Service robot dynamic navigation planning method and system

Abstract

The application belongs to the technical field of robot navigation and path planning, and specifically discloses a dynamic navigation planning method and system for a service robot. The application overcomes the limitations of single-dimensional analysis in a complex dynamic scene by constructing a dynamic social heat map and deducing group dynamic intention data, and improves the perception accuracy of the environment structure and the crowd situation. A linkage mechanism is established between the dynamic social heat map, individual behavior characteristics and path planning, the passing efficiency and social etiquette are quantitatively evaluated according to the future direction of the group, and a social cost function is generated to optimize the navigation path. The actual influence of the robot navigation behavior on the environment is monitored in real time, and the planning strategy is adjusted combined with the feedback data, so that the adaptability of the navigation decision and the continuous improvement of the environment acceptance are realized.

Description

Technical Field

[0001] This invention belongs to the field of robot navigation and path planning technology, and relates to a dynamic navigation planning method and system for service robots. Background Technology

[0002] When service robots navigate in complex and dynamic environments, they need to comprehensively consider multiple factors such as environmental structure, pedestrian flow, and social etiquette. Their core objective is to generate a navigation path that is both efficient and harmonious with human behavior by perceiving and analyzing surrounding environmental data. In typical application scenarios, such as shopping malls, hospitals, or airports, robots need to identify functional areas such as passageways, entrances, and gathering areas, and analyze the density, speed, and main direction of pedestrian flow within these areas to construct a dynamic social heatmap to characterize environmental constraints. Simultaneously, robots also need to extract individual behavioral characteristics to predict future group movements.

[0003] Existing technologies typically rely on static maps and simple path planning algorithms to achieve navigation through pre-defined rules or fixed parameters. These methods often generate navigation paths using geometric path optimization, based on known environmental layout information combined with real-time sensor data. Some improved technologies attempt to introduce behavior prediction models, using analysis of individual posture orientation or spatial relationships to help determine potential behavioral intentions. However, when dealing with complex dynamic scenarios, these methods still primarily rely on single-dimensional analysis, lacking a comprehensive quantitative assessment of social etiquette and traffic efficiency.

[0004] The aforementioned existing technologies have certain limitations when dealing with highly dynamic and diverse service scenarios. Because the correlation between dynamic social heatmaps and individual behavioral characteristics has not been fully explored, existing methods have limited accuracy in predicting the future movements of groups, which can easily lead to path planning results deviating from actual needs.

[0005] Furthermore, the assessment of social intrusion costs and potential conflict risks during path planning is rather coarse, failing to fully reflect the realities of human-computer interaction. The ability to monitor the actual impact of robot navigation behavior on the environment and adjust planning strategies accordingly has also not been fully realized, leaving room for improvement in the adaptability and environmental acceptance of navigation decisions. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a dynamic navigation planning method and system for service robots is proposed.

[0007] The objective of this invention can be achieved through the following technical solutions: The first aspect of this invention provides a dynamic navigation planning method for service robots, comprising: environmental perception analysis: the service robot's operating area is designated as the target area, real-time perception data of a multi-source sensor array deployed within the target area is obtained, and environmental state fusion analysis is performed based on this data to generate a dynamic social heat map and extract individual behavioral characteristics.

[0008] Intent inference generation: Based on the dynamic social heatmap and the individual behavioral characteristics, by analyzing the behavioral tendencies of individual behavioral characteristics under the environmental constraints represented by the dynamic social heatmap, dynamic intention data representing the future trend of the group is inferred.

[0009] Path planning generation: A social cost function is generated based on the dynamic intention data of the group. The social cost function is used to quantitatively evaluate social etiquette and traffic efficiency, and a socialized navigation path is planned based on the social cost function.

[0010] Path correction and optimization: The navigation process data of the service robot executing the social navigation path is collected in real time using a multi-source sensor array. A navigation effect distribution map is generated by comparing the navigation process data before and after navigation. The social cost function generation parameters are adjusted based on the navigation effect distribution map.

[0011] A second aspect of the present invention provides a dynamic navigation planning system for a service robot, comprising: an environmental perception module, which records the service robot's operating area as the target area, acquires real-time perception data from a multi-source sensor array deployed within the target area, performs environmental state fusion analysis based on the data to generate a dynamic social heat map, and extracts individual behavioral characteristics.

