Path optimization and control method and system of industrial mobile robot equipment considering communication delay

Abstract

The invention relates to the technical field of robot path optimization, in particular to a path optimization and control method and system for industrial mobile robot equipment considering communication delay, and the method comprises the steps: setting a starting point and a target point on a rasterized map; adding the attraction potential field, the repulsion potential field and the power potential field together to obtain a total potential field; calculating a total potential field corresponding to the adjacent grids by the robot, and selecting the grid with the minimum total potential field as a next path point; when the robot has a planned stagnation condition, executing an escape strategy: calculating the direction of the repulsive force of the robot; anticlockwise rotating the direction of the repulsive force by 90 degrees to generate an escape direction; setting a virtual target point at a preset distance along the escape direction; the robot ignores the total potential field of the target point and moves towards the virtual target point; and after the robot arrives at the virtual target point, the virtual target point is removed, and the normal path planning process based on the target point is recovered. The reliability of task execution of the robot is improved.

Description

Technical Field

[0001] This invention relates to the field of robot path optimization technology, specifically to a path optimization and control method and system for industrial mobile robot equipment that takes into account communication latency. Background Technology

[0002] With the development of industrial automation and intelligent manufacturing, industrial mobile robots are increasingly widely used in warehousing and logistics, flexible production lines, and smart factories. Path planning, as one of the core technologies for autonomous navigation of mobile robots, directly affects the robot's operating efficiency and the safety of system operation. Industrial mobile robots rely heavily on wireless communication during operation to achieve functions such as control command transmission and reception, status feedback, autonomous positioning, path navigation, and task scheduling, requiring high real-time performance and stability. However, industrial environments are complex, with numerous walls, equipment, and other obstructions. Wireless signals undergo reflection, diffraction, and scattering during propagation, creating multipath effects that cause signals to arrive at the receiving end at different times, leading to uncertainties in communication latency. Especially in large-area or structurally complex workshops and warehouses, unstable "blind spots" with extremely weak wireless signals and high communication latency may occur in some areas. If robot path planning does not consider these factors, it may lead to increased communication latency or even connection interruption, affecting the safe operation of equipment and task execution efficiency.

[0003] Currently, various solutions have been proposed for the path planning problem, including local obstacle avoidance methods based on the A algorithm and artificial potential field (APF) models, sampling planning methods based on rapid expanding random trees (RRT), and planning methods based on trajectory optimization. For example, some existing methods improve real-time performance in dynamic environments by introducing a sliding window update mechanism to enhance the A algorithm (e.g., CN109059924B), or combine swarm intelligence algorithms to optimize path search performance (e.g., CN104571113A). However, these methods mainly focus on static obstacle avoidance or path geometry optimization, without modeling the quality of communication links during robot operation. Furthermore, while methods based on artificial potential field methods (e.g., CN105629974A) or velocity obstacle models (e.g., CN105223956A) improve obstacle avoidance capabilities, they still rely on ideal communication assumptions and do not consider the impact of signal fading or communication delays. While improved RRT algorithms (such as CN111752281A) optimize path sampling efficiency, their objectives are limited to kinematic feasibility and path length, neglecting wireless channel awareness. Existing technologies generally lack a systematic consideration of the spatial distribution characteristics of wireless signals and the dynamic changes in communication delays. This can lead to planned paths traversing areas with weak signal coverage or high communication latency, affecting the communication stability between the robot and the scheduling system and failing to meet the demands of efficient and reliable communication in industrial scenarios. Therefore, a method that combines communication channel characteristics and path optimization is urgently needed to improve the overall performance of industrial mobile robot systems. Summary of the Invention

[0004] To address the technical problem that existing path planning technologies fail to consider the quality of wireless communication in industrial environments, leading to communication delays, jitter, or even interruptions in communication blind spots or weak areas, thus affecting system reliability and task efficiency, this invention provides a path optimization and control method and system for industrial mobile robots that considers communication latency. This invention constructs a power potential field positively correlated with communication quality and integrates it with the attractive and repulsive potential fields in traditional artificial potential field methods to form a total potential field that guides the robot to actively avoid weak communication areas. Simultaneously, it introduces an efficient VTP-90 local minimum escape strategy and combines it with nonlinearly compensated PID tracking control, thereby significantly improving robot communication stability, reducing task execution latency in complex industrial environments, and ensuring that path planning in complex topologies possesses accessibility, real-time performance, and smooth motion.

