Intelligent installation and path planning robot system for bus trunking

Abstract

This invention discloses a robot system for intelligent installation and path planning of busbar trunking, relating to the field of busbar trunking path planning technology. The system includes an environmental perception unit, a path planning unit, an installation execution unit, a collaborative control unit, and a power supply guarantee unit. Each unit interacts with the others via an industrial-grade wireless communication link. The frequency band of this communication link is adapted to the electromagnetic environment characteristics of the busbar trunking installation scenario, and its signal transmission characteristics match the structural distribution characteristics of the busbar trunking installation space. By collecting environmental parameters of the installation space in real time through the environmental perception unit, and combining this with the dynamic planning and obstacle avoidance decisions of the path planning unit, deep collaboration between environmental perception and path planning is achieved, solving the problems of low efficiency and poor adaptability in traditional manual planning.

Description

Technical Field

[0001] This invention relates to the field of busbar path planning technology, and in particular to a robot system for intelligent installation and path planning of busbars. Background Technology

[0002] Busbar trunking, as the core carrier of power transmission, is widely used in factories, building power distribution rooms, pipe corridors, and other scenarios. Its installation accuracy and route rationality directly determine the efficiency and safety stability of power transmission. Traditional busbar trunking installation relies heavily on manual measurement and positioning, as well as manual handling and installation, which presents numerous technical challenges.

[0003] First, installation path planning relies on manual on-site surveys, which is inefficient and easily affected by subjective factors. It is difficult to adapt to the obstacle avoidance requirements in complex spaces, often resulting in path redundancy or installation interference. Second, during manual installation, it is difficult to accurately control the positioning accuracy and connection tightness of busbar trunking. Installation deviations can easily lead to increased contact resistance, severe overheating, and even safety hazards. Third, existing intelligent installation equipment is mostly a single-action actuator, lacking deep coordination between environmental perception and path planning. Moreover, the fault detection mechanism is lagging behind, and it cannot provide real-time feedback on installation quality and equipment status. As a result, deviations during installation cannot be corrected in a timely manner, affecting the overall installation efficiency and reliability. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a busbar intelligent installation and path planning robot system. The technical solution is as follows:

[0005] A busbar trunking intelligent installation and path planning robot system is provided, which includes an environmental perception unit, a path planning unit, an installation execution unit, a collaborative control unit, and a power supply protection unit.

[0006] Each unit achieves data interaction through an industrial-grade wireless communication link. The frequency band of the communication link is adapted to the electromagnetic environment characteristics of the bus trunking installation scenario, and its signal transmission characteristics are matched with the structural distribution characteristics of the bus trunking laying space.

[0007] The output data dimension of the environmental perception unit is adapted to the core features of the busbar installation space size, obstacle distribution, and installation reference surface, and the data format is physically adapted to the input interface of the path planning unit.

[0008] The path planning unit has a built-in path generation chip. The chip's storage area contains a planning strategy mapping table built based on the bus trunking laying path optimization mechanism and the installation space avoidance mechanism. The parameter association logic of the mapping table is derived from the bus trunking installation specifications and spatial physical constraints.

[0009] The installation execution unit integrates a busbar trunking identification reading module, which uses the inherent physical identifier of the busbar trunking as a unique index. The execution board presets physical installation links based on the busbar trunking connection structure and installation spacing requirements, and the link action timing is synchronously adapted with the path planning results.

[0010] The output of the collaborative control unit establishes a bidirectional hardware signal link with the parameter configuration module of the path planning unit and the power drive module of the installation execution unit. Its feedback path is driven by the adaptation response parameters corresponding to the installation characteristics of the bus trunking itself. Each unit forms a full-process hardware linkage structure through the above hardware link and mechanism mapping link.

[0011] Installation scenarios include factories, building power distribution rooms, and pipe corridors. The communication link adopts a dual-mode architecture of LoRa and 5G, and the input interface adopts a standard industrial bus interface.

[0012] Preferably, the environmental sensing unit includes a spatial size sensing module, an obstacle detection component, and a data integration module, wherein the response characteristics of each sensing element of the spatial size sensing module are matched with the busbar trunking dimensions and the installation space gap range.

[0013] The obstacle detection component detects spatial obstacles that affect the installation path, and the analysis logic of the detection signal is adapted to the structural constraint mechanism of the installation space. The output of the data integration module establishes a physical data input link with the map building chip of the path planning unit through a state calibration link. The output environmental state data is adapted to the busbar path planning mechanism analysis logic of the map building chip, and the data integration rules are derived from the multiple physical constraints of the busbar installation space.

[0014] The characteristics of spatial obstacles include pipeline diameter, beam and column cross-sectional dimensions, and wall spacing. The status calibration link uses shielded twisted-pair cable transmission.

[0015] Preferably, the spatial dimension sensing module includes a slot width sensing element, an installation spacing sensing element, and a wall reference sensing element deployed at the front end of the robot, and also includes a corner angle sensing element integrated on the side of the robot.

[0016] The response range of the channel width sensing element covers the standard channel width range of the busbar trunking, the sensing threshold of the installation spacing sensing element matches the spacing constraint range of the busbar trunking installation specification, and the response curve of the wall reference sensing element fits the flatness variation characteristic curve of the installation reference surface.

[0017] The signal transmission carrier of each sensing element is a polytetrafluoroethylene insulated transmission structure that is compatible with the electromagnetic environment of the installation scene. The dielectric properties of this structure are adapted to the electric field distribution characteristics inside the building installation environment. Independent signal microchannels are arranged inside, and the spacing between the microchannels is inversely proportional to the expected laying density of the busbar trunking. The channel transmission rate is dynamically matched with the sampling frequency of the sensing element.

[0018] The dielectric constant of the polytetrafluoroethylene insulated transmission structure is precisely matched with the electric field distribution characteristics of the building installation environment, and there are 6 independent signal microchannels.

