Intelligent clamp path planning method for industrial robot

Abstract

The invention discloses an intelligent clamp path planning method for an industrial robot, and relates to the technical field of clamp path planning, and the method comprises the steps: carrying out the feature recognition of a working environment space of an intelligent clamp, and obtaining a space partition; establishing spatial constraint features according to a corresponding relation between the spatial partitions and spatial recognition features; according to the working parameters of the intelligent clamp, control constraint information is obtained; performing constraint matching analysis to obtain a control parameter strategy space; task process decomposition is conducted on the industrial robot clamp task, and a task environment space sequence is obtained; and carrying out space partitioning strategy local optimization to obtain a path planning result. The technical problems that in the prior art, path planning of the intelligent clamp in the complex industrial environment lacks accuracy and high efficiency, and collision risks exist are solved, and the technical effect of improving the accuracy, high efficiency and safety of path planning of the intelligent clamp in the complex industrial environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fixture path planning, and in particular to an intelligent fixture path planning method for an industrial robot. Background Art

[0002] In the field of modern industrial automation production, industrial robots play an extremely important role. As a key component of industrial robots to perform tasks such as grasping, handling, and assembly, the rationality and efficiency of the path planning of intelligent fixtures directly affect the performance of the entire production system. With the continuous development of industrial production, production scenarios are becoming increasingly complex and diverse, and the requirements for production accuracy, efficiency, and safety continue to rise. Traditional fixture path planning often relies on simple preset trajectories or relatively rough spatial perception and obstacle avoidance strategies. In the face of complex working environments, such as the presence of many static equipment of various shapes, other dynamically running robots or material transmission devices, this traditional method exposes many disadvantages. On the one hand, it is difficult to accurately identify various obstacles in the working environment and accurately evaluate their impact on the fixture path, which can easily lead to collision accidents, which will not only damage equipment and workpieces, but also cause production interruptions and bring huge economic losses. On the other hand, it is impossible to flexibly adjust the path planning strategy according to the fixture's own working parameters and task requirements, resulting in low production efficiency and the inability to give full play to the performance advantages of industrial robots and intelligent fixtures.

[0003] The existing technology has technical problems such as lack of accuracy and efficiency in path planning of intelligent fixtures in complex industrial environments, and the risk of collision. Summary of the invention

[0004] The present application provides a path planning method for an intelligent fixture for an industrial robot, which is used to solve the technical problems in the prior art that the path planning of intelligent fixtures in complex industrial environments lacks accuracy and efficiency, and there is a risk of collision.

[0005] In view of the above problems, the present application provides an intelligent fixture path planning method for an industrial robot, the method comprising: Based on the working parameters of the intelligent fixture, the working environment space of the intelligent fixture is feature identified to obtain space partitions, and the space partitions are clustered and segmented according to the space identification features; according to the correspondence between the space partitions and the space identification features, space constraint features are established; according to the working parameters of the intelligent fixture, control constraint information is obtained; according to the space constraint features and the control constraint information, constraint matching analysis is performed to obtain a control parameter strategy space, and the control parameter strategy in the control parameter strategy space has a workspace position identifier; the task process of the industrial robot fixture task is decomposed to obtain a task environment space sequence; according to the task environment space sequence, spatial matching is performed with the workspace position identifier of the control parameter strategy space, the control parameter strategy space is used to perform local optimization of the space partition strategy, and the local optimization result is used to perform full task path optimization to obtain a path planning result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: Based on the working parameters of the intelligent fixture, the working environment space of the intelligent fixture is characterized to obtain the space partition; according to the correspondence between the space partition and the space identification feature, the space constraint feature is established; according to the working parameters of the intelligent fixture, the control constraint information is obtained; the constraint matching analysis is performed to obtain the control parameter strategy space; the task process of the industrial robot fixture task is decomposed to obtain the task environment space sequence; according to the task environment space sequence, the space position identification of the control parameter strategy space is spatially matched, and the control parameter strategy space is used to perform local optimization of the space partition strategy, and the local optimization result is used to optimize the full task path to obtain the path planning result. The technical effect of improving the accuracy, efficiency and safety of the path planning of the intelligent fixture in a complex industrial environment is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of an intelligent fixture path planning method for an industrial robot provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the process of obtaining spatial partitioning in the intelligent fixture path planning method for an industrial robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present application provides an intelligent fixture path planning method for industrial robots, which is used to solve the technical problems in the prior art that the path planning of intelligent fixtures in complex industrial environments lacks accuracy and efficiency, and there is a risk of collision.

[0011] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0012] Examples, such as Figure 1 As shown, the present application provides an intelligent fixture path planning method for an industrial robot, the method comprising: Step S100: Based on the working parameters of the intelligent fixture, feature recognition is performed on the working environment space of the intelligent fixture to obtain space partitions, wherein the space partitions are clustered and segmented according to the space recognition features.

[0013] Specifically, based on the working parameters of the intelligent fixture, such as the size of the clamping force, the opening and closing range, and the movement speed, its working environment space is comprehensively analyzed. Taking the automobile parts assembly workshop as an example, if the clamping force of the intelligent fixture is small, when identifying the working environment space, it is necessary to focus on the storage area and operation space of lightweight parts; if the movement speed is slow, it is necessary to consider the spatial layout and obstacle distribution within a short distance. Through these working parameters, the static avoidance targets (such as fixed equipment, shelves, etc.) and dynamic avoidance targets (such as moving transport carts, other robots in operation, etc.) in the working environment are identified, and their working spaces are determined, and then the static avoidance space characteristics and dynamic avoidance space characteristics are obtained. Then, the minimum working space of the intelligent fixture is used to fit these features to obtain the passage influence characteristics, such as determining whether the fixture can pass smoothly in the narrow channel area; the maximum working space is used for passage fitting to obtain the operation influence characteristics, such as judging whether the movement of the fixture in the open area is restricted. Finally, the spatial characteristics are discretely analyzed according to the traffic impact characteristics and operation impact characteristics, the characteristic distribution dispersion is calculated and spatial clustering is performed based on this, and the space is divided according to the clustering results to obtain spatial partitions, such as dividing the parts assembly area and material transportation area into different partitions.

[0014] Step S200: establishing space constraint features according to the correspondence between the space partitions and the space identification features.

[0015] Specifically, the spatial constraint features are established based on the correspondence between spatial partitions and spatial identification features. For each spatial partition, how its spatial identification features affect the movement of the intelligent fixture is analyzed. For example, in the narrow parts assembly area, the narrow space feature corresponds to the spatial constraint feature that restricts the large-scale movement of the intelligent fixture; in the material transportation area, the dynamic feature of frequent exchanges of personnel and vehicles corresponds to the spatial constraint feature that the intelligent fixture needs to avoid at any time.

[0016] Step S300: obtaining control constraint information according to the working parameters of the intelligent fixture.

[0017] Specifically, the control constraint information is obtained according to the working parameters of the intelligent fixture. First, the control nodes of the intelligent fixture are determined, such as the mobile arm control node and the clamping mechanism control node. Then, the multi-node control parameter combination is performed according to the adjustment parameter range, and the morphological changes of the fixture under different combinations are analyzed to obtain the edge space constraint, such as the space range required when the fixture is opened to the maximum. At the same time, the passage adjustment constraint is determined according to the adjustment parameter range of the mobile arm control node, such as the limit of the turning radius at different speeds. The control constraint information is obtained by combining these factors.

[0018] Step S400: performing constraint matching analysis according to the spatial constraint feature and the control constraint information to obtain a control parameter strategy space, wherein the control parameter strategy in the control parameter strategy space has a workspace location identifier.

[0019] Specifically, constraint matching analysis is performed based on spatial constraint features and control constraint information, the constraint distribution space is determined based on spatial constraint features, and the fixture control operation constraint space is clarified based on control constraint information. The minimum safety distance is configured for different types of avoidance space features (static and dynamic). For example, for static equipment, the safety distance is determined based on its edge shape and the response parameters of the intelligent fixture; for dynamic targets, the position is predicted by obtaining its operating parameters and path characteristics, and then the safety distance is determined. Then, the feasible working parameters of the intelligent fixture are searched in the safe operation space, and the control parameter strategy space is constructed. Each strategy is marked with a workspace location for subsequent calls.

