A path planning method for realizing Z-axis orientation constraint based on a mechanical arm SDK

Abstract

The application discloses a kind of path planning methods for realizing Z-axis orientation constraint based on mechanical arm SDK, it is related to industrial robot control field, the path planning methods for realizing Z-axis orientation constraint based on mechanical arm SDK, including target positioning, path planning and analysis, split mechanical arm operating track and dynamically solve Z-axis optimal parameter, visual detection and posture real-time adjustment and transport data record analysis, relies on the real-time visual data of positioning detection system acquisition, realizes Z-axis pose dynamic monitoring, based on solving data, call mechanical arm SDK combined with Cartesian planning algorithm, real-time adjustment Z-axis orientation and end handling component posture and speed, can promptly correct motion error, the Z-axis deviation caused by workpiece deviation, combined with moveit2 frame collision detection capability and SDK fast response capability, realize the path planning method for Z-axis orientation constraint, effectively avoid collision risk, improve the stability and security of mechanical arm operation.

Description

Technical Field

[0001] This invention relates to the field of industrial robot control technology, specifically a path planning method based on a robotic arm SDK to achieve Z-axis orientation constraints. Background Technology

[0002] Robotic arms are commonly used in scenarios such as picking, placing, and transferring. As core equipment for automated operations, they can complete complex tasks such as high-precision picking, assembly, and handling. They have advantages such as flexible movement, high repeatability and positioning accuracy, and stable and reliable operation. They can effectively replace manual labor in repetitive, high-intensity, or high-risk scenarios, improving production efficiency and operational safety.

[0003] However, existing robotic arms still have the following shortcomings when in operation: 1. When a robotic arm uses suction cups or lifting devices to move heavy objects, there are requirements for the tilt angle of the end effector. Tilting the suction cup will reduce the effective vertical suction force and generate harmful shearing force, which will fundamentally threaten the stability and safety of the handling system.

[0004] 2. By default, OMPL planning does not consider the end angle. When the end angle sampling constraint is added, the sampling success rate decreases as the deviation range of the end angle decreases. When the end angle is limited to ±2 degrees, the planning time can easily exceed 10 seconds, which is unacceptable.

[0005] 3. Currently, in material handling operations with Z-axis orientation constraints, the path planning and trajectory execution of robotic arms are statically designed. During the planning stage, the Z-axis operating parameters are determined only based on preset scene data. There is no real-time pose detection and dynamic adjustment mechanism. As a result, the Z-axis orientation is prone to deviating from the preset threshold due to robotic arm motion errors and workpiece placement deviations, and the actual operating height may touch obstacles, resulting in insufficient operational stability and safety.

[0006] 4. Existing robotic arms lack self-learning and iterative optimization capabilities in path planning and Z-axis parameter setting. Each operation only plans parameters based on single scene positioning data, without systematically storing and analyzing Z-axis attitude adjustment and path execution data from multiple complete runs. This results in planning parameters remaining at the initial design level, making it difficult to adapt to the personalized needs of different handling scenarios. Consequently, the planning accuracy and operational efficiency cannot be continuously improved over the long term.

[0007] To address the shortcomings of existing technologies, this invention provides a path planning method based on a robotic arm SDK to implement Z-axis orientation constraints, thereby solving the aforementioned problems. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a path planning method based on a robotic arm SDK to implement Z-axis orientation constraints, which solves the problem of easy collisions caused by the inability to adjust the robotic arm in real time during operation.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a path planning method based on a robotic arm SDK to implement Z-axis orientation constraints, comprising the following steps: Step S1, Target Localization: The orientation, angle and height of the target to be transported are located by the localization detection system. At the same time, the three-dimensional obstacle space boundary information of the transport scene is recorded to narrow down the search range for subsequent precise localization. Meanwhile, a scene space Z-axis height mapping table is established. Step S2, Path Planning and Analysis: Based on the analysis and processing of positioning data and scene space Z-axis height mapping table, the running stroke of the robotic arm is determined, and the orientation and height threshold of the Z-axis at the end of the robotic arm are constrained. The basic running parameters of the Z-axis are planned simultaneously. Step S3: Decompose the robotic arm's running trajectory and dynamically calculate the optimal Z-axis parameters: Based on the above analysis data, perform coordinate decomposition. For each segmented sub-path, match the scene space Z-axis height mapping table and calculate the optimal safe Z-axis running height of the path in real time to form the optimal Z-axis running solution. Step S4, Visual Inspection and Real-time Attitude Adjustment: Based on the positioning detection system and real-time analysis of 3D obstacle space boundary data, the running height of the Z-axis and the coordinates in the actual space are calculated. Using the robotic arm SDK combined with the Cartesian programming algorithm, the Z-axis orientation of the robotic arm and the running attitude of the end-effector are adjusted in real time. Step S5: Operational data recording and analysis: Based on the complete operation process of the robotic arm multiple times and the real-time adjustment posture data of the Z-axis, the operation parameters are stored and analyzed. The actual operation data of the robotic arm is continuously enriched by the database for subsequent optimization and judgment of the Z-axis operation posture.