[0005] The technical means employed in this invention are as follows: A method for path optimization and control of industrial mobile robot equipment considering communication latency includes the following steps: A gridded map is established in the robot's working area, and a starting point and a target point are set on the gridded map. An attractive potential field is constructed based on the starting point, the target point, and the attractive gain; a repulsive potential field is constructed based on the starting point, the repulsive influence radius, and the repulsive gain; and a power potential field is constructed based on the received signal power and the power potential field gain coefficient. The attractive potential field, the repulsive potential field, and the power potential field are added together to obtain the total potential field. In each decision cycle, the robot calculates the total potential field corresponding to its neighboring grids and selects the grid with the smallest total potential field as the next path point. When the robot encounters a planning stagnation, an escape strategy is executed, which includes: Calculate the direction of the robot's repulsive force; rotate the direction of the repulsive force counterclockwise by 90° to generate the escape direction; A virtual target point is set at a preset distance along the escape direction; the robot ignores the total potential field of the target point and moves towards the virtual target point; after the robot reaches the virtual target point, the virtual target point is removed and the normal path planning process based on the target point is restored.

[0006] Furthermore, the formula for calculating the attractive potential field is:

[0007] in, To attract the potential field, To attract gain, Let Euclidean distance be the distance between the target point and the current position. The formula for calculating the Euclidean distance between the target point and the current position is:

[0008] in, For the target point, This is the current location.

[0009] Furthermore, the formula for calculating the repulsive potential field is as follows:

[0010] in, To repel potential fields, To exclude gain, To exclude the radius of influence, For the m-th obstacle, The total number of obstacles. Let be the absolute distance between the m-th obstacle and the car. The formula for calculating the absolute distance between the m-th obstacle and the car is:

[0011] in, Let be the coordinates of the m-th obstacle.

[0012] Furthermore, the formula for calculating the power potential field is:

[0013] in, For power potential field, The power potential field gain coefficient is... This represents the received signal power at the current location.

[0014] Furthermore, the process of the robot moving to the next path point or virtual target point includes: A proportional-integral-derivative (PI-DE) control law based on position error is used to convert the reference velocity into a trajectory tracking control command. The position error is the deviation between the robot's actual position and the planned path point. The PI-DE control law is as follows:

[0015] in, For control signals, Let k be the distance error, and k be a discrete time point. , , These are the proportional, integral, and differential gain coefficients, respectively.

[0016] Furthermore, the process of generating the received signal power at the current location point includes: A parameterized received signal power intensity prediction model is constructed. Several sampling points are selected in the rasterized map, and the measured signal power values ​​of each sampling point are measured and recorded in the field using a signal receiving device to form a measurement dataset. The measurement dataset is used as a benchmark to fit the undetermined parameters in the received signal power intensity prediction model to minimize the error between the model's predicted value and the measured value, thereby determining the final parameters of the prediction model. During the path planning process, for any current location point, its coordinates are input into the received signal power intensity prediction model that has completed parameter fitting, and the received signal power of the current location point is calculated.

[0017] Furthermore, during the movement of the robot, the random density function PDF is used to describe the random distribution of the received signal strength.

[0018] Furthermore, the planning stagnation situations include being trapped at a local minimum point or experiencing path loops. The preset distance is 3 to 5 times the grid step size, and the number of steps the robot takes to move towards the virtual target point in a single attempt is set to 1 step. If an escape attempt fails to escape the planning stagnation state, the escape strategy is repeated to set a new virtual target point until the robot escapes.

