Route planning method and system based on Cartesian coordinate robot conveying

Abstract

The invention relates to the technical field of path planning, in particular to a path planning method and system based on Cartesian coordinate robot conveying, and provides the following scheme that temperature field data are collected through an infrared thermal sensor, the principal component direction is calculated according to the temperature gradient, and a traversal strategy is dynamically adjusted; it is ensured that the robot carries out treatment in the most remarkable temperature change direction; in the processing process, the candidate grids are screened through the temperature transfer relation between the temperature gradient and the outer ring grid, and redundant processing is avoided. Through heat conduction simulation, the system evaluates a temperature transfer effect, optimizes a grid processing sequence, and reduces unnecessary computing resource consumption; and finally, through an iteration mode, the robot efficiently and accurately completes a grid processing task, and optimization of path planning is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a path planning method and system based on rectangular coordinate robot transportation. Background Art

[0002] In modern automated working environments, path planning and mesh processing are key technologies to ensure efficient operation. Especially in application scenarios involving complex temperature fields, such as cooling systems and air cooling systems of industrial equipment, how to accurately locate the areas that need to be processed and optimize the processing paths has always been a technical challenge. In existing technologies, traditional path planning methods often rely on simple mesh division and regularized scanning strategies, which are difficult to cope with environments with drastic or uneven temperature changes. In addition, many existing methods cannot effectively handle the problems of multi-mesh stacking and occlusion in complex scenarios, resulting in inefficient path planning and serious waste of computing resources.

[0003] The existing technology has the problems raised by this background technology: it is unable to effectively handle the problems of multi-grid stacking and occlusion in complex scenes, resulting in inefficient path planning and serious waste of computing resources. To solve the above problems, this application designs a path planning method and system based on rectangular coordinate robot transportation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the shortcomings of the existing technology and provide a path planning method and system based on rectangular coordinate robot transportation. The temperature field data is collected by infrared thermal sensors, and the main component direction is calculated according to the temperature gradient. The traversal strategy is dynamically adjusted to ensure that the robot processes along the direction of the most significant temperature change. During the processing, the candidate grids are screened based on the temperature gradient and the temperature transfer relationship with the outer circle grid to avoid redundant processing. Through heat conduction simulation, the system evaluates the temperature transfer effect, optimizes the grid processing sequence, and reduces unnecessary computing resource consumption. Ultimately, through an iterative approach, the robot efficiently and accurately completes the grid processing task and achieves the optimization of path planning.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A path planning method based on rectangular coordinate robot transportation is applicable to an automated working environment with a track conveying system. The target to be processed is a planar structure including multiple grids arranged at an angle. The rectangular coordinate robot is equipped with an infrared thermal sensor with a radiation range of one grid. The track conveying system includes track nodes corresponding to each grid of the planar structure, so that the rectangular coordinate robot radiates all grids by reaching each track node. The path planning method includes:

[0007] Accomplishing infrared radiation of a plurality of adjacent grids by traversing to a plurality of adjacent orbital nodes to obtain a temperature field of each grid, wherein the plurality of adjacent grids form a current scanning temperature map;

[0008] Locating multiple currently processed grids according to the current scan temperature map, selecting one of the grids for processing, and determining a path to a corresponding orbital node according to the currently processed grid;

[0009] Follow the path to the track node.

[0010] The track nodes are distributed according to a rectangular coordinate system, and each track node corresponds to the geometric center of the grid in the planar structure.

[0011] The infrared radiation of multiple adjacent grids is realized by traversing to multiple adjacent orbital nodes to obtain the temperature field of each grid, including:

[0012] According to the current position of the rectangular coordinate robot, the current temperature field is collected;

[0013] Calculate the principal component direction of the current temperature field, and if the angle between the principal component direction and the X-axis of the rectangular coordinate system is less than a preset angle threshold, traverse the adjacent track nodes row by row along the X-axis according to a zigzag strategy;

[0014] If the angle between the principal component direction and the X-axis of the rectangular coordinate system is greater than or equal to a preset angle threshold, traverse the adjacent track nodes column by column along the Y-axis according to the column priority strategy;

[0015] Perform a traversal operation, collect the temperature fields of adjacent track nodes according to the determined strategy, and use the temperature fields as the current temperature fields until the temperature change rate of the temperature fields collected N times in succession is less than the preset temperature threshold or the track nodes of the current row and column have been traversed, and output the current scan temperature map.

[0016] Locating a plurality of current to-be-processed grids according to the current scanned temperature map, including:

[0017] Performing Gaussian filtering and noise reduction processing on the current scanning temperature image, and constructing a two-dimensional temperature gradient vector field;

[0018] Calculating the temperature gradient value of each grid in the X-axis direction and the Y-axis direction according to the two-dimensional temperature gradient vector field to generate a temperature gradient vector;

[0019] If the temperature gradient vector is greater than or equal to a preset temperature gradient threshold, the corresponding grid is marked as a candidate grid;

[0020] An iterative operation is performed on the candidate grid to obtain a temperature-related region of the candidate grid, and the grid within the temperature-related region is used as a current grid to be processed.

[0021] Performing an iterative operation on the candidate grid to obtain a temperature-related area of ​​the candidate grid includes:

[0022] Create an independent set for each candidate grid and calculate the average main gradient direction of the set;

[0023] An iterative operation is performed to obtain the current main gradient direction of the adjacent grids of any grid in the set. If the angle between the current main gradient direction and the average main gradient direction is less than the preset same-direction threshold, the adjacent grid is added to the set. This process is repeated until the angles between the current main gradient direction and the average main gradient direction of the adjacent grids are all greater than or equal to the same-direction threshold. The set is then output as a temperature-associated region.

[0024] The average main gradient direction is determined based on a local density parameter and a temperature gradient value.

[0025] Locate multiple meshes to be processed and select one of them for processing, including:

[0026] Randomly select a candidate grid from the current grid to be processed as the initial processing grid;

[0027] Determine a first outer ring grid and a second outer ring grid of the initial processing grid;

[0028] Based on the principle of heat conduction, the temperature transfer process of the initial processing grid during the processing is simulated to determine and predict whether the first outer ring grid and the second outer ring grid need further processing;

[0029] If further processing is required, selecting the candidate grid with the smallest Euclidean distance to the initial processing grid from the remaining unprocessed candidate grids as the next processing grid, wherein the candidate grid is different from the first outer circle grid and the second outer circle grid;

[0030] If no further processing is required, a candidate grid is randomly selected from the remaining unprocessed candidate grids as the next processing grid;

[0031] The simulation process is repeated until there are no unprocessed candidate grids. It is determined whether there are any remaining grids in the grid to be processed that need further processing. If further processing is required, the rectangular coordinate robot is used to process them one by one.

