placement point. The generation of the constraint potential field is dynamic, capable of real-time calculation and updating based on the relative positional relationship between the rebar to be installed and the reference rebar, as well as preset engineering constraints (such as minimum protective layer thickness, rebar lap length, etc.). In practical applications, the constraint potential field is updated in real-time according to the dynamic changes of the reference rebar to ensure the real-time performance and accuracy of the placement point calculation. For example, during construction, the reference reinforcement may experience slight displacement or deformation due to external disturbances (such as wind, vibration, or adjacent construction activities), or thermal expansion and contraction due to temperature changes. These dynamic changes are captured by sensors in real time and immediately fed back into the 3D digital scene, triggering the recalculation and updating of the constraint potential field to reflect the latest actual situation. Furthermore, based on the potential energy distribution of each candidate placement point, the reinforcement to be installed is guided to the candidate placement point with the lowest potential energy value. The aim is to find one or a group of placement points with the lowest potential energy values ​​in the constraint potential field through an optimization algorithm; these points represent the most ideal placement position under the current constraints. Automated hoisting equipment will move precisely according to this guidance information. Further, when the minimum potential energy value is higher than a preset safety threshold, the corresponding structural safety potential energy component weight is adjusted, assigning a high penalty weight to the structural safety potential energy component weight to obtain the adjusted potential energy value. The candidate placement point corresponding to the adjusted potential energy value is defined as the optimal compromise placement point. This is a key optimization mechanism aimed at ensuring that structural safety is always the highest priority. The preset safety threshold represents the minimum acceptable safety standard. If the calculated optimal placement point (i.e., the point with the lowest potential energy) still fails to meet this safety standard, the system will proactively increase the weight of the structural safety-related potential energy components, making them have a greater impact on the total potential energy value, thereby "punishing" those placement points that perform poorly in terms of structural safety. In this way, even when the ideal placement point is unattainable, the system can forcibly guide the automated hoisting equipment to choose a "compromise" placement point that performs best in terms of structural safety, even if that point may be slightly inferior in terms of geometric fit.

[0065] This application's solution effectively addresses the problem of calculating the optimal compromise placement point that satisfies both geometric fit and structural safety in complex and dynamic construction environments by introducing the concept of a constraint potential field and combining it with a real-time update mechanism and a dynamic weight adjustment strategy. Specifically, the constraint potential field quantifies the relative positional relationship between the rebar to be installed and the reference rebar, as well as the engineering constraints, into potential energy values, allowing the quality of the placement point to be intuitively reflected. Real-time updates to the constraint potential field ensure that the system can adapt to the dynamic changes of the reference rebar, avoiding placement deviations caused by data lag. More importantly, when the initially calculated minimum potential energy value fails to reach the preset safety threshold, by assigning a high penalty weight to the potential energy component weighting for structural safety, the system can forcibly prioritize structural safety, even if this means making a certain "compromise" in geometric fit. The ultimately selected optimal compromise placement point will necessarily meet structural safety requirements. It is precisely because of this dynamic adaptation and safety-first mechanism that this application provides a more robust and safer method for precise rebar positioning control in complex and ever-changing environments.

[0066] In some preferred embodiments, it is assumed that during the hoisting of a steel cage in a high-rise building, a steel rebar to be installed needs to be placed in a predetermined position. Real-time three-dimensional morphological information of the steel rebar to be installed is obtained through multi-source sensor data acquisition, while sensor scanning of the target installation area obtains the actual position information of the reference rebar. Based on this information, the system constructs a three-dimensional digital scene and generates a constraint potential field in real time. In the initial stage, the constraint potential field assigns potential energy values ​​to each candidate placement point according to geometric fit (e.g., the alignment degree between the steel rebar to be installed and the reference rebar) and engineering constraints (e.g., minimum protective layer thickness, rebar 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 light winds at the construction site or slight settlement of the foundation, the reference rebar undergoes a small dynamic displacement. The system monitors this change in real time and immediately updates the constraint potential field. At this point, the candidate placement point with the minimum potential energy value may no longer be the optimal choice due to the displacement of the reference rebar, and may even lead to the minimum protective layer thickness not meeting the requirements, thereby increasing its potential energy value (especially the potential energy component related to structural safety). If the calculated minimum potential energy value is still higher than the preset safety threshold (e.g., indicating a severely insufficient minimum protective layer thickness), the system will immediately activate the safety assurance mechanism. It will adjust the weights of the corresponding structural safety potential energy components, assigning them a high penalty weight. This means that even if a placement point appears geometrically perfect, if it has structural safety defects, 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 a new candidate placement point with the lowest adjusted potential energy value. This new placement point may not be geometrically "perfect," but it is sufficiently guaranteed in terms of structural safety and is thus defined as the optimal compromise placement point. The automated hoisting equipment will then precisely place the equipment based on this new optimal compromise placement point, ensuring the quality of the rebar installation and structural safety.

