Robot path planning method and device based on multi-dimensional space modeling and medium

Abstract

The invention discloses a robot path planning method and device based on multi-dimensional space modeling and a medium, and the method comprises the steps: carrying out the three-dimensional topology dynamic modeling of building structure data, so as to obtain an initial weighted three-dimensional connected graph; based on the elevator state parameters, weight dynamic updating is conducted on weight nodes of the initial weighted three-dimensional connected graph, and the weighted three-dimensional connected graph is determined; based on the weighted three-dimensional connected graph, through analysis of an improved A * algorithm, a robot global advancing path plan is obtained; according to the robot advancing path planning, robot local advancing path planning is obtained through robot reversing strategy decision analysis; and based on the robot global travel path planning and the robot local travel path planning, through asset counting trajectory analysis, determining a counting scanning line spacing, and performing building asset counting. According to the method, the technical problem that the dynamic constraint condition in the three-dimensional space cannot meet the instant requirement in the existing SLAM technology is solved.

Description

Technical Field

[0001] This application relates to the technical field, and in particular to a robot path planning method, device and medium based on multi-dimensional space modeling. Background Art

[0002] In recent years, with the rapid growth of the demand for intelligent building automation, mobile robots based on SLAM technology have been gradually applied to scenarios such as equipment inspection and security patrol. The traditional SLAM framework constructs a two-dimensional plane map through lidar or visual sensors, and combines dynamic window method, A* algorithm, etc. to achieve path planning and basic obstacle avoidance, which has a certain degree of maturity in open space scenarios. However, the intelligent building environment has significant three-dimensional, high-dynamic and intensive characteristics. Cross-floor vertical transportation depends on elevators, access control systems open and close frequently, the channels in equipment rooms are narrow and obstacles are randomly distributed.

[0003] The existing technology is still limited to static navigation within a single plane layer, lacking the ability to co-model three-dimensional space semantics, dynamic constraint conditions and complex terrain, resulting in the robot's difficulty in meeting the dual requirements of high-frequency task execution efficiency and safety. Traditional SLAM technology only processes single-layer plane paths, without integrating three-dimensional space elements such as elevators and security doors; it does not consider dynamic constraint conditions such as elevator waiting time and access control switch status; for asset inventory, it is necessary to frequently travel back and forth between equipment rooms in the computer room, and the existing algorithms cannot handle dynamic obstacle avoidance in narrow channels between cabinets. Summary of the Invention

[0004] The embodiments of this application provide a robot path planning method, device and medium based on multi-dimensional space modeling, which solves the technical problem that the existing SLAM technology cannot meet the immediate needs of dynamic constraint conditions in three-dimensional space.

[0005] In a first aspect, the embodiments of this application provide a robot path planning method based on multi-dimensional space modeling, which is characterized in that the method includes: obtaining building structure data, and performing three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; obtaining elevator state parameters, and based on the elevator state parameters, dynamically updating the weights of the weight nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph; based on the weighted three-dimensional connected graph, through the analysis of an improved A* algorithm, obtaining a global travel path plan for the robot; according to the robot travel path plan, through the decision-making analysis of the robot reverse strategy, obtaining a local travel path plan for the robot; based on the robot global travel path plan and the robot local travel path plan, through the analysis of the asset inventory trajectory, determining the inventory scan line spacing, and according to the inventory scan line spacing, performing building asset inventory.

[0006] In one embodiment of the present application, three-dimensional topological dynamic modeling is performed on building structure data to obtain an initial weighted three-dimensional connected graph, which specifically includes: dividing the building structure data into three-dimensional voxel grids, and extracting key nodes from the three-dimensional voxel grids to obtain key weighted nodes; wherein, the key weighted nodes include: elevators, access controls, and channels; based on the key weighted nodes, through the abstraction of the three-dimensional voxel grids, an initial weighted three-dimensional connected graph is obtained.

[0007] In one embodiment of the present application, based on elevator state parameters, the weight nodes of the initial weighted three-dimensional connected graph are dynamically updated in weight to determine a weighted three-dimensional connected graph, which specifically includes: screening car information from the elevator state parameters, and fusing access control information with the car information obtained by screening the car information to obtain weight characteristic parameters; wherein, the weight characteristic parameters include: elevator weight characteristic parameters, access control weight characteristic parameters, and channel weight characteristic parameters; performing weight initialization on the weight characteristic parameters to obtain an initial weight; wherein, the weight initialization analysis formula for the elevator weight characteristic parameters is:

[0008] W_e = (T_wait + T_trans) × N_robot

[0009] wherein, W_e is the elevator passage weight, T_wait is the elevator response time, T_trans is the elevator running time, and N_robot is the number of robots; based on the initial weight, the weight nodes of the initial weighted three-dimensional connected graph are dynamically updated in weight to determine a weighted three-dimensional connected graph.