[0032] If the first outer circle grid and the second outer circle grid include a candidate grid, the candidate grid is deleted from the candidate grid set.

[0033] A path planning system based on rectangular coordinate robot transportation is suitable for an automated working environment with a track transportation system. The target to be processed is a planar structure including multiple grids arranged at an angle. The rectangular coordinate robot is equipped with an infrared thermal sensor with a radiation range of one grid. The track transportation system includes track nodes corresponding to each grid of the planar structure, so that the rectangular coordinate robot radiates all grids by reaching each track node. The path planning system includes a temperature acquisition module, a grid positioning module, a grid processing module and a path planning module, wherein:

[0034] The temperature acquisition module realizes infrared radiation of multiple adjacent grids by traversing to multiple adjacent orbital nodes to obtain the temperature field of each grid, wherein the multiple adjacent grids form a current scanning temperature map;

[0035] The grid positioning module positions a plurality of grids to be processed according to the current scanning temperature map;

[0036] The grid processing module selects one of the grids to be processed according to the current grid to be processed;

[0037] The path planning module determines the path to the corresponding track node based on the currently processed grid.

[0038] The grid positioning module includes:

[0039] a gradient construction unit, configured to perform Gaussian filtering and noise reduction processing on the current scanning temperature map, and construct a two-dimensional temperature gradient vector field;

[0040] Grid marking unit, used to generate temperature gradient vectors and select candidate grids;

[0041] The associated region determining unit is used to perform an iterative operation on the candidate grid, obtain the temperature associated region of the candidate grid, and use the grids in the region as the current grid to be processed.

[0042] The grid processing module includes:

[0043] An initial grid selection unit is used to randomly select a candidate grid from the current grid to be processed as the initial processing grid;

[0044] An outer ring processing judgment unit is used to determine the first outer ring grid and the second outer ring grid of the initial processing grid, simulate the temperature transfer process based on the heat conduction principle, and judge whether the outer ring grid needs further processing;

[0045] A grid selection unit, if the outer circle grid needs to be further processed, selects the candidate grid with the smallest Euclidean distance to the initial processing grid and different from the outer circle grid from the remaining unprocessed candidate grids as the next processing grid;

[0046] If not, the next processing grid is randomly selected, and after the processing is completed, the remaining outer circle grids to be processed are processed by the rectangular coordinate robot.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] By incorporating heat conduction simulation and outer mesh screening technology, this system accurately identifies and focuses on areas most affected by temperature conduction, avoiding duplicate processing and missing critical areas. The intelligent screening mechanism, which uses temperature gradient thresholds and unidirectional thresholds, further enhances the system's adaptability, ensuring more flexible and efficient processing. It also effectively handles multi-mesh stacking and occlusion issues in complex scenarios, improving path planning efficiency and reducing wasted computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0050] Figure 1 This is a flow chart of a path planning method for conveying based on rectangular coordinate robots according to Example 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of the target structure to be processed in Example 1 of the present invention;

[0052] Figure 3 This is a schematic diagram of temperature collection in Example 1 of the present invention;

[0053] Figure 4 This is a schematic diagram of positioning of the grid to be processed in Example 1 of the present invention;

[0054] Figure 5 A schematic diagram of temperature-related regions is constructed for Example 1 of the present invention;

[0055] Figure 6 This is a schematic diagram of the outer circle expansion of the initial processing grid in Example 1 of the present invention;

[0056] Figure 7 This is a schematic diagram of candidate mesh elimination in Example 1 of the present invention;

[0057] Figure 8 This is a schematic diagram of a grid selection process according to embodiment 1 of the present invention;

[0058] Figure 9 This is a module diagram of the path planning system based on rectangular coordinate robot transportation in Example 2 of the present invention. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] Example 1

[0061] See also Figure 1 The present invention provides an embodiment of a path planning method based on rectangular coordinate robot transportation, which is applicable to an automated working environment with a track conveying system. The target to be processed is a planar structure including multiple grids arranged at an angle. The rectangular coordinate robot is equipped with an infrared thermal sensor with a radiation range of one grid. The track conveying system includes track nodes corresponding to each grid of the planar structure, so that the rectangular coordinate robot radiates all grids by reaching each track node. The specific steps of the path planning method are as follows:

[0062] S1: By traversing to multiple adjacent orbital nodes, the infrared radiation of multiple adjacent grids is realized to obtain the temperature field of each grid. Multiple adjacent grids form a current scanning temperature map;

[0063] In this embodiment, the rectangular coordinate robot traverses multiple adjacent track nodes, achieving infrared radiation coverage of multiple adjacent grids. Using an infrared thermal sensor with a coverage range of one grid, the robot can scan each grid individually and accurately obtain temperature information based on the infrared radiation data. By gradually scanning rather than covering the entire area all at once, unnecessary computation is reduced, improving overall path planning efficiency. This ensures that the robot can flexibly and in real time adapt to ambient temperature changes, enhancing path planning accuracy.

[0064] S2: Locate multiple grids to be processed according to the current scan temperature map;

[0065] In this embodiment, the robot can locate multiple meshes requiring processing based on the temperature map generated by the current scan. By processing the current temperature field data in real time, the areas requiring processing are effectively screened from the entire mesh. Instead of cleaning every mesh individually, the robot quickly locates the meshes requiring processing based on the hot spots in the temperature map. This not only saves time but also avoids excessive calculations and redundant operations, improving work efficiency and response speed. By precisely locating the meshes to be processed, the computational burden is reduced and subsequent path planning is accelerated.

[0066] S3: Select one of the grids to be processed according to the current grid to be processed, and determine the path to the corresponding track node according to the currently processed grid;

[0067] In this embodiment, the robot quickly generates a corresponding path by selecting one of the currently processed grids as the starting point. The robot then analyzes the state of the currently processed grid and uses a pre-set path planning algorithm to calculate and determine the optimal path to the corresponding track node in that grid. This ensures accurate path planning, especially when navigating inclined surfaces. The robot can select the most appropriate path based on the specific location and state of the target grid, rather than relying on a pre-set fixed path.