[0010] Preferably, in step S1, the positioning and detection system includes at least a laser 3D scanning sensor, a structured light 3D camera, and a lidar. Through their cooperation, the system accurately positions the three-dimensional orientation, placement angle, and actual stacking height of the items to be transported, thereby obtaining basic spatial positioning data of the items to be transported and narrowing the search range for the subsequent precise positioning of the luggage grabbing point by the robotic arm.

[0011] Preferably, in step S1, the three-dimensional obstacle spatial boundary information in the baggage handling scenario is simultaneously recorded. The three-dimensional obstacles include all static obstacles in the baggage stack, shelves, and handling trolley scenario. The recorded obstacle spatial boundary information is analyzed and modeled in coordinate form to establish a scene space Z-axis height mapping table covering the entire handling operation area. This mapping table contains the highest height values ​​of obstacles corresponding to each x / y plane coordinate position in the handling operation area, providing spatial data support for the dynamic calculation of the subsequent Z-axis running height of the robotic arm.

[0012] Preferably, in step S2, a comprehensive analysis and planning is carried out based on the three-dimensional positioning data of the luggage to be transported obtained from the aforementioned positioning detection and the established scene space Z-axis height mapping table. Specifically, the three-dimensional positioning data of the luggage to be transported and the scene space Z-axis height mapping table are first processed to unify the coordinate system. Based on the fused data, the entire motion range of the robotic arm from the origin to the luggage grabbing point and then to the placement point is analyzed to accurately determine the overall running stroke of the robotic arm.

[0013] Preferably, in step S2, based on the above-mentioned running stroke and dual constraints, the basic running parameters of the robot arm's Z-axis are calculated and planned. The basic running parameters include the initial height, target height, vertical lifting rate, and constant height value of the Z-axis in each movement segment, providing a data basis for subsequent rapid splitting and optimal Z-axis parameter calculation.

[0014] Preferably, in step S3, based on the running distance, Z-axis running basic parameters, and scene space Z-axis height mapping table analysis data obtained from the aforementioned path planning, the entire process running trajectory of the robotic arm is divided into coordinate segments according to motion logic, and divided into multiple continuous sub-paths: from the origin to the gripping and lowering point, straight down gripping, straight up lifting, from the gripping point to the intermediate stopping point, from the intermediate stopping point to the placement and lowering point, straight down placement, and straight up return to position, and the starting point and ending point three-dimensional coordinates of each sub-path are clearly defined.

[0015] Preferably, in step S3, for each segment of the split sub-path, the scene space Z-axis height mapping table is matched point by point according to its x / y plane coordinate interval, the highest height data of obstacles under the corresponding coordinates is extracted, and the optimal safe Z-axis running height of the sub-path is calculated in real time, that is, the sum of the highest height of the obstacle and the preset 0.03m-0.05m safety redundancy height. At the same time, the parameters are adapted in combination with the movement speed of the robotic arm and the Z-axis orientation constraint requirements of the end effector, and a unique Z-axis running parameter is matched for each segment of the sub-path. The optimal safe Z-axis height and adaptation parameters of all sub-paths are integrated to form the optimal solution for the Z-axis running of the robotic arm throughout the entire process.

[0016] Preferably, in step S4, during the operation of the robotic arm along the planned trajectory, the structured light 3D camera and laser 3D scanning sensor of the positioning and detection system continuously collect real-time visual data. Combined with the pre-recorded three-dimensional obstacle space boundary information, multi-dimensional joint analysis is carried out. Through point cloud analysis and coordinate transformation algorithms, the actual operating height of the Z-axis of the robotic arm end and the real-time three-dimensional coordinates of the robotic arm end in the work space are accurately calculated. At the same time, the deviation value between the actual orientation angle of the Z-axis and the preset vertical downward orientation is calculated.

[0017] Preferably, in step S4, based on the calculated real-time pose data, the robotic arm SDK is called and combined with the Cartesian programming algorithm to dynamically adjust the robotic arm's operating posture in real time. During the adjustment process, the Z-axis orientation constraint is prioritized. If the actual Z-axis orientation angle is detected to deviate from the preset threshold, the joint fine-tuning command is issued through the SDK to correct the Z-axis orientation so that the tilt angle is always controlled within the threshold range.

[0018] Preferably, in step S5, after the robotic arm completes each complete handling operation, various types of data during the entire process are automatically collected, including the preset optimal safe height, actual operating height, orientation angle and real-time adjustment parameters of each sub-path Z-axis, the attitude adjustment data of the end-effector, and key information such as path planning time and collision risk warning.