[0019] The present invention also includes a path optimization and control system for industrial mobile robot equipment that takes into account communication latency, for implementing the above-mentioned path optimization and control method for industrial mobile robot equipment that takes into account communication latency, comprising: A grid map construction module is used to create a gridded map in the robot's working area and set a start point and a target point on the gridded map; a signal power measurement and estimation module is used to acquire and estimate the received signal power at each location in the gridded map; a potential field calculation module, connected to the grid map construction module and the signal power measurement and estimation module, is used to construct an attractive potential field based on the start point, target point, and attraction gain; construct a repulsive potential field based on the obstacle position, repulsive influence radius, and repulsive gain; construct a power potential field based on the received signal power and power potential field gain coefficient; and add the attractive potential field, repulsive potential field, and power potential field to obtain the total potential field; a virtual target generation module, connected to the potential field calculation module, is used to calculate the robot's target in each decision cycle. The total potential field of neighboring grids is calculated, and the grid with the smallest total potential field is selected as the next path point. The path point selection module is connected to the potential field calculation module and the path point selection module. When the robot's planning stall is detected, an escape strategy is executed: the direction of the repulsive force currently acting on the robot is calculated, the direction of the repulsive force is rotated 90° counterclockwise to generate an escape direction, and a virtual target point is set at a preset distance along this direction. The controller module is connected to the path point selection module and the virtual target generation module. It is used to generate drive commands to control the robot to move towards the next path point or the virtual target point. When the virtual target point is activated, the robot is controlled to ignore the attractive potential field of the original target point and move towards the virtual target point. After reaching the virtual target point, the virtual target point is removed and normal path planning is resumed.

[0020] Furthermore, the formula for calculating the attractive potential field is:

[0021] in, To attract the potential field, To attract gain, Let Euclidean distance be the distance between the target point and the current position. The formula for calculating the Euclidean distance between the target point and the current position is:

[0022] in, For the target point, For the current location, the formula for calculating the repulsive potential field is:

[0023] in, To repel potential fields, To exclude gain, To exclude the radius of influence, For the m-th obstacle, The total number of obstacles. Let be the absolute distance between the m-th obstacle and the car. The formula for calculating the absolute distance between the m-th obstacle and the car is:

[0024] in, Let be the position coordinates of the m-th obstacle. The formula for calculating the power potential field is:

[0025] in, For power potential field, The power potential field gain coefficient is... This represents the received signal power at the current location.

[0026] Compared with the prior art, the present invention has the following advantages: This invention introduces a power potential field, using the communication signal distribution as a planning objective to guide the robot to prioritize high-quality communication areas, thereby effectively reducing communication latency and jitter risks. Simultaneously, by employing the VTP-90 escape strategy, it achieves efficient escape from local minima, significantly improving path reachability in complex environments while ensuring real-time algorithm performance. Furthermore, by combining nonlinear compensation and PID tracking control, it achieves compatibility with robot kinematic / dynamic constraints, generating smoother and more easily engineering-implemented tracking trajectories.

[0027] This invention incorporates a power potential field into the traditional artificial potential field path planning framework to account for communication latency. Specifically, it maps indicators such as the received signal power and communication stability of the wireless channel to spatial distribution characteristics, which are then used as part of the potential field cost function. This allows the robot to proactively avoid weak signal areas and regions with high communication latency and large fluctuations during path search, thereby achieving joint optimization of path quality and communication quality. This mechanism prioritizes planning paths through areas with good signal coverage, stable links, and low communication latency, improving communication reliability and real-time performance during task execution.

[0028] Based on the above reasons, this invention can be widely applied in fields such as robot path optimization. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating a path optimization and control method for an industrial mobile robot device that takes into account communication latency, according to the present invention.

[0031] Figure 2 This is a schematic diagram of the industrial mobile robot equipment management and control system based on cloud-edge-device collaboration and considering communication latency, as per the present invention.

[0032] Figure 3 This is a schematic map illustrating the path planning of this invention.

[0033] Figure 4 This is a schematic diagram of the received power field intensity distribution of the present invention.

[0034] Figure 5 This is a top plan view of the power field intensity received by the present invention.

[0035] Figure 6 This is a top-view planar view of the total potential field intensity divided by a grid according to the traditional method.