[0068] S4: driving to a track node along the path;

[0069] It should be noted that the path planning method in this embodiment can be applied to the automated cleaning and temperature control optimization of the air-cooling system of a direct-cooling unit, particularly in addressing fin blockage and maintaining efficient heat dissipation in the air-cooling island. By employing infrared thermal imaging technology and an intelligent temperature field measurement and control system, real-time temperature monitoring and intelligent cleaning of the fins are achieved, which not only improves operational efficiency but also significantly reduces back pressure, coal consumption, and energy consumption, thereby achieving energy conservation and efficiency improvement. Combining robotic technology with the human-free operation of an intelligent control system optimizes the fin flushing process and enables dynamic adjustments based on real-time temperature data, maximizing the heat exchange capacity of the air-cooling system and reducing the impact of environmental factors on the cooling effect.

[0070] Specifically, when processing inclined planar structure grids, traditional path planning methods often require detailed processing of each grid, which not only requires a large amount of calculations but also leads to a decrease in system performance. In practical applications, faced with complex environments, such as the stacking and irregular arrangement of multiple grids, it is impossible to efficiently distinguish the priorities of different grids, resulting in redundancy and unnecessary computational burden in path planning. However, this application reduces the number of processed grids and improves the efficiency of path planning by utilizing an efficient grid positioning algorithm and temperature field information.

[0071] In this embodiment, the first step of the path planning method is to scan with an infrared thermal sensor, collect and generate the current scanning temperature map in real time. This process traverses the temperature radiation of multiple adjacent track nodes, gradually completes the scanning of the grid, and accurately obtains the temperature data of each grid. It solves the problem of excessive scanning range or redundant processing in traditional path planning. Specifically, this application uses intelligent temperature field dimensionality reduction. After obtaining the temperature information, combined with temperature gradient analysis, it only processes the grids with significant temperature changes, thereby greatly reducing the number of grids that need further processing. In this way, it is possible to focus on processing the most critical grid areas without wasting computing resources, avoiding unnecessary repetitive operations.

[0072] Furthermore, another innovation of this application is to accurately select the grids to be processed through an iterative algorithm. By calculating the temperature gradient vector of each grid and analyzing its correlation with the surrounding grids, the range of grids that currently need to be processed is dynamically adjusted. This not only improves the flexibility of path planning, but also enables the system to dynamically adjust strategies according to the real-time environment. For example, in a complex environment, when the temperatures of multiple grids vary greatly, this application uses a heat conduction model simulation to determine which grids no longer need to be processed after the previous grid processing, and automatically adjusts the path according to the changes in the temperature field, thereby ensuring that the robot can reach the target grid efficiently and accurately.

[0073] This application significantly improves path planning efficiency and reduces unnecessary computational burden by reducing the number of grids that need to be processed. It can intelligently adjust the path based on environmental changes, avoiding the limitations of fixed path planning methods. Furthermore, the path planning method of this application, through dynamic temperature analysis and intelligent decision-making mechanisms, enables robots to achieve precise operations in complex operating environments, ensuring the accuracy and flexibility of path planning.

[0074] See also Figure 2 , a schematic diagram of the structure of the target to be processed in an embodiment of the present invention, where the target to be processed is arranged obliquely by a 7x7 grid. The grid corresponds to a specific facility in a specific industrial environment. The black nodes in the figure represent track nodes, and each track node corresponds to the geometric center of the grid in the surface structure. The solid line represents the rectangular coordinate system, which corresponds to the distribution of the track nodes. The dotted line represents the conveyor track between the track nodes. The figure is only an example. In practice, the conveyor track can be connected to each track node to allow the rectangular coordinate robot to run on the conveyor track.

[0075] The specific steps of S1 are as follows:

[0076] S1.1: Collect the current temperature field according to the current position of the rectangular coordinate robot;

[0077] In this embodiment, a rectangular coordinate robot uses an infrared thermal sensor to collect temperature fields. The robot's current position is determined by a real-time positioning system (such as an encoder or inertial measurement unit), ensuring that it accurately understands its position in the coordinate system. This positioning accuracy enables the robot to accurately collect the temperature of the surrounding grid within a predetermined range. During the temperature field collection process, the infrared thermal sensor emits infrared radiation into the surrounding space and receives thermal radiation signals reflected by objects. These signals are converted into temperature data, and the temperature field distribution in the area where the robot is located is updated in real time.

[0078] S1.2: Calculate the principal component direction of the current temperature field. If the angle between the principal component direction and the X-axis of the rectangular coordinate system is less than a preset angle threshold, traverse the adjacent track nodes row by row along the X-axis using a zigzag strategy.

[0079] In this embodiment, in order to more accurately determine the direction of the robot's path planning, principal component analysis is first performed on the currently collected temperature field data. Principal component analysis is a common dimensionality reduction method that calculates the covariance matrix of the temperature field data to extract the principal components representing the direction of maximum data variation. Simply put, the principal component direction is the direction in which the temperature changes most significantly in the temperature field data. This direction usually corresponds to the direction in which the temperature changes the most and the hot spots are most concentrated. After calculating the principal component direction through principal component analysis, the robot can understand the most important change trend in the current temperature field, and thus give priority to scanning along the direction in which the temperature changes the most significantly during path planning, so as to improve processing efficiency and accuracy.

[0080] Specifically, the temperature value of the grid in the temperature field is first used as input data, and the data is analyzed using principal component analysis to obtain the principal component vector of the temperature change direction. Then, by calculating the angle between the principal component direction and the X-axis of the rectangular coordinate system, the angle of the direction is obtained. If the angle is less than the preset angle threshold (the angle threshold can be set according to the actual application requirements), this indicates that the main direction of the temperature field change is roughly parallel to the X-axis, and you can choose to traverse along the X-axis. If the angle is greater than the preset angle threshold, it means that the change trend of the temperature field is more significant in the Y-axis direction, and the robot will choose to traverse along the Y-axis direction.

[0081] Furthermore, the zigzag strategy means that when the robot traverses the grid, it starts from the starting point of a row and scans along the X-axis direction to the end point, and then goes to the next row to continue scanning until the entire area is covered. During this process, the robot always maintains a horizontal scanning mode, and continuously switches directions between each row during the scanning process, forming a zigzag trajectory. When the traversed area is large, simply scanning in one direction is likely to produce blind spots, resulting in some grids not being scanned. The zigzag strategy ensures that each grid is scanned by alternating movements, while avoiding repeated scanning or omissions that may occur due to one-way traversal. By performing zigzag scanning in the X-axis direction, the robot can effectively allocate scanning time and computing resources, avoid excessive repetition or omission of certain areas when scanning a large area, and thus improve scanning efficiency.