[0020] Step S500: Decompose the task process of the industrial robot fixture task to obtain the task environment space sequence.

[0021] Specifically, the task flow of industrial robot fixture tasks is comprehensively and meticulously decomposed. For example, in the process of assembling automobile engines, the entire task can be decomposed into multiple subtasks, including grabbing the engine cylinder block from the parts warehouse, transporting the cylinder block to a specific station of the assembly line, installing internal parts such as pistons at the station, and transporting the engine to the inspection area after assembly. For each subtask, its corresponding work environment space is determined. The subtask of grabbing the engine cylinder block corresponds to the space of the parts warehouse, which has the characteristics of densely placed parts and numerous shelves; the subtask of transporting to the assembly line station involves the transportation channel space inside the factory, which may be characterized by limited channel width, the presence of other transportation equipment and personnel flow; when installing internal parts at the assembly station, it corresponds to the assembly work area space, which has various assembly tools, equipment and strict operating space requirements; and the subtask of transporting to the inspection area corresponds to the inspection area space, which is arranged with various testing instruments and has special provisions for the motion accuracy and operating space of the fixture. Through such task flow decomposition, each subtask is matched one by one with its corresponding work environment space, thereby obtaining a task environment space sequence. This sequence accurately describes the order of different working environment spaces that the industrial robot fixture experiences during the entire task process, laying a solid foundation for subsequent spatial matching according to the task environment space sequence and control parameter strategy space and achieving precise path planning, ensuring that the fixture can complete operations efficiently and safely in the appropriate environment space at different task stages, thereby improving the efficiency and quality of the entire industrial production process.

[0022] Step S600: According to the task environment space sequence, spatial matching is performed with the workspace location identifier of the control parameter strategy space, the control parameter strategy space is used to perform local optimization of the spatial partition strategy, and the local optimization result is used to optimize the full task path to obtain a path planning result.

[0023] Specifically, strictly follow the task environment space sequence, and carefully match the work environment space corresponding to each subtask with the work space position mark in the control parameter strategy space one by one. For example, when the task is carried out to the fine assembly operation in the parts assembly area, the spatial characteristics of the assembly area are identified from the task environment space sequence, and then the corresponding control parameter strategy with the assembly area position mark is accurately found in the control parameter strategy space. The matched control parameter strategy space is used to carry out local optimization of the spatial partition strategy for the current spatial partition. In the specific spatial partition of the assembly area, according to the corresponding control parameter strategy, such as optimizing the motion speed curve of the fixture to meet the requirements of precision assembly for speed stability, adjusting the posture control parameters of the fixture to ensure the accuracy of part grasping and installation, screening and optimizing possible motion paths, calculating various performance indicators under different paths, such as path length, motion time, collision risk, etc., to find the optimal local path strategy in the partition. Subsequently, the local optimization results of each spatial partition are used to optimize the path of the entire task. Considering the smooth connection between each subtask and the efficient completion goal of the overall task, the optimal local path strategies of different partitions are spliced ​​and adjusted. For example, in the process of transporting from the parts grabbing area to the assembly area, it is necessary to ensure that the transport path is perfectly connected with the exit of the grabbing area and the entrance of the assembly area. At the same time, the local optimization results of the two areas are combined to further optimize the parameters such as transportation speed and turning timing. On the entire task path, the control parameter strategies of different spatial partitions are coordinated and coordinated to calculate the total evaluation value of the entire task path, which comprehensively considers factors such as path length, movement time, collision risk, energy consumption, etc. Finally, after multiple iterations of optimization and evaluation, the splicing parameter strategy with the largest total evaluation value after correction is determined and used as the final path planning result. This result ensures that the industrial robot fixture can complete various operations with the optimal path, highest efficiency and lowest risk throughout the task process, effectively improving the automation level and production efficiency of industrial production, reducing production costs, and improving product quality and production safety.

[0024] In one possible implementation, Figure 2 As shown, step S100 also includes: Step S110: According to the working parameters of the intelligent clamp, a relationship between the working parameters and the volume state of the clamp is established to obtain a maximum working space and a minimum working space.

[0025] Step S120: identifying static avoidance targets and dynamic avoidance targets in the work environment space, and determining static avoidance space characteristics and dynamic avoidance space characteristics based on the work space where the static avoidance targets and dynamic avoidance targets are located.

[0026] Step S130: using the minimum working space to perform traffic fitting on the static avoidance space features and the dynamic avoidance space features to obtain traffic impact features.

[0027] Step S140: using the maximum working space to perform a pass fit on the static avoidance space characteristics and the dynamic avoidance space characteristics to obtain operation impact characteristics.

[0028] Step S150: performing spatial feature discrete analysis according to the traffic impact characteristics and the operation impact characteristics, performing spatial clustering according to the feature distribution discreteness, and performing spatial segmentation according to the spatial clustering results to obtain the spatial partitions.

[0029] Specifically, based on the working parameters of the smart fixture, such as the maximum extension length, maximum clamping width, rotation angle range, etc., the relationship between them and the clamp volume state is deeply analyzed. For example, when the maximum extension length of the smart fixture is longer, the range it can reach in the workspace is wider, and the corresponding maximum workspace volume increases; when the clamping width is larger, the operating space requirements in certain directions will also increase. Through the precise calculation of these parameter relationships, the maximum and minimum working spaces of the smart fixture in different states are determined. The maximum working space represents the space range occupied by the fixture in extreme operating states such as fully expanded and rotated to the maximum angle, while the minimum working space is the space requirement of the fixture in the minimum operating state such as retracted and returned to the original position.

[0030] Perform a comprehensive scan of the work environment space to accurately identify the static avoidance targets and dynamic avoidance targets. For example, in a machining workshop, static avoidance targets may include fixed machine tools, large equipment bases, warehouse shelves, etc., which are fixed in position during the work process; dynamic avoidance targets include running transport carts, operators, other collaborative robots, etc., whose positions and motion states change at any time. After determining the workspace where these targets are located, further analysis is performed to obtain static avoidance space characteristics and dynamic avoidance space characteristics. The static avoidance space characteristics mainly involve the restrictions on the motion path of the intelligent fixture caused by the shape, size, and position relationship of the target. For example, the guardrails around the machine tool and the protruding parts of the equipment base will form specific spatial obstructions; the dynamic avoidance space characteristics need to consider factors such as the target's motion trajectory, speed range, and motion cycle, such as the driving route of the transport cart, the operator's range of activities, and the habitual motion path.

[0031] The previously obtained minimum workspace is used to fit the static avoidance space features and dynamic avoidance space features. Assuming that the minimum workspace is a cuboid of a specific shape and volume, it is moved along different paths in the work environment space to simulate the intelligent fixture passing through the space around the static and dynamic avoidance targets in the minimum operation state. Through this fitting process, the impact of the fixture passing around the avoidance target in the minimum workspace is analyzed, such as whether a collision will occur due to narrow space, whether it can bypass the target smoothly, etc., so as to obtain the passage impact characteristics. These features include key information such as the feasible passage direction of the minimum workspace near the avoidance target, the minimum turning radius, and the minimum safety gap.

[0032] A similar method is used to fit the static avoidance space characteristics and dynamic avoidance space characteristics using the maximum workspace to obtain the operation impact characteristics. When fitting the maximum workspace, the movement of the intelligent fixture under extreme operating conditions is considered. For example, when the fixture is extended to its longest length and rotated to its maximum angle, the running trajectory and space requirements in the space around the avoidance target. The operation impact characteristics may involve the maximum movement speed limit of the maximum workspace in different avoidance target areas, the maximum operating range limit, the maximum occupancy of the surrounding space, etc. These characteristics reflect the operation capabilities and space requirements of the intelligent fixture in different avoidance target environments during normal operation.