[0019] Preferably, the obstacle detection component includes a lidar detector, a visual recognition component, and an acoustic warning component;

[0020] The detection frequency band of the laser radar detector covers the contour size feature range of the obstacles in the installation space. The detection parameters of the visual recognition device match the types of obstacles such as pipelines, beams and columns on the busbar installation path. The triggering reference of the acoustic warning device is the difference in distance characteristics between the obstacle and the robot. Its detection trigger end establishes a hardware signal connection with the timing sensor of the robot installation operation.

[0021] The output of this component is connected to the data integration module together with the output of the spatial size sensing module. The integrated signal format is compatible with the spatial avoidance mechanism signal receiving interface of the path planning unit. The integrated dataset contains the associated information of spatial coordinates, obstacle type, and size parameters.

[0022] The detection range of the lidar detector is adapted to the size of the installation space, and the vision recognition component adopts an industrial-grade CMOS image sensor;

[0023] Preferably, the path planning unit includes a map building module, a path generation chip, and a timing coordination module. The modeling logic of the map building module matches the three-dimensional structural mechanism of the busbar installation space, and the modeling accuracy is adapted to the allowable range of installation errors.

[0024] The planning algorithm of the path generation chip is adapted to the optimization mechanism of the bus trunking laying path. The algorithm parameters are derived from the physical constraints of the installation space and the connection process requirements of the bus trunking. The planning results include the associated parameters of path node coordinates, turning angles, and moving speed.

[0025] The synchronization benchmark of the timing coordination module is the operation time scale of the bus trunking segment installation. Its output timing planning data provides a physical data carrier of the time dimension for the multi-action allocation board of the installation execution unit through the timestamp encapsulation board, and the timestamp format is compatible with the installation timing mechanism mapping link of the action allocation board.

[0026] The map building module uses 3D point cloud modeling technology, and the timestamp encapsulation board supports millisecond-level time synchronization;

[0027] Preferably, the path planning unit further includes an obstacle avoidance decision component. The decision logic of this component matches the structural constraint mechanism of the busbar installation space. It has a built-in spatial interference judgment link based on the installation path and obstacles. The interference judgment parameters are derived from the obstacle size and the space required for the busbar installation.

[0028] The obstacle avoidance decision component has a built-in obstacle avoidance strategy storage chip. The storage chip has embedded obstacle avoidance path adjustment logic corresponding to different obstacle types. The adjustment logic includes the relationship between path offset, corner correction value, and movement speed adaptation parameters.

[0029] The obstacle avoidance command output of this component establishes a hardware control link with the robotic arm drive module on which the execution unit is installed. Its command parameters are linked and adapted to the motion trajectory adaptation mechanism of the robotic arm, and the command execution timing is uniformly adapted to the time scale of path planning.

[0030] The obstacle avoidance strategy storage chip uses Flash storage medium, and the hardware control link uses a CAN bus interface.

[0031] Preferably, the dynamic correction link of the path generation chip is adapted to the real-time data output of the environmental sensing unit, the correction logic is constructed based on the deviation compensation mechanism of the bus trunking installation path, and the correction triggering condition is derived from the degree of difference between the environmental data and the initial planning data.

[0032] The dynamic correction link can adjust the physical parameters such as the turning angle and spacing of the planned path in real time according to environmental changes, and the adjustment range is adapted to the remaining redundancy of the installation space.

[0033] The chip's output is connected to the parameter input interface of the path evaluation component of the cooperative control unit. Its planning data is adapted to the installation feasibility coupling constraint logic of the evaluation component. The evaluation feedback result can trigger a secondary path correction through the hardware link.

[0034] The parameter input interface uses an SPI communication interface, and the response time of the secondary correction matches the rhythm of the installation operation.

[0035] Preferably, the installation execution unit includes a robotic arm assembly, a positioning calibration module, and an installation locking assembly. The motion parameters of the robotic arm assembly are matched with the structural adaptation mechanism of the busbar installation interface, and the degree of freedom of motion is adapted to the connection process requirements of the busbar.

[0036] The calibration benchmark of the positioning calibration module is compatible with the positioning mechanism of the bus trunking installation hole, and the calibration accuracy matches the allowable range of installation error. The calibration process includes associated actions of hole identification, attitude adjustment, and position locking.

[0037] The locking force of the installation locking component is adapted to the mechanical characteristics of the busbar trunking connection structure, and the locking action timing is linked and adapted to the positioning calibration completion signal; the execution data output end of this unit establishes a physical link with the data source interface of the installation effect evaluation component of the collaborative control unit, and the output installation data format is adapted to the installation quality mechanism evaluation logic of the evaluation component.

[0038] Preferably, the adaptive clamping component of the robotic arm assembly adopts a flexible adaptable structure, the contour of the clamping surface matches the external structural mechanism of the busbar trunking shell, the material properties of the clamping surface are adapted to the surface protection requirements of the busbar trunking shell, and the clamping posture can be adaptively adjusted according to the changes in the size of the trunking.

[0039] The positioning calibration module has a built-in hole position recognition chip. Its recognition parameters are adapted to the hole diameter and spacing characteristics of the busbar mounting holes. The recognition logic is derived from the physical characteristics and imaging mechanism of the mounting holes.

[0040] The calibration signal of the positioning calibration module is synchronously linked with the action execution timing of the robotic arm component. The linkage logic is derived from the compensation mechanism of the mounting hole positioning accuracy and the robotic arm action error, ensuring the coordination and consistency of positioning and installation actions.

[0041] Preferably, the collaborative control unit includes a busbar digital twin component and an installation effect evaluation component. The constraint equation chip of the digital twin component has a coupling coefficient table based on the multi-physics coupling mechanism of the busbar, installation environment, robot's spatial field, mechanical field, and motion field. The coupling coefficient is calibrated by experimental data on the mechanism of busbar installation deviation and path planning.

[0042] The evaluation chip of the installation effect evaluation component integrates a data integration link based on the installation quality mechanism of the bus trunking throughout its entire life cycle. It integrates the execution data of the installation execution unit with the physical simulation data of the digital twin component. The evaluation dimensions include related parameters such as installation position accuracy, connection tightness, and path adaptability. The output adjustment end of this unit establishes a two-way hardware link with the path generation chip of the path planning unit and the robotic arm component of the installation execution unit. The parameters of its adjustment signal are linked and adapted to the installation quality data mechanism of the installation execution unit. The adjustment range is derived from the degree of difference between the evaluation result and the installation specification.