[0067] Specifically, determining the potential energy gradient based on the potential energy distribution of each candidate placement point involves analyzing the potential energy values ​​corresponding to each candidate placement point in the 3D digital scene, calculating the direction and magnitude of the fastest change in potential energy value, and thus forming a "force field" or "trend" to guide the movement of the steel reinforcement to be installed. This potential energy gradient can be regarded as the direction of the driving force guiding the steel reinforcement to be installed towards the region of minimum potential energy.

[0068] The acquisition of kinematic and dynamic constraint information for automated hoisting equipment aims to ensure that the planned path is actually executable by the equipment. Kinematic constraints can be understood as the geometric limitations and range of motion of the equipment in space. For example, the size of the end effector of a robotic arm refers to the physical size of the component that the hoisting equipment uses to grip the steel bars to be installed, which affects its maneuverability in confined spaces; joint limits refer to the maximum and minimum rotational or extension angles of each joint of the robotic arm, restricting the arm's posture and reachable space. Dynamic constraints involve the speed and acceleration limitations of the equipment during movement. For example, the maximum speed limit refers to the maximum permissible speed of each component or the entire equipment, and the maximum acceleration limit refers to the maximum acceleration the equipment can withstand during startup, shutdown, or change of direction. These constraints directly affect the time efficiency and smoothness of path planning.

[0069] In practical applications, path planning based on potential energy gradients and the kinematic and dynamic constraints of automated hoisting equipment refers to using this information to calculate an optimal path from the current position of the rebar to be installed to the candidate placement point with the minimum potential energy value. This path must not only follow the guiding direction of the potential energy gradient but also strictly meet the kinematic and dynamic constraints of the automated hoisting equipment. For example, it must ensure that the robotic arm does not exceed its joint limits, and that its speed and acceleration do not exceed their maximum limits. Simultaneously, path planning must also consider avoiding collisions with other structures or obstacles in the 3D digital scene, thereby generating a collision-free guided path. This collision-free guided path is the sequence of instructions used by the automated hoisting equipment to actually perform the movement operation.

[0070] Therefore, driving an automated hoisting device along a collision-free guide path to move the steel bar to be installed to the candidate placement point with the minimum potential energy value means converting the planned collision-free guide path into control commands that the automated hoisting device can recognize and execute. For example, a series of joint angle, speed, or end effector pose commands, enabling the automated hoisting device to accurately move the steel bar to be installed along the path until it reaches the predetermined optimal compromise placement point.

[0071] This application's solution effectively addresses the problems of path infeasibility, inefficiency, or collision risks that may arise when guidance is based solely on potential energy distribution by introducing potential energy gradients, kinematic constraints, and dynamic constraints of the automated hoisting equipment, and then performing path planning. Specifically, the potential energy gradient provides clear directional guidance for the movement of the rebar to be installed, ensuring it approaches the region with the minimum potential energy. Simultaneously, by acquiring and integrating the kinematic and dynamic constraints of the automated hoisting equipment, such as the size of the robotic arm's end effector, joint limits, maximum speed limits, and maximum acceleration limits, the path planning algorithm generates a path that perfectly matches the actual operational capabilities of the equipment. Because these physical constraints and potential obstacle information are fully considered during path planning, a collision-free guidance path is generated, preventing interference between the equipment and the environment or reference rebar during movement, ensuring the safety and smoothness of the hoisting operation. Finally, the automated hoisting equipment, driven along this collision-free guidance path, can accurately, efficiently, and safely move the rebar to the optimal compromise placement point.

[0072] In some preferred embodiments, during the process of guiding the rebar to be installed to the candidate placement point with the lowest potential energy value, the system first calculates the potential energy gradient in real time using numerical differentiation or gradient descent algorithms based on the pre-calculated potential energy distribution of each candidate placement point in the 3D digital scene. This gradient indicates the direction in which the potential energy decreases the fastest. For example, if the potential energy value decreases sharply in a certain direction, the gradient value in that direction will be larger, indicating that this direction is the preferred direction for movement.

[0073] Simultaneously, the kinematic and dynamic constraints of the automated lifting equipment are pre-loaded into the system. For example, for a multi-joint robotic arm, its kinematic constraints might include the rotational angle range of each joint (joint limits), the length of each link in the robotic arm, and the geometry and dimensions of the end effector. The dynamic constraints might include the maximum rotational speed and maximum acceleration of each joint, as well as the maximum linear and angular velocities of the robotic arm as a whole. This information is typically obtained through the equipment's CAD model, manufacturer's specifications, or on-site calibration.