[0019] The technical effects and advantages of this invention are as follows: 1. This path planning method based on the robotic arm SDK for Z-axis orientation constraints relies on a positioning and detection system to collect real-time visual data. Combined with the joint analysis of 3D obstacle boundary information, it accurately calculates the actual Z-axis running height, real-time 3D coordinates, and orientation angle deviation, achieving dynamic monitoring of Z-axis pose. Based on the calculated data, the robotic arm SDK is called in conjunction with the Cartesian programming algorithm to adjust the Z-axis orientation and the posture and speed of the end-effector in real time. This can promptly correct Z-axis offset caused by motion errors and workpiece placement deviations. Combining the collision detection capabilities of the MoveIt2 framework and the fast response capabilities of the SDK, this method for Z-axis orientation constraint path planning compensates for the shortcomings of static planning, ensures accurate Z-axis posture constraints throughout the process, effectively avoids collision risks, and improves the stability and safety of robotic arm operations.

[0020] 2. This path planning method based on the robotic arm SDK, which implements Z-axis orientation constraints, performs fine-grained coordinate decomposition of the robotic arm's entire stroke, dividing it into multiple sub-paths that conform to motion logic. It clarifies the start and end three-dimensional coordinates of each segment, making trajectory planning more systematic. Simultaneously, for each sub-path, it matches the scene's Z-axis height mapping table point-by-point according to the x / y coordinate interval, extracts the highest obstacle height, and combines it with a 0.03m-0.05m safety redundancy height to calculate the optimal Z-axis safety height in real time. It also considers the movement speed and Z-axis orientation constraints for parameter adaptation, forming the optimal solution for the entire Z-axis operation. This step ensures precise adaptation of the Z-axis height to scene obstacles, adhering to orientation constraints while avoiding path redundancy from single-height planning, improving the rationality and accuracy of path planning, and guaranteeing collision-free operation of each movement segment of the robotic arm from a planning perspective.

[0021] 3. This path planning method based on the robotic arm SDK, which implements Z-axis orientation constraints, automatically collects core operational data throughout the entire process after each handling operation. After classification, organization, and removal of invalid and abnormal data, the data is stored in a dedicated database. Simultaneously, cluster analysis is performed on multiple datasets of the same scenario and type of target to extract Z-axis posture adjustment patterns, continuously enriching the actual operational dataset. In subsequent path planning, historical optimization data can be called upon, combined with the current scene positioning data and the Z-axis height mapping table, to iteratively optimize the preset Z-axis posture parameters, making the planning scheme more in line with actual operational needs, gradually improving planning accuracy and scene adaptability, realizing the accumulation and reuse of operational data, and forming a self-learning and self-optimization closed loop for robotic arm handling. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the target localization process in this invention; Figure 3 This is a flowchart illustrating the path planning and analysis process in this invention. Figure 4 This is a flowchart of the process of splitting the robotic arm's trajectory and dynamically calculating the optimal Z-axis parameters in this invention; Figure 5 This is a logic diagram of visual detection and real-time posture adjustment in this invention; Figure 6 This is a schematic diagram of the data recording and analysis process in this invention; Figure 7 This is a schematic diagram of the operation process of the robotic arm in this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This embodiment discloses a path planning method based on a robotic arm SDK to implement Z-axis orientation constraints, according to the appendix. Figure 1 To be continued Figure 7 As shown, it includes the following steps: Step S1, Target Localization: The positioning and detection system locates the orientation, angle, and height of the target to be transported. Simultaneously, it records the 3D obstacle space boundary information of the transport scene to narrow down the search range for subsequent precise positioning. A scene space Z-axis height mapping table is also established. The positioning and detection system collects the visual and spatial features of the target to be transported, and calculates the target's orientation, deflection angle, and spatial height information in the scene coordinate system in real time, completing the coarse target localization. Simultaneously collect environmental data within the transportation scene, input obstacle outlines, equipment layout, and three-dimensional obstacle space boundary information of passage restrictions, construct a constraint model of the passable area of ​​the scene, effectively narrowing the search range for subsequent precise positioning and improving positioning efficiency and safety; During the environmental modeling process, the height features of different areas of the scene are sampled and fitted in a grid, and a Z-axis height mapping table of the scene space is established. This provides a height benchmark and constraint basis for the subsequent robotic arm motion planning, realizing the integrated preprocessing of target position, environmental boundary and height features, and laying the data foundation for path planning and precise grasping.

[0026] Step S2, Path Planning and Analysis: Based on the analysis and processing of positioning data and scene space Z-axis height mapping table, the running stroke of the robotic arm is determined, and the orientation and height of the Z-axis at the end of the robotic arm are constrained. The basic running parameters of the Z-axis are planned simultaneously. Based on the target coarse positioning data and scene space Z-axis height mapping table obtained in the previous step, a comprehensive analysis and processing is carried out in combination with scene obstacle boundary constraints. Based on the target grasping point and the initial posture of the robotic arm, the motion trajectory is rationally planned and the overall operating stroke of the robotic arm is determined. At the same time, the orientation of the Z-axis of the robotic arm is constrained by the scene height characteristics, and a safe operating height is set to avoid interference or collision with scene obstacles or equipment. On this basis, the motion parameters of the Z-axis are planned simultaneously, and the basic operating parameters of lifting stroke, speed curve, and start and stop points are determined. Under the premise of ensuring motion stability and safety, reliable trajectory constraints and parameter support are provided for subsequent precise positioning and grasping actions.