[0043] Beneficial effects

[0044] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0045] By collecting environmental parameters of the installation space in real time through the environmental perception unit and combining them with the dynamic planning and obstacle avoidance decision-making of the path planning unit, deep collaboration between environmental perception and path planning is achieved, solving the problems of low efficiency and poor adaptability of traditional manual planning. Through the precise positioning and fastening control of the installation execution unit, combined with the closed-loop evaluation and dynamic correction of the collaborative control unit, the installation accuracy and connection reliability are improved, effectively avoiding safety hazards caused by installation deviations, and comprehensively improving the efficiency and quality stability of busbar installation.

[0046] The path planning unit constructs a planning strategy based on an improved A* algorithm and multiple physical constraints. It achieves dynamic path correction through quantitative evaluation of spatial adaptability and obstacle avoidance rationality. It can accurately adapt to obstacle avoidance and installation process requirements in complex spaces. Compared with fixed path planning, its adaptability and rationality are significantly improved. The installation execution unit adopts vision + laser dual calibration technology and adaptive clamping structure. Combined with real-time monitoring of installation accuracy and connection reliability, it achieves accurate positioning and stable connection of busbar trunking, solving the technical pain points of large positioning deviation and weak connection in traditional installation.

[0047] The collaborative control unit uses a digital twin model to visualize and simulate the installation process and evaluate the installation effect from multiple dimensions, forming a closed-loop mechanism for the entire process. It can adjust the path parameters and installation parameters in reverse according to the evaluation results to ensure continuous optimization of installation quality. The power supply unit adopts a dual-mode complementary power supply mode to dynamically adapt to the load requirements of each unit. While improving the system's energy efficiency, it ensures the stability of power supply under high-intensity operation and provides support for the continuous and reliable operation of the system. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A flowchart of the intelligent installation and path planning robot system for busbar trunking provided in the embodiments of this application. Detailed Implementation

[0050] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0051] To facilitate understanding of the embodiments of this application, the following points will be explained first:

[0052] First, in this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but does not exclude the possibility of indicating an "and" relationship. The specific meaning can be understood in the context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one of a, b, or c" can represent: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0053] Second, in this application, the use of prefixes such as "first" and "second" is merely for the purpose of distinguishing and describing different things belonging to the same name category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no temporal sequence, size, or priority relationship between them.

[0054] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0055] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0056] like Figure 1 The diagram shown is a structural schematic of the intelligent busbar installation and path planning robot system provided in this application embodiment, including an environmental perception unit, a path planning unit, an installation execution unit, a collaborative control unit, and a power supply guarantee unit.

[0057] The environmental sensing unit is used to collect environmental parameters of the busbar installation space in real time. The environmental parameters include at least the space dimensions, obstacle distribution, and flatness of the installation reference surface. The collected environmental parameters are then transmitted to the collaborative control unit. The collaborative control unit is used to receive the environmental parameters transmitted by the environmental sensing unit and generate corresponding path planning instructions and installation execution instructions.

[0058] In this embodiment, the environmental perception unit integrates a spatial dimension sensing module, obstacle detection components, and a data integration module to collect real-time, multi-dimensional physical environment information of the installation space, providing the system with accurate external state input. These raw environmental parameters are reliably transmitted to the collaborative control unit, which, as the core decision-making hub of the system, uses a built-in multi-source data fusion algorithm and a predefined environment, path, and installation mapping model to perform real-time data analysis and correlation decisions. It can identify complex scenarios such as narrow spaces requiring path optimization, dense obstacles requiring segmented avoidance, uneven reference surfaces requiring adjustment of installation posture, etc. Based on this, it dynamically generates two sets of key instructions: one is a control instruction for the path planning unit, guiding the optimization of parameters such as path node coordinates, movement speed, and obstacle avoidance strategies; the other is a control instruction for the installation execution unit, triggering adjustments to parameters such as positioning calibration, clamping force, and locking torque. This closed-loop process from environmental perception to intelligent decision-making transforms the system from passive execution to active adaptation, laying a core foundation for the accuracy of subsequent path planning and installation execution.

[0059] The path planning unit receives path planning instructions transmitted by the collaborative control unit, obtains path planning parameters, and obtains spatial adaptability and obstacle avoidance rationality based on the path planning parameters. These parameters characterize the degree of adaptability between the planned path and the installation space, as well as the effectiveness of obstacle avoidance. Based on the spatial adaptability and obstacle avoidance rationality, the unit determines whether to perform dynamic path correction to achieve accurate adaptation between the planned path and the complex space. If so, the path information is sent to the installation execution unit after correction; otherwise, the path information is sent directly to the installation execution unit.

[0060] It should be noted that the path planning parameters include the installation space channel width, obstacle dimensions, busbar trunking turning radius, and installation spacing requirements. The installation space channel width refers to the minimum lateral distance allowed for robot passage and operation within the installation area. This is measured by scanning the two boundaries of the installation space with a LiDAR detector, and the straight-line distance between the two points is the channel width. Obstacle dimensions refer to the cross-sectional dimensions and extension length of obstacles such as pipelines, beams, and columns along the installation path. These dimensions are calculated by acquiring obstacle images using a visual recognition device, extracting contour features through image segmentation algorithms, and calculating the dimensional parameters. The busbar trunking turning radius refers to the minimum allowable bending radius of the busbar trunking itself, which is directly obtained from the busbar trunking product specifications. The installation spacing requirements refer to the minimum safe distance between the busbar trunking and walls, obstacles, and adjacent busbar trunking, determined according to national electrical installation standards.