[0074] Based on this information, the path planning module can employ algorithms such as fast random tree algorithms, probabilistic route graph algorithms, or A* search algorithms, combined with the collision detection module, to search for a collision-free path in the 3D digital scene from the current position of the rebar to be installed to the target placement point. During the path search process, the algorithm continuously checks whether the generated path segments meet the kinematic and dynamic constraints of the automated hoisting equipment. For example, it ensures that every point on the path is within the reachable workspace of the robotic arm, and that the velocity and acceleration variations between adjacent path points do not exceed the equipment's limits. If a potential collision or constraint violation is detected, the algorithm will replan the path.

[0075] Once a collision-free guide path satisfying all constraints is generated, it is converted into a series of discrete path points or joint trajectories. This trajectory data is then sent to the controller of the automated lifting equipment. Based on these instructions, the controller drives the various joints of the robotic arm to move precisely along the planned trajectory, enabling the rebar to be installed to move smoothly and safely to the candidate placement point with the lowest potential energy, thus achieving precise positioning.

[0076] Specifically, constructing a candidate path set based on potential energy gradients means that the system, according to the potential energy distribution between the rebar to be installed and the reference rebar, uses path search algorithms such as fast random tree algorithms, probabilistic route graph algorithms, or A* algorithms 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 a 3D digital scene. These paths constitute a preliminary candidate path set. Collision detection and feasibility screening of the candidate path set aim to eliminate paths that collide with known obstacles in the environment, as well as paths that do not conform to the kinematic constraints (e.g., end effector size, joint limits) and dynamic constraints (e.g., maximum speed limits, maximum acceleration limits) of the automated hoisting equipment, thereby obtaining a preliminary set of feasible and effective paths.

[0077] Furthermore, path smoothing and joint trajectory optimization are performed on the effective path set to improve path execution efficiency and equipment motion stability. Path smoothing optimization includes continuity adjustment based on curvature constraints, specifically employing methods such as spline interpolation and Bézier curve fitting to make the path geometrically smoother, avoiding sharp turns or convoluted path segments, thereby reducing impact and vibration during equipment movement and improving placement accuracy. Joint trajectory optimization includes time parameterization based on velocity profiles. Specifically, based on the dynamic characteristics of the automated lifting equipment, appropriate velocity, acceleration, and jerk curves are planned for each joint of the robotic arm to ensure coordinated movement of all joints, avoiding unnecessary pauses or jitters, thereby improving the efficiency and smoothness of lifting operations.

[0078] Furthermore, during the optimization process, the system combines information on obstacle distribution and dynamic changes at the construction site to adjust the positions and order of path nodes in the optimized effective path set in real time, resulting in an adjusted path set. This means the system can continuously monitor the construction site environment, for example, by acquiring real-time obstacle data through additional sensors (such as LiDAR and visual sensors). When a new obstacle is detected or the position of an existing obstacle changes, the system can respond quickly and dynamically modify the nodes of the planned path, for example, through local path replanning or obstacle avoidance algorithms, so that the adjusted path can bypass or avoid these dynamic obstacles. The adjusted path set is then subjected to collision detection and feasibility screening again to ensure that the path after real-time adjustment is still collision-free and feasible, in order to cope with the complexity and uncertainty of the construction site, and ultimately obtain a safe and reliable collision-free guidance path.

[0079] The proposed solution first constructs a candidate path set based on potential energy gradients and performs preliminary collision detection and feasibility screening to ensure the basic usability of the paths. Furthermore, by performing path smoothing and joint trajectory optimization on the effective path set, particularly by introducing continuity adjustment based on curvature constraints and time parameterization based on velocity profiles, the generated guiding paths are not only collision-free but also possess excellent smoothness and executability. This avoids sudden stops, sharp turns, or vibrations of the equipment during movement, thereby improving the stability and accuracy of the hoisting process. In addition, the optimization process incorporates real-time information on obstacle distribution and dynamic changes at the construction site, adjusting path nodes to allow the system to dynamically adapt to changes in the construction environment. Even if new obstacles appear or existing obstacles move, the path can be adjusted promptly to ensure the safety of the hoisting process, effectively solving the problem of insufficient adaptability of traditional path planning in dynamic and complex environments.