[0010] In one embodiment of the present application, based on the weighted three-dimensional connected graph, through the improved A* algorithm analysis, a global traveling path planning for the robot is obtained, which specifically includes: dividing the robot path of the weighted three-dimensional connected graph to obtain the current global path; based on the current global path, through the improved A* algorithm analysis, a global traveling path planning for the robot is obtained; wherein, the cost function of the improved A* algorithm analysis is:

[0011] T = α·p + β·(T_wait + T_trans) + γ·ω

[0012] wherein, α, β, and γ are weight coefficients, α + β + γ = 1, β ≥ 0.4, T_wait is the elevator response time, T_trans is the elevator running time, and ω is the energy consumption coefficient; the heuristic function of the A* algorithm analysis is:

[0013]

[0014] wherein, D is the Euclidean distance from the robot to the target point, τ is the estimated remaining path time to the target, m is the average elevator response time threshold, s is the cumulative slope value, and P is the maximum climbing ability of the robot.

[0015] In one embodiment of the present application, according to the robot travel path planning, through the robot reverse driving strategy decision analysis, the robot local travel path planning is obtained, which specifically includes: based on the robot travel path planning, through the path channel width monitoring, the robot reverse driving strategy is determined; in the case that the robot reverse driving strategy is the forward decision plan, the passability score of the forward decision plan is performed to determine the first narrow channel processing plan score; in the case that the robot reverse driving strategy is the reverse decision plan, the steering action weight score of the reverse decision plan is performed to determine the second narrow channel processing plan score; the narrow channel processing plan with a score greater than the execution score threshold in the first narrow channel processing plan score and the second narrow channel processing plan score is set as the current robot travel plan, so as to obtain the robot local travel path planning.

[0016] In one embodiment of the present application, based on the robot global travel path planning and the robot local travel path planning, through the asset inventory trajectory analysis, the inventory scan line spacing is determined, which specifically includes: based on the robot global travel path planning and the robot local travel path planning, the robot asset inventory path planning is determined; according to the robot asset inventory path planning, through the dynamic analysis of the asset cabinet spacing, the robot scan line density is determined; based on the robot scan line density, the inventory scan line spacing is determined.

[0017] In one embodiment of the present application, after determining the robot asset inventory path planning based on the robot global travel path planning and the robot local travel path planning, the method further includes: based on the robot asset inventory path planning, determining the robot task queue, and making an elevator time window reservation for the robot task queue to determine the elevator reservation time; according to the elevator reservation time, through the access control dynamic interaction, the access control interaction instruction is obtained.

[0018] In one embodiment of the present application, before performing the building asset inventory according to the inventory scan line spacing, the method further includes: based on the robot global travel path planning and the robot local travel path planning, through the robot energy consumption analysis, determining the minimum consumption path; wherein, the energy consumption calculation formula of the robot energy consumption analysis is:

[0019] E = 0.8*d + 2.5*s′ + 1.2*T r

[0020] E is the robot energy consumption prediction value, d is the robot moving distance, s′ is the slope integral, T r is the number of robot steering times.

[0021] In a second aspect, an embodiment of the present application further provides a robot path planning device based on multi-dimensional space modeling, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: obtain building structure data, and perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; obtain elevator state parameters, and based on the elevator state parameters, perform dynamic weight update on the weighted nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph; based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, obtain a global travel path planning for the robot; according to the robot travel path planning, through robot reverse strategy decision analysis, obtain a local travel path planning for the robot; based on the robot global travel path planning and the robot local travel path planning, through asset inventory trajectory analysis, determine the inventory scan line spacing, and according to the inventory scan line spacing, perform building asset inventory.

[0022] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for robot path planning based on multi-dimensional space modeling, storing computer-executable instructions, characterized in that the computer-executable instructions are set to: obtain building structure data, and perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; obtain elevator state parameters, and based on the elevator state parameters, perform dynamic weight update on the weighted nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph; based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, obtain a global travel path planning for the robot; according to the robot travel path planning, through robot reverse strategy decision analysis, obtain a local travel path planning for the robot; based on the robot global travel path planning and the robot local travel path planning, through asset inventory trajectory analysis, determine the inventory scan line spacing, and according to the inventory scan line spacing, perform building asset inventory.