[0061] The channel width adaptation factor is interleaved with the analysis results of the ratio of installation space channel width to busbar turning radius to obtain the channel adaptation influence component; the obstacle avoidance factor is interleaved with the analysis results of the ratio of obstacle size to installation spacing requirements to obtain the avoidance influence component; the path redundancy factor is interleaved with the analysis results of the ratio of planned path length to straight-line distance to obtain the redundancy influence component; the channel adaptation influence component, avoidance influence component, and redundancy influence component are superimposed to obtain spatial adaptability and obstacle avoidance rationality. The specific constraint expressions for obtaining spatial adaptability and obstacle avoidance rationality are as follows:

[0062] ;

[0063] In the formula, S represents spatial adaptability and obstacle avoidance rationality; This represents the channel width adaptation factor obtained from the path planning database; This represents the obstacle avoidance factor obtained from the path planning database; This represents the path redundancy factor obtained from the path planning database; W represents the width of the installation space channel; R represents the turning radius of the busbar trunking; O represents the obstacle size. Indicates the installation spacing requirements; L represents the planned path length; L represents the straight-line distance from the installation start point to the end point.

[0064] It's important to understand that the installation space channel width, obstacle size, busbar turning radius, and installation spacing requirements are not independent but rather form a mutually restrictive and adaptable relationship: the channel width must be greater than the sum of the busbar turning radius and the robot's operational redundancy; otherwise, turning cannot be completed. Obstacle sizes must be smaller than the installation spacing requirements; otherwise, path adjustments are necessary for obstacle avoidance. The smaller the ratio of the planned path length to the straight-line distance, the lower the path redundancy and the higher the installation efficiency. Simultaneously, there is a positive correlation between installation space channel width and spatial adaptability and obstacle avoidance rationality; the wider the channel, the greater the space for path adjustment, resulting in higher adaptability and rationality. Conversely, there is a negative correlation between obstacle size and spatial adaptability and obstacle avoidance rationality; the larger the obstacle, the more difficult it is to avoid, leading to lower adaptability and rationality. Finally, there is a negative correlation between path redundancy and spatial adaptability and obstacle avoidance rationality; the higher the path redundancy, the more ineffective travel, resulting in lower adaptability and rationality.

[0065] Furthermore, the specific steps to determine whether to perform dynamic path correction are as follows:

[0066] Based on the comparison between spatial adaptability and obstacle avoidance rationality and the adaptation reference value, if the spatial adaptability and obstacle avoidance rationality are greater than or equal to the adaptation reference value, then no dynamic path correction is performed; otherwise, dynamic path correction is performed based on the adaptation deviation amount, which represents the degree of negative deviation between the spatial adaptability and obstacle avoidance rationality and the adaptation reference value. The dynamic path correction includes node coordinate optimization and obstacle avoidance strategy adjustment.

[0067] It is important to understand that the process begins by obtaining path planning parameters, calculating spatial adaptability and obstacle avoidance rationality, and comparing them with adaptation reference values. If corrections are needed, the process proceeds to the optimization branch. In the node coordinate optimization stage, the coordinate offset factor is obtained by querying the mapping table based on the adaptation deviation and channel width deviation, and the path node positions are adjusted. In the obstacle avoidance strategy adjustment stage, based on the type and size of obstacles, detour, overpass, or segmented installation strategies are selected to generate the corrected path. Finally, the corrected path information is transmitted to the installation execution unit to ensure that the path adapts to the complex spatial environment.

[0068] It should be further explained that the specific process for node coordinate optimization is as follows:

[0069] The difference between the width of the installation space channel and the turning radius of the busbar trunking is obtained and recorded as the channel width deviation. The channel width deviation is input into a predefined deviation and offset mapping table, and the corresponding node coordinate offset factor is output. The mapping relationship is configured such that: the larger the channel width deviation, the smaller the mapped node coordinate offset factor, so as to reduce path redundancy; the smaller the channel width deviation, the larger the mapped node coordinate offset factor, so as to ensure the working space.

[0070] Establish a correlation rule between node coordinate offset factor and path node coordinates, wherein the correlation rule is specifically as follows:

[0071] When the deviation of the channel width is greater than the upper limit threshold of the deviation, it is determined to be a sufficient space scenario. The node coordinate offset factor and the adaptation deviation are input into the predefined offset and coordinate mapping table, and the corresponding node lateral offset value is output. The lateral coordinate of the current path node is combined with the node lateral offset value to obtain the target node coordinate, thereby shortening the path length while ensuring the working space. The upper limit threshold of the deviation represents the critical deviation value at which the channel width fully meets the working requirements.

[0072] Node coordinate optimization also includes:

[0073] When the channel width deviation is less than the lower limit threshold, it is determined to be a narrow space scenario. The node coordinate offset factor and the adaptation deviation are input into the predefined offset and coordinate mapping table, and the corresponding node longitudinal offset value is output. The longitudinal coordinate of the current path node is combined with the node longitudinal offset value to obtain the target node coordinate. Thus, path adaptation is achieved through segmented avoidance. The lower limit threshold represents the critical deviation value at which the channel width can only meet the basic passage requirements.

[0074] When the channel width deviation is within the deviation threshold range, it is determined to be a normal spatial scenario. In this case, node coordinate optimization is not performed, and the current path node coordinates are maintained. The deviation threshold range refers to the closed interval formed by the lower deviation threshold and the upper deviation threshold.

[0075] Under the dynamically adjusted path node coordinates, the path nodes are reconnected according to the obstacle avoidance strategy adjustment results to generate a continuous corrected path until the spatial adaptability and obstacle avoidance rationality are greater than or equal to the adaptability reference value, thus completing the path dynamic correction process.

[0076] In this embodiment, by establishing a correlation rule between channel width deviation and node coordinate offset factor, precise path optimization is achieved under different spatial scenarios. First, in scenarios with ample space, the path length is shortened by optimizing the lateral offset value of nodes, ensuring working space while improving installation efficiency. Second, in scenarios with narrow space, segmented obstacle avoidance is achieved by using the longitudinal offset value of nodes, solving the path adaptation problem in complex spaces. For scenarios with normal space, the path node coordinates are kept unchanged to avoid unnecessary adjustment costs. Finally, a continuous path is reconstructed based on the corrected node coordinates to ensure that spatial adaptability and obstacle avoidance rationality meet the requirements, providing precise path support for subsequent installation execution.