[0036] Figure 7 This is a schematic diagram of the construction of the VTP-90 virtual target point of the present invention.

[0037] Figure 8 This is a top-down view of the potential field and the planned path diagram under different potential fields according to the present invention.

[0038] Figure 9 This is the power intensity distribution curve at the path point of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0041] This invention aims to address the technical challenges commonly found in existing industrial mobile robot path planning methods, such as insufficient adaptability to uncertain communication environments and inadequate guarantee of path reliability and real-time performance. The main objective of this invention is to provide a method and system for path planning and access control of industrial mobile robots based on a power potential field, comprehensively considering uncertain wireless communication delays. This method innovatively introduces a "power potential field," reflecting the characteristics of wireless signal strength and communication delay, into the traditional attractive and repulsive potential fields. This enables path planning not only to achieve obstacle avoidance and target arrival but also to prioritize the generation of paths in areas with strong communication signals, low communication delays, and stability, thereby significantly improving the communication connection reliability and real-time scheduling performance of the mobile robot.

[0042] Furthermore, the VTP-90 (Virtual Target Point - 90°) strategy proposed in this invention effectively solves the problem of easily getting trapped in local minima in traditional artificial potential field path planning. By dynamically generating auxiliary target points, it guides the robot to escape the local minimum potential field trap, thereby improving the global search capability and robustness of path planning.

[0043] Based on the aforementioned path planning method, a path optimization system employing a cloud-edge-device collaborative architecture was further designed. Global path planning and strategy optimization are performed in the cloud, while real-time task scheduling and local decision-making are handled at the edge. Combined with the mobile robot device at the edge, an implementable control law was designed to achieve tracking control and dynamic adjustment of the planned trajectory. This collaborative approach effectively enhances the system's intelligence and response speed, ensuring the safe, efficient, and stable operation of the mobile robot in complex industrial environments and under conditions of uncertain communication latency.

[0044] Example 1 like Figure 1 As shown, this invention provides a path optimization and control method for industrial mobile robot devices that considers communication latency, comprising the following steps: S1. Create a gridded map in the robot's working area, and set the starting point and target point on the gridded map.

[0045] S2. Based on the starting point, target point, and attraction gain, construct the attraction potential field; based on the starting point, repulsion influence radius, and repulsion gain, construct the repulsion potential field; based on the received signal power and power potential field gain coefficient, construct the power potential field; add the attraction potential field, repulsion potential field, and power potential field together to obtain the total potential field.

[0046] Specifically, the formula for calculating the attractive potential field is:

[0047] in, To attract the potential field, To attract gain, Let be the Euclidean distance between the target point and the current position. The formula for calculating the Euclidean distance between the target point and the current position is:

[0048] in, For the target point, This is the current location.

[0049] The formula for calculating the repulsive potential field is:

[0050] in, To repel potential fields, To exclude gain, To exclude the radius of influence, For the m-th obstacle, The total number of obstacles. Let be the absolute distance between the m-th obstacle and the car. The formula for calculating the absolute distance between the m-th obstacle and the car is:

[0051] in, Let be the coordinates of the m-th obstacle.

[0052] The formula for calculating the power potential field is:

[0053] in, For power potential field, The power potential field gain coefficient is... This represents the received signal power at the current location.

[0054] In potential field weight The selection criteria provide a preferred range and simulation examples: , , , (Grid unit), Simulation starting point ,Target Using this parameter set in the example scenario, the average received power of the planned path is increased by approximately 31.45% compared to the traditional APF.

[0055] Taking the logarithm of the power in the power potential field is used to suppress extreme values ​​in the potential field caused by differences in power intensity. The received signal power at the current location is obtained through offline electromagnetic simulation, online measurement, or based on a priori signal strength maps.

[0056] Specifically, the process of generating the received signal power at the current location includes: The first step is to construct a parameterized received signal power intensity prediction model.

[0057] The second step is to select several sampling points in the rasterized map, measure and record the measured signal power of each sampling point in the field using a signal receiving device, and form a measurement dataset.