[0012] The intent deduction module, based on the dynamic social heatmap and the individual behavioral characteristics, analyzes the behavioral tendencies of individual behavioral characteristics under the environmental constraints represented by the dynamic social heatmap, and deduces the dynamic intention data of the group that represents the future trend of the group.

[0013] The path planning module generates a social cost function based on the group's dynamic intention data. The social cost function is used to quantitatively evaluate social etiquette and traffic efficiency, and plans a socialized navigation path based on the social cost function.

[0014] The path correction module uses a multi-source sensor array to collect navigation process data in real time as the service robot executes the socialized navigation path. It generates a navigation effect distribution map by comparing the navigation process data before and after navigation, and adjusts the social cost function generation parameters based on the navigation effect distribution map.

[0015] The database stores the baseline spatial reconstruction features and baseline attitude reconstruction features of different partitions of the target area before navigation.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] 1. This invention overcomes the limitations of single-dimensional analysis in complex dynamic scenarios by constructing dynamic social heat maps and inferring group dynamic intention data, thereby improving the accuracy of perception of environmental structure and crowd flow.

[0018] 2. This invention establishes a linkage mechanism between dynamic social heatmaps, individual behavioral characteristics, and path planning. It quantitatively assesses traffic efficiency and social etiquette based on the future movement of the group, generating a social cost function to optimize navigation paths. By monitoring the actual impact of robot navigation behavior on the environment in real time and adjusting planning strategies based on feedback data, it achieves continuous improvement in the adaptability of navigation decisions and environmental acceptance. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram illustrating the implementation steps of the method of the present invention.

[0021] Figure 2 This is a schematic diagram of the system module connections of the present invention.

[0022] Figure 3 This is a schematic diagram of the dynamic social heatmap generation process in an embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram illustrating the process of extrapolating group dynamic intent data in an embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram illustrating the process of constructing the social cost function in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0026] Please see Figure 1 As shown, the first aspect of the present invention provides a dynamic navigation planning method for a service robot, comprising:

[0027] Environmental perception analysis: The service robot's operating area is designated as the target area. Real-time perception data from the multi-source sensor array deployed within the target area is acquired, and environmental state fusion analysis is performed based on this data to generate a dynamic social heat map and extract individual behavioral characteristics.

[0028] In a preferred embodiment of the present invention, the multi-source sensor array includes a lidar, a depth camera, an ultrasonic sensor, and an infrared sensor.

[0029] In a preferred embodiment of the present invention, the specific process of acquiring real-time sensing data of a multi-source sensor array deployed within the target area includes: synchronously acquiring spatial geometric features output by a lidar, attitude orientation features output by a depth camera, distance distribution features output by an ultrasonic sensor, and temperature field features output by an infrared sensor, and integrating the spatial geometric features, attitude orientation features, distance distribution features, and temperature field features into the real-time sensing data.

[0030] Further explanation is needed: LiDAR outputs spatial geometric features, i.e., obstacle distribution and spatial structure information within the target area; depth cameras output attitude and orientation features to capture changes in the attitude and orientation of people in the environment; ultrasonic sensors provide distance distribution features to detect the relative distance between the robot and surrounding objects; and infrared sensors output temperature field features, reflecting thermal imaging information about the distribution of people within the target area. These features are integrated into real-time perception data after synchronous acquisition, serving as the basis for subsequent analysis.

[0031] For a preferred embodiment of the present invention, please refer to Figure 3 The specific process of generating a dynamic social heatmap through environmental state fusion analysis, as shown, includes:

[0032] Extract spatial geometric features, orientation features, and distance distribution features of the target area from real-time sensing data.

[0033] Functional zones are classified according to the mapping relationship between spatial geometric features and distance distribution features.

[0034] Preferably, the specific method for classifying the functional areas is as follows: Based on the spatial geometric features acquired by the lidar, the physical structural parameters of the target area are determined, including the channel width and open area. Combined with the distance distribution features collected by the ultrasonic sensor, the distribution density in different areas is calculated. Areas where the channel width is greater than a preset first-level threshold and the open area is greater than a preset first-level threshold are set as baseline conditions. These areas, combined with the distribution density in the distance distribution features, are classified as first-level functional areas, i.e., channel areas. Areas where the channel width is greater than a preset second-level threshold but less than or equal to the preset first-level threshold, and the open area is greater than the preset second-level threshold but less than or equal to the preset first-level threshold, are classified as second-level functional areas, i.e., transition areas. Areas where the channel width is less than or equal to the preset second-level threshold and the distribution density in the distance distribution features is less than or equal to the preset threshold are classified as third-level functional areas, i.e., aggregation areas. This completes the functional area classification.