[0082] S1.3: If the angle between the principal component direction and the X-axis of the rectangular coordinate system is greater than or equal to a preset angle threshold, traverse the adjacent track nodes column by column along the Y-axis according to a column-first strategy;

[0083] Specifically, similar to the scanning method of S1.2, based on the changing characteristics of the temperature field in the Y-axis direction, such as areas where temperature changes are more concentrated or there are obvious gradients, choosing to traverse along the Y-axis direction can ensure efficient capture of temperature changes.

[0084] S1.4: Perform a traversal operation, collect the temperature fields of adjacent track nodes according to the determined strategy, and use the temperature fields as the current temperature fields until the temperature change rate of the temperature fields collected N times in a row is less than a preset temperature threshold or the track nodes in the current row and column have been traversed, and output the current scan temperature map;

[0085] See also Figure 3 , a schematic diagram of temperature acquisition according to an embodiment of the present invention, in which the rectangular coordinate robot is located at the center grid of the temperature field, and the principal component direction indicates the direction of the most significant temperature change in the temperature field. In the figure on the left, the robot calculates the principal component direction of the temperature field and finds that this direction is close to the X-axis. Therefore, the system chooses to scan along the X-axis according to a zigzag strategy. The figure on the right shows that under the zigzag strategy, the robot will traverse adjacent grids row by row along the X-axis to ensure the integrity of the scan and avoid missing areas.

[0086] Specifically, the robot continues to traverse adjacent track nodes using either a zigzag or column-first strategy, following the aforementioned strategy. After each traversal, the robot updates and recollects the current temperature field data. Each newly collected temperature data point is compared with the previously collected data point to calculate the temperature change rate. If the temperature change rate for N consecutive acquisitions is less than a preset temperature threshold, indicating that the temperature field has stabilized, the system stops collecting data and outputs the current scan temperature map.

[0087] It should be noted that the fact that the temperature field changes tend to be stable does not mean that the remaining grids do not need to be processed. Rather, based on the solution of this embodiment, in actual applications, although the temperature changes in most areas have tended to be stable, there are still a few grids that may be affected by the temperature conduction of the surrounding grids in the subsequent scan. Therefore, it may still have a certain impact on the remaining grids, especially under the heat conduction effect between adjacent grids. The remaining unscanned grids will also be affected by the processed grids. Since the temperature changes of these grids may depend on the temperature information of the processed grids, even if the temperature change rate reaches a stable state, the temperature field of the remaining unprocessed grids may still be affected by the cooling process, resulting in fine-tuning of the temperature changes in the local area. This heat conduction effect ensures the accuracy and continuity of the scanned temperature map. Even for grids that are not directly scanned, their temperature data can be corrected through the temperature changes of adjacent grids.

[0088] The specific steps of S2 are as follows:

[0089] S2.1: performing Gaussian filtering to reduce noise on the current scanned temperature image and constructing a two-dimensional temperature gradient vector field;

[0090] Specifically, the currently collected temperature field usually contains noise data caused by environmental factors, sensor errors, etc., which can affect the accuracy of path planning and grid processing. To this end, Gaussian filtering is first used to reduce the noise of the temperature map. Gaussian filtering is a smoothing operation that can remove high-frequency noise in the image through a weighted average method. The temperature values ​​in the neighborhood are weighted averaged using a Gaussian kernel function. The weights of pixels in the neighborhood (i.e., grid temperature data) are larger, and the weights of pixels far from the center are smaller, thereby achieving smoothing of the temperature field data.

[0091] S2.2: Calculate the temperature gradient value of each grid in the X-axis direction and the Y-axis direction according to the two-dimensional temperature gradient vector field to generate a temperature gradient vector;

[0092] Specifically, the temperature gradient reflects the rate of temperature change and its direction at a point in the temperature field. The temperature gradient is a vector that indicates the direction of the greatest temperature change. By calculating the temperature gradient value for each grid, we can determine the rate of temperature change for each grid in different directions, i.e., the temperature gradient vector.

[0093] In this embodiment, the temperature change rate in the X-axis direction is calculated by calculating the difference between the current grid temperature and the temperature of the adjacent grids on the left and right. To ensure the accuracy of the calculation, the temperature values ​​of adjacent grids are usually selected for the difference operation. For example, the temperature of the current grid is subtracted from the temperature of the grid to the right, and then divided by the distance between the grids (assuming that the distance between each grid in the X-axis direction is constant).

[0094] Similarly, the temperature change of the grid along the Y-axis is calculated. The temperature difference between the current grid and the grids above and below it is used to determine the temperature change rate along the Y-axis. Similar to the X-axis, the temperature change is calculated using a differential operation and then normalized based on the distance between the grids.

[0095] Furthermore, the calculated temperature gradient values ​​in the X-axis and Y-axis directions are combined into a two-dimensional temperature gradient vector. This vector indicates the most significant direction of temperature change at the grid location, and its size (modulus) represents the rate of temperature change. The direction of this gradient vector is the direction of the largest temperature change, indicating the direction of the fastest temperature change starting from this grid. For each grid in the temperature field, the above method is used for calculation, and finally the temperature gradient vector of each grid in the entire area is obtained. These vectors can form a two-dimensional temperature gradient vector field, which provides detailed direction and change rate information for subsequent path planning and grid selection.

[0096] S2.3: If the temperature gradient vector is greater than or equal to a preset temperature gradient threshold, mark the corresponding grid as a candidate grid;

[0097] In this embodiment, candidate grids are grids whose current temperature has exceeded a preset threshold and require further processing. Specifically, candidate grids are marked based on the comparison of their temperature gradient vectors with preset temperature gradient thresholds. The temperature gradient vector of each grid represents the rate and direction of temperature change in the X and Y axes. When the magnitude of the temperature gradient vector for a grid is greater than or equal to the preset temperature gradient threshold, it indicates that the temperature change in the area where the grid is located is significant, exceeding the normal range. The temperature change is large, indicating that there may be a temperature anomaly or an area requiring special attention. Therefore, the grid is marked as a candidate grid for subsequent processing.

[0098] Specifically, the magnitude of the temperature gradient vector reflects the rate of temperature change of a particular grid relative to the surrounding grids. If the temperature gradient exceeds a threshold, it indicates overheating, heat accumulation, or temperature unevenness in that area, which often requires priority attention. For example, issues such as uneven cooling, clogged fins, and abnormal temperatures are often closely related to equipment efficiency, cooling effectiveness, and overall system performance, and therefore require priority inspection or repair.