[0080] In some preferred embodiments, assuming an automated hoisting device is moving a rebar to a predetermined position at a construction site, the system first generates a series of potential candidate paths based on the potential energy gradient between the rebar to be installed and the reference rebar. These paths undergo preliminary collision detection to eliminate obviously infeasible paths, resulting in a set of valid paths. Subsequently, to ensure a smooth and efficient hoisting process, the system refines these valid paths. For example, by applying algorithms such as Bézier curves or spline interpolation, the paths are smoothed to ensure that the speed and acceleration changes of the robotic arm's end effector are gradual during movement, avoiding abrupt acceleration, deceleration, or directional changes. This can be achieved by adjusting the curvature constraints of the path. Simultaneously, the motion trajectories of each joint of the robotic arm are optimized. For example, by using time parameterization techniques, a suitable velocity profile is assigned to each joint to ensure coordinated movement of all joints, achieving optimal motion efficiency and accuracy. During the hoisting process, if temporary obstacles (such as moving construction vehicles or personnel) enter the preset path area, the system will acquire information about these dynamic obstacles in real time and immediately adjust the positions and sequences of the optimized path nodes to generate a new, collision-free guide path. For example, the system may plan a detour around the obstacle or pause briefly if necessary, waiting for the obstacle to be removed before continuing. Finally, the system will perform collision detection and feasibility screening on the adjusted path again to ensure that it remains safe and feasible in the new environment, and use this as the final collision-free guide path to guide the automated hoisting equipment to accurately place the rebar.

[0081] This application further proposes steps for real-time generation of constrained potential fields, including:

[0082] Real-time monitoring of ambient noise; when transient or localized ambient noise is detected, the noise suppression module is activated.

[0083] The noise suppression module adjusts the sensor data acquisition parameters according to the ambient noise and filters the sensor data affected by the ambient noise to generate processed sensor data.

[0084] Based on the type and intensity of environmental noise, the weights of the potential energy components affected by noise compensation in the calculation of the constraint potential field are dynamically adjusted. Based on the processed sensor data and the adjusted weights of the potential energy components of noise compensation, the constraint potential field is generated in real time in the three-dimensional digital scene.

[0085] Specifically, real-time environmental noise monitoring refers to continuously acquiring noise signals in the environment through acoustic sensors, vibration sensors, or other environmental monitoring equipment deployed at the construction site. Environmental noise can be understood as any non-target signal that may interfere with sensor data acquisition, such as the operating noise of construction machinery, sounds generated by personnel activities, and vibrations caused by wind. When transient or localized environmental noise is detected, such as when the acoustic signal amplitude changes drastically within a preset time window and exceeds a preset threshold, or when the vibration intensity in a localized area abnormally increases, the system will automatically activate the noise suppression module.

[0086] The noise suppression module is configured to intelligently adjust sensor data acquisition parameters based on the characteristics of the monitored ambient noise. For example, for optical sensors, exposure time or gain can be adjusted; for acoustic sensors, sampling frequency or sensitivity can be adjusted. Simultaneously, the noise suppression module also filters sensor data affected by ambient noise. This filtering can employ various techniques, such as Kalman filtering, wavelet denoising, median filtering, or adaptive filtering, with the aim of effectively separating and removing noise components from the raw sensor data, thereby generating cleaner and more accurate processed sensor data.

[0087] Furthermore, the weights of the potential energy components affected by noise compensation in the constraint potential field calculation are dynamically adjusted according to the type and intensity of environmental noise. For example, when high-intensity impact noise is detected, the weights of the potential energy components related to noise compensation may be significantly increased to ensure that the impact of noise on the potential field calculation is fully considered and compensated. This noise-compensated potential energy component aims to quantify the potential impact of noise on the positioning accuracy of the rebar and incorporate it into the potential field calculation. Based on these processed sensor data and the adjusted potential energy component weights for noise compensation, a more accurate and robust constraint potential field is generated in real time within the 3D digital scene.

[0088] This application's solution effectively addresses the problem of traditional methods being susceptible to noise interference when generating constraint potential fields in complex construction environments 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. This module adjusts sensor data acquisition parameters and filters affected data to ensure higher purity and accuracy of the sensor data input into the potential field calculation. Simultaneously, the potential energy component weights for noise compensation are dynamically adjusted according to the type and intensity of the noise, enabling the constraint potential field to more accurately reflect the actual engineering constraints and reinforcement position relationships, maintaining its effectiveness even in noisy environments. It is precisely this effective management and compensation of noise that makes the generated constraint potential field more stable and reliable, providing a solid foundation for subsequent calculations of the optimal compromise placement point.

[0089] In some preferred embodiments, it is assumed that automated hoisting equipment is precisely positioning the reinforcing bars at a rebar installation site in a high-rise building. Various environmental noises may be present at the site, such as continuous low-frequency vibrations from a nearby concrete pump truck or instantaneous impact noise from a sudden falling heavy object.