[0077] It should be further explained that the specific process for adjusting the obstacle avoidance strategy is as follows:

[0078] The system identifies obstacle types on the installation path in real time, classifying them into fixed obstacles (beams, columns, walls) and moving obstacles (tools, personnel). It obtains the size parameters and position coordinates of each type of obstacle and inputs the obstacle size parameters and installation spacing requirements into a predefined size and strategy mapping table. The mapping relationship maps the ratio of obstacle size to installation spacing requirements into discrete obstacle avoidance levels, including mild avoidance, moderate avoidance, severe avoidance, and no avoidance.

[0079] Based on the obstacle avoidance level and adaptation deviation, a predefined level and strategy mapping table is queried, and the corresponding target obstacle avoidance strategy is output. The strategy is configured such that the higher the obstacle avoidance level, the higher the complexity of the corresponding obstacle avoidance strategy and the greater the path adjustment range.

[0080] The specific steps for adjusting the obstacle avoidance strategy are as follows:

[0081] If the obstacle avoidance level is light avoidance, complex obstacle avoidance actions will not be performed; only the path node coordinates will be finely adjusted to maintain the current obstacle avoidance strategy.

[0082] If the obstacle avoidance level is moderate, the moderate obstacle avoidance level and the adaptation deviation amount are input into the predefined level and strategy mapping table, and the corresponding path detour angle is output. The current path and the path detour angle are combined to obtain the target detour path, so as to achieve safe isolation from obstacles and prevent collision risks during the installation process.

[0083] If the obstacle avoidance level is severe avoidance, the severe avoidance level and the adaptation deviation amount are input into the predefined level and strategy mapping table, and the corresponding segmented installation mark is output. The installation path is split into multiple short paths, and obstacle avoidance is achieved by segmented transportation and segmented installation, effectively avoiding installation difficulties caused by obstacle obstruction.

[0084] If the obstacle avoidance level is "unavoidable", a path adjustment warning signal is sent to the collaborative control unit, which then regenerates the installation start and end points and initiates secondary path planning.

[0085] In this embodiment, obstacle avoidance strategies are precisely adapted through obstacle type identification and obstacle avoidance level classification. In mild obstacle avoidance scenarios, only minor path adjustments are needed to meet requirements and ensure installation efficiency. In moderate obstacle avoidance scenarios, safe isolation is achieved by adjusting the detour angle, balancing efficiency and safety. In severe obstacle avoidance scenarios, a segmented installation strategy is adopted to solve the problem of avoiding complex obstacles. In scenarios where obstacle avoidance is impossible, a timely warning signal is sent to initiate secondary planning, avoiding ineffective operations. The entire obstacle avoidance mechanism significantly improves the system's adaptability and reliability in complex spatial environments through accurate obstacle perception and graded response.

[0086] The installation execution unit receives installation execution instructions and path information from the path planning unit transmitted by the collaborative control unit, obtains installation execution parameters, and calculates installation accuracy and connection reliability based on these parameters. These parameters characterize the precision of the busbar installation position and the stability of the connection structure. The unit determines whether to adjust the installation parameters based on the installation accuracy and connection reliability to prevent potential power transmission hazards caused by installation deviations. If the adjustment is made, the unit proceeds to the evaluation stage of the collaborative control unit; otherwise, it proceeds directly to the evaluation stage of the collaborative control unit.

[0087] It should be explained that the installation execution parameters include positioning deviation, locking torque, clamping pressure, and installation attitude angle. The positioning deviation refers to the three-dimensional coordinate difference between the actual installation position and the target position of the busbar, calculated by combining the vision and laser sensors of the positioning calibration module. The locking torque refers to the tightening torque of the bolts by the installation locking assembly, collected in real time by the torque sensor built into the electric wrench. The clamping pressure refers to the clamping force of the adaptive clamping component of the robotic arm on the busbar, measured by the pressure sensor built into the clamping component. The installation attitude angle refers to the horizontal and vertical tilt angles of the busbar after installation, collected by the gyroscope on the side of the robot.

[0088] The positioning influence component is obtained by interacting with the ratio analysis results of the positioning adaptation factor and the positioning deviation value to the allowable installation accuracy value; the torque influence component is obtained by interacting with the ratio analysis results of the torque adaptation factor and the locking torque value to the standard torque value; the attitude influence component is obtained by interacting with the ratio analysis results of the attitude adaptation factor and the installation attitude angle to the allowable attitude value; the positioning influence component, torque influence component, and attitude influence component are superimposed to obtain the installation accuracy and connection reliability. The specific limiting expressions for installation accuracy and connection reliability are as follows:

[0089] ;

[0090] In the formula, K represents the installation accuracy and connection reliability; m1 represents the positioning adaptation factor obtained from the installation execution database; m2 represents the torque adaptation factor obtained from the installation execution database; m3 represents the attitude adaptation factor obtained from the installation execution database; and ΔP represents the positioning deviation value. Indicates the allowable installation accuracy; T represents the locking torque value; Indicates the standard torque value; Indicates the installation attitude angle; This indicates the permissible attitude value.

[0091] It's important to understand that positioning deviation, locking torque, clamping pressure, and installation attitude angle are interconnected: excessive positioning deviation leads to uneven locking torque distribution, affecting connection reliability; insufficient clamping pressure causes busbar displacement during installation, increasing positioning deviation; excessive installation attitude angle deviation exacerbates uneven current transmission, increasing the risk of overheating. Furthermore, positioning deviation is negatively correlated with installation accuracy and connection reliability; a smaller positioning deviation results in higher installation accuracy and better connection reliability. Locking torque is positively correlated with installation accuracy and connection reliability; the closer the locking torque is to the standard value, the more stable the connection and the higher the reliability. Finally, installation attitude angle is negatively correlated with installation accuracy and connection reliability; a smaller installation attitude angle deviation results in more uniform current transmission and higher reliability.

[0092] Furthermore, the specific steps to determine whether to perform installation parameter adjustments are as follows:

[0093] Based on the comparison between installation accuracy and connection reliability and reliability reference value, if the installation accuracy and connection reliability are greater than or equal to the reliability reference value, no installation parameter adjustment is performed; otherwise, installation parameter adjustment is performed according to the reliability deviation, whereby the reliability deviation represents the degree of negative deviation between installation accuracy and connection reliability and reliability reference value. The installation parameter adjustment includes positioning calibration optimization and locking torque adjustment.