[0099] S2.4: Perform an iterative operation on the candidate grid to obtain a temperature-related region of the candidate grid, and use the grid within the temperature-related region as the current grid to be processed;

[0100] In this embodiment, once the candidate grids are marked, the system enters an iterative operation step. For each candidate grid, the system analyzes the temperature gradient values ​​and correlations of the surrounding grids, iteratively marking grids related to the temperature changes of the candidate grid as temperature-correlated regions. This process is based on the thermal conductivity characteristics of the temperature field. Because heat in a temperature field is generally transferred between adjacent regions, the temperature values ​​of adjacent grids have a certain correlation. By calculating the temperature-correlated regions, the system can identify a group of related grids that are commonly affected by a certain temperature source and may need to be processed together.

[0101] Specifically, the iterative operation ensures that when processing candidate grids with significant temperature gradients, surrounding grids with similar temperature change trends are also included in the processing range, thereby optimizing the processing strategy. The robot can intelligently identify related grid areas, avoiding the independent processing of individual grids and improving processing efficiency. Especially when faced with a large temperature field, the practice of treating temperature-related areas as the current grid to be processed can make the processing more systematic and coherent, reduce the possibility of omissions and redundant processing, and ultimately improve the overall efficiency of path planning.

[0102] The specific steps of S2.4 are as follows:

[0103] S2.4.1: Create a separate set for each candidate grid and calculate the average principal gradient direction of the set;

[0104] In this embodiment, candidate grids are those selected in the previous steps based on the temperature gradient threshold. These grids exhibit significant temperature variations and therefore require further processing. The area represented by each candidate grid may have a certain temperature correlation with surrounding grids. To more accurately identify grid clusters within a region and further determine their processing strategy, a separate set is created for each candidate grid. This set includes the current candidate grid and its neighboring grids.

[0105] Specifically, by creating a set for each candidate grid, subsequent correlation analysis can be easily performed. The main gradient direction of each grid represents the direction in which the grid's temperature changes most significantly, which is determined by the degree of change in the temperature field and the temperature gradient.

[0106] Furthermore, the system performs a weighted average of the main gradient directions for each grid within the collection to generate the overall gradient direction for the collection. This eliminates deviations from individual grids, providing a more reliable and stable grid processing direction. The average main gradient direction represents the common temperature variation trend for the grids within the entire collection, providing a foundation for subsequent operations.

[0107] In this embodiment, the average main gradient direction is determined based on a local density parameter and a temperature gradient value. The local density parameter measures the spatial distribution density of grids within a grid set, reflecting the distance and correlation between a grid and other grids in its neighborhood. The temperature gradient value describes the rate of temperature change of each grid in a specific direction. Combining these two factors effectively constructs a more accurate average main gradient direction.

[0108] S2.4.2: Perform an iterative operation to obtain the current main gradient direction of the adjacent grids of any grid in the set. If the angle between the current main gradient direction and the average main gradient direction is less than a preset synchrotron threshold, add the adjacent grid to the set until the angles between the current main gradient direction and the average main gradient direction of the adjacent grids are all greater than or equal to the synchrotron threshold. Output the set as a temperature-related region.

[0109] In this embodiment, an iterative process analyzes the temperature gradients of each grid in the candidate grid set and its adjacent grids one by one. The current dominant gradient direction of each grid is then calculated and compared with the average dominant gradient direction within the set. If the angle between the dominant gradient direction of the current grid and the average dominant gradient direction within the set is less than a preset symmetric threshold, the adjacent grid is deemed to have a consistent temperature change trend with the grids within the set and is added to the current set. This process continues until the angles between the dominant gradient directions of all adjacent grids and the set are greater than or equal to the symmetric threshold.

[0110] Specifically, the temperature-related area is gradually expanded in an iterative manner, that is, the areas with similar temperature change trends to the current grid are aggregated together. In this way, the system not only processes a single grid, but can identify groups of grids with similar temperature patterns. In this way, the temperature-related area can cover areas with relatively consistent temperature changes, avoiding the mistaken inclusion of areas with smaller temperature changes in the processing area. In actual operation, setting an appropriate threshold can ensure that the system only selects grids with similar temperature change trends. If the threshold is too low, too many unrelated grids may be mistakenly aggregated together, and if the threshold is too high, some grids with similar temperature change trends may be missed. Therefore, the same-direction threshold needs to be determined by technicians in this field through a large number of experiments.

[0111] It should be noted that the grids in the temperature correlation area are not necessarily grids whose temperatures have exceeded the preset threshold. However, in a complex temperature field, the heat conduction effect between grids may affect the grids that originally had lower temperatures, forming a certain temperature gradient. Therefore, when selecting grids to be processed, grids with relatively small temperature gradient values ​​may also be associated with the temperature gradient of the candidate grids through the temperature transfer effect, especially these grids have a strong temperature gradient association with grids with higher temperatures. By selecting the temperature correlation area, even in areas where the temperature change is not obvious, the temperature correlation effect caused by heat conduction can be effectively included in the processing range, thereby ensuring the integrity and accuracy of the overall path planning and grid processing.

[0112] See also Figure 4 and Figure 5 , a schematic diagram of the grid positioning to be processed in the embodiment of the present invention, Figure 4 In the figure, based on the temperature gradient threshold, the system determines candidate grid 1, candidate grid 2, and candidate grid 3 by calculating the gradient value of the temperature field. These grids are areas with more significant temperature changes and require further processing. In the figure, these candidate grids are marked as key areas that need to be processed. By calculating the temperature gradient value, the system can intelligently screen out these key grids, thereby effectively improving the efficiency of path planning. Figure 5In the process, an iterative operation is performed on candidate grids 1, 2, and 3 to obtain the current main gradient direction of their adjacent grids. By comparing the angle between the main gradient direction of an adjacent grid and the average main gradient direction in the set, if the angle is less than the preset same-direction threshold, the adjacent grid is added to the current set. The iterative process will continue until the angle between the main gradient direction of all adjacent grids and the average main gradient direction is greater than or equal to the same-direction threshold. At this point, the grids in the set form a temperature correlation region, indicating that these grids have a certain correlation in temperature change trends. The system will then process these grids as the current grids to be processed. Finally, temperature correlation region 1, temperature correlation region 2, and temperature correlation region 3 are obtained.