[0023] The embodiment of the present application provides a robot path planning method, device and medium based on multi-dimensional space modeling. Through weighted three-dimensional connected graph, improved A* algorithm analysis and robot reverse strategy decision analysis, it solves the technical problem that the existing SLAM technology cannot meet the immediate needs of dynamic constraint conditions in three-dimensional space, realizes the immediate path planning of the robot in the intelligent building, elevator access interaction and dynamic obstacle avoidance on the path, and improves the building travel efficiency and asset inventory efficiency of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0025] Figure 1 It is a flowchart of a robot path planning method based on multi-dimensional space modeling provided by an embodiment of the present application;

[0026] Figure 2 It is a specific logic diagram of a robot path planning method based on multi-dimensional space modeling provided by an embodiment of the present application;

[0027] Figure 3 It is a schematic diagram of the internal structure of a robot path planning device based on multi-dimensional space modeling provided by an embodiment of the present application. Specific embodiments

[0028] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] The embodiments of the present application provide a robot path planning method, device and medium based on multi-dimensional space modeling. By analyzing the weighted three-dimensional connected graph, improved A* algorithm, and robot reverse strategy decision-making, the technical problem that the existing SLAM technology cannot meet the immediate needs under dynamic constraints in the three-dimensional space is solved, and the immediate path planning of the robot in the intelligent building, elevator access interaction on the path, and dynamic obstacle avoidance are realized, improving the building travel efficiency and asset inventory efficiency of the robot.

[0030] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the drawings.

[0031] Figure 1 It is a flowchart of a robot path planning method based on multi-dimensional space modeling provided by an embodiment of the present application. As Figure 1 shown, a robot path planning method based on multi-dimensional space modeling provided by an embodiment of the present application specifically includes the following steps:

[0032] Step 101, obtain building structure data, and perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph.

[0033] Exemplarily, traditional SLAM technology only processes single-layer planar paths and cannot perform three-dimensional topological dynamic modeling of the complete building structure based on key nodes within the building. By performing three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connectivity graph, the construction of the weighted connectivity graph of the building structure is realized, breaking through the limitations of traditional two-dimensional planar modeling.

[0034] Specifically, performing three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connectivity graph includes: dividing the building structure data into three-dimensional voxel grids and extracting key nodes from the three-dimensional voxel grids to obtain key weighted nodes; among them, the key weighted nodes include: elevators, access controls, and passages; based on the key weighted nodes, through the abstraction of the three-dimensional voxel grids, an initial weighted three-dimensional connectivity graph is obtained.

[0035] In one embodiment, first, a three-dimensional topological structure based on the Building Information Model (BIM) is constructed, the building structure data is divided into three-dimensional voxel grids, and decomposed into a weighted connectivity graph model including elevator shaft core nodes, access control nodes, and passage connection nodes, where each node is accurately positioned through the spatial coordinate system (x, y, z) and the type attributes are marked.

[0036] Then, the edge weights synthesize static passing distances and dynamic parameters. The elevator node weight is associated with a real-time waiting time algorithm, and the access control node sets a status variable (0 / 1) and realizes the permission control step through double verification by an RFID reader and an infrared sensor.

[0037] Finally, according to the confirmation of the permission control node, key weighted nodes are obtained, and through the abstraction of the three-dimensional voxel grids, an initial weighted three-dimensional connectivity graph is obtained.

[0038] 102. Obtain elevator state parameters, and based on the elevator state parameters, perform dynamic weight updates on the weight nodes of the initial weighted three-dimensional connectivity graph to determine the weighted three-dimensional connectivity graph.

[0039] Exemplarily, after determining the initial weighted three-dimensional connectivity graph, the weights of the key stages need to be dynamically adjusted according to the current building facility status, and the weights of each part of the weighted three-dimensional connectivity graph are allocated, realizing the dynamic update of the weighted three-dimensional connectivity graph.

[0040] Specifically, based on the elevator state parameters, the weight nodes of the initial weighted three-dimensional connected graph are dynamically updated in weight to determine the weighted three-dimensional connected graph, including: screening the car information from the elevator state parameters, and fusing the access control information of the car information obtained by screening the car information to obtain weight characteristic parameters; wherein, the weight characteristic parameters include: elevator weight characteristic parameters, access control weight characteristic parameters, and channel weight characteristic parameters; initializing the weights of the weight characteristic parameters to obtain initial weights; wherein, the weight initialization analysis formula for the elevator weight characteristic parameters is:

[0041] W_e = (T_wait + T_trans) × N_robot

[0042] wherein, W_e is the elevator passage weight, T_wait is the elevator response time, T_trans is the elevator running time, and N_robot is the number of robots; based on the initial weights, the weight nodes of the initial weighted three-dimensional connected graph are dynamically updated in weight to determine the weighted three-dimensional connected graph.