[0090] The method of this application first monitors these environmental noises in real time using multiple acoustic and vibration sensors deployed on-site. When the acoustic sensors detect a transient impact sound whose amplitude rapidly increases from twice the average amplitude of the background noise to ten times within 0.1 seconds, the system immediately identifies it as transient environmental noise and activates the noise suppression module.

[0091] The noise suppression module adjusts the scanning frequency and data integration time of the lidar sensor (used to acquire real-time three-dimensional morphological information of the rebar to be installed) based on the characteristics of the impact sound, such as its frequency range and duration. It also performs wavelet transform-based denoising on the point cloud data returned by the lidar to remove point cloud jitter and outliers caused by impact vibration. Simultaneously, for continuous low-frequency vibrations, the noise suppression module uses adaptive Kalman filtering to process the data from the reference rebar position sensor (e.g., a high-precision GNSS or total station) to eliminate measurement drift caused by vibration.

[0092] Based on this, 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, its corresponding noise compensation weight is 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 bars to be installed to the candidate placement point with the minimum potential energy value, thereby ensuring the accurate positioning and installation quality of the steel bars.

[0093] The instantaneous environmental noise mentioned in the above real-time generation of the constrained potential field is specifically defined as follows:

[0094] Transient environmental noise is an impact-type noise event in which the amplitude of the acoustic signal changes 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.

[0095] Specifically, transient environmental noise can be understood as a sudden, short-duration noise event with high energy. The "preset time window" refers to a pre-defined duration for observing changes in the acoustic signal, such as a time range from milliseconds to seconds, to capture rapidly changing noise events. "A change in acoustic signal amplitude" means that the sound pressure or intensity in the environment fluctuates significantly when the noise event occurs. "The rate of change exceeds a preset rate threshold" means that this amplitude change is rapid and drastic, rather than slow or gradual; this rate threshold can be set according to the background noise characteristics of the actual construction environment. "The peak amplitude after the change exceeds a 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. "Impact noise events" refer to noise caused by physical events such as mechanical impacts, collisions, and knocks, characterized by concentrated energy, short duration, and a broad spectrum.

[0096] This application's solution, by precisely defining transient environmental noise, enables the noise suppression module to more accurately identify and distinguish such noise. During the real-time generation of the constraint potential field, when transient environmental noise matching the above characteristics is detected, the noise suppression module can be activated specifically, and the sensor data acquisition parameters and filtering strategies can be adjusted according to its characteristics. This effectively filters out or compensates for the impact of transient noise on sensor data, ensuring higher accuracy and reliability of the processed sensor data, thereby making the calculation of the constraint potential field more precise.

[0097] The steps to obtain the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars include:

[0098] Real-time acquisition of construction environmental factor information, including temperature and humidity information;

[0099] Real-time acquisition of structural material status information, including concrete strength information;

[0100] Based on information on construction environment factors and structural material status, the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars are dynamically adjusted. The engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars include the minimum protective layer thickness and the lap length of the reinforcing bars.

[0101] Specifically, real-time acquisition of construction environment information, such as temperature and humidity, refers to the continuous monitoring and collection of environmental parameters in the current construction area through environmental sensors deployed at the construction site. These parameters have a significant impact on the physical properties of the reinforcing steel and the solidification process of the concrete. Simultaneously, real-time acquisition of structural material status information, such as concrete strength, can be obtained through non-destructive testing methods or embedded sensors to reflect the actual load-bearing capacity and maturity of the structural materials. Concrete strength information is a key basis for assessing structural safety and determining the thickness of the reinforcing steel cover. Based on this real-time acquisition of construction environment information and structural material status information, the engineering constraints between the reinforcing steel to be installed and the reference reinforcing steel can be dynamically adjusted. For example, when the ambient temperature is too high or the concrete strength has not yet reached the design requirements, the minimum cover thickness or the lap length of the reinforcing steel can be adjusted accordingly to ensure the safety and durability of the structure. The minimum cover thickness refers to the minimum distance between the outer surface of the reinforcing steel and the concrete surface, its purpose being to protect the reinforcing steel from corrosion and provide sufficient fire resistance. The lap length of the reinforcing steel refers to the minimum overlap length between two reinforcing steel bars when connected, its purpose being to ensure effective force transfer and avoid stress concentration.

[0102] This application's solution overcomes the limitations of traditional methods where engineering constraints are often static preset values ​​and cannot adapt to dynamic changes at the construction site. Specifically, changes in ambient temperature or humidity affect the thermal expansion and contraction of reinforcing steel and the solidification characteristics of concrete, thus influencing the actual position and stress state of the steel. Real-time acquisition of this environmental information allows for timely adjustment of constraints such as the minimum protective layer thickness, ensuring effective protection of the reinforcing steel under different environments. Simultaneously, real-time acquisition of concrete strength information accurately reflects the actual performance of structural materials, preventing improper steel placement when concrete strength is insufficient, thereby ensuring the overall structural safety. Therefore, dynamically adjusted engineering constraints more accurately reflect actual construction needs, making the subsequently generated constraint potential field more consistent with the actual site conditions, resulting in more reliable and safer calculated optimal compromise placement points.