[0058] The third step involves using the measurement dataset as a benchmark to fit the undetermined parameters in the received signal power intensity prediction model, minimizing the error between the model's predicted values ​​and the measured values, thereby determining the final parameters of the prediction model.

[0059] Fourth step: During the path planning process, for any current location point, input its coordinates into the received signal power intensity prediction model that has completed parameter fitting, and calculate the received signal power of the current location point.

[0060] S3. In each decision cycle, the robot calculates the total potential field corresponding to the neighboring grid (e.g., 8-neighborhood) and selects the grid with the smallest total potential field as the next path point.

[0061] As a preferred embodiment, when the robot encounters a planning stagnation, an escape strategy is executed. The VTP-90 escape strategy includes: The first step is to calculate the direction of the robot's repulsive force.

[0062] The second step is to rotate the direction of the repulsive force counterclockwise by 90° to generate the escape direction.

[0063] The third step is to set a virtual target point at a preset distance along the escape direction.

[0064] Step 4: The robot ignores the total potential field of the target point and moves toward the virtual target point.

[0065] Step 5: After the robot reaches the virtual target point, remove the virtual target point and resume the normal path planning process based on the target point.

[0066] Planning stagnation occurs when the robot is stuck at a local minimum or when a path loop occurs. The preset distance is 3 to 5 times the grid step size. The number of steps the robot takes to move towards the virtual target point at one time is set to 1 step. If the escape fails to get out of the planning stagnation state, the escape strategy is repeated to set a new virtual target point until the robot escapes.

[0067] As a preferred embodiment, the process of the robot moving to the next path point or virtual target point includes: The first step is to use a proportional-integral-derivative (PID) control law based on position error to convert the reference velocity into a trajectory tracking control command. The position error is the deviation between the robot's actual position and the planned path point. The PID control law is as follows:

[0068] in, For control signals, Let k be the distance error, and k be a discrete time point. , , These are the proportional, integral, and differential gain coefficients, respectively.

[0069] The second step involves combining nonlinear compensation control to compensate for the robot's nonlinear motion model, making it approximate as a linear second-order system. To achieve smooth trajectory tracking.

[0070] As a preferred embodiment, when a complete signal strength map cannot be obtained on site, online real-time sampling and spatial interpolation technology, or machine learning fitting method based on prior scene information, can be used to dynamically estimate the received signal power at each location, thereby ensuring the effective construction of the power potential field under partial observation conditions.

[0071] As a preferred embodiment, for highly dynamic environments or multi-robot collaborative operation scenarios, the power potential field can be coupled with time-varying indicators that reflect real-time communication performance, including time delay variance and packet loss rate, and then a composite communication potential field can be designed to meet the system's more stringent requirements for communication link stability and reliability.

[0072] As a preferred embodiment, if it is necessary to further improve the smoothness of the path and the comfort of movement, after the grid path points are selected, spline curve-based fitting or trajectory optimization smoothing processing can be added, and kinematic constraints such as acceleration and jerk can be introduced into the controller to make the final generated trajectory more consistent with the actual dynamic characteristics of the industrial mobile robot equipment.

[0073] Example 2 Based on Embodiment 1, the present invention provides a specific implementation method for Embodiment 1: (1) For example Figure 3 As shown, a raster map is created on a two-dimensional plane (x-axis range [-7.5, 7.5], y-axis range [-5, 5] in the example). The scene contains 8 rooms and a central corridor. The positions of the walls are known in advance, and the transmitting antenna is placed in the center of the map. Starting point. Target point .

[0074] (2) Obtain the received power distribution at each grid point on the map through full-wave electromagnetic simulation or on-site measurement. And save it as a signal strength map.

[0075] (3) Divide the map into several grid points, construct attractive, repulsive, and power potential fields according to the technical solution, take the sum of the intensities of these three potential fields as the total potential field intensity, and set the total potential field intensity value for each grid point. The total potential field is as follows: Figure 4 and Figure 5 As shown. When certain grid points If the value is below the threshold, communication becomes impossible. The potential field of that grid point can be set to a maximum value to prevent the path from passing through.