[0043] In one embodiment, first, the car running state data stream is obtained by real-time docking with the elevator control system through the OPC UA protocol, the elevator call request queue is processed by using the sliding time window algorithm, and the elevator arrival time is predicted by combining the Kalman filter.

[0044] Then, the weight nodes of the initial weighted three-dimensional connected graph are dynamically updated in weight. Since each initial weight is a constant, it is necessary to fuse the access control information of the car information obtained by screening the car information to obtain weight characteristic parameters. The weight characteristic parameters include: elevator weight characteristic parameters, access control weight characteristic parameters, and channel weight characteristic parameters. The weight initialization analysis formula for the elevator weight characteristic parameters is:

[0045] W_e = (T_wait + T_trans) × N_robot (1)

[0046] wherein, W_e is the elevator passage weight, T_wait is the elevator response time, T_trans is the elevator running time, and N_robot is the number of robots.

[0047] Further, the access control weight W d is 0 (access control closed state) or ∞ (access control open state), and the RFID detection period is 200 ms.

[0048] Further, the channel weight is W c = L × C; wherein, L is the channel length, C is the channel congestion coefficient, and C = 1.5 (main channel of the machine room).

[0049] Further, the channel nodes dynamically adjust the congestion coefficient according to the detection of the traffic flow, and then update the channel weight.

[0050] Finally, a three-dimensional grid map containing semantic features such as cabinet location and power distribution unit is established. Through spatial hashing indexing, the association mapping between equipment assets and topological nodes is realized, and finally an enhanced semantic navigation map supporting multi-objective optimized path planning is generated.

[0051] Step 103: Based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, a global travel path planning for the robot is obtained.

[0052] Exemplarily, after obtaining the weighted three-dimensional connected graph through three-dimensional topological dynamic modeling, it is necessary to plan the travel strategy of the robot for the entire building. Through improved A* algorithm analysis, the global travel path planning of the robot based on the weighted three-dimensional connected graph is realized, which improves the rationality of the global travel path of the robot and optimizes the elevator scheduling during the robot's travel.

[0053] Specifically, based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, a global travel path planning for the robot is obtained, including: dividing the robot path on the weighted three-dimensional connected graph to obtain the current global path; based on the current global path, through improved A* algorithm analysis, a global travel path planning for the robot is obtained; where the cost function of the improved A* algorithm analysis is:

[0054] T = α·p + β·(T_wait + T_trans) + γ·ω

[0055] Among them, α, β, and γ are weight coefficients, α + β + γ = 1, β ≥ 0.4, T_wait is the elevator response time, T_Trans is the elevator running time, and ω is the energy consumption coefficient; the heuristic function of the A* algorithm analysis is:

[0056]

[0057] Among them, D is the Euclidean distance from the robot to the target point, τ is the estimated remaining path time of the target, m is the average elevator response time threshold, s is the cumulative slope value, and P is the maximum climbing ability of the robot.

[0058] In one embodiment, first, based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, a global travel path planning for the robot is obtained. Divide the robot path on the weighted three-dimensional connected graph to obtain the current global path; based on the current global path, through improved A* algorithm analysis, a global travel path planning for the robot is obtained; where the cost function of the improved A* algorithm analysis is explained by the following formula:

[0059] T = α·p + β·(T_wait + T_trans) + γ·ω (2)

[0060] Among them, α, β, and γ are weight coefficients, where α + β + γ = 1, β ≥ 0.4, T_wait is the elevator response time, T_trans is the elevator running time, and ω is the energy consumption coefficient; the heuristic function analyzed by the A* algorithm is explained by the following formula:

[0061]

[0062] Among them, D is the Euclidean distance from the robot to the target point, τ is the estimated remaining path time of the target, m is the average elevator response time threshold, s is the cumulative slope value, and P is the maximum climbing ability of the robot.

[0063] Then, through multi-dimensional constraint conditions, dynamically balance the distance, time, and terrain factors, use a priority queue to optimize the node expansion order, accelerate the search for neighboring nodes through spatial hashing in a three-dimensional grid map, and at the same time introduce a path smoothing module to eliminate jagged trajectories, generating a three-dimensional optimal path that meets the requirements of elevator collaborative scheduling, access control permission verification, and terrain passability.

[0064] Finally, through the edge computing unit, real-time fuse the elevator status, access control logs, and laser SLAM positioning data, use the time window mechanism to perform a moving average filter on the dynamic weights, ensure the robustness of the planning results in the case of elevator waiting time fluctuations, sudden access control locks, etc., send a reservation request (HTTP POST / elevator / reserve) to the elevator control system, and detect and replan the path conflicts of multi-robot across floors through cloud collaborative computing.