[0103] In some preferred embodiments, assuming that during the rebar installation process of a large construction project, the rebar to be installed needs to be placed in an area where some concrete has already been poured. At the initial stage of construction, the ambient temperature is 25°C and the humidity is 60%. At this time, the system, based on preset parameters and real-time monitoring data, determines that the minimum protective layer thickness is 30mm and the rebar lap length is 500mm. However, during the middle stage of construction, the weather changes abruptly, with the ambient temperature plummeting to 10°C and the humidity rising to 85%. Simultaneously, non-destructive testing reveals that the early strength growth rate of concrete in some areas is lower than expected due to the low temperature. At this point, the method of this application acquires these construction environmental factors (temperature 10°C, humidity 85%) and structural material status information (concrete strength lower than expected) in real time. The system dynamically adjusts the engineering constraints based on this real-time data. For example, to address the impact of low temperature on concrete strength and rebar protection, the system may adjust the minimum protective layer thickness to 35mm to provide more adequate protection; simultaneously, to ensure the reliability of force transmission under slow concrete strength growth, the rebar lap length may be adjusted to 550mm. These adjusted engineering constraints will be used in subsequent constraint potential field generation and optimal compromise placement point calculations to ensure that the placement of the reinforcing bars to be installed still meets strict engineering specifications and safety requirements, even in dynamically changing construction environments.

[0104] The steps for assigning potential energy values ​​to each candidate placement point include:

[0105] Determine the spatial coordinates of each candidate placement point in the 3D digital scene;

[0106] Based on the relative positional relationship between the reinforcing bars to be installed and the reference reinforcing bars, the geometric fit potential energy components of each candidate placement point are calculated.

[0107] Based on the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars, calculate the constraint compliance potential energy components of each candidate placement point.

[0108] The geometric fit potential energy component and the constraint compliance potential energy component of each candidate placement point are weighted and summed according to the preset fit constraint weights to obtain the comprehensive potential energy value of each candidate placement point. The comprehensive potential energy value of each candidate placement point is then used as the result of the constraint potential field assigning potential energy values ​​to each candidate placement point.

[0109] In this context, determining the spatial coordinates of each candidate placement point in a 3D digital scene refers to defining the 3D position (e.g., X, Y, Z coordinates) and orientation (e.g., pitch, yaw, roll angles) of each potential rebar placement location in the constructed digital model. These coordinate points constitute the set of all discrete or continuous spatial positions that the rebar to be installed may occupy.

[0110] Furthermore, based on the relative positional relationship between the reinforcing bar to be installed and the reference reinforcing bar, the geometric fit potential energy component of each candidate placement point is calculated. The geometric fit potential energy component aims to quantify the degree of geometric matching between the reinforcing bar to be installed and the surrounding reference reinforcing bars at a specific candidate placement point. For example, this component can be calculated based on factors such as the minimum distance between the reinforcing bar to be installed and the reference reinforcing bars, the presence of collisions or excessive gaps, etc. A lower potential energy value indicates a high degree of geometric matching, meaning reasonable space utilization and no interference; conversely, a higher potential energy value indicates a penalty if there are collisions or unreasonable gaps.

[0111] Furthermore, based on the engineering constraints between the reinforcing bars to be installed and the reference reinforcing bars, the constraint compliance potential energy component is calculated for each candidate placement point. Engineering constraints are the specifications and standards that must be followed when placing reinforcing bars, such as minimum cover thickness and lap length. The constraint compliance potential energy component is used to assess whether the reinforcing bars to be installed meet all relevant engineering constraints at a specific candidate placement point. If any engineering constraint is violated, this component will assign a higher potential energy value to reflect the degree of non-compliance with the specifications. For example, when the calculated cover thickness is less than the specification requirement, the potential energy value will increase significantly.

[0112] Finally, the geometric fit potential energy component and the constraint compliance potential energy component of each candidate placement point are weighted and summed according to the preset fit constraint weights to obtain the comprehensive potential energy value of each candidate placement point. The fit constraint weights are adjustable parameters used to balance the importance of geometric fit and engineering constraints in the overall evaluation. For example, engineering constraints can be assigned higher weights based on the specific requirements of the project or safety priorities. The obtained comprehensive potential energy value is used as the result of assigning potential energy values ​​to each candidate placement point by the constraint potential field. This comprehensive potential energy value comprehensively reflects the overall performance of each candidate placement point in terms of geometric feasibility and engineering compliance.