[0076] (4) Figure 7 middle F att Indicates the strength of the attractive potential field. F rep This represents the repulsive potential field strength. The industrial mobile robot detects the total potential field value of its 8-neighborhood grid at its current position and selects the smallest value as the next path point. If local stagnation or looping occurs, VTP-90 is activated: it calculates the current repulsive force direction and rotates 90° counterclockwise, such as... Figure 6 As shown, in this direction by distance Set a temporary virtual target point, and use this virtual target to temporarily generate an attractive potential field to move forward. Step by step, then restore the original target attraction and continue planning; to avoid repetition, path history points can be marked as non-reselectable.

[0077] (5) The motion of the industrial mobile robot is approximated by a nonlinear compensation controller. And a PID control law is used to generate the speed at the wheel end. Angular velocity reference This enables the tracking of planned path points.

[0078] In the MATLAB environment, parameters are used , , , VTP-90 , The planning paths of the traditional APF (without power potential field) and the method of this invention are compared respectively. Figure 7 and Figure 8 As shown, like Figure 6 and Figure 8As shown in the simulation results, the path planned by the method of this invention (red line) has higher signal strength at each path point compared to the path planned by the traditional method that does not consider signal reception strength and communication delay (blue line). Therefore, it has lower delay and jitter, which is more conducive to the control of the mobile robot. Especially in the strong signal area closer to the center of the map in the middle section of the path, the average received signal power at the path point is increased from 0.1784mW in the traditional method to 0.2345mW, an increase of about 31.45%, thus verifying the advantages of this invention in reducing communication delay and jitter.

[0079] Example 3 Based on Example 1, the present invention also provides a path optimization and control system for industrial mobile robot devices that considers communication latency, comprising: The grid map building module is used to create a gridded map in the robot's working area and set the start point and target point on the gridded map.

[0080] The signal power measurement and estimation module is used to acquire and estimate the received signal power at each location in the rasterized map.

[0081] The potential field calculation module, connected to the grid map construction module and the signal power measurement and estimation module, is used to construct an attractive potential field based on the starting point, target point and attractive gain, a repulsive potential field based on the obstacle position, repulsive influence radius and repulsive gain, and a power potential field based on the received signal power and power potential field gain coefficient. The attractive potential field, repulsive potential field and power potential field are added together to obtain the total potential field.

[0082] The virtual target generation module is connected to the potential field calculation module. It is used to calculate the total potential field of the robot's neighboring grids in each decision cycle and select the grid with the smallest total potential field as the next path point.

[0083] The path selection module is connected to the potential field calculation module and the path selection module. When the robot is detected to be stuck in the planning process, the escape strategy is executed: the direction of the repulsive force currently acting on the robot is calculated, the direction of the repulsive force is rotated 90° counterclockwise to generate the escape direction, and a virtual target point is set at a preset distance along this direction.

[0084] The controller module, connected to the path point selection module and the virtual target generation module, is used to generate drive commands to control the robot to move to the next path point or virtual target point. When the virtual target point is activated, the robot is controlled to ignore the attractive potential field of the original target point and move to the virtual target point. After arriving at the virtual target point, the virtual target point is removed and normal path planning is restored.

[0085] Example 4 like Figure 2The diagram shows a flowchart of a path optimization and control method for an industrial mobile robot device that takes into account communication latency, according to the present invention.

[0086] Based on Example 3, the present invention provides an industrial mobile robot equipment management and control system that takes into account communication latency based on cloud-edge-device collaboration.

[0087] Figure 2 This invention presents a path planning and control system for mobile robots that considers communication signal strength and latency distribution. The system consists of an MQTT server, an edge control server, an operator workstation, and a cloud-based path planning service module. Information interconnection and interoperability between the system modules and the mobile robot device are achieved through wireless networks, local area networks (LANs), and wide area networks (WANs). The mobile robot device connects to the LAN via a WiFi access point and uses MQTT to report its location, status, and sensor data to the server, while simultaneously receiving scheduling and path instructions. The edge control server is responsible for task management, vehicle scheduling, and processing and forwarding path planning requests. For simple tasks, processing can be done locally on the edge control server for rapid response; for more complex optimization calculations, the cloud-based path optimization algorithm service can be invoked to obtain path planning that considers latency distribution. The edge control server sends task and operation control instructions to the mobile robot device via MQTT. The local controller of the mobile robot device executes motion control, path tracking, obstacle avoidance using LiDAR, and continuously reports real-time operating status based on the received instructions. The operator workstation is used to view maps, monitor vehicle status, and issue tasks.