[0035] By overlaying the posture orientation features, the functional region levels are semantically corrected, and a dynamic social heatmap labeled with channel areas, entrance areas, and gathering areas is output.

[0036] It needs further explanation that, from the perspective of correction logic, the levels defined by spatial geometry and distance distribution may have the same level but different actual functions. Preferably, a region's width and population density meet the geometric conditions of a passageway, but if the depth camera captures chaotic postures and orientations of people in that region without a unified direction of movement, contrary to the consistent orientation characteristic of a passageway, then that region is determined to be a temporary stopping area, not a passageway. Similarly, if a region's population density is close to that of a gathering area, but all postures and orientations point towards a specific entrance point, this feature can be overridden to classify it as an entrance area, rather than simply a transition zone. After semantic correction, regions that originally only reflected hierarchical differences are given clear functional semantics: regions with uniform postures and orientations extending in a fixed direction are labeled as passageways; regions with postures and orientations mostly pointing towards specific points within the region are labeled as entrance areas; and regions with chaotic and irregular postures and orientations without a unified direction are labeled as gathering areas. This ultimately forms a dynamic social heatmap that combines spatial distribution and functional semantics, providing precise environmental constraints for subsequent path planning.

[0037] Preferably, when the directional features within a certain area indicate that the crowd is generally facing the same direction, that area is labeled as a channel area; when the directional features indicate that the crowd is dispersed and has no obvious directionality, that area is labeled as a cluster area. The final output dynamic social heatmap not only labels different functional areas but also reflects the distribution of social dynamics in the current environment.

[0038] It should be noted that the purpose of generating dynamic social heatmaps is to provide service robots with accurate environmental constraints and social situational information for navigation in complex and dynamic scenarios. It can intuitively present the environmental structure and pedestrian distribution, laying the foundation for inferring group dynamic intentions and supporting the construction of social cost functions. Ultimately, this helps robots plan paths that balance traffic efficiency and social etiquette, overcoming the limitations of single-dimensional analysis and improving the adaptability of navigation to dynamic environments.

[0039] Intent inference generation: Based on the dynamic social heatmap and the individual behavioral characteristics, by analyzing the behavioral tendencies of individual behavioral characteristics under the environmental constraints represented by the dynamic social heatmap, dynamic intention data representing the future trend of the group is inferred.

[0040] For a preferred embodiment of the present invention, please refer to Figure 4 The specific process of inferring the dynamic intention data of the group to represent the future trend of the group, as shown, includes:

[0041] Extract the orientation and temperature field features of the target area from real-time sensing data.

[0042] The rate of change of the motion trajectory of the target area within the time window is calculated in real time. If there is a significant consistency in direction within a short period of time, the target area is identified as having a group movement trend point, and the group movement trend point in the attitude orientation feature is extracted in this way.

[0043] Preferably, if the number of people whose movement trajectory change rate is lower than the preset value in the target area within the preset time window exceeds the preset threshold, and the average movement direction deviation is less than the preset threshold, then it is determined that there is a group movement trend in the area, and the corresponding center point or direction starting point of the area is the group movement trend point.

[0044] Calculate the slope of temperature fluctuation in the target area and detect the slope change of the temperature fluctuation slope at the corresponding coordinates.

[0045] It should be noted that the temperature fluctuation slope is calculated as follows: the temperature field data of the target area collected by the infrared sensor is divided into several sub-regions according to the coordinate grid, and the average temperature of each sub-region is continuously recorded at fixed time intervals to obtain the time-temperature data sequence under different coordinates. The ratio of the temperature change between adjacent time points to the time interval is calculated by linear fitting, which is the temperature fluctuation slope corresponding to each coordinate.

[0046] When the population movement trend point coincides with the region where the temperature fluctuation slope changes significantly, population dynamic intention data representing the future movement of the population is generated.