[0113] The specific steps for S3 are as follows:

[0114] S3.1: Randomly select a candidate grid from the current grid to be processed as the initial processing grid;

[0115] In this embodiment, processing begins by randomly selecting a grid from all current candidate grids. The core concept of this method is to use random selection, rather than processing each grid individually from a fixed starting point. This avoids the repetitive operations and local optimization that can result from fixed path selection in traditional methods. By randomly selecting the initial grid, the robot can explore different processing paths without being restricted to a specific direction. This helps it explore a wider area and reduces the risk of being misled by local temperature fields.

[0116] S3.2: Determine a first outer grid and a second outer grid of the initial processing grid;

[0117] In this embodiment, the outer ring of grids refers to the area of ​​grids adjacent to the current grid, with a distance of one grid unit. It is divided into a first outer ring and a second outer ring, which represent the grids directly adjacent to the initial grid and the grids slightly further away, respectively. Specifically, the first outer ring of grids refers to the grids directly adjacent to the initial processing grid and most likely to be affected by temperature transfer, while the second outer ring of grids refers to the grids slightly further away from the initial processing grid but still within the range of temperature transfer.

[0118] Specifically, by defining an outer mesh, the system can focus on the mesh's neighborhood, ensuring that processing doesn't overextend to unnecessary areas. This outer mesh helps the system lock in the spatial scope of processing, avoiding inefficient calculations in areas too far away, while also providing critical adjacency data for temperature conduction simulations.

[0119] It's important to note that temperature transfer is a nonlinear process, and its impact range is not only related to the physical distance between the grids, but also closely related to factors such as temperature differences and the heat transfer medium. Therefore, the specific settings of the first and second outer ring grids are not fixed, but can be experimentally optimized and dynamically adjusted according to the needs of different environments and actual applications. Generally, the definition of these outer ring grids can be determined based on experimental data and the characteristics of ambient temperature changes. For example, in areas with more drastic temperature changes, the first outer ring may need to include more grids to ensure that the propagation and impact of temperature are fully considered; while in areas with more gradual temperature changes, the second outer ring may not require too many grids to avoid computational redundancy.

[0120] Preferably, the ranges of the first outer ring grid and the second outer ring grid may be dynamically adjusted according to the local characteristics of the temperature field to optimize the efficiency of path planning and temperature conduction simulation.

[0121] Specifically, in areas with more drastic temperature changes, the temperature field changes with a higher gradient due to the strong heat conduction effect, directly affecting the temperature state of adjacent grids. Therefore, the definition of the first outer ring grid is no longer limited to a single grid unit adjacent to the initial processing grid, but is appropriately expanded to include multiple adjacent grids, ensuring that the system can fully capture the propagation range of temperature changes. This design allows heat conduction in the temperature field to be accurately reflected and propagates the temperature influence from the initial grid to multiple adjacent areas, improving the accuracy and rationality of the temperature conduction simulation.

[0122] Furthermore, in areas with relatively gentle temperature changes, the temperature field changes more slowly, the temperature differences between adjacent grids are small, and the impact of heat conduction on adjacent grids is limited. Therefore, the second outer grid ring does not need to include too many grids, which would cause redundant calculations and unnecessary resource waste in areas with less significant temperature changes. In this case, the scope of the second outer grid ring can be appropriately narrowed to only include areas with significant temperature changes among adjacent grids, avoiding redundant calculations and improving computational efficiency.

[0123] S3.3: Based on the principle of heat conduction, simulate the temperature transfer process of the initial processing grid during the processing process, and determine and predict whether the first outer ring grid and the second outer ring grid need further processing;

[0124] Specifically, because temperature is conductive, when processing one mesh, its temperature will affect adjacent meshes, especially the first and second outer meshes. Therefore, during path planning, it is necessary to predict the effect of temperature transfer and determine whether these outer meshes require further processing.

[0125] In this embodiment, the heat conduction simulation considers heat exchange between grids in the temperature field. The system simulates the impact of the initial grid's temperature change on its neighboring grids by calculating temperature differences. The system then determines whether the temperature changes of the first and second outer grids have reached a threshold requiring processing. If these outer grids still experience significant temperature changes, they are significantly affected by the initial grid's temperature change, and therefore these grids are processed further.

[0126] S3.4: If further processing is required, select the candidate grid with the smallest Euclidean distance to the initial processing grid from the remaining unprocessed candidate grids as the next processing grid, wherein the candidate grid is different from the first outer ring grid and the second outer ring grid;

[0127] Specifically, Euclidean distance is a standard for measuring the straight-line distance between two points. Selecting the grid with the smallest distance means selecting the area closest to the current processing area, which helps reduce the distance the robot moves and improves the efficiency of path planning. It is also most likely to cover the first outer circle grid and the second outer circle grid.

[0128] In this embodiment, by selecting the grid with the smallest Euclidean distance as the next processing grid, the system can efficiently cover the adjacent area. However, it must be understood that selecting the grid with the smallest Euclidean distance does not necessarily completely cover the first and second outer grids, because the temperature changes and spatial distributions of these outer grids may not completely overlap with the grid with the smallest Euclidean distance.

[0129] Preferably, when selecting the grid with the smallest Euclidean distance, not only the distance from the initial processing grid is considered, but also the distribution of the outer grids is considered. In the Euclidean distance selection process, candidate grids close to the outer grids are prioritized to ensure that the next processing grid selected spatially overlaps the outer grids, thereby optimizing the processing path.

[0130] Specifically, when selecting the grid with the smallest Euclidean distance, the system can determine whether the current grid can transfer temperature to the outer grids. If the grid with the smallest Euclidean distance can cover the first or second outer grid within its temperature transfer influence range, the system will select that grid as the next processing target.

[0131] Furthermore, if the mesh with the smallest Euclidean distance doesn't cover the first or second outer mesh within its temperature transfer influence range, further analysis of the temperature field can reveal which meshes have a greater heat transfer influence on the outer meshes. In this case, even if a candidate mesh doesn't have the smallest Euclidean distance, if it has sufficient heat transfer capability to effectively transfer temperature to the outer meshes, it can be selected as the next mesh to be processed.

[0132] S3.5: If no further processing is required, a candidate grid is randomly selected from the remaining unprocessed candidate grids as the next processing grid;

[0133] Specifically, the random selection strategy means the system doesn't fix the processing order, but rather flexibly selects the grids to be processed from the remaining grids. This approach provides flexibility and randomness in path planning, avoiding overly rigid processing procedures and allowing the processing order to be dynamically adjusted. Although this randomness carries a certain degree of uncertainty, it effectively prevents the system from being constrained by fixed paths in complex environments, making the processing process more adaptable and capable of handling diverse workloads.