[0027] Step S3: Decompose the robotic arm's running trajectory and dynamically calculate the optimal Z-axis parameters: Based on the above analysis data, perform coordinate decomposition. For each segment of the decomposed sub-path, match the scene space Z-axis height mapping table and calculate the optimal safe Z-axis running height of the path in real time to form the optimal Z-axis running solution. Based on the previous positioning information, scene constraints and preliminary planning parameters, decompose the overall running trajectory of the robotic arm into segments, dividing the continuous motion into multiple independent and smoothly connected sub-paths. For each sub-path segment, the scene space Z-axis height mapping table and obstacle space boundary constraints are matched in real time. Combined with the target posture and end effector motion characteristics, the optimal safe height, posture tilt angle and running speed of the corresponding segment are dynamically calculated. The optimal balance between obstacle avoidance, stability and motion efficiency is achieved, and the optimal solution of Z-axis operation for the entire trajectory is finally formed, providing a refined and adaptive height parameter basis for the real-time control of the robotic arm.

[0028] Step S4, Visual Inspection and Real-time Attitude Adjustment: Based on the positioning detection system and real-time analysis of 3D obstacle space boundary data, the running height of the Z-axis and the coordinates in the actual space are calculated. Using the robotic arm SDK combined with Moveit2 and Cartesian programming algorithm, the orientation of the robotic arm's Z-axis and the running attitude of the end-effector are adjusted in real time. Based on the positioning detection system, scene visual data is continuously collected, and real-time dynamic analysis is carried out in combination with the recorded 3D obstacle space boundary information to calculate the current running height of the robotic arm's Z-axis and the coordinates in the actual space. Based on the calculation results, the robot arm SDK interface is called, and the Cartesian spatial planning algorithm is integrated to perform closed-loop adjustment of the Z-axis orientation. At the same time, according to the real-time attitude of the target and the handling requirements, the pitch angle and yaw angle of the end-effector are dynamically corrected to assist in the real-time linkage of visual detection and attitude adjustment, and to compensate for deviations caused by positioning errors and environmental interference. For example, when the angle is less than 2 degrees, the Moveit2 built-in OMPL planning algorithm cannot meet the requirements of such operating attitudes, thus affecting the stability of the robot arm. However, by using the Moveit2 Cartesian algorithm in this method to plan a collision-free path and using the linear motion API of the robot arm SDK to execute the planned trajectory, it can be ensured that the robot arm always maintains accurate spatial positioning and stable operating attitude in complex scenarios, providing a guarantee for safe and efficient grasping and handling.

[0029] Step S5: Operational data recording and analysis: Based on the complete operation process of the robotic arm multiple times and the real-time adjustment posture data of the Z-axis, the operation parameters are stored and analyzed. The actual operation data of the robotic arm is continuously enriched by the database for subsequent optimization judgment of the Z-axis operation posture. The entire process of the robotic arm's multiple complete operation processes is collected, and the trajectory operation status, Z-axis height change, posture adjustment amount, obstacle avoidance response time and end-positioning deviation are recorded in real time. Then, the raw data and processing results are uniformly stored in the database for standardized storage. By statistically analyzing and comparing multiple sets of measured operating parameters, the Z-axis motion law and attitude optimization features under different scenarios are extracted. The scenario adaptation sample library and operating experience model are continuously expanded. At the same time, the accumulated historical data forms the basis for optimization decision-making, providing data support for Z-axis trajectory pre-planning and attitude adaptive adjustment under similar working conditions, and realizing the continuous iterative upgrade of algorithms and control strategies.

[0030] In step S1, the positioning and detection system includes at least a laser 3D scanning sensor, a structured light 3D camera, and a lidar. Through the cooperation of these multiple components, the three-dimensional orientation, placement angle, and actual stacking height of the items to be transported are accurately located, and the basic spatial positioning data of the items to be transported is obtained, thereby narrowing the search range for the subsequent precise positioning of the luggage grabbing point by the robotic arm end. In step S1, the three-dimensional obstacle spatial boundary information in the baggage handling scene is recorded synchronously. The three-dimensional obstacles include all static obstacles in the baggage stacks, shelves, and handling carts. The recorded obstacle spatial boundary information is analyzed and modeled in coordinate form to establish a scene space Z-axis height mapping table covering the entire handling operation area. This mapping table contains the highest height values ​​of obstacles corresponding to each x / y plane coordinate position in the handling operation area, providing spatial data support for the dynamic calculation of the subsequent Z-axis running height of the robotic arm.