[0065] Step 104: According to the robot's travel path planning, through the analysis of the robot's reverse strategy decision-making, obtain the local travel path planning of the robot.

[0066] Exemplarily, for relatively narrow or absolutely narrow paths, it is necessary to immediately analyze whether the robot can pass. And in order to consider the optimal travel mode when the robot has options for passing and turning, it is also necessary to further evaluate through the robot's turning (corresponding to energy consumption and time consumption). Through the analysis of the robot's reverse strategy decision-making, obtain the local travel path planning of the robot, realizing the local travel path planning of the robot in a narrow environment, and improving the analysis accuracy and system robustness of the path planning of the intelligent skeleton and the middle robot.

[0067] Specifically, according to the robot's travel path planning, through the decision-making analysis of the robot's reverse driving strategy, the local travel path planning of the robot is obtained, including: based on the robot's travel path planning, through the monitoring of the path channel width, the robot's reverse driving strategy is determined; in the case where the robot's reverse driving strategy is the forward decision-making plan, the passability of the forward decision-making plan is scored to determine the score of the first narrow channel processing plan; in the case where the robot's reverse driving strategy is the reverse decision-making plan, the steering action weight of the reverse decision-making plan is scored to determine the score of the second narrow channel processing plan; the narrow channel processing plan with a score greater than the execution score threshold among the scores of the first narrow channel processing plan and the second narrow channel processing plan is set as the current robot travel plan to obtain the local travel path planning of the robot.

[0068] In one embodiment, based on the robot's travel path planning, through the monitoring of the path channel width, the robot's reverse driving strategy is determined, and the robot's travel parameters are set by the Dynamic Window Approach (DWA).

[0069] Among them, the speed range is 0 - 1.5 m / s, the speed limit in the computer room is 0.8 m / s, the angular velocity range is ±0.5 rad / s, and it drops to ±0.3 rad / s in narrow channels; the weight of the evaluation function is 60% for the obstacle distance and 30% for the path fitting degree.

[0070] First, detect the channel width. If the channel width is less than twice the width of the robot, that is, when the channel width < 1.2 m (the robot width is 0.6 m × 2), start the reverse driving strategy decision tree.

[0071] Then, score different plans according to the reverse driving strategy decision tree to obtain the final action of the current robot. Select the forward plan: passability score = remaining space / safety threshold;

[0072] Select the reverse plan: steering action weight score = 1 / (number of turns × 0.2 + path length × 0.8);

[0073] Finally, select the plan with the final score > 0.7 to obtain the local travel path planning of the robot.

[0074] Furthermore, according to the coordinates of the computer room equipment, generate an asset inventory trajectory. When it is detected that a temporary obstacle continuously blocks for more than 5 seconds, the elevator waiting time exceeds the estimated value by 200%, or the path cost caused by the access control closure > 150% of the alternative path, trigger local path replanning. When the channel width < the width of the robot body + 20 cm (the robot width is 0.6 m + 0.2 m = 0.8 m), force the reverse mode to be enabled.

[0075] Step 105: Based on the global path planning and local path planning of the robot, determine the inventory scanning line spacing through asset inventory trajectory analysis, and conduct building asset inventory according to the inventory scanning line spacing.

[0076] Specifically, based on the global path planning and local path planning of the robot, determining the inventory scanning line spacing through asset inventory trajectory analysis includes: based on the global path planning and local path planning of the robot, determining the robot asset inventory path planning; according to the robot asset inventory path planning, determining the robot scanning line density through dynamic analysis of the asset cabinet spacing; and determining the inventory scanning line spacing based on the robot scanning line density.

[0077] Further, after determining the robot asset inventory path planning based on the global path planning and local path planning of the robot, the method further includes: determining the robot task queue based on the robot asset inventory path planning, making an elevator time window reservation for the robot task queue, and determining the elevator reservation time; and obtaining the access control interaction instruction through dynamic access control interaction according to the elevator reservation time.

[0078] Further, before conducting the building asset inventory according to the inventory scanning line spacing, the method further includes: determining the minimum consumption path through robot energy consumption analysis based on the global path planning and local path planning of the robot; wherein the energy consumption calculation formula for robot energy consumption analysis is:

[0079] E = 0.8*d + 2.5*s′ + 1.2*T r

[0080] E is the predicted value of robot energy consumption, d is the moving distance of the robot, s′ is the slope integral, and T r is the number of robot turning times.