[0094] It's important to understand that the process begins with acquiring installation execution parameters, calculating installation accuracy and connection reliability, comparing them with reliability reference values, and then proceeding to the optimization branch if adjustments are needed. In the positioning and calibration optimization phase, a calibration factor is obtained by querying a mapping table based on reliability deviation and positioning deviation, and the robotic arm's posture is adjusted to achieve precise positioning. In the locking torque adjustment phase, a target torque value is output based on the torque deviation and the busbar material characteristics, and tightening is completed using an electric wrench. Finally, the adjusted installation data is transmitted to the collaborative control unit for evaluation to ensure that the installation quality meets requirements.

[0095] It should be further explained that the specific steps for positioning calibration optimization are as follows:

[0096] The difference between the positioning deviation value and the allowable installation accuracy value is obtained and recorded as the positioning deviation margin. The positioning deviation margin is input into a predefined margin and calibration mapping table, and the corresponding positioning calibration factor is output. The mapping relationship is configured as follows: the larger the positioning deviation margin, the larger the mapped positioning calibration factor, so as to achieve rapid positioning correction; the smaller the positioning deviation margin, the smaller the mapped positioning calibration factor, so as to achieve fine positioning and fine adjustment.

[0097] Establish a correlation rule between the positioning calibration factor and the adjustment angle of the robotic arm, wherein the correlation rule is specifically as follows:

[0098] When the positioning deviation margin exceeds the upper limit threshold, it is determined to be a large deviation scenario. The positioning calibration factor and reliability deviation amount are input into the predefined calibration and angle mapping table, and the corresponding lateral and longitudinal adjustment angles of the robotic arm are output. The current posture angle of the robotic arm is combined with the adjustment angle to obtain the target posture angle, thereby quickly correcting the positioning deviation and ensuring installation accuracy. The upper limit threshold of the margin represents the critical margin value at which the positioning deviation needs to be adjusted significantly.

[0099] Positioning calibration optimization also includes:

[0100] When the positioning deviation margin is less than the lower limit threshold, it is determined to be a minor deviation scenario. The positioning calibration factor and the reliability deviation are input into the predefined calibration and angle mapping table, and the corresponding robot arm fine-tuning angle is output. The current robot arm posture angle is combined with the fine-tuning angle to obtain the target posture angle, thereby achieving high-precision positioning correction and avoiding secondary deviations caused by over-adjustment. The lower limit threshold represents the critical margin value at which the positioning deviation only needs fine adjustment.

[0101] When the positioning deviation margin is within the margin threshold range, it is determined to be a qualified deviation scenario. In this case, positioning calibration optimization is not performed, and the current robotic arm posture angle is maintained. The margin threshold range refers to the closed interval formed by the lower margin threshold and the upper margin threshold.

[0102] Under the dynamically adjusted posture angle of the robotic arm, the installation position of the busbar is corrected in real time using a combination of vision and laser calibration technology based on the positioning calibration factor, until the positioning deviation is less than the allowable installation accuracy value, thus completing the positioning calibration optimization process.

[0103] In this embodiment, precise positioning correction under different deviation scenarios is achieved through the correlation rules between positioning deviation margin and positioning calibration factor. In scenarios with large deviations, the deviation is quickly corrected by adjusting the horizontal and vertical angles of the robotic arm, improving positioning efficiency. In scenarios with small deviations, high-precision positioning is achieved through fine-tuning, ensuring installation quality. For scenarios with acceptable deviations, the current posture is maintained to avoid unnecessary adjustments. Finally, combining visual and laser dual calibration technology ensures that the positioning deviation meets installation requirements, providing a fundamental guarantee for the reliability of subsequent connections.

[0104] It should be further explained that the specific steps for adjusting the lock-up torque are as follows:

[0105] The difference between the locking torque value and the standard torque value is obtained and recorded as the torque deviation. The torque deviation is input into a predefined deviation-torque mapping table, and the corresponding torque adjustment factor is output. The mapping relationship is configured such that: the larger the absolute value of the torque deviation, the larger the mapped torque adjustment factor, so as to achieve rapid torque correction; the smaller the absolute value of the torque deviation, the smaller the mapped torque adjustment factor, so as to achieve fine torque adjustment.

[0106] Establish a correlation rule between the torque adjustment factor and the output torque of the electric wrench, wherein the correlation rule is specifically as follows:

[0107] When the torque deviation exceeds the upper limit threshold, it is determined to be a torque insufficient scenario. The torque adjustment factor and the reliability deviation are input into the predefined adjustment and output mapping table, and the corresponding torque increase value is output. The current output torque of the electric wrench is combined with the torque increase value to obtain the target output torque, thereby enhancing the connection tightness and preventing the risk of loosening due to insufficient torque. The upper limit threshold of torque represents the critical deviation value at which the torque needs to be significantly increased.

[0108] When the torque deviation is less than the lower limit threshold, it is determined to be an excessive torque scenario. The torque adjustment factor and the reliability deviation are input into the predefined adjustment and output mapping table, and the corresponding torque reduction value is output. The current output torque of the electric wrench is combined with the torque reduction value to obtain the target output torque, thereby avoiding damage to the busbar housing caused by excessive torque. The lower limit threshold represents the critical deviation value at which the torque needs to be significantly reduced.

[0109] When the torque deviation is within the torque threshold range, it is determined to be a qualified torque scenario. In this case, the locking torque adjustment is not performed, and the current electric wrench output torque is maintained. The torque threshold range refers to the closed interval formed by the lower torque threshold and the upper torque threshold.

[0110] Under the dynamically adjusted output torque of the electric wrench, the connecting bolts of the busbar trunking are tightened one by one according to the torque adjustment factor. The locking torque value is monitored in real time until the locking torque value is within the allowable deviation range of the standard torque value, thus completing the locking torque adjustment process.