[0033] The spatial characteristics are discretely analyzed according to the traffic impact characteristics and operation impact characteristics. The working environment space is divided into multiple small units, and the distribution of traffic impact characteristics and operation impact characteristics in each unit is calculated, such as the distribution frequency of the minimum safety gap in a certain area, the change trend of the maximum movement speed limit, etc. Spatial clustering is performed according to the discreteness of these characteristic distributions, and spatial units with similar characteristic distributions are clustered into one category. For example, areas with small minimum safety gaps and dense distribution are classified into one category, which means that the area has greater traffic restrictions on smart fixtures. Spatial segmentation is performed according to the spatial clustering results, and the working environment space is divided into different spatial partitions. Each spatial partition has relatively uniform traffic and operation impact characteristics, which lays a solid foundation for the subsequent establishment of spatial constraint characteristics, the formulation of targeted control strategies, and the realization of efficient path planning of smart fixtures.

[0034] In a possible implementation, step S140 further includes: Step S141: obtaining multi-level operating state volumes according to the relationship between the working parameters and the clamp volume state.

[0035] Step S142: using the multi-level operation state volume, respectively performing pass fitting on the static avoidance space feature and the dynamic avoidance space feature to obtain a multi-level operation restriction feature.

[0036] Step S143: Integrate the multi-level operation restriction features to obtain the operation impact features.

[0037] Specifically, first, the working parameters of the intelligent clamp are carefully studied, which cover many key aspects of the clamp, such as the length range of the telescopic arm, the degree of opening and closing of the clamp, the angle range of the rotation joint, and the size specifications of the overall structure. By establishing an accurate mathematical relationship model between these working parameters and the volume state of the clamp, the multi-level operating state volume is determined. For example, for an intelligent clamp with a telescopic function, the extension length of its telescopic arm is an important working parameter. When the telescopic arm is in a fully retracted state, the overall volume of the clamp is small, and the corresponding first-level operating state volume is at this time. This volume is mainly determined by the basic structure of the clamp and the space occupied by the clamp part in the minimum gripping state. As the telescopic arm gradually extends, each time it extends to a specific length value, the overall shape and space occupancy of the clamp will change, resulting in different volume states. Assuming that the telescopic arm has three adjustable extension gears, each gear corresponds to a different length value, then when the telescopic arm is extended to the first gear length, combined with the degree of opening and closing of the clamp and the angle of the rotation joint at this time (these parameters are also within the working parameter range and are interrelated with the clamp volume), the second-level operating state volume is calculated. Similarly, when the telescopic arm is extended to the second and third gear lengths, the corresponding third and fourth level operating state volumes are calculated respectively. In addition, the degree of opening and closing of the jaws will also affect the volume. When the jaws are opened to the maximum extent to grasp larger workpieces, even if the length of the telescopic arm remains unchanged, the overall volume of the fixture will increase, which also constitutes part of the different levels of operating state volume. Through comprehensive analysis and precise calculation of various combinations and changes of these working parameters, we can finally obtain a multi-level operating state volume that can accurately reflect the intelligent fixture in different operating modes and postures, laying the foundation for the subsequent in-depth analysis of the fixture's passage and operating characteristics in the working environment.

[0038] Based on the multi-level operation state volume obtained above, the operation restrictions of the intelligent fixture in a complex working environment are deeply explored. The static avoidance space characteristics are analyzed. In a workshop scene full of large fixed equipment (such as machine tools, shelves, etc.), the operation state volumes of each level are fitted with the spaces where these static avoidance targets are located one by one. For example, when considering the first-level operation state volume, it is virtually placed in the space around the machine tool to simulate whether the fixture can successfully bypass the protruding parts such as the corners and brackets of the machine tool in this volume state, and whether it can pass through the narrow passages between the machine tools. At the same time, the minimum safe distance required to be maintained between the fixture and the machine tool in this process is analyzed. This distance is the partial operation restriction feature of the first-level operation state volume for the static avoidance space characteristics. For the second-level and above operation state volumes, the above passage fitting process is repeated. As the operation state volume increases, such as when the telescopic arm is extended or the gripper is opened, the changes in its passage in the space around the machine tool are observed. It may be found that when the volume increases to a certain extent, the narrow passage that was originally passable becomes impassable, or a larger turning radius is required when bypassing the machine tool. These newly added restrictive factors constitute the operation restriction characteristics under the static avoidance space characteristics corresponding to the operation state volume of this level. Subsequently, similar operations are performed for the dynamic avoidance space characteristics. In the working area with dynamic targets such as transport carts and mobile robots, the operation state volumes of each level are fitted with the motion space of these dynamic targets. Consider whether the fixtures of the operation state volumes of each level can avoid in time under different speeds and driving routes of the transport cart, and the lead time and buffer space that need to be reserved to avoid collision. For example, for the first-level operation state volume, when the transport cart is driving in a straight line at a low speed, the fixture may only need to maintain a small lateral avoidance distance, but when the cart turns at a high speed, the fixture needs a larger avoidance space. This is part of the operation restriction characteristics of the first-level operation state volume in this dynamic avoidance scenario. Similarly, for different levels of operation state volumes, as their volumes change, the operation restriction characteristics in the dynamic avoidance space will also change. For example, when the volume increases, the reaction time of the fixture may become longer, and it needs to sense the movement of the dynamic target earlier and make avoidance actions, which will cause changes in its operable space and safety range in the dynamic avoidance space. Through such a comprehensive and detailed pass fitting process, for the static and dynamic avoidance space characteristics, multi-level operation restriction characteristics corresponding to each level of operation state volume are obtained respectively. These characteristics accurately describe the restrictions of the smart fixture in complex working environments under different operation states, providing a key basis for the subsequent integration of complete operation impact characteristics.

[0039] The multi-level operation restriction features previously obtained for the static avoidance space features and dynamic avoidance space features are summarized and sorted out. For example, in terms of static avoidance, the minimum safety distance and maximum allowable approach angle required by the fixture when bypassing the fixed equipment under each level of operation state volume are listed one by one; in terms of dynamic avoidance, the shortest reaction time, minimum avoidance speed, maximum allowable collision risk coefficient and other restriction features corresponding to different operation state volumes when the fixture avoids the moving target are also presented in a concentrated manner. The internal connection and mutual influence between these multi-level operation restriction features are deeply analyzed. For example, the larger safety distance requirement of the fixture in static avoidance under a certain level of operation state volume may lead to a longer reaction time in dynamic avoidance, because the larger safety distance means that the fixture needs to perceive the movement changes of the dynamic target earlier. By establishing such an association model, the scattered operation restriction features at each level are organically combined. Then, the repeated and redundant information is removed, and the interrelated restriction features are optimized and integrated. For example, if the minimum avoidance speed of two adjacent operation state volumes in dynamic avoidance is very small, they can be reasonably merged or averaged according to the actual situation to simplify the feature description. Finally, after comprehensive integration, a comprehensive, comprehensive and accurate operation impact feature is formed. This operation impact feature covers key information such as the operation capability, constraints and space requirements of the intelligent fixture in the entire working environment space (including static and dynamic avoidance target areas) under different operating conditions, providing a solid and accurate basis for the subsequent path planning and control strategy formulation of the intelligent fixture, ensuring that the fixture can operate efficiently and safely in a complex working environment.

[0040] In a possible implementation, step S150 further includes: Step S151: marking the location distribution of the traffic impact features according to the work environment space to obtain a traffic impact distribution map.

[0041] Step S152: According to the position distribution of the operation impact features, distribution annotation is performed in the work environment space to obtain an operation impact distribution map.

[0042] Step S153: The traffic impact distribution map and the operation impact distribution map are merged and superimposed, and the dispersion is calculated according to the distribution annotation to obtain the feature distribution dispersion, which is used to characterize the discrete proportion of the traffic impact feature and the operation impact feature in the workspace.

[0043] Step S154: performing spatial feature clustering according to the feature distribution discreteness.