[0113] This application's solution refines the calculation of potential energy into geometric fit potential energy components and constraint compliance potential energy components, and then performs a weighted summation, enabling a comprehensive and quantitative assessment of the feasibility of each candidate placement point. The geometric fit potential energy component directly reflects the spatial matching degree between the rebar to be installed and the reference rebar, ensuring physical placement feasibility; while the constraint compliance potential energy component transforms engineering specifications and safety requirements into quantifiable penalties, ensuring the compliance of the placement scheme. Through this fractional and weighted fusion approach, the final comprehensive potential energy value more accurately reflects the installation difficulty, risk, and compliance with engineering requirements of the rebar to be installed at a specific location, thus providing a more reliable basis for the subsequent selection of the optimal compromise placement point.

[0114] The above technical solution overcomes the limitations of traditional methods in assessing potential energy values, which are often insufficiently comprehensive or accurate. Specifically, by introducing geometric fit potential energy components and constraint compliance potential energy components and weighting them together, the calculated potential energy value more accurately and comprehensively reflects the actual placement conditions and engineering requirements of the reinforcing bars to be installed within the target installation area. This not only improves the realism and reliability of the virtual assembly simulation but also significantly enhances the accuracy and efficiency of calculating the optimal compromise placement point, thereby effectively reducing rework rates and potential risks during on-site construction and ensuring the precise positioning and installation quality of the longitudinal reinforcing bars in the structure.

[0115] In some preferred embodiments, suppose that during the construction of a reinforced concrete structure, a rebar to be installed needs to be placed between existing reference rebars. First, the system generates multiple candidate placement points in a 3D digital scene and determines their spatial coordinates. For each candidate placement point, the system calculates its geometric fit potential energy component. For example, by analyzing the minimum gap and overlapping area between the rebar to be installed and the surrounding reference rebars, a higher potential energy value is assigned if the gap is too small or there is overlap. Simultaneously, the system dynamically acquires and adjusts engineering constraints such as the minimum protective layer thickness and rebar lap length based on current construction environmental factors such as temperature and humidity, and structural material state information such as concrete strength. Based on these engineering constraints, the constraint compliance potential energy component is calculated. For example, if a candidate placement point would result in a protective layer thickness lower than the specification requirement, a significant penalty potential energy is applied. Finally, according to preset fit constraint weights (e.g., geometric fit weight of 0.6 and constraint compliance weight of 0.4), these two components are weighted and summed to obtain the comprehensive potential energy value of the candidate placement point. By repeating this process for all candidate placement points, a potential energy distribution map is formed, where the point with the lowest potential energy value is the optimal compromise placement point, indicating that the location is geometrically best suited and best meets engineering constraints.

[0116] This application further proposes that after obtaining the collision-free guidance path, the smoothness and efficiency of the collision-free guidance path be evaluated, and the generation parameters of the collision-free guidance path be adjusted according to the evaluation results.

[0117] Specifically, after generating a collision-free guided path, its quality needs to be evaluated. "Evaluating the smoothness and efficiency of the collision-free guided path" refers to quantitatively analyzing the generated path to determine its feasibility and optimization level in practical operation. Smoothness can be evaluated based on indicators such as the rate of change of curvature, acceleration, or jerk. For example, by calculating the curvature change at various points on the path, drastic changes indicate low smoothness. Efficiency can be evaluated based on indicators such as the total path length, estimated completion time, energy consumption, or required kinematic / dynamic resources. For example, a shorter path and shorter completion time indicate higher efficiency. These evaluations can be performed using pre-defined mathematical models or simulation tools.

[0118] "Adjusting the generation parameters of the collision-free guided path based on the evaluation results" refers to dynamically adjusting the internal parameters of the algorithm or model used to generate the collision-free guided path based on the evaluation results of smoothness and efficiency. These generation parameters may include, but are not limited to: step size, number of iterations, optimization objective weights (e.g., smoothness weights, path length weights) in the path planning algorithm, parameters of the smoothing algorithm (e.g., order of spline curves, number of control points), and tolerance of kinematic and dynamic constraints of automated hoisting equipment. For example, if the evaluation results show insufficient path smoothness, the weight of smoothness optimization in the path generation algorithm can be increased, or the continuity adjustment parameters based on curvature constraints in path smoothing optimization can be adjusted; if efficiency is low, relevant parameters in path length optimization or time parameterization can be adjusted.