[0031] The positioning and detection system adopts a multi-device collaborative architecture of laser 3D scanning sensor, structured light 3D camera and lidar. Through hardware complementarity and data fusion, it achieves high-precision positioning and scene modeling. With its high-speed point cloud acquisition capability, the laser 3D scanning sensor performs full-area scanning of the handling area, accurately captures the three-dimensional contour features of the items to be handled, and calculates the three-dimensional orientation coordinates, placement deflection angle and actual stacking height of the items in the world coordinate system. The structured light 3D camera performs fine imaging of the target local area, supplements the surface texture and detailed features of the object, corrects the edge contour deviation that may exist in the laser scan, and improves the integrity and accuracy of the positioning data. The lidar focuses on scanning the scene environment, quickly captures the spatial distribution information of all static obstacles such as luggage piles, shelves, and handling carts, and outputs the three-dimensional boundary coordinates of the obstacles in real time. After synchronous calibration, the data collected by multiple devices is integrated and processed through a data fusion algorithm: on the one hand, effective positioning data is filtered out, outliers caused by environmental interference are eliminated, and the core position parameters of the items to be transported are accurately located, reducing the search range of the subsequent robotic arm end gripping point to a 5-10cm area around the target, which greatly improves the precision positioning efficiency. On the other hand, the input three-dimensional obstacle information is analyzed in coordinate form, and an obstacle space boundary model is established according to the principle of grid division of the work area, clarifying the passage restrictions and safety distance values ​​of each area. The computing devices relied upon by this data fusion algorithm include industrial control computers, edge computing modules, and embedded controllers installed in the robotic arm. Based on this, a scene space Z-axis height mapping table covering the entire handling operation area is constructed: using the x / y plane coordinates of the operation area as an index, the maximum height value of the obstacle corresponding to each coordinate point is determined through interpolation calculation and boundary fitting, forming a global, high-precision height constraint dataset. This mapping table is linked to the changes in obstacle position in real time and dynamically updates the height information, providing reliable spatial data support for the dynamic calculation of the robot arm's Z-axis running height and obstacle avoidance path planning, ensuring the safety and accuracy of the robot arm's movement, and transmitting the data to the industrial-grade storage server inside the machine.

[0032] In step S2, a comprehensive analysis and planning is carried out based on the three-dimensional positioning data of the luggage to be transported obtained from the aforementioned positioning detection and the established scene space Z-axis height mapping table. Specifically, the three-dimensional positioning data of the luggage to be transported and the scene space Z-axis height mapping table are first processed to unify the coordinate system. Based on the fused data, the entire motion range of the robotic arm from the origin to the luggage grabbing point and then to the placement point is analyzed to accurately determine the overall running stroke of the robotic arm. Meanwhile, in response to the operational requirements of suction cups or lifting devices in luggage handling scenarios, dual constraints are applied to the Z-axis of the robotic arm's end effector. First, the Z-axis of the robotic arm's end effector is constrained to be vertically downward, with a tilt angle threshold of ≤2°. Second, the operating height of the Z-axis of the robotic arm's end effector is constrained to be no less than the sum of the highest obstacle height recorded in the scene space Z-axis height mapping table under the corresponding x / y plane coordinates and the preset safety redundancy height. Based on the above operating stroke and dual constraints, the basic operating parameters of the robotic arm's Z-axis are calculated and planned. The basic operating parameters include the initial height, target height, vertical lifting rate, and constant height value of the Z-axis during each movement segment, providing a data foundation for subsequent rapid decomposition and optimal Z-axis parameter calculation. Relying on an industrial control computer as the core processing device, and loading the developed path planning software, it first reads the three-dimensional positioning data (x1, y1, z1) of the object to be transported output by the positioning and detection system (laser 3D scanning sensor, structured light 3D camera, lidar). At the same time, it retrieves the scene space Z-axis height mapping table stored in the industrial-grade storage server in the machine. Through the coordinate transformation algorithm built into the industrial control computer, the two types of data are unified to the base coordinate system of the robotic arm, completing the coordinate system unification process and eliminating the deviation caused by the difference in data sources. Based on the fused data, an industrial-grade control computer can further analyze the entire spatial motion range of the robotic arm from the origin (initial docking position) to the luggage grabbing point and then to the target placement point. Combined with the joint motion limit parameters of the robotic arm, the overall running stroke of the robotic arm can be accurately calculated through the moveit2 Cartesian space planning algorithm, and the spatial range boundary of each motion stage can be clearly defined. To meet the operational adaptation requirements of suction cups or lifting devices, a dual constraint is applied to the Z-axis of the robotic arm's end effector via an industrial-grade control computer: First, attitude constraints are performed: the attitude control interface of the robotic arm's embedded controller is called to force the Z-axis of the end effector to face vertically downwards, and the tilt angle data is fed back in real time through the laser 3D scanning sensor to ensure that the tilt angle threshold is ≤2°; Then, height constraints are imposed: the industrial-grade control computer matches the x / y plane coordinates and Z-axis height mapping table of each movement path in real time, extracts the highest height value of the obstacle at the corresponding position, and adds a 3-5cm preset safety redundancy height to form the minimum running height value of the Z-axis, prohibiting the end effector from running below this value; Finally, based on the above-mentioned running stroke and dual constraints, the industrial-grade control computer calculates the basic parameters of Z-axis operation, including the initial height of each movement segment, the target height, the vertical lifting rate (0.1-0.3m / s, dynamically adjusted to adapt to the load weight), and the constant height value during planar translation. The parameter data is synchronized to the robotic arm control system and storage server via an industrial Ethernet switch, providing a standardized data foundation for subsequent trajectory splitting and optimal Z-axis parameter calculation.