[0134] S3.6: Repeat the simulation process until there are no unprocessed candidate grids, and determine whether there are any remaining grids in the grid to be processed that need further processing. If further processing is required, process them one by one using the rectangular coordinate robot;

[0135] Specifically, by repeating the simulation process, the temperature changes of the current grid and adjacent grids are continuously evaluated until all candidate grids have been processed. In each iteration, the system will continue to determine whether the first outer ring grid and the second outer ring grid require further processing based on the aforementioned heat conduction principle. Each time a grid is processed, the system will update the temperature field data and re-evaluate whether there are other grids that need to be processed. When all unprocessed grids have been scanned and determined to require processing, the system will make a final decision based on the real-time temperature field data, and finally use the rectangular coordinate robot to process the grids that require further processing one by one in sequence.

[0136] It should be noted that, during the simulation process, if the first outer circle grid and the second outer circle grid contain a candidate grid, the candidate grid will be deleted from the candidate grid set.

[0137] In this embodiment, the candidate grid set includes all regions selected in the previous step as having significant temperature variations. In practice, these regions need to be processed individually. However, during the path planning and temperature processing of this application, the first and second outer-ring grids are directly adjacent to the initially processed grid and are affected by its temperature variations. In this case, if a candidate grid falls within the range of the first or second outer-ring grid, this means that the temperature variation of that grid is likely already covered by the outer-ring grid. In other words, the temperature variation trend of the candidate grid is similar or correlated to that of the outer-ring grid, and the processing of the outer-ring grid has indirectly covered or affected the candidate grid. Therefore, there is no need to process the candidate grid separately, thus avoiding duplicate calculations and ineffective path planning. By removing grids from the candidate grid set that are already included in the outer-ring grid, the system can effectively avoid duplicate processing, thereby improving processing efficiency and reducing wasted computing resources. For example, when processing a grid, the system already knows that the temperature variation of its adjacent outer-ring grid will affect it, and therefore no longer needs to treat it as a separate grid to be processed.

[0138] See also Figure 6 , schematic diagram of the outer ring expansion of the initial processing grid according to the embodiment of the present invention, Figure 6 In the example, candidate grid 1 is randomly selected as the initial processing grid. Based on the characteristics of the temperature field, the system defines a first outer ring of grids and a second outer ring of grids. The two elliptical areas in the figure represent the adjacent areas of candidate grid 1. The first outer ring of candidate grid 1 refers to the area adjacent to and directly affected by the initial grid, while the second outer ring of grids is located slightly further away from the initial grid. Temperature changes in these grids may be transmitted from the initial grid's temperature changes. As can be seen in the figure, candidate grid 2 is within the second outer ring of candidate grid 1.

[0139] See also Figure 7 , schematic diagram of candidate mesh elimination according to an embodiment of the present invention, Figure 7 In the example, since Candidate Grid 2 is already within the second outer circle of Candidate Grid 1, it is directly related to the temperature change of Candidate Grid 1 and will not be processed again in subsequent processing. Therefore, Candidate Grid 2 is removed from the candidate grid set and only remains as a pending grid, not considered as a candidate grid. This operation effectively avoids redundant processing, improving system processing efficiency and path planning accuracy.

[0140] See also Figure 8 , the embodiment of the present invention processes the grid selection principle diagram, in Figure 8In the example above, the system uses the principle of heat conduction to determine the first and second outer rings of candidate grid 1 and determines that one grid in the outer ring still needs to be processed. Next, the system calculates the Euclidean distance and determines that candidate grid 3 has the shortest distance from the initial processing grid, so it is selected as the next grid to be processed. This Euclidean distance calculation effectively selects the grid closest to the current processing area and the one that requires the most processing priority, further reducing the distance the robot must travel and optimizing the path planning process.

[0141] Example 2

[0142] See also Figure 9 The present invention provides an embodiment of a path planning system based on rectangular coordinate robot transportation, suitable for an automated working environment with a track conveying system, wherein the target to be processed is a planar structure including multiple grids arranged at an angle, the rectangular coordinate robot is equipped with an infrared thermal sensor with a radiation range of one grid, the track conveying system includes a track node corresponding to each grid of the planar structure, so that the rectangular coordinate robot radiates all grids by reaching each track node, and the system includes a temperature acquisition module, a grid positioning module, a grid processing module, and a path planning module, wherein:

[0143] The temperature acquisition module realizes infrared radiation of multiple adjacent grids by traversing to multiple adjacent orbital nodes to obtain the temperature field of each grid, wherein the multiple adjacent grids form a current scanning temperature map;

[0144] The grid positioning module positions a plurality of grids to be processed according to the current scanning temperature map;

[0145] The grid processing module selects one of the grids to be processed according to the current grid to be processed;

[0146] The path planning module determines the path to the corresponding track node based on the currently processed grid.

[0147] The grid positioning module includes:

[0148] a gradient construction unit, configured to perform Gaussian filtering and noise reduction processing on the current scanning temperature map, and construct a two-dimensional temperature gradient vector field;

[0149] Grid marking unit, used to generate temperature gradient vectors and select candidate grids;

[0150] The associated region determining unit is used to perform an iterative operation on the candidate grid, obtain the temperature associated region of the candidate grid, and use the grids in the region as the current grid to be processed.

[0151] The grid processing module includes:

[0152] An initial grid selection unit is used to randomly select a candidate grid from the current grid to be processed as the initial processing grid;

[0153] An outer ring processing judgment unit is used to determine the first outer ring grid and the second outer ring grid of the initial processing grid, simulate the temperature transfer process based on the heat conduction principle, and judge whether the outer ring grid needs further processing;

[0154] A grid selection unit, if the outer circle grid needs to be further processed, selects the candidate grid with the smallest Euclidean distance to the initial processing grid and different from the outer circle grid from the remaining unprocessed candidate grids as the next processing grid;

[0155] If not, the next processing grid is randomly selected, and after the processing is completed, the remaining outer circle grids to be processed are processed by the rectangular coordinate robot.

[0156] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A path planning method based on rectangular coordinate robot conveying is suitable for an automated working environment with a track conveying system, characterized in that: The target to be processed is a planar structure including multiple grids arranged at an angle. The rectangular coordinate robot is equipped with an infrared thermal sensor with a radiation range of one grid. The track conveying system includes a track node corresponding to each grid of the planar structure, so that the rectangular coordinate robot radiates all grids by reaching each track node. The path planning method includes: Accomplishing infrared radiation of a plurality of adjacent grids by traversing to a plurality of adjacent orbital nodes to obtain a temperature field of each grid, wherein the plurality of adjacent grids form a current scanning temperature map; Locating multiple currently processed grids according to the current scan temperature map, selecting one of the grids for processing, and determining a path to a corresponding orbital node according to the currently processed grid; Follow the path to the track node.