[0044] Specifically, according to the actual layout and size of the working environment space, detailed location distribution annotation is performed for the traffic impact features. For example, in a complex industrial workshop, the working environment space contains various equipment, passages and operating areas. For traffic impact features, such as in narrow passage areas, since the passage width limits the minimum working space for the smart fixture to pass through, this area is marked as an area with greater traffic impact, and the annotation information includes the minimum safe gap, feasible traffic direction, etc.; in open areas, the traffic impact is relatively small, and the corresponding characteristic values ​​are marked. By annotating the entire working environment space one by one in this way, a traffic impact distribution map is finally obtained. The map intuitively shows the distribution of traffic impact features in the working environment space, allowing staff to clearly see which areas have greater restrictions on the passage of smart fixtures and which areas are relatively loose.

[0045] According to the position distribution of the operation impact characteristics, similar distribution annotations are made in the working environment space. Taking into account the operation impact characteristics of the smart fixture under different operating conditions, for example, around large equipment, the existence of the equipment limits the maximum operating space of the fixture. This area is marked as an area with significant operation impact, and the annotation content covers the maximum movement speed limit, maximum operating range limit, etc.; in open areas far away from the equipment, the operation impact is relatively weak, and the corresponding characteristic values ​​are marked. After such an annotation process, an operation impact distribution map is obtained. This map clearly presents the distribution status of the operation impact characteristics in the working environment space, which helps to fully understand the operation capabilities and limitations of smart fixtures in different locations.

[0046] The obtained traffic impact distribution map and operation impact distribution map are fused and superimposed. In this process, the two maps are integrated under the same coordinate system, so that each location point in the work environment space has the annotation information of traffic impact characteristics and operation impact characteristics at the same time. For example, in a certain workshop area, the traffic impact distribution map marks that the passage here is narrow and the minimum workspace is restricted. At the same time, the operation impact distribution map shows that the area is close to large equipment and the maximum workspace is restricted. The dispersion is calculated for the distribution annotations after fusion and superposition. The dispersion calculation is achieved through a specific mathematical algorithm, which considers the degree of difference between the traffic impact characteristics and the operation impact characteristics at each location point and the distribution of these characteristics in the entire workspace. During the calculation process, the frequency and amplitude of the changes in the annotation information in each area are analyzed. For example, in an area, whether the transition from a larger restriction to a smaller restriction of the traffic impact characteristics is frequent, and whether it is synchronized with the change of the operation impact characteristics. Through the comprehensive evaluation of these factors, a numerical feature distribution dispersion is obtained. This feature distribution dispersion can accurately characterize the discrete proportion of traffic impact characteristics and operation impact characteristics in the workspace. If the discreteness is high, it means that the distribution of these features in the workspace is relatively scattered, and the characteristics of different areas are obviously different. For example, there are open areas in the workshop with small traffic and operation impacts, and complex equipment areas with large impacts, and the characteristics of the transition area change dramatically; on the contrary, if the discreteness is low, it means that the distribution of features is relatively concentrated, and the traffic and operation impact features of most areas are relatively similar. It is possible that the environment of the entire workshop has relatively uniform restrictions on the intelligent fixture. The discreteness of feature distribution provides an important basis for the subsequent spatial feature clustering, which helps to divide the work environment space more scientifically to achieve optimized control and efficient operation of intelligent fixtures.

[0047] Spatial feature clustering is performed based on the calculated feature distribution discreteness, and the work environment space is divided into different clustering areas according to the size and distribution of discreteness. When the discreteness is high, multiple clusters with obvious differences are formed, and the traffic impact characteristics and operation impact characteristics within each cluster are relatively similar; when the discreteness is low, the number of clusters may be relatively small, and the feature consistency of the spatial area is high. Through such spatial feature clustering, the complex work environment space is effectively classified according to the traffic impact and operation impact characteristics, which provides an important basis for the subsequent spatial segmentation and precise control of the intelligent fixture, so that the intelligent fixture can adopt appropriate operation strategies according to the characteristics of different clustering areas to improve work efficiency and safety.

[0048] In a possible implementation, step S300 further includes: Step S310: obtaining control nodes of the intelligent clamp, wherein the control nodes at least include a moving arm control node and a clamping mechanism control node.

[0049] Step S320: performing multi-node control parameter combinations according to the adjustment parameter range of the control node to obtain the fixture shape changes of each control parameter combination.

[0050] Step S330: Obtain edge space constraints according to the change in the fixture shape.

[0051] Step S340: Determine the traffic adjustment constraint according to the adjustment parameter range of the mobile arm control node.

[0052] Step S350: Obtain the control constraint information according to the edge space constraint and the traffic adjustment constraint.

[0053] Specifically, first of all, it is necessary to conduct in-depth research on the overall structure and functional mechanism of the intelligent fixture to accurately locate its control nodes. As a key component of industrial robots for performing grasping and operation tasks, the control system of the intelligent fixture is relatively complex. Among them, the mobile arm control node is the key to achieving precise positioning and flexible movement of the fixture in the three-dimensional workspace. It receives control instructions and accurately adjusts the telescopic length, rotation angle, and translation speed of the mobile arm to ensure that the fixture can reach the target position accurately. For example, on the automotive parts assembly line, when it is necessary to grasp parts at a specific position, the mobile arm control node accurately controls the mobile arm to extend to the appropriate length, rotate to the correct angle, and move at the appropriate speed according to the preset program or real-time instructions, so that the fixture can accurately approach the parts. The clamping mechanism control node focuses on controlling the action of the clamp and is the core element for achieving reliable grasping of workpieces of various shapes and sizes. It can flexibly adjust the opening and closing degree of the clamp, the clamping force, and the opening and closing speed according to the characteristics of the workpiece, such as shape, size, material, etc. For example, when grasping fragile electronic components, the clamping mechanism control node will control the gripper to gently grasp the components with a smaller gripping force and a slower opening and closing speed to prevent them from being damaged; while when grasping heavy mechanical parts, the gripping force will be increased to ensure that the parts are firmly grasped. Through a comprehensive analysis of the structure and function of the intelligent fixture, these key control nodes, including at least the mobile arm control node and the clamping mechanism control node, are successfully identified and obtained, laying a solid foundation for the subsequent parameter analysis and control constraint information acquisition based on the control nodes, thereby realizing efficient and precise operation of the intelligent fixture in a complex industrial environment.

[0054] Clarify the adjustment parameter range of each control node, which is the basis for parameter combination. For the mobile arm control node, its adjustment parameter range covers key parameters such as telescopic length (for example, it can be adjusted between 0.5 meters and 1.5 meters), rotation angle (such as being able to rotate within the range of -180° to 180°), and moving speed (which may vary between 0.1 meters / second and 1 meter / second); the adjustment parameter range of the clamping mechanism control node includes the degree of opening and closing of the clamping jaws (from fully closed to the maximum opening distance, such as 0 cm to 10 cm), the clamping force (according to the requirements of different workpieces, it can be set between 10 Newtons and 100 Newtons) and the opening and closing speed (such as 0.05 seconds to 0.5 seconds to complete an opening and closing action). Based on these parameter ranges, a comprehensive and systematic combination of multi-node control parameters is carried out. Taking the telescopic length, rotation angle and moving speed of the mobile arm control node, and the degree of opening and closing of the clamping jaws, the clamping force and the opening and closing speed of the clamping mechanism control node as examples, if several typical values ​​in each parameter range are selected for combination, many different parameter combinations will be generated. For example, when the telescopic length of the mobile arm is 0.8 meters, the rotation angle is 45°, the moving speed is 0.3 meters per second, and the clamping mechanism jaws are open and closed at a degree of 5 centimeters, the clamping force is 30 Newtons, and the opening and closing speed is 0.1 seconds, a specific control parameter combination is formed. For each control parameter combination, its influence on the fixture morphology is deeply analyzed to obtain the fixture morphology changes under each combination. Under the above parameter combination, the mobile arm of the fixture is in a specific extension position and angle, the jaws present a corresponding opening and closing state, and the overall fixture forms a unique shape in space. This shape not only determines the occupation range and posture of the fixture itself in the workspace, but is also directly related to its interaction with the surrounding environment. By analyzing a large number of different control parameter combinations one by one, a detailed mapping relationship between control parameters and fixture morphology changes is constructed, which provides a key basis for the subsequent accurate evaluation of the edge space constraints of the fixture under different working conditions, and further promotes the optimization process of intelligent fixture path planning.