[0081] In one embodiment, first, based on the global path planning generated by the weighted three-dimensional connected graph and local obstacle avoidance path planning, construct a dedicated path for asset inventory through cabinet space topology analysis, wherein the scanning line density is dynamically calculated according to the cabinet arrangement direction and equipment interval size, and the cabinet surface is projected onto a two-dimensional plane using an adaptive grid division algorithm.

[0082] Then, fit the cabinet edge contour based on the lidar point cloud data, and deduce the scanning line spacing according to the minimum overlapping coverage principle in combination with the preset RFID reading angle and the effective identification distance of the UHF reader.

[0083] After completing the path planning, the inventory area is divided into independent subtasks by floor through the task decomposition engine. The idle window of the car is predicted for each time period by combining the elevator operation cycle model, and a task queue marked with timestamps is established using a doubly linked list structure. The access control module sends an RS485 control instruction to unlock the electromagnetic lock 200 ms before the robot arrives through the spatio-temporal matching of the pre-authorization token and the path time sequence, and synchronously verifies the infrared human body sensing signal to prevent tailgating.

[0084] Next, based on the global path planning of the robot and the local path planning of the robot, the minimum consumption path is determined through the robot energy consumption analysis; among them, the energy consumption calculation formula for the robot energy consumption analysis is:

[0085] E = 0.8*d + 2.5*s′ + 1.2*T r

[0086] E is the predicted value of the robot energy consumption, d is the moving distance of the robot, s′ is the slope integral, and T r is the number of robot turning times. The optimal solution that meets the energy consumption requirements and the inventory coverage rate ≥ 98% is screened from the candidate path set through Monte Carlo tree search, and the dynamic voltage and frequency scaling (DVFS) technology is introduced to adjust the driving motor power in real time.

[0087] Finally, the digital twin engine synchronizes the physical space and the virtual map state. When a temporary obstacle or equipment displacement is detected, an online replanning algorithm based on Lyapunov stability is triggered to ensure that the inventory path remains continuous and feasible within the mechanical vibration tolerance range (±5 cm). Finally, the inventory coordinate mapping is realized through multi-modal data fusion, and the asset change information is updated to the BIM operation and maintenance platform in real time.

[0088] Figure 2 This is the specific logic diagram of a robot path planning method based on multi-dimensional space modeling provided by the embodiment of the present application.

[0089] In one embodiment, after the user terminal uploads the building information model (BIM) to the building information modeling module, the floor structure, cabinet coordinates, and equipment space relationship are extracted through IFC format parsing, and a three-dimensional weighted graph model including the elevator shaft topology, access control node coordinates, and channel connectivity is generated and pushed to the path calculation engine.

[0090] When a user initiates a route request from point A to point B, the path calculation engine combines real-time elevator status data (e.g., elevator 1 stops at shaft 1 on the 5th floor) with the access permission list and uses an improved A* algorithm to calculate the global path. The weight matrix integrates the elevator response time prediction value (based on the historical wait time exponential smoothing method) and the access control switch delay parameter to generate an optimal path solution that includes the cross-floor elevator scheduling timing. After receiving the global path, the motion control module uses the dynamic perception module's lidar and visual SLAM data to construct an occupancy grid map of the environment in real time. It uses the dynamic window algorithm (DWA) for local path optimization and fuses the IMU and wheel encoder data through the Kalman filter to achieve centimeter-level positioning. When a temporary obstacle (such as moving people or equipment) is detected and triggers an alarm, the local replanning module is immediately activated, generating an obstacle avoidance path using the rapidly expanding random tree (RRT) algorithm and simultaneously updating the velocity vector constraints of the dynamic window algorithm.

[0091] The elevator collaboration module sends a car reservation instruction to the elevator control system via the Modbus TCP protocol 200ms in advance based on the path timing, ensuring that the elevator door is open when the robot arrives at the shaft. At the same time, the access control system receives encrypted authorization instructions via the RS485 bus, releases the electromagnetic lock before the robot arrives, and links the infrared sensor to verify that there is no tailgating risk; the global status monitoring module continuously compares the deviation between the actual motion trajectory and the planned path. When the cumulative error exceeds a threshold (such as 50cm) or the elevator status suddenly changes (such as a fault shutdown), the global replanning mechanism is triggered. After recalculating the path, the change notification is pushed to the user terminal via the MQTT protocol.