2. The path planning method based on rectangular coordinate robot transportation according to claim 1, characterized in that: The track nodes are distributed according to a rectangular coordinate system, and each track node corresponds to the geometric center of the grid in the planar structure.

3. The path planning method based on rectangular coordinate robot transportation according to claim 1, characterized in that: The infrared radiation of multiple adjacent grids is realized by traversing to multiple adjacent orbital nodes to obtain the temperature field of each grid, including: According to the current position of the rectangular coordinate robot, the current temperature field is collected; Calculate the principal component direction of the current temperature field, and if the angle between the principal component direction and the X-axis of the rectangular coordinate system is less than a preset angle threshold, traverse the adjacent track nodes row by row along the X-axis according to a zigzag strategy; If the angle between the principal component direction and the X-axis of the rectangular coordinate system is greater than or equal to a preset angle threshold, traverse the adjacent track nodes column by column along the Y-axis according to the column priority strategy; Perform a traversal operation, collect the temperature fields of adjacent track nodes according to the determined strategy, and use the temperature fields as the current temperature fields until the temperature change rate of the temperature fields collected N times in succession is less than the preset temperature threshold or the track nodes of the current row and column have been traversed, and output the current scan temperature map.

4. The path planning method based on rectangular coordinate robot transportation according to claim 1, characterized in that: Locating a plurality of current to-be-processed grids according to the current scanned temperature map, including: Performing Gaussian filtering and noise reduction processing on the current scanning temperature image, and constructing a two-dimensional temperature gradient vector field; Calculating the temperature gradient value of each grid in the X-axis direction and the Y-axis direction of the rectangular coordinate system according to the two-dimensional temperature gradient vector field to generate a temperature gradient vector; If the temperature gradient vector is greater than or equal to a preset temperature gradient threshold, the corresponding grid is marked as a candidate grid; An iterative operation is performed on the candidate grid to obtain a temperature-related region of the candidate grid, and the grid within the temperature-related region is used as a current grid to be processed.

5. The path planning method based on rectangular coordinate robot transportation according to claim 4, characterized in that: Performing an iterative operation on the candidate grid to obtain a temperature-related area of ​​the candidate grid includes: Create an independent set for each candidate grid and calculate the average main gradient direction of the set; An iterative operation is performed to obtain the current main gradient direction of the adjacent grids of any grid in the set. If the angle between the current main gradient direction and the average main gradient direction is less than the preset same-direction threshold, the adjacent grid is added to the set. This process is repeated until the angles between the current main gradient direction and the average main gradient direction of the adjacent grids are all greater than or equal to the same-direction threshold. The set is then output as a temperature-associated region.

6. The path planning method based on rectangular coordinate robot transportation according to claim 5, characterized in that: The average main gradient direction is determined based on a local density parameter and a temperature gradient value.

7. The path planning method based on rectangular coordinate robot transportation according to claim 4, characterized in that: Locate multiple meshes to be processed and select one of them for processing, including: Randomly select a candidate grid from the current grid to be processed as the initial processing grid; Determine a first outer ring grid and a second outer ring grid of the initial processing grid; Based on the principle of heat conduction, the temperature transfer process of the initial processing grid during the processing is simulated to determine and predict whether the first outer ring grid and the second outer ring grid need further processing; If further processing is required, selecting the candidate grid with the smallest Euclidean distance to the initial processing grid from the remaining unprocessed candidate grids as the next processing grid, wherein the candidate grid is different from the first outer circle grid and the second outer circle grid; If no further processing is required, a candidate grid is randomly selected from the remaining unprocessed candidate grids as the next processing grid; The simulation process is repeated until there are no unprocessed candidate grids. It is determined whether there are any remaining grids in the grid to be processed that need further processing. If further processing is required, the rectangular coordinate robot is used to process them one by one.

8. The path planning method based on rectangular coordinate robot transportation according to claim 7, characterized in that: If the first outer circle grid and the second outer circle grid include a candidate grid, the candidate grid is deleted from the candidate grid set.

9. A path planning system based on rectangular coordinate robot conveying, suitable for an automated working environment with a track conveying system, for implementing the path planning method based on rectangular coordinate robot conveying according to any one of claims 1 to 8, characterized in that: The target to be processed is a planar structure including multiple grids arranged at an angle. The rectangular coordinate robot is equipped with an infrared thermal sensor with a radiation range of one grid. The track conveying system includes a track node corresponding to each grid of the planar structure, so that the rectangular coordinate robot radiates all grids by reaching each track node. The path planning system includes a temperature acquisition module, a grid positioning module, a grid processing module, and a path planning module, wherein: The temperature acquisition module realizes infrared radiation of multiple adjacent grids by traversing to multiple adjacent orbital nodes to obtain the temperature field of each grid, wherein the multiple adjacent grids form a current scanning temperature map; The grid positioning module positions a plurality of grids to be processed according to the current scanning temperature map; The grid processing module selects one of the grids to be processed according to the current grid to be processed; The path planning module determines the path to the corresponding track node based on the currently processed grid.

10. The path planning system based on rectangular coordinate robot transportation according to claim 9, characterized in that: The grid positioning module includes: a gradient construction unit, configured to perform Gaussian filtering and noise reduction processing on the current scanning temperature map, and construct a two-dimensional temperature gradient vector field; Grid marking unit, used to generate temperature gradient vectors and select candidate grids; The associated region determining unit is used to perform an iterative operation on the candidate grid, obtain the temperature associated region of the candidate grid, and use the grids in the region as the current grid to be processed.

11. The path planning system based on rectangular coordinate robot transportation according to claim 10, characterized in that: The grid processing module includes: An initial grid selection unit is used to randomly select a candidate grid from the current grid to be processed as the initial processing grid; An outer ring processing judgment unit is used to determine the first outer ring grid and the second outer ring grid of the initial processing grid, simulate the temperature transfer process based on the heat conduction principle, and judge whether the outer ring grid needs further processing; A grid selection unit, if the outer circle grid needs to be further processed, selects the candidate grid with the smallest Euclidean distance to the initial processing grid and different from the outer circle grid from the remaining unprocessed candidate grids as the next processing grid; If not, the next processing grid is randomly selected, and after the processing is completed, the remaining outer circle grids to be processed are processed by the rectangular coordinate robot.

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