[0055] The edge space constraint is determined based on the change in the fixture shape. When the fixture shape changes, the actual space range it occupies in the workspace and its relative position relationship with the surrounding environment will also change. For example, as the jaws open or close, the overall outer contour size of the fixture will change accordingly. If the jaws are opened to the maximum extent to grab a large workpiece, the horizontal and vertical extension range of the fixture will increase, so sufficient space needs to be reserved around it to avoid collisions with surrounding equipment, workpieces or other obstacles. Through a detailed analysis of various possible fixture shape changes, the minimum safe distance that must be maintained between the edge of the fixture and the surrounding environment in different shapes is calculated, thereby determining the edge space constraint. This constraint ensures that the edge of the fixture is always within a safe space during movement and operation, preventing collision accidents caused by insufficient space.

[0056] The passage adjustment constraints are determined according to the adjustment parameter range of the mobile arm control node. The adjustment parameter range of the mobile arm, including the telescopic length, rotation angle and moving speed, directly affects the passage capacity and flexibility of the fixture in the workspace. For example, when the mobile arm is extended to a longer length, its passage in a narrow channel may be restricted, and a larger channel width or more flexible turning space is required; if the moving speed is fast, a larger turning radius is required when turning to ensure that the fixture can pass smoothly and safely. Through in-depth research on the impact of various parameters of the mobile arm control node on passage under different values, the passage adjustment constraints of the fixture in the workspace under different parameter combinations are determined, such as the minimum turning radius, channel width requirements, and passage speed limits. These constraints provide clear guidance for the safe passage of the fixture in a complex working environment.

[0057] The control constraint information is obtained by combining the edge space constraint and the passage adjustment constraint. The edge space constraint focuses on the space limitation caused by the change of the fixture's own shape, while the passage adjustment constraint focuses on the influence of the control node parameters of the mobile arm on the passage. The two complement each other. These constraint information are integrated together to form a comprehensive and systematic control constraint information. This information accurately reflects the various space limitations that the smart fixture is subject to due to its own structure and motion control characteristics during actual work. In the subsequent path planning and operation control, based on these control constraint information, the smart fixture can avoid obstacles more intelligently and efficiently, select the optimal motion path, ensure the safe and stable completion of grasping and operation tasks in complex industrial environments, and improve the automation level and work efficiency of industrial production.

[0058] In a possible implementation, step S400 further includes: Step S410: Obtaining a constraint distribution space according to the spatial constraint characteristics.

[0059] Step S420: Obtaining the fixture control operation constraint space according to the control constraint information.

[0060] Step S430: configuring a minimum safety distance according to the spatial constraint characteristics.

[0061] Step S440: Based on the minimum safety distance, the fixture is used to control the operation constraint space to identify the operation safety space in the constraint distribution space to obtain a safe operation space.

[0062] Step S450: Searching for the working parameters of the intelligent fixture in the safe operation space, obtaining all feasible working parameters, and constructing the control parameter strategy space.

[0063] Specifically, the constraint distribution space is obtained based on the spatial constraint features. The spatial constraint features are derived from an in-depth analysis of the working environment space of the intelligent fixture, including the spatial restrictions formed by static avoidance targets (such as fixed equipment, walls, etc.) and dynamic avoidance targets (such as moving robots, transport vehicles, etc.) in the working environment. By accurately describing these constraint features, such as the location, shape, and motion trajectory of obstacles, the workspace is divided into areas with different degrees of restrictions to determine the constraint distribution space. For example, in a machining workshop, the large machine tools around them have restricted areas for the range of motion of the intelligent fixture due to their fixed position and large volume. These areas together constitute part of the constraint distribution space.

[0064] The control operation constraint space of the fixture is obtained based on the control constraint information. The control constraint information is determined based on the control nodes of the intelligent fixture itself (such as the mobile arm control node and the clamping mechanism control node) and their adjustment parameter ranges. Considering the morphological changes of the fixture under different control parameter combinations and the resulting edge space constraints and passage adjustment constraints, the feasible area and restriction conditions of the fixture during movement and operation are determined. For example, when the mobile arm is extended to a certain length and the gripper is in a specific opening and closing state, the movable range of the fixture in the workspace will be limited. The space formed by this restricted range is the control operation constraint space of the fixture.

[0065] Configure the minimum safety distance according to the spatial constraint characteristics. For static avoidance space features, such as sharp corners or protruding parts of fixed equipment, a suitable minimum safety distance is determined in combination with the operation avoidance response parameters of the intelligent fixture (such as the detection accuracy of the sensor, the braking ability of the fixture, etc.) to prevent the fixture from colliding with these static targets during movement. For dynamic avoidance space features, such as moving robots or transport vehicles, by obtaining their operating parameters (such as speed, movement direction, movement cycle, etc.), analyze their operation path characteristics (such as periodic round-trip paths or random dynamic paths). For periodic paths, determine the periodic avoidance position according to their periodic laws, and determine the minimum safety distance in combination with the operation avoidance response parameters of the intelligent fixture; for dynamic paths, obtain real-time control parameters for position prediction by connecting to the control interface of the dynamic avoidance target, and then determine the minimum safety distance based on the prediction results and the operation avoidance response parameters.

[0066] Based on the configured minimum safety distance, the fixture control operation constraint space is used to identify the operation safety space in the constraint distribution space. The fixture control operation constraint space is combined with the constraint distribution space, and the minimum safety distance is used as a buffer to screen out areas that meet the fixture's own control requirements and avoid various constraints in the workspace. These areas are the safe operation space. For example, in a workshop environment with both large equipment and mobile vehicles, the safe operation space is the area where the fixture can operate safely and effectively after considering the safety distance around the equipment and the predicted safety range of the vehicle's motion trajectory.

[0067] After the safe operation space is determined, a comprehensive search for the working parameters of the intelligent fixture is carried out in this space. It is clear that the working parameters of the intelligent fixture cover several key aspects, such as the telescopic speed, rotation angular velocity, telescopic length range of the mobile arm, as well as the clamping force of the clamping mechanism, the opening and closing speed and degree of the clamping jaws. In the safe operation space, different values ​​are tried one by one for these parameters. For example, for the telescopic speed of the mobile arm, starting from its minimum allowed value, the value is gradually increased in a certain step length, and combined tests are performed with different rotation angular velocities and telescopic lengths. Under each parameter combination, it is evaluated whether the intelligent fixture can successfully complete a series of simulated operation tasks in the safe operation space, including approaching the target workpiece, grabbing the workpiece, moving the workpiece, and releasing the workpiece in different positions and postures. Through a large number of simulation tests and precise calculations, it is determined whether each parameter combination meets the safe operation requirements, that is, whether it can avoid collisions with any obstacles in the safe operation space, while ensuring that the fixture can stably and accurately grab and operate the workpiece. The parameter combinations that meet these conditions are screened out, and these are the feasible working parameters. Finally, all feasible working parameters are sorted and classified, and the control parameter strategy space is constructed according to their applicability and effectiveness in different workspace positions. In this space, each control parameter strategy clearly corresponds to a specific workspace position identifier, so that in the actual path planning process, the intelligent fixture can quickly and accurately select the most appropriate parameter strategy from the control parameter strategy space according to the current workspace position, thereby achieving efficient, accurate and safe operation, and improving the automation level and production efficiency of the entire industrial production process.

[0068] In a possible implementation, step S430 further includes: Step S431: For the static avoidance space feature, the minimum safety distance is determined according to the edge feature of the static avoidance target and the operation avoidance response parameter of the intelligent fixture.

[0069] Step S432: For the dynamic avoidance space feature, the dynamic avoidance target is taken as the collaborative target to obtain the operation parameters of the dynamic avoidance target, and the operation path feature is obtained. The operation path feature includes a periodic path and a dynamic path.