[0088] In summary, this invention models the wireless signal propagation characteristics of mobile robots in complex industrial environments, comprehensively considering the uncertainties of wireless channel communication quality and communication latency, and using signal strength and communication reliability as important constraints for path planning. Based on the traditional artificial potential field method, a power potential field is innovatively introduced, integrating the spatial distribution characteristics of wireless signals into the path planning process, enabling proactive avoidance of mobile robots in areas with weak signals or high latency. The system adopts a cloud-edge-device collaborative architecture based on the Industrial Internet of Things (IIoT). The mobile robot in the system, acting as the "device," integrates sensing units for real-time acquisition and perception of environmental signal states, enabling it to acquire multi-dimensional information including its own position, motion state, and surrounding wireless signal strength, achieving continuous perception of the working environment and trajectory tracking and operational control of the mobile robot. The field server, acting as the "edge," is deployed in the industrial field, responsible for unified management and edge computing processing of multiple connected mobile robots, including functions such as status aggregation, anomaly detection, and local real-time scheduling of operation plans, ensuring low-latency response and high reliability. The edge server also has the capability for local storage and preliminary analysis of robot data, supporting autonomous operation in the field during network interruptions or high latency. The cloud platform, acting as the "cloud," interconnects with edge servers via high-speed internet such as 5G, enabling autonomous global path planning and task distribution. The cloud also provides support for system operation and maintenance, remote monitoring, and big data optimization, achieving efficient collaboration and intelligent management between the cloud, edge, and endpoints. This invention not only improves the operational safety and path planning efficiency of industrial mobile robots in complex wireless communication environments but also provides strong technical support for the intelligent scheduling and management of industrial IoT systems.

[0089] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0090] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of path optimization and management of an industrial mobile robot device taking into account communication latency, characterized by, The method comprises the following steps: establishing a rasterized map in a robot working area, setting a start point and a target point on the rasterized map; constructing an attractive potential field based on the start point, the target point and an attractive gain, constructing a repulsive potential field based on the start point, a repulsive influence radius and a repulsive gain, constructing a power potential field based on a received signal power and a power potential field gain coefficient, and adding the attractive potential field, the repulsive potential field and the power potential field together to obtain a total potential field; the robot calculates the total potential field corresponding to adjacent grids in each decision-making period, and selects a grid with the minimum total potential field as a next path point; when the robot appears to be in a planning stagnation state, an escape strategy is executed, the escape strategy comprising: calculating a direction of a repulsive force of the robot; rotating the direction of the repulsive force counterclockwise by 90° to generate an escape direction; setting a virtual target point at a preset distance along the escape direction; the robot ignores the total potential field of the target point and moves to the virtual target point; after the robot reaches the virtual target point, the virtual target point is removed, and a normal path planning process based on the target point is restored.

2. The method of claim 1, wherein, a calculation formula of the attractive potential field is: wherein, is an attractive potential field, is an attractive gain, is the Euclidean distance between the goal point and the current position point, which is calculated as: wherein is a target point, is a current position point.

3. The method of claim 1, wherein the method further comprises: a calculation formula of the repulsive potential field is: wherein, is a repulsion potential, is a repulsion gain, is a repulsion influence radius, is the mth obstacle, is the total number of obstacles, is the absolute distance between the mth obstacle and the car, the calculation formula of the absolute distance between the mth obstacle and the car is: wherein, is the position coordinate of the mth obstacle.

4. The method of claim 1, wherein the method further comprises: a calculation formula of the power potential field is: wherein, is the power potential field, is the power potential field gain coefficient, is the received signal power at the current position point.