[0119] This application's solution introduces an evaluation and adjustment mechanism after generating a collision-free guiding path, enabling the path planning process to form a closed-loop feedback system. Specifically, after generating the collision-free guiding path, its smoothness and efficiency are quantitatively evaluated to obtain objective feedback on path quality. Based on this evaluation result, the system can intelligently identify the deficiencies of the current path planning parameters and adjust these parameters accordingly. For example, when the path smoothness is poor, smoothness-related optimization weights can be increased or smoothing algorithm parameters can be adjusted; when the path efficiency is low, path length or time-related optimization parameters can be adjusted. This iterative optimization process ensures that subsequently generated paths better meet the needs of smooth and efficient operation in actual construction, thereby ensuring that automated hoisting equipment can move the reinforcing bars to be installed to the target location in a more optimized manner.

[0120] In some preferred embodiments, assuming that during a rebar hoisting task, the automated hoisting equipment moves according to an initially generated collision-free guide path, but the system detects significant acceleration changes at certain path points by the robotic arm's end effector, indicating insufficient path smoothness. In this case, the system calculates that the smoothness score of the current path is below a safety threshold based on a preset smoothness evaluation model. Based on this evaluation result, the system automatically adjusts the smoothness optimization weight in the path generation algorithm, for example, increasing it from 0.5 to 0.8, and may simultaneously adjust the continuity adjustment parameters based on curvature constraints in the path smoothness optimization. Subsequently, the system uses these adjusted parameters to regenerate a collision-free guide path. After evaluation, the new path exhibits significantly reduced acceleration changes and an improved smoothness score, enabling the automated hoisting equipment to accurately place the rebar in a smoother and more efficient manner.

[0121] refer to Figure 2 This application proposes a precise positioning control system for longitudinal reinforcing bars in a structure. This system aims to implement the aforementioned precise positioning control method for longitudinal reinforcing bars in a structure. The system includes:

[0122] The processing module acquires multi-source sensor data for the steel bars 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 morphological information of the steel bars to be installed.

[0123] The location information acquisition module is used to perform sensor scanning on the target installation area to generate the actual location information of the reference reinforcement.

[0124] The placement point calculation module performs virtual assembly simulation and verifies engineering constraints based on the real-time three-dimensional shape information of the steel bars to be installed and the actual position information of the reference steel bars, in order to calculate the optimal compromise placement point.

[0125] The placement control module drives the automated hoisting equipment to place the steel bars to be installed into position according to the optimal compromise placement point, and performs conformity confirmation and correction after placement.

[0126] The processing module is configured to acquire multi-source sensor data of the rebar to be installed, fuse the multi-source sensor data, and evaluate the quality of the multi-source sensor data to generate real-time three-dimensional morphological information of the rebar to be installed. Specifically, multi-source sensors can include, but are not limited to, LiDAR, vision sensors, and inertial measurement units, used to acquire information such as the geometric dimensions, orientation, and surface features of the rebar to be installed. Data fusion can employ algorithms such as Kalman filtering, extended Kalman filtering, or particle filtering to improve the accuracy and robustness of the data. Data quality assessment can be based on indicators such as the signal-to-noise ratio, consistency, and completeness of the sensor data to ensure the high reliability of the generated three-dimensional morphological information.

[0127] The location information acquisition module is configured to perform sensor scanning on the target installation area to generate the actual location information of the reference reinforcement. This module can employ similar or different sensor technologies as the processing module, such as a 3D laser scanner or a high-precision RTK-GNSS system, to accurately acquire the spatial coordinates, orientation, and geometry of the installed reference reinforcement within the target installation area. Through a comprehensive scan of the target area, a digital model of the reference reinforcement can be constructed, providing an accurate reference for subsequent virtual assembly.

[0128] The placement point calculation module is configured to perform virtual assembly simulation and verify engineering constraints based on the real-time 3D shape information of the rebar to be installed and the actual position information of the reference rebar, in order to calculate the optimal compromise placement point. This module receives the outputs from the processing module and the position information acquisition module, simulates the relative positional relationship between the rebar to be installed and the reference rebar in a digital environment, and considers engineering constraints such as minimum protective layer thickness and rebar lap length. Through iterative calculation and optimization algorithms, such as those based on the potential field method or optimization search algorithms, it finds the placement position that satisfies all constraints and has the best overall performance—the optimal compromise placement point.

[0129] The placement control module is configured to drive automated hoisting equipment to place the rebar to be installed into position based on the optimal compromise placement point, and to perform post-placement conformity verification and correction. This module sends the calculated optimal compromise placement point as an instruction to the automated hoisting equipment, such as a smart crane equipped with a high-precision robotic arm or cable-driven system. During placement, the module can monitor the actual position of the rebar in real time and compare it with the target position. If any deviation exists, fine-tuning and correction are performed to ensure that the rebar is ultimately placed accurately in the predetermined position.

[0130] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein 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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