[0092] At the energy consumption management level, the motion control module dynamically adjusts the motor torque output according to the slope integral formula, and optimizes the number of turns through the PID controller to reduce energy loss, ensuring that the prediction error rate of the composite energy consumption model is ≤5%; finally, the system uses the digital twin engine to bind the robot inventory data (such as cabinet RFID tag information) with the BIM model coordinates, realizing the sub-meter spatial visualization of asset change information on the operation and maintenance platform, and avoiding path intersection through the timestamp conflict detection algorithm in the multi-robot collaborative scenario, completing efficient inspection and dynamic management of assets throughout the building, and solving the technical problem that the existing SLAM technology cannot achieve dynamic constraints in three-dimensional space and cannot meet immediate needs.

[0093] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a robot path planning device based on multi-dimensional space modeling, whose structure is as follows Figure 3 shown.

[0094] Figure 3 This is a schematic diagram of the internal structure of a robot path planning device based on multi-dimensional space modeling provided in an embodiment of the present application.Figure 3 As shown, the device includes:

[0095] At least one processor 301;

[0096] And a memory 302 communicatively connected to the at least one processor;

[0097] Wherein, the memory 302 stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor 301 to enable the at least one processor 301 to:

[0098] Obtain building structure data, perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; obtain elevator state parameters, and based on the elevator state parameters, dynamically update the weights of the weighted nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph; based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, obtain a global travel path plan for the robot; according to the robot travel path plan, through robot reverse strategy decision analysis, obtain a local travel path plan for the robot; based on the robot global travel path plan and the robot local travel path plan, through asset inventory trajectory analysis, determine the inventory scan line spacing, and according to the inventory scan line spacing, perform building asset inventory.

[0099] Some embodiments of the present application provide a Figure 1 non-volatile computer storage medium for robot path planning based on multi-dimensional space modeling corresponding to

[0100] Obtain building structure data, perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; obtain elevator state parameters, and based on the elevator state parameters, dynamically update the weights of the weighted nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph; based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, obtain a global travel path plan for the robot; according to the robot travel path plan, through robot reverse strategy decision analysis, obtain a local travel path plan for the robot; based on the robot global travel path plan and the robot local travel path plan, through asset inventory trajectory analysis, determine the inventory scan line spacing, and according to the inventory scan line spacing, perform building asset inventory.

[0101] The various embodiments in the present application are all described in a progressive manner. The same or similar parts among the various embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0102] The system, medium, and method provided by the embodiments of the present application correspond one by one. Therefore, the system and the medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and the medium will not be elaborated here.

[0103] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0105] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0107] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0109] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0110] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0111] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A robot path planning method based on multi-dimensional space modeling, characterized in that, The method includes: Obtaining building structure data and performing three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; Obtaining elevator state parameters and, based on the elevator state parameters, dynamically updating the weights of the weighted nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph; Based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, obtaining a global robot travel path plan; According to the robot travel path plan, through robot reverse driving strategy decision analysis, obtaining a local robot travel path plan; Based on the global robot travel path plan and the local robot travel path plan, through asset inventory trajectory analysis, determining the inventory scan line spacing, and according to the inventory scan line spacing, performing building asset inventory.

2. The robot path planning method based on multi-dimensional space modeling according to claim 1, wherein Performing three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph, specifically including: Dividing the building structure data into three-dimensional voxel grids and extracting key nodes from the three-dimensional voxel grids to obtain key weighted nodes; wherein, the key weighted nodes include: elevators, access controls, and channels; Based on the key weighted nodes, through three-dimensional voxel grid abstraction, obtaining the initial weighted three-dimensional connected graph.

3. A robot path planning method based on multi-dimensional space modeling according to claim 1, characterized in that, Based on the elevator state parameters, dynamically updating the weights of the weighted nodes of the initial weighted three-dimensional connected graph to determine a weighted three-dimensional connected graph, specifically including: Screening car information from the elevator state parameters and fusing access control information with the car information obtained by screening the car information to obtain weight characteristic parameters; wherein, the weight characteristic parameters include: elevator weight characteristic parameters, access control weight characteristic parameters, and channel weight characteristic parameters; Initializing the weights of the weight characteristic parameters to obtain initial weights; wherein, the weight initialization analysis formula for the elevator weight characteristic parameters is: W_e = (T_wait + T_trans) × N_robot where, W_e is the elevator passage weight, T_wait is the elevator response time, T_trans is the elevator running time, and N_robot is the number of robots; Based on the initial weights, dynamically updating the weights of the weighted nodes of the initial weighted three-dimensional connected graph to determine the weighted three-dimensional connected graph.