[0070] Step S433: determining a periodic avoidance position according to the periodic path, and determining the minimum safety distance according to the periodic avoidance position in combination with the operation avoidance response parameters of the intelligent fixture.

[0071] Step S434: According to the dynamic path, connect to the control interface of the dynamic avoidance target, obtain real-time control parameters for position prediction.

[0072] Step S435: Determine the minimum safety distance according to the position prediction result and the operation avoidance response parameters of the intelligent fixture.

[0073] Specifically, for static avoidance targets, such as large machine tools and fixed shelves in workshops, accurate measurements are first performed. Professional measuring tools are used to obtain their three-dimensional dimensions, including length, width, height, and detailed geometric information such as the coordinate positions of each corner. Then, computer-aided design (CAD) or three-dimensional modeling software is used to construct an accurate three-dimensional model of the static avoidance target. At the same time, the smart fixture itself is also modeled in detail, including the shape, size, and extension range of the gripper, as well as the length and rotation angle range of the moving arm, to ensure that the model can accurately reflect the spatial form of the fixture under different working conditions. According to the work tasks and operation procedures of the smart fixture, its possible motion trajectory when approaching, bypassing, or operating close to the static avoidance target is analyzed. For example, when the fixture needs to grab a workpiece located near a static obstacle from one side, the extension direction of its moving arm, the change in rotation angle, and the opening and closing path of the gripper are determined. Based on the above analysis, combined with the model of the static avoidance target and the motion trajectory of the fixture, the safe distance is determined by geometric calculation. Taking into account the uncertainty of the fixture during movement, such as possible slight offset or vibration, the safety distance is usually set to the sum of the maximum size of the static avoidance target (such as the longest side length, maximum diameter, etc.) and the maximum extension length of the fixture in the corresponding direction, and an additional safety margin is added. For example, if the longest side length of the static obstacle is 2 meters and the maximum extension length of the fixture in this direction is 1 meter, based on experience and safety standards, an additional safety margin of 0.5 meters may be added, thus determining the minimum safety distance to be 3.5 meters.

[0074] Using advanced collision detection algorithms, the models of the static avoidance target and the intelligent fixture are imported into a dedicated simulation software environment. These algorithms can accurately handle complex geometric shapes, not only considering the basic shape of the object, but also accurately judging the collision of corners, depressions and other details. In the simulation software, various possible movement scenarios of the intelligent fixture around the static avoidance target are set, including different combinations of parameters such as speed, acceleration, starting position and movement direction. The simulated fixture starts to move from the initial position and gradually approaches the static avoidance target. At each time step during the movement, the collision detection algorithm calculates the distance between the fixture model and the static avoidance target model and determines whether a collision occurs. Through a large number of simulation tests, the distance between the fixture and the static avoidance target when a collision is about to occur in different scenarios is recorded. The results of multiple simulation tests are statistically analyzed to find the minimum distance value. Taking into account various factors in the actual working environment, such as sensor errors, environmental interference, etc., this minimum distance value is appropriately corrected and finally determined as the minimum safe distance for the static avoidance space characteristics. For example, after multiple simulations, it was found that the closest distance between the fixture and the static avoidance target in a specific motion scenario was 0.3 meters, but considering the actual situation, the minimum safety distance was set to 0.5 meters to ensure sufficient safety margin. The motion speed characteristics of the intelligent fixture are studied in depth, including its maximum speed, average speed, and speed change law in different working modes (such as fast grasping, precise placement, etc.). At the same time, the operation avoidance response parameters of the intelligent fixture are accurately measured and analyzed to determine the time (response time) required from detecting an obstacle to starting to take braking or avoidance measures, as well as parameters such as deceleration during braking. According to the kinematic formula, the distance that the fixture may move within the response time at the current motion speed of the fixture is calculated. For example, if the maximum motion speed of the fixture is 1 meter / second and the response time is 0.5 seconds, the fixture may move 0.5 meters during this period. At the same time, considering that the fixture will continue to move a certain distance during the braking process, the braking distance is calculated based on the braking deceleration. Assuming the braking deceleration is 2 meters / second², the braking distance calculated according to the formula is 0.125 meters. Adding the moving distance and braking distance in the response time, the size of the buffer area is 0.625 meters. Set a buffer area of ​​this size around the static avoidance target to ensure that the gripper has enough space and time to stop or adjust the direction of movement to avoid collision.

[0075] For dynamic avoidance targets, such as moving transport carts and other robots at work, their operating parameters are collected in real time through a variety of sensors (such as visual sensors, laser radars, position sensors, etc.). Visual sensors can obtain information such as the outline and position changes of dynamic avoidance targets. Laser radars can accurately measure the distance and relative speed between them and the intelligent fixture, and position sensors provide their coordinate position information in the workspace. A data acquisition system is established to collect, process and store the data obtained by these sensors in real time to ensure the accuracy and timeliness of the data. According to the collected operating parameters, the running path characteristics of the dynamic avoidance targets are analyzed. For dynamic avoidance targets with periodic motion laws, such as transport carts that periodically travel back and forth on fixed tracks, the periodic path characteristics such as the period length, the point of change in the direction of movement, and the law of speed change are determined by analyzing the data of their position changes over time. For example, it is found that the transport cart completes a round trip every 20 seconds, with a speed of 0 at the two end points of the track and a constant speed of 0.5 m / s in the middle section. For dynamic avoidance targets with irregular motion trajectories, such as manually operated transport robots, their position changes are tracked in real time and their motion trajectories are predicted and analyzed using data processing algorithms (such as Kalman filtering) to determine their dynamic path characteristics, including the approximate direction of motion, speed change range, etc.

[0076] According to the periodic path characteristics of the dynamic avoidance target, the key periodic avoidance positions are determined. For example, for the above-mentioned transport trolley, the two end points of its track (the positions where the speed is 0 and the direction of movement changes) and the positions in the middle of the track that may intersect with the motion path of the smart fixture are considered as periodic avoidance positions. These positions are areas with a high risk of collision and require special attention. Considering the movement of the smart fixture near these periodic avoidance positions, the minimum safe distance is calculated in combination with its running avoidance response parameters. Similar to the static avoidance space characteristics, the distance that the fixture may move within the response time at the current movement speed and the braking distance are calculated. Assuming that the movement speed of the fixture when approaching the periodic avoidance position of the transport trolley is 0.8 m / s, the response time is 0.4 s, and the braking deceleration is 1.5 m / s², the calculated moving distance within the response time is 0.32 m, and the braking distance is 0.21 m, which is added together to get 0.53 m. Then, based on factors such as the stay time of the transport vehicle at the periodic avoidance position and the starting acceleration, an additional safety margin is added, such as 0.2 meters, and the final minimum safety distance at the periodic avoidance position is determined to be 0.73 meters.

[0077] For avoidance targets with dynamic paths, their precise control parameters, such as motor drive signals, speed setting values, steering instructions, etc., are obtained in real time by connecting to their control interfaces (if possible) or using advanced communication technologies. These parameters can more accurately reflect the motion intention and future motion trend of the dynamic avoidance target. Based on the acquired real-time control parameters, the prediction algorithm is used to predict the future position of the dynamic avoidance target. For example, based on the current speed setting value and steering instruction, combined with its kinematic model, its position change in the next few seconds is predicted. At the same time, considering the interference factors in the actual environment, such as changes in ground friction and load changes, the prediction results are corrected and optimized.

[0078] According to the position prediction results, the possible collision between the smart fixture and the dynamic avoidance target in the future is analyzed. Considering the operation avoidance response parameters of the smart fixture, the minimum distance required for the fixture to safely stop or avoid before the predicted collision moment is calculated. For example, it is predicted that the dynamic avoidance target will intersect with the current motion path of the fixture in 3 seconds. At this time, the movement speed of the fixture is 0.6 m / s, the response time is 0.35 seconds, and the braking deceleration is 1.2 m / s². The calculated moving distance within the response time is 0.21 m, the braking distance is 0.15 m, and a total safety distance of 0.36 m is required. At the same time, according to the motion uncertainty of the dynamic avoidance target (such as possible sudden acceleration or steering), a certain safety factor is added, such as 0.15 m, and the minimum safety distance is finally determined to be 0.51 m. In addition, during the movement of the dynamic avoidance target, its position prediction and minimum safety distance calculation are updated in real time to ensure that the smart fixture always maintains a safe distance with the dynamic avoidance target to ensure operation safety. Through the above detailed methods for static and dynamic avoidance space characteristics, the minimum safe distance of the intelligent fixture in a complex working environment can be accurately determined, providing a solid guarantee for the safe and efficient path planning of the intelligent fixture, effectively avoiding the occurrence of collision accidents, and improving the automation level and reliability of industrial production.