[0111] In this embodiment, precise control of the locking torque is achieved through the correlation rule between torque deviation and torque adjustment factor. In cases of insufficient torque, the output torque is increased to enhance connection tightness; in cases of excessive torque, the output torque is reduced to protect the busbar housing; for cases with acceptable torque, the current torque is maintained, ensuring connection reliability while avoiding over-operation. The entire torque adjustment mechanism, through real-time monitoring and dynamic correction, ensures that the torque of each connecting bolt meets standard requirements, significantly improving the stability and consistency of the busbar connection.

[0112] The collaborative control unit receives installation data from the installation execution unit, integrates a busbar trunking digital twin component and an installation effect evaluation component, simulates the installation process through the digital twin model, combines the installation data to complete a multi-dimensional evaluation of the installation effect, and generates evaluation results; based on the evaluation results, it sends correction instructions to the path planning unit and the installation execution unit to form a closed-loop control.

[0113] The power supply unit provides power to the environmental perception unit, path planning unit, installation execution unit, collaborative control unit, and robot mobile chassis. It outputs appropriate voltage and power according to the path planning and installation execution instructions from the collaborative control unit.

[0114] In this embodiment, the power supply unit adopts a lithium battery + AC mains complementary power supply mode. The lithium battery has a capacity of 200Ah and a voltage of 24V, while the AC mains power is supplied after conversion by an AC220V to DC24V power module. When the AC mains power is normal, it prioritizes supplying power through the AC mains power and charging the lithium battery; when the AC mains power is interrupted, it automatically switches to lithium battery power supply, with a battery life of ≥8 hours.

[0115] The unit has a built-in power management chip that can dynamically adjust the power supply (50-500W) according to the instructions of the collaborative control unit: when the installation execution unit is performing high-intensity operations (such as robotic arm handling and locking), the power supply is automatically increased to 300-500W; when the environmental sensing unit is performing low-load operations (such as continuous monitoring and data transmission), the power supply is reduced to 50-100W to achieve optimal system energy efficiency. Simultaneously, the power supply protection unit has overvoltage, overcurrent, and overheat protection functions. When the output voltage deviation exceeds ±5%, the output current exceeds 10A, or the module temperature exceeds 60℃, the power supply is automatically cut off and a warning signal is sent to ensure the system's electrical safety.

[0116] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added, removed, or rearranged compared to the disclosed example embodiments.

[0117] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0118] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0119] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, one or more processors can also be operable to support the performance of related operations in a cloud computing environment or as Software as a Service (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors), where these operations can be accessed via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application programming interface (API)).

[0120] The performance of certain operations can be distributed across processors, residing not only within a single machine but also deployed across multiple machines. In some example embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., within a home environment, office environment, or server cluster). In other example embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0121] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0122] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, as inventions, for convenience only, and if more than one disclosure or concept is disclosed in fact, it is not intended to limit the scope of this application to any single disclosure or concept.

[0123] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A robot system for intelligent installation and path planning of busbar trunking, characterized in that, It includes an environmental perception unit, a path planning unit, an installation execution unit, a collaborative control unit, and a power supply protection unit; Each unit achieves data interaction through an industrial-grade wireless communication link. The frequency band of the communication link is adapted to the electromagnetic environment characteristics of the bus trunking installation scenario, and its signal transmission characteristics are matched with the structural distribution characteristics of the bus trunking laying space. The output data dimension of the environmental perception unit is adapted to the core features of the busbar installation space size, obstacle distribution, and installation reference surface, and the data format is physically adapted to the input interface of the path planning unit. The path planning unit has a built-in path generation chip. The chip's storage area contains a planning strategy mapping table built based on the bus trunking laying path optimization mechanism and the installation space avoidance mechanism. The parameter association logic of the mapping table is derived from the bus trunking installation specifications and spatial physical constraints. The installation execution unit integrates a busbar trunking identification reading module, which uses the inherent physical identifier of the busbar trunking as a unique index. The execution board presets physical installation links based on the busbar trunking connection structure and installation spacing requirements, and the link action timing is synchronously adapted with the path planning results. The output of the collaborative control unit establishes a bidirectional hardware signal link with the parameter configuration module of the path planning unit and the power drive module of the installation execution unit. Its feedback path is driven by the adaptation response parameters corresponding to the installation characteristics of the bus trunking itself. Each unit forms a full-process hardware linkage structure through the above hardware link and mechanism mapping link.

2. The intelligent installation and path planning robot system for busbar trunking as described in claim 1, characterized in that, The environmental perception unit includes a spatial size sensing module, an obstacle detection component, and a data integration module. The response characteristics of each sensing element of the spatial size sensing module are matched with the busbar trunking size and the installation space gap range. The obstacle detection component detects spatial obstacles that affect the installation path, and the analysis logic of the detection signal is adapted to the structural constraint mechanism of the installation space. The output of the data integration module establishes a physical data input link with the map building chip of the path planning unit through a state calibration link. The output environmental state data is adapted to the busbar path planning mechanism analysis logic of the map building chip, and the data integration rules are derived from the multiple physical constraints of the busbar installation space.

3. The intelligent installation and path planning robot system for busbar trunking as described in claim 2, characterized in that, The spatial dimension sensing module includes a slot width sensing element, an installation spacing sensing element, and a wall reference sensing element deployed at the front end of the robot, as well as a corner angle sensing element integrated on the side of the robot. The response range of the channel width sensing element covers the standard channel width range of the busbar trunking, the sensing threshold of the installation spacing sensing element matches the spacing constraint range of the busbar trunking installation specification, and the response curve of the wall reference sensing element fits the flatness variation characteristic curve of the installation reference surface. The signal transmission carrier of each sensing element is a polytetrafluoroethylene insulated transmission structure that is compatible with the electromagnetic environment of the installation scene. The dielectric properties of this structure are adapted to the electric field distribution characteristics inside the building installation environment. Independent signal microchannels are arranged inside, and the spacing between the microchannels is inversely proportional to the expected laying density of the busbar trunking. The channel transmission rate is dynamically matched with the sampling frequency of the sensing element.