[0033] In step S3, based on the running distance, Z-axis running basic parameters and scene space Z-axis height mapping table data obtained from the aforementioned path planning, the entire running trajectory of the robotic arm is divided according to the motion logic coordinates, and the starting point and ending point three-dimensional coordinates of each sub-path are determined. For each segment of the sub-path after splitting, the scene space Z-axis height mapping table is matched point by point according to its x / y plane coordinate interval. The maximum height data of obstacles under the corresponding coordinates is extracted, and the optimal safe height for Z-axis operation of the sub-path is calculated in real time, which is the sum of the maximum height of the obstacle and the preset safe redundancy height of 0.03m-0.05m. At the same time, the parameters are adapted in combination with the movement speed of the robotic arm and the Z-axis orientation constraint requirements of the end effector. A unique Z-axis operation parameter is matched for each segment of the sub-path. The optimal safe height of Z-axis and the adaptation parameters of all sub-paths are integrated to form the optimal solution for the Z-axis operation of the robotic arm throughout the entire process. During operation, the system first reads the running distance, Z-axis basic parameters, and scene space Z-axis height mapping table determined in the path planning stage. The entire trajectory of the robotic arm is then decomposed into eight continuous sub-paths according to motion logic, including the origin, grab and drop point, straight down, straight up, intermediate stop point, placement and drop point, straight down, straight up, and return to the origin. The industrial-grade control computer uses a coordinate splitting algorithm to accurately calculate the three-dimensional coordinates of the starting point, ending point, and intermediate transition points of each sub-path, thus constructing a complete trajectory segmentation model. For each sub-path segment, the industrial control computer matches the scene space Z-axis height mapping table point by point according to its x / y plane coordinate interval, and extracts the maximum height data of the obstacle at the corresponding position. In the specific implementation, attention should be paid to calculating the intermediate stopping point. For example, using x and y to represent the plane coordinate system and the Z-axis to represent the height, the coordinates of the grab point are (x1, y1), the coordinates of the placement point are (x2, y2), and the coordinates of the intermediate stopping point are (x1, x2) / 2, y2). The height of the intermediate stopping point is: Z = item height + 0.03 + placement item height. To avoid encountering the stacking environment, the Z-axis is required to be at least higher than the preset minimum height. The pose of the intermediate stopping point adopts the placement pose. Through the above processing, a unique Z-axis running parameter is matched for each sub-path segment, including the optimal safe height, lifting rate, and attitude adjustment amount. The industrial-grade control computer integrates and optimizes the parameters of all sub-paths to form the optimal solution for the entire Z-axis operation, which is then sent to the embedded controller of the robotic arm to provide high-precision and high-safety motion parameter support for subsequent precise execution.