[0079] In a possible implementation, step S600 further includes: Step S610: sorting the control parameter strategy space according to the task environment space sequence, and establishing a mapping association between the task space sequence relationship and the control parameter strategy space.

[0080] Step S620: Decompose the spatial tasks based on the task execution objectives to obtain the target tasks of each space.

[0081] Step S630: Optimize the control parameter strategy space according to the target task, screen strategies according to the optimization results, obtain control parameter strategies that meet the evaluation threshold, and sort strategies according to the evaluation values, wherein the control parameter strategies have optimization evaluation labels.

[0082] Step S640: splicing all task parameter strategies of the task environment space sequence according to the optimization evaluation labeling to obtain a total evaluation value of the spliced ​​parameter strategy.

[0083] Step S650: Using the spatial connection adjustment coefficient of the task environment spatial sequence as the parameter splicing evaluation coefficient, the total evaluation value of the splicing parameter strategy is corrected to obtain the path planning result, and the path planning result is the splicing parameter strategy with the largest total evaluation value after correction.

[0084] Specifically, the control parameter strategy space is deeply analyzed, which contains many parameter combination strategies for smart fixtures in different working states and spatial positions. Each strategy has unique performance characteristics and applicable scenarios. At the same time, the task environment space sequence is studied, which describes in detail the various workspace areas and their sequence that the smart fixture goes through in the entire task process. Then, based on the task environment space sequence, each strategy in the control parameter strategy space is carefully sorted according to its most suitable spatial position. For example, in an industrial scenario involving parts processing and assembly, if the task starts in the raw material storage area, then the control parameter strategy that matches the operating characteristics of the area (such as the initial gripping speed, gripping force and other parameter combinations of the fixture in the area) will be placed at the front of the sequence; when the task is advanced to the processing area, the strategies corresponding to the precise positioning, high-speed motion and other characteristics required for the processing operation are arranged in sequence. Through this sorting process, a clear and definite mapping relationship is established between each space in the task environment space sequence and the corresponding strategy in the control parameter strategy space. This mapping relationship ensures that when the intelligent fixture performs a task, it can quickly and accurately obtain and apply the most appropriate control parameter strategy according to the specific task environment space in which it is located, laying a solid foundation for subsequent efficient path planning and precise operation.

[0085] Clarify the task execution goal, which is the core orientation of the entire task and covers the expected results from the initial operation to the final completion. For example, in the task of assembling automobile parts, the task execution goal may be to assemble the parts of a specific model into a complete automobile part in a precise position and order, and to ensure that the assembly quality meets strict process standards, and to complete the assembly task within the specified time to meet the production cycle requirements. Then, according to the task execution goal, the entire task is comprehensively and meticulously decomposed into spatial tasks. Considering that the assembly of automobile parts involves multiple different working areas, such as parts storage area, pretreatment area, assembly area and inspection area, the corresponding target tasks are determined according to the functional characteristics and operation requirements of each area. In the parts storage area, the target task may be to accurately identify and grasp the required parts, which requires the intelligent fixture to quickly locate the target parts in the complex spatial environment of the storage area and grasp them with appropriate posture and strength to ensure that the grasping process is stable and does not damage the parts; in the assembly area, the target task is transformed into assembling the grasped parts with other parts according to precise positions and angles, which requires the intelligent fixture to have high-precision positioning capabilities and flexible posture adjustment capabilities to achieve a perfect fit between parts. Through such a spatial task decomposition process, the complex overall task is broken down into specific target tasks for each space, which provides a clear direction and basis for the subsequent targeted optimization of the control parameter strategy space according to the target tasks, and helps the intelligent fixture to complete various operations efficiently and accurately in different workspaces, thereby ensuring the smooth completion of the entire task.

[0086] For each spatial target task obtained from the decomposition of the spatial task, its unique operation requirements, accuracy standards and environmental constraints are deeply analyzed. For example, in the precision electronic component assembly task, in the component picking space, the target task requires the fixture to grasp the tiny components with gentle and precise movements, which requires the control parameter strategy to ensure moderate clamping force, stable opening and closing speed of the clamp and extremely high positioning accuracy; while in the component assembly space, the target task is to accurately install the components to the specified position of the circuit board. At this time, the control parameter strategy needs to focus on the accuracy of the motion trajectory of the mobile arm and the accuracy of the posture adjustment. According to the specific requirements of these target tasks, the parameter optimization is fully carried out in the control parameter strategy space, and the numerous control parameter combinations in the strategy space (covering the speed, acceleration, rotation angle of the mobile arm, the clamping force, opening and closing speed of the clamp and other parameters) are carefully evaluated. Through simulation experiments, the performance of each parameter combination in achieving the target task is measured, such as the accuracy, efficiency, stability of the operation, and the impact on the workpiece and the fixture itself. Then, strict strategy screening is carried out according to the optimization results, and a series of evaluation thresholds are set. These thresholds are determined based on the key performance indicators of the task. For example, in the component picking space, the component grasping position error must not exceed ±0.05 mm, and the clamping force fluctuation range must be controlled within the minimum value; in the component assembly space, the assembly position accuracy is required to reach ±0.02 mm, etc. Only control parameter strategies that meet these evaluation thresholds are retained. For the screened strategies, they are further sorted according to the comprehensive evaluation value. The comprehensive evaluation value comprehensively considers multiple factors, such as the weight of operation accuracy, the impact of operation efficiency, energy consumption, and the degree of equipment wear. Each control parameter strategy is given an optimization evaluation label, which clearly records its detailed performance in various evaluation indicators, so as to intuitively compare the pros and cons of different strategies. Through such sorting, the control parameter strategy that best suits each target task can be quickly determined, providing high-quality strategy selection for subsequent full-task path planning.

[0087] According to the optimization evaluation annotations obtained in the previous steps, the optimal control parameter strategies corresponding to each target task in the task environment space sequence are spliced ​​in order. For example, in a complex electronic product assembly task, if the task environment space sequence includes the component picking area, the circuit board installation area, the shell assembly area, etc., the optimal parameter strategy found in the component picking area (such as the clamping force, opening and closing speed and other parameter combinations suitable for grasping tiny components), the precise positioning and installation parameter strategy of the circuit board installation area, and the assembly parameter strategy of the shell assembly area will be spliced ​​in the order of task execution to form a complete full-task parameter strategy chain. In the splicing process, the transition between the various spaces should be fully considered. For example, from the component picking area to the circuit board installation area, it is necessary to ensure that the speed, posture adjustment and other parameters of the fixture during the movement process can be smoothly transitioned to avoid instability or collision risks caused by parameter mutations. Then, the total evaluation value of this splicing parameter strategy is calculated. The calculation of the total evaluation value comprehensively considers the various performances of each target task under its corresponding parameter strategy. For example, for the parts picking area, factors such as the accuracy and efficiency of picking and the degree of protection of parts will be considered; for the circuit board installation area, the focus is on the accuracy of the installation position and the stability of the installation process; for the shell assembly area, the focus is on the tightness of the assembly and the integrity of the appearance. These factors are given different weights according to their importance, and the total evaluation value of the splicing parameter strategy is obtained through calculation methods such as weighted summation. This total evaluation value comprehensively reflects the overall performance level of the entire task path planning under the current parameter strategy combination, and provides an important reference for further optimization of the path planning in the future. It helps to judge whether the current splicing parameter strategy meets the task requirements and whether it needs to be adjusted or re-optimized.