4. The robot path planning method based on multidimensional space modeling according to claim 1, characterized in that: Based on the weighted three-dimensional connected graph, through improved A* algorithm analysis, obtaining a global robot travel path plan, specifically including: Dividing the robot path of the weighted three-dimensional connected graph to obtain the current global path; Based on the current global path, through improved A* algorithm analysis, obtaining a global robot travel path plan; wherein, the cost function of the improved A* algorithm analysis is: T = α·p + β·(T_wait + T_trans) + γ·ω where, α, β, and γ are weight coefficients, α + β + γ = 1, β ≥ 0.4, T_wait is the elevator response time, T_trans is the elevator running time, and ω is the energy consumption coefficient; The heuristic function of the A* algorithm analysis is: Wherein, D is the Euclidean distance from the robot to the target point, τ is the estimated remaining path time of the target, m is the average elevator response time threshold, s is the cumulative slope value, and P is the maximum climbing ability of the robot.

5. A robot path planning method based on multi-dimensional space modeling according to claim 1, characterized in that, According to the robot travel path planning, through the analysis of the robot reverse driving strategy decision, the local travel path planning of the robot is obtained, specifically including: Based on the robot travel path planning, the robot reverse driving strategy is determined by monitoring the path channel width. In the case where the robot reverse driving strategy is the forward decision plan, the passability of the forward decision plan is scored to determine the score of the first narrow channel processing plan. In the case where the robot reverse driving strategy is the reverse decision plan, the steering action weight of the reverse decision plan is scored to determine the score of the second narrow channel processing plan. The narrow channel processing plan with a score greater than the execution score threshold among the score of the first narrow channel processing plan and the score of the second narrow channel processing plan is set as the current robot travel plan to obtain the local travel path planning of the robot.

6. The robot path planning method based on multi-dimensional space modeling according to claim 1, wherein, Based on the robot global travel path planning and the robot local travel path planning, the inventory scan line spacing is determined through the analysis of the asset inventory trajectory, specifically including: Based on the robot global travel path planning and the robot local travel path planning, the robot asset inventory path planning is determined. According to the robot asset inventory path planning, the robot scan line density is determined through the dynamic analysis of the asset cabinet spacing. Based on the robot scan line density, the inventory scan line spacing is determined.

7. A robot path planning method based on multi-dimensional space modeling according to claim 6, characterized in that, After determining the robot asset inventory path planning based on the robot global travel path planning and the robot local travel path planning, the method further includes: Based on the robot asset inventory path planning, the robot task queue is determined, and the elevator time window is reserved for the robot task queue to determine the elevator reservation time. According to the elevator reservation time, the access control interaction instruction is obtained through the access control dynamic interaction.

8. The robot path planning method based on multidimensional space modeling according to claim 1, characterized in that: Before performing the building asset inventory according to the inventory scan line spacing, the method further includes: Based on the robot global travel path planning and the robot local travel path planning, the minimum consumption path is determined through the robot energy consumption analysis; wherein, the energy consumption calculation formula of the robot energy consumption analysis is: E = 0.8*d + 2.5*s′ + 1.2*T r E is the predicted value of robot energy consumption, d is the robot moving distance, s′ is the slope integral, T r is the number of turns of the robot.

9. A robot path planning device based on multi-dimensional space modeling, characterized in that, The device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Obtain the building structure data, and perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph. Obtain the elevator state parameters, and based on the elevator state parameters, perform weight dynamic update on the weight nodes of the initial weighted three-dimensional connected graph to determine the weighted three-dimensional connected graph. Based on the weighted three-dimensional connected graph, through the improved A* algorithm analysis, the robot global travel path planning is obtained. According to the robot travel path planning, through the decision-making analysis of the robot reverse driving strategy, the local travel path planning of the robot is obtained; Based on the global travel path planning of the robot and the local travel path planning of the robot, through the analysis of the asset inventory trajectory, the inventory scanning line spacing is determined, and the building assets are inventoried according to the inventory scanning line spacing.

10. A non-volatile computer storage medium for robot path planning based on multi-dimensional space modeling, storing computer executable instructions, characterized in that: The computer-executable instructions are set as: Obtain the building structure data, and perform three-dimensional topological dynamic modeling on the building structure data to obtain an initial weighted three-dimensional connected graph; Obtain the elevator state parameters, and based on the elevator state parameters, perform dynamic weight update on the weighted nodes of the initial weighted three-dimensional connected graph to determine the weighted three-dimensional connected graph; Based on the weighted three-dimensional connected graph, through the analysis of the improved A* algorithm, the global travel path planning of the robot is obtained; According to the robot travel path planning, through the decision-making analysis of the robot reverse driving strategy, the local travel path planning of the robot is obtained; Based on the global travel path planning of the robot and the local travel path planning of the robot, through the analysis of the asset inventory trajectory, the inventory scanning line spacing is determined, and the building assets are inventoried according to the inventory scanning line spacing.

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