5. The method of claim 1, wherein the method further comprises: a process in which the robot moves to the next path point or the virtual target point comprises: a proportional-integral-derivative control law based on a position error is used to convert a reference speed into a trajectory tracking control command, the position error being a deviation between an actual position of the robot and a planned path point, and the proportional-integral-derivative control law being: wherein is a control signal, is a distance error, k is a discrete time point, , , are proportional, integral and derivative gain coefficients, respectively.

6. The method of claim 4, wherein the method further comprises: a generation process of the received signal power of the current position point comprises: a parameterized received signal power intensity prediction model is constructed; a plurality of sampling points are selected in the rasterized map, signal power measured values of the sampling points are measured and recorded through a signal receiving device to form a measurement data set; the measurement data set is a reference, and undetermined parameters in the received signal power intensity prediction model are fitted to minimize an error between a prediction value of the model and a measured value, so as to determine final parameters of the prediction model; in a path planning process, for any current position point, coordinates of the current position point are input into the received signal power intensity prediction model which has completed parameter fitting, and a received signal power of the current position point is calculated.

7. The method of claim 1, wherein the method further comprises: in a moving process of the robot, a random density function PDF is used to describe a random distribution condition of the received signal intensity.

8. The method of claim 1, wherein the method further comprises: the planning stagnation state comprises being trapped in a local minimum value point or appearing to be in a path cycle, the preset distance is 3-5 times of a grid step length, a step number of a single advance of the robot to the virtual target point is set to 1 step, if one-time escape is unsuccessful in escaping from the planning stagnation state, the escape strategy is repeatedly executed to set a new virtual target point until the robot escapes.

9. A path optimization and control system for an industrial mobile robot device taking into account communication latency for implementing the method of path optimization and control for an industrial mobile robot device taking into account communication latency according to any one of the preceding claims 1-8, characterized in that, comprise: a raster map construction module configured to establish a rasterized map in a robot working area and set a start point and a target point on the rasterized map; a signal power measurement and estimation module configured to acquire and estimate received signal powers of positions in the rasterized map; The potential field calculation module is connected with the grid map construction module and the signal power measurement and estimation module, and is configured to construct an attractive potential field based on the start point, the target point and an attractive gain, construct a repulsive potential field based on the obstacle position, a repulsive influence radius and a repulsive gain, construct a power potential field based on the received signal power and a power potential field gain coefficient, and add the attractive potential field, the repulsive potential field and the power potential field to obtain a total potential field; The virtual target generation module is connected with the potential field calculation module, and is configured to calculate the total potential field of the robot in the vicinity of a grid in each decision cycle, and select a grid with the minimum total potential field as a next path point; The path point selection module is connected with the potential field calculation module and the path point selection module, and is configured to execute an escape strategy when it is detected that the robot appears to be in planning stagnation: calculate the direction of the repulsive force currently suffered by the robot, rotate the direction of the repulsive force counterclockwise by 90° to generate an escape direction, and set a virtual target point at a preset distance along the direction; The controller module is connected with the path point selection module and the virtual target generation module, and is configured to generate a driving command to control the robot to move to the next path point or the virtual target point; when the virtual target point is activated, control the robot to ignore the attractive potential field of the original target point and move to the virtual target point, remove the virtual target point after reaching and restore normal path planning.

10. The path optimization and management system of an industrial mobile robot device taking into account communication latency according to claim 9, characterized in that, The calculation formula of the attractive potential field is: wherein, is an attractive potential field, is an attractive gain, is the Euclidean distance between the goal point and the current position point, which is calculated as: wherein, is a target point, is a current position point, and a calculation formula of the repulsive potential field is: wherein, is a repulsion potential, is a repulsion gain, is a repulsion influence radius, is the mth obstacle, is the total number of obstacles, is the absolute distance between the mth obstacle and the car, the calculation formula of the absolute distance between the mth obstacle and the car is: wherein, is the position coordinate of the mth obstacle, and the calculation formula of the power potential field is: wherein, is a power potential field, is a power potential field gain coefficient, is a received signal power at the current position point.

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