[0034] In step S4, during the operation of the robotic arm along the planned trajectory, the structured light 3D camera and 3D scanning sensor of the positioning and detection system continuously collect real-time visual data. Combined with the pre-recorded three-dimensional obstacle space boundary information, multi-dimensional joint analysis is carried out. Through point cloud parsing and coordinate transformation algorithms, the actual operating height of the robotic arm end Z-axis and the real-time three-dimensional coordinates of the robotic arm end in the work space are accurately calculated. At the same time, the deviation value between the actual orientation angle of the Z-axis and the preset vertical downward orientation is calculated. In step S4, based on the calculated real-time pose data, the robotic arm SDK is called and combined with the Cartesian programming algorithm to dynamically adjust the robotic arm's operating posture in real time. During the adjustment process, the Z-axis orientation constraint is prioritized. If the actual orientation angle of the Z-axis is detected to deviate from the preset threshold, the joint fine-tuning command is issued through the SDK to correct the Z-axis orientation so that the tilt angle is always controlled within the threshold range. If the actual operating height of the Z-axis is detected to be close to the highest height of the obstacle at the corresponding coordinate, the operating height of the Z-axis is adjusted to the optimal safe height in real time through the SDK while keeping the Z-axis orientation unchanged. The spatial posture of the end-effector is adjusted synchronously to ensure that the end-effector does not contact the target to be transported or the scene obstacles. This achieves precise and real-time control of the operating posture of the robotic arm's Z-axis and the end-effector, ensuring the stability and safety of the entire transport process. Throughout the entire process of the robotic arm performing the handling operation according to the planned trajectory, the industrial control computer serves as the core control hub. Relying on the structured light 3D camera and laser 3D scanning sensor in the positioning and detection system, a high-frequency visual data acquisition link is established to realize the real-time perception of the scene and the posture of the robotic arm end. The 3D scanning sensor simultaneously outputs high-precision point cloud data, and performs multi-dimensional joint analysis with the three-dimensional obstacle space boundary model pre-recorded in the industrial-grade storage server. Through point cloud registration, coordinate transformation and posture estimation algorithms, the actual running height of the robotic arm end Z axis and the three-dimensional coordinates (x, y, z) in the working space are calculated in real time. At the same time, the actual orientation of the Z axis and the angle deviation value of the preset "vertical downward" are calculated, providing a high-precision input basis for closed-loop adjustment. Based on the real-time pose data obtained from the calculation, the industrial-grade control computer calls the robotic arm SDK interface and integrates the Cartesian space programming algorithm to dynamically correct and precisely adjust the end-effector's running posture. The entire adjustment process prioritizes the Z-axis orientation constraint: when the actual tilt angle of the Z-axis is detected to exceed 2° deviation, the SDK immediately issues a joint angle fine-tuning command to correct the robotic arm joint angle in real time, so that the end-effector orientation quickly returns to the safe angle range, ensuring that the suction cup or lifting device is always perpendicular to the target. Meanwhile, the laser 3D scanning sensor continuously monitors the height changes of obstacles in the area below the end effector. When it detects that the Z-axis running height is close to the highest height of the obstacle at the corresponding x / y coordinate position, the industrial control computer dynamically adjusts the Z-axis height to the optimal safe height (the highest height of the obstacle + 0.03-0.05m redundancy) while maintaining the Z-axis orientation stability. Simultaneously, it fine-tunes the pitch and rotation spatial attitude of the end effector to avoid potential interference and collision risks during operation. This enables precise and real-time control of the robot arm's Z-axis height, orientation, and end effector attitude, ensuring that the entire robot arm's handling process is stable, safe, and collision-free.

[0035] In step S5, after the robotic arm completes each complete handling operation, various types of data during the entire process are automatically collected, including the preset optimal safe height, actual operating height, orientation angle and real-time adjustment parameters of each sub-path Z-axis, posture adjustment data of the end-effector, and key information such as path planning time and collision risk warning. The above data is categorized and organized by work batch, and after removing invalid and abnormal data, it is stored in a dedicated database. At the same time, cluster analysis is performed on multiple operation data of the same scenario and the same type of target to be transported to extract the regular features of Z-axis posture adjustment, continuously enriching the dataset of actual operation of the robotic arm. When carrying out path planning in the future, historical optimization data in the database can be called, and combined with the positioning data of the current operation scenario and the Z-axis height mapping table, the preset parameters of Z-axis running posture can be iteratively optimized to improve the accuracy and adaptability of planning, and realize the self-learning and continuous optimization of robotic arm handling operations.

[0036] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A path planning method based on a robotic arm SDK to implement Z-axis orientation constraints, characterized in that, Includes the following steps: Step S1, Target Localization: The orientation, angle and height of the target to be transported are located by the localization detection system. At the same time, the three-dimensional obstacle space boundary information of the transport scene is recorded to narrow down the search range for subsequent precise localization. Meanwhile, a scene space Z-axis height mapping table is established. Step S2, Path Planning and Analysis: Based on the analysis and processing of positioning data and scene space Z-axis height mapping table, the running stroke of the robotic arm is determined, and the orientation and height threshold of the Z-axis at the end of the robotic arm are constrained. The basic running parameters of the Z-axis are planned simultaneously. Step S3: Decompose the robotic arm's running trajectory and dynamically calculate the optimal Z-axis parameters: Based on the above analysis data, perform coordinate decomposition. For each segmented sub-path, match the scene space Z-axis height mapping table and calculate the optimal safe Z-axis running height of the path in real time to form the optimal Z-axis running solution. Step S4, Visual Inspection and Real-time Attitude Adjustment: Based on the positioning detection system and real-time analysis of 3D obstacle space boundary data, the running height of the Z-axis and the coordinates in the actual space are calculated. Using the robotic arm SDK combined with the Cartesian programming algorithm, the Z-axis orientation of the robotic arm and the running attitude of the end-effector are adjusted in real time. Step S5: Operational data recording and analysis: Based on the complete operation process of the robotic arm multiple times and the real-time adjustment posture data of the Z-axis, the operation parameters are stored and analyzed. The actual operation data of the robotic arm is continuously enriched by the database for subsequent optimization and judgment of the Z-axis operation posture.