[0088] The significance of the spatial connection adjustment coefficient is clarified. It is a quantitative indicator that comprehensively considers the transition characteristics between task environment spaces. For example, in an industrial scenario involving multiple processing stations and material transfer areas, from one processing station to an adjacent material transfer area, the fixture needs to change its movement speed, adjust its posture, and adapt to different spatial layouts. These factors will affect the performance of the overall path planning. Next, the coefficient is introduced as the parameter splicing evaluation coefficient. The total evaluation value of the splicing parameter strategy has been obtained before. This total evaluation value reflects the overall performance of the parameter strategy combination corresponding to each target task under ideal conditions. However, various factors in the spatial connection process in actual work will affect the total evaluation value. At this time, the spatial connection adjustment coefficient is used to correct the total evaluation value. For example, if the transition between two adjacent spaces is more complex and requires the fixture to make large posture adjustments and speed changes, the corresponding spatial connection adjustment coefficient may reduce the total evaluation value; conversely, if the transition is smoother, the impact on the total evaluation value is small. The corrected total evaluation value is obtained by calculating and correcting the spatial connection adjustment coefficient and the total evaluation value. Then, among the numerous splicing parameter strategies, the strategy with the largest total evaluation value after correction is selected, which is the final path planning result. This path planning result fully considers the characteristics of each area in the task environment space sequence and the connection relationship between spaces, ensuring that the intelligent fixture can operate smoothly in a complex workspace with optimal performance when performing tasks, effectively avoiding collision risks, improving the accuracy and efficiency of operations, and meeting the high requirements for intelligent fixture path planning in industrial production.

[0089] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0091] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent fixture path planning method for an industrial robot, characterized in that: The method comprises: Based on the working parameters of the intelligent fixture, feature recognition is performed on the working environment space of the intelligent fixture to obtain space partitions, wherein the space partitions are clustered and segmented according to the space recognition features; Establishing spatial constraint features according to the correspondence between the spatial partitions and the spatial identification features; Obtaining control constraint information according to the working parameters of the intelligent fixture; According to the spatial constraint feature and the control constraint information, constraint matching analysis is performed to obtain a control parameter strategy space, wherein the control parameter strategy in the control parameter strategy space has a workspace location identifier; Decompose the task process of the industrial robot fixture task to obtain the task environment space sequence; According to the task environment space sequence, spatial matching is performed with the workspace position identifier of the control parameter strategy space, the control parameter strategy space is used to perform local optimization of the space partition strategy, and the local optimization result is used to optimize the full task path to obtain the path planning result.

2. The intelligent fixture path planning method for an industrial robot according to claim 1, characterized in that: The method of performing feature recognition on the working environment space of the intelligent fixture based on the working parameters of the intelligent fixture to obtain space partitions includes: According to the working parameters of the intelligent fixture, a relationship between the working parameters and the volume state of the fixture is established to obtain a maximum working space and a minimum working space; Identify static avoidance targets and dynamic avoidance targets in the work environment space, and determine static avoidance space characteristics and dynamic avoidance space characteristics based on the work space where the static avoidance targets and dynamic avoidance targets are located; Using the minimum working space, the static avoidance space characteristics and the dynamic avoidance space characteristics are subjected to traffic fitting to obtain traffic impact characteristics; Using the maximum working space, the static avoidance space characteristics and the dynamic avoidance space characteristics are fitted to obtain operation impact characteristics; A spatial feature discrete analysis is performed according to the traffic impact characteristics and the operation impact characteristics, spatial clustering is performed according to the feature distribution discreteness, and spatial segmentation is performed according to the spatial clustering results to obtain the spatial partition.

3. The intelligent fixture path planning method for an industrial robot according to claim 2, characterized in that: The obtaining of the operation impact characteristics further includes: According to the relationship between the working parameters and the clamp volume state, a multi-level operating state volume is obtained; Using the multi-level operation state volume, respectively performing pass fitting on the static avoidance space feature and the dynamic avoidance space feature to obtain a multi-level operation restriction feature; The multi-level operation restriction characteristics are integrated to obtain the operation impact characteristics.

4. The intelligent fixture path planning method for an industrial robot according to claim 2, characterized in that: According to the traffic impact characteristics and the operation impact characteristics, spatial feature discrete analysis is performed, and spatial clustering is performed according to the feature distribution dispersion, including: According to the working environment space, the location distribution of the traffic impact features is marked to obtain a traffic impact distribution map; According to the position distribution of the operation impact features, distribution annotation is performed in the work environment space to obtain an operation impact distribution map; The traffic impact distribution map and the operation impact distribution map are merged and superimposed, and the dispersion is calculated according to the distribution annotation to obtain the feature distribution dispersion, which is used to characterize the discrete proportion of the traffic impact feature and the operation impact feature in the workspace; Spatial feature clustering is performed according to the feature distribution discreteness.

5. The intelligent fixture path planning method for an industrial robot according to claim 1, characterized in that: According to the working parameters of the intelligent fixture, control constraint information is obtained, including: Obtaining control nodes of the intelligent clamp, wherein the control nodes at least include a moving arm control node and a clamping mechanism control node; According to the adjustment parameter range of the control node, a multi-node control parameter combination is performed to obtain the fixture shape change of each control parameter combination; According to the change of the fixture morphology, edge space constraints are obtained; Determining a traffic adjustment constraint according to an adjustment parameter range of the mobile arm control node; The control constraint information is obtained according to the edge space constraint and the traffic adjustment constraint.

6. The intelligent fixture path planning method for an industrial robot according to claim 2, characterized in that: According to the spatial constraint feature and the control constraint information, constraint matching analysis is performed to obtain a control parameter strategy space, including: Obtaining a constraint distribution space according to the spatial constraint characteristics; According to the control constraint information, obtaining the fixture control operation constraint space; According to the spatial constraint characteristics, a minimum safety distance is configured; Based on the minimum safety distance, the fixture is used to control the operation constraint space to identify the operation safety space in the constraint distribution space to obtain a safe operation space; The working parameters of the intelligent fixture are searched in the safe operation space, all feasible working parameters are obtained, and the control parameter strategy space is constructed.

7. The intelligent fixture path planning method for an industrial robot according to claim 6, characterized in that: According to the spatial constraint characteristics, the minimum safety distance is configured, including: For the static avoidance space characteristics, the minimum safety distance is determined according to the edge characteristics of the static avoidance target and the operation avoidance response parameters of the intelligent fixture; For the dynamic avoidance space feature, the dynamic avoidance target is taken as the collaborative target to obtain the operation parameters of the dynamic avoidance target, and the operation path feature is obtained, wherein the operation path feature includes a periodic path and a dynamic path; Determine a periodic avoidance position according to the periodic path, and determine the minimum safety distance according to the periodic avoidance position combined with the operation avoidance response parameter of the intelligent fixture; According to the dynamic path, connecting to the control interface of the dynamic avoidance target, obtaining real-time control parameters for position prediction; The minimum safety distance is determined according to the position prediction result and in combination with the operation avoidance response parameters of the intelligent fixture.

8. The intelligent fixture path planning method for an industrial robot according to claim 1, characterized in that: The obtaining of the path planning result includes: The control parameter strategy space is sorted according to the task environment space sequence to establish a task space sequence relationship and a mapping association with the control parameter strategy space; Based on the task execution goal, the spatial tasks are decomposed to obtain the target tasks of each space; Optimizing the control parameter strategy space according to the target task, screening strategies according to the optimization results, obtaining control parameter strategies that meet the evaluation threshold, and sorting strategies according to the evaluation values, wherein the control parameter strategies have optimization evaluation labels; According to the optimization evaluation annotation, all task parameter strategies of the task environment space sequence are spliced ​​to obtain the total evaluation value of the spliced ​​parameter strategy; The spatial connection adjustment coefficient of the task environment spatial sequence is used as the parameter splicing evaluation coefficient, and the total evaluation value of the splicing parameter strategy is corrected to obtain the path planning result. The path planning result is the splicing parameter strategy with the largest total evaluation value after correction.

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