4. The intelligent installation and path planning robot system for busbar trunking as described in claim 2, characterized in that, The obstacle detection component includes a lidar detection element, a visual recognition element, and an acoustic warning element; The detection frequency band of the laser radar detector covers the contour size feature range of the obstacles in the installation space. The detection parameters of the visual recognition device match the types of obstacles such as pipelines, beams and columns on the busbar installation path. The triggering reference of the acoustic warning device is the difference in distance characteristics between the obstacle and the robot. Its detection trigger end establishes a hardware signal connection with the timing sensor of the robot installation operation. The output of this component is connected to the data integration module together with the output of the spatial size sensing module. The integrated signal format is compatible with the spatial avoidance mechanism signal receiving interface of the path planning unit. The integrated dataset contains the associated information of spatial coordinates, obstacle type, and size parameters.

5. The intelligent installation and path planning robot system for busbar trunking as described in claim 1, characterized in that, The path planning unit includes a map building module, a path generation chip, and a timing coordination module. The modeling logic of the map building module matches the three-dimensional structural mechanism of the busbar installation space, and the modeling accuracy is adapted to the allowable range of installation errors. The planning algorithm of the path generation chip is adapted to the optimization mechanism of the bus trunking laying path. The algorithm parameters are derived from the physical constraints of the installation space and the connection process requirements of the bus trunking. The planning results include the associated parameters of path node coordinates, turning angles, and moving speed. The synchronization benchmark of the timing coordination module is the operation time scale of the bus trunking segment installation. Its output timing planning data provides a physical data carrier of the time dimension for the multi-action allocation board of the installation execution unit through the timestamp encapsulation board, and the timestamp format is compatible with the installation timing mechanism mapping link of the action allocation board.

6. The intelligent installation and path planning robot system for busbar trunking as described in claim 5, characterized in that, The path planning unit also includes an obstacle avoidance decision component. The decision logic of this component matches the structural constraint mechanism of the busbar installation space. It has a built-in spatial interference judgment link based on the installation path and obstacles. The interference judgment parameters are derived from the obstacle size and the space required for the busbar installation. The obstacle avoidance decision component has a built-in obstacle avoidance strategy storage chip. The storage chip has embedded obstacle avoidance path adjustment logic corresponding to different obstacle types. The adjustment logic includes the relationship between path offset, corner correction value, and movement speed adaptation parameters. The obstacle avoidance command output of this component establishes a hardware control link with the robotic arm drive module on which the execution unit is installed. Its command parameters are linked and adapted to the motion trajectory adaptation mechanism of the robotic arm, and the command execution timing is uniformly adapted to the time scale of path planning.

7. The intelligent installation and path planning robot system for busbar trunking as described in claim 5, characterized in that, The dynamic correction link of the path generation chip is adapted to the real-time data output of the environmental sensing unit. The correction logic is constructed based on the deviation compensation mechanism of the bus trunking installation path, and the correction triggering condition is derived from the degree of difference between the environmental data and the initial planning data. The dynamic correction link can adjust the physical parameters such as the turning angle and spacing of the planned path in real time according to environmental changes, and the adjustment range is adapted to the remaining redundancy of the installation space. The chip's output is connected to the parameter input interface of the path evaluation component of the cooperative control unit. Its planning data is adapted to the installation feasibility coupling constraint logic of the evaluation component. The evaluation feedback result can trigger a secondary path correction through the hardware link.

8. The intelligent installation and path planning robot system for busbar trunking as described in claim 1, characterized in that, The installation execution unit includes a robotic arm assembly, a positioning and calibration module, and an installation locking assembly. The motion parameters of the robotic arm assembly are matched with the structural adaptation mechanism of the busbar installation interface, and the degree of freedom of motion is adapted to the busbar connection process requirements. The calibration benchmark of the positioning calibration module is compatible with the positioning mechanism of the bus trunking installation hole, and the calibration accuracy matches the allowable range of installation error. The calibration process includes associated actions of hole identification, attitude adjustment, and position locking. The locking force of the installation locking component is adapted to the mechanical characteristics of the busbar trunking connection structure, and the locking action timing is linked and adapted to the positioning calibration completion signal; the execution data output end of this unit establishes a physical link with the data source interface of the installation effect evaluation component of the collaborative control unit, and the output installation data format is adapted to the installation quality mechanism evaluation logic of the evaluation component.

9. The intelligent installation and path planning robot system for busbar trunking as described in claim 8, characterized in that, The adaptive clamping component of the robotic arm assembly adopts a flexible adaptable structure. The contour of the clamping surface matches the external structural mechanism of the busbar trunking shell, and the material properties of the clamping surface are adapted to the surface protection requirements of the busbar trunking shell. The clamping posture can be adaptively adjusted according to the changes in the size of the trunking. The positioning calibration module has a built-in hole position recognition chip. Its recognition parameters are adapted to the hole diameter and spacing characteristics of the busbar mounting holes. The recognition logic is derived from the physical characteristics and imaging mechanism of the mounting holes. The calibration signal of the positioning calibration module is synchronously linked with the action execution timing of the robotic arm component. The linkage logic is derived from the compensation mechanism of the mounting hole positioning accuracy and the robotic arm action error, ensuring the coordination and consistency of positioning and installation actions.

10. The intelligent installation and path planning robot system for busbar trunking as described in claim 1, characterized in that, The collaborative control unit includes a busbar digital twin component and an installation effect evaluation component. The constraint equation chip of the digital twin component has a coupling coefficient table based on the coupling mechanism of multiple physics fields such as busbar, installation environment, robot spatial field, mechanical field, and motion field. The coupling coefficient is calibrated by experimental data on the mechanism of busbar installation deviation and path planning. The evaluation chip of the installation effect evaluation component integrates a data integration link based on the installation quality mechanism of the bus trunking throughout its entire life cycle. It integrates the execution data of the installation execution unit with the physical simulation data of the digital twin component. The evaluation dimensions include related parameters such as installation position accuracy, connection tightness, and path adaptability. The output adjustment end of this unit establishes a two-way hardware link with the path generation chip of the path planning unit and the robotic arm component of the installation execution unit. The parameters of its adjustment signal are linked and adapted to the installation quality data mechanism of the installation execution unit. The adjustment range is derived from the degree of difference between the evaluation result and the installation specification.

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