2. The path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 1, characterized in that, In step S1, the positioning and detection system includes at least a laser 3D scanning sensor, a structured light 3D camera, and a lidar. Through the cooperation of these components, the three-dimensional orientation, placement angle, and actual stacking height of the items to be transported are accurately located, and the spatial positioning data of the items to be transported is obtained, thereby narrowing the search range for the subsequent precise positioning of the luggage grabbing point by the robotic arm.

3. The path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 2, characterized in that, In step S1, the three-dimensional obstacle spatial boundary information in the baggage handling scene is recorded synchronously. The three-dimensional obstacles include all static obstacles in the baggage stacks, shelves, and handling carts. The recorded obstacle spatial boundary information is analyzed and modeled in coordinate form to establish a scene space Z-axis height mapping table covering the entire handling operation area. This mapping table contains the highest height values ​​of obstacles corresponding to each x / y plane coordinate position in the handling operation area, providing spatial data support for the dynamic calculation of the subsequent Z-axis running height of the robotic arm.

4. The path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 1, characterized in that, In step S2, a comprehensive analysis and planning is carried out based on the three-dimensional positioning data of the luggage to be transported obtained from the aforementioned positioning detection and the established scene space Z-axis height mapping table. Specifically, the three-dimensional positioning data of the luggage to be transported and the scene space Z-axis height mapping table are first processed to unify the coordinate system. Based on the fused data, the entire motion range of the robotic arm from the origin to the luggage grabbing point and then to the placement point is analyzed to accurately determine the overall running stroke of the robotic arm.

5. A path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 4, characterized in that, In step S2, based on the above-mentioned running stroke and dual constraints, the basic operating parameters of the robot arm's Z-axis are calculated and planned. The basic operating parameters include the initial height, target height, vertical lifting rate, and constant height value of the Z-axis in each movement segment, providing a data basis for subsequent rapid splitting and optimal Z-axis parameter calculation.

6. The path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 1, characterized in that, In step S3, based on the running stroke, Z-axis running basic parameters and scene space Z-axis height mapping table data obtained from the aforementioned path planning, the entire process running trajectory of the robotic arm is divided into coordinate segments according to motion logic, which are divided into multiple continuous sub-paths: from the origin to the gripping and lowering point, straight down gripping, straight up lifting, from the gripping point to the intermediate stopping point, from the intermediate stopping point to the placement and lowering point, straight down placement, and straight up return to position. The three-dimensional coordinates of the starting point and ending point of each sub-path are clearly defined.

7. A path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 6, characterized in that, In step S3, for each segment of the split sub-path, the scene space Z-axis height mapping table is matched point by point according to its x / y plane coordinate interval, the maximum height data of obstacles under the corresponding coordinates is extracted, and the optimal safe Z-axis running height of the sub-path is calculated in real time, which is the sum of the maximum height of the obstacle and the preset 0.03m-0.05m safety redundancy height. At the same time, the parameters are adapted in combination with the robot arm movement speed and the Z-axis orientation constraint requirements of the end effector, and a unique Z-axis running parameter is matched for each sub-path. The optimal safe Z-axis height and adaptation parameters of all sub-paths are integrated to form the optimal Z-axis running solution of the robot arm throughout the entire process.

8. A path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 1, characterized in that, In step S4, during the operation of the robotic arm along the planned trajectory, the structured light 3D camera and laser 3D scanning sensor of the positioning and detection system continuously collect real-time visual data. Combined with the pre-recorded three-dimensional obstacle space boundary information, multi-dimensional joint analysis is carried out. Through point cloud analysis and coordinate transformation algorithms, the actual operating height of the Z-axis of the robotic arm end and the real-time three-dimensional coordinates of the robotic arm end in the work space are accurately calculated. At the same time, the deviation value between the actual orientation angle of the Z-axis and the preset vertical downward orientation is calculated.

9. A path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 8, characterized in that, In step S4, based on the calculated real-time pose data, the robotic arm SDK is called and combined with the Cartesian programming algorithm to dynamically adjust the robotic arm's operating posture in real time. During the adjustment process, the Z-axis orientation constraint is prioritized. If the actual Z-axis orientation angle is detected to deviate from the preset threshold, the joint fine-tuning command is issued through the SDK to correct the Z-axis orientation so that the tilt angle is always controlled within the threshold range.

10. A path planning method based on a robotic arm SDK to implement Z-axis orientation constraints according to claim 1, characterized in that, In step S5, after the robotic arm completes each complete handling operation, various types of data are automatically collected during the entire process, including the preset optimal safe height, actual operating height, orientation angle and real-time adjustment parameters of each sub-path Z-axis, the attitude adjustment data of the end-effector, and key information such as path planning time and collision risk warning.

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