[0047] It should be further explained that the group movement trend points, derived from posture orientation features, reflect the group's subjective tendency to move in that direction. However, this may lead to misjudgments of merely pointing without any actual movement. Areas with significant changes in temperature fluctuation slope, based on infrared data, reflect the objective behavior of people actually gathering or dispersing. When these two coincide, it indicates that the group's subjective intention to move is consistent with its objective actions, eliminating single-dimensional bias. At this point, integrating the directional information of the trend points, the rate of temperature change, and the attributes of the heatmap regions generates dynamic group intention data indicating that the group is moving in a certain direction at a certain rate. This provides a core basis for the robot's proactive path planning.

[0048] Path planning generation: A social cost function is generated based on the dynamic intention data of the group. The social cost function is used to quantitatively evaluate social etiquette and traffic efficiency, and a socialized navigation path is planned based on the social cost function.

[0049] For a preferred embodiment of the present invention, please refer to Figure 5 The specific process for generating the social cost function, as shown, includes:

[0050] Identify the coordinates of the channel areas marked in the dynamic social heat map, and calculate the physical travel cost of alternative paths traversing the area.

[0051] Preferably, a method for calculating physical passage cost is as follows: The coordinates of the passage area are determined based on a dynamic social heatmap, and the physical width of the passage and the real-time population density are extracted from the spatial geometric features of the area and the distance distribution features. Based on preset quantification rules, the base cost, superimposed cost, and additional cost are calculated, and then summed to obtain the physical passage cost of the passage area. The quantification rules are: for every unit decrease in passage width, the base cost value increases by 1; for every unit increase in population density, the superimposed cost value increases by 1. Then, the angle between the alternative path and the passage extension direction is calculated; for every unit increase in the angle, the additional cost increases by 0.5.

[0052] Identify the coordinates of clustered areas marked in the dynamic social heatmap and assess the social intrusion cost caused by a path traversing that area.

[0053] Preferably, a method for calculating the cost of social intrusion is as follows: Based on a dynamic social heatmap, the coordinates of the cluster area are determined, and the population density from the distance distribution characteristics and the population orientation consistency from the posture orientation characteristics of the area are extracted. Based on preset quantification rules, the basic intrusion cost and the superimposed intrusion cost are calculated, and summed to obtain the social intrusion cost of traversing the cluster area. The quantification rules are: for every unit increase in population density, the basic intrusion cost increases by 1; for every unit increase in population orientation consistency, the superimposed intrusion cost increases by 1.

[0054] Based on the dynamic intention data of the group, the potential conflict risk between alternative paths and the future trajectory of the personnel is predicted, and the expected conflict cost is obtained.

[0055] Preferably, a method for quantifying expected conflict costs is as follows: Based on dynamic social heatmaps and group dynamic intent data, determine the coordinates of alternative paths and the future trajectories of individuals, and extract the shortest estimated distance between individuals and paths, and the time difference between trajectory intersections. Calculate the basic conflict cost and the superimposed conflict cost according to preset rules, and sum them to obtain the expected conflict cost. The quantification rules are: for every unit reduction in the shortest estimated distance between individuals and paths, the basic conflict cost increases by 1; for every unit reduction in the trajectory intersection time difference, the superimposed conflict cost increases by 1.

[0056] The social cost function is constructed by combining the physical access costs, social intrusion costs, and anticipated conflict costs.

[0057] Preferably, the social cost function is calculated as follows: the social cost function is obtained by summing the physical access cost, social intrusion cost, and expected conflict cost according to preset weights.

[0058] It should be further explained that when constructing the social cost function, it is necessary to combine the core needs of the service robot's application scenario and set preset weights for the three types of costs according to the appropriate scenario. Finally, the three types of costs are integrated by weighted summation. That is, the social cost function is obtained by multiplying the physical access cost, social intrusion cost, and expected conflict cost by their respective preset weights and then adding them together. This quantitatively evaluates the comprehensive adaptability of the navigation path and provides a basis for path planning.

[0059] In a preferred embodiment of the present invention, the specific process of planning a socialized navigation path based on the social cost function includes:

[0060] Locate the group movement trend point in the group dynamic intent data, and query the functional attributes of the location identifier area corresponding to the trend point in the dynamic social heat map.

[0061] If the area is a clustered area, a slow-moving mode will be triggered and an alternative route will be selected first.

[0062] It's important to explain that the core characteristics of the clustered area are high population density, chaotic postures and orientations, and the potential for temporary stops and interactions. Based on the social cost function assessment, the social intrusion cost and anticipated conflict cost in this area are significantly higher than in other areas. If the robot moves at normal speed, it is prone to collisions with randomly moving people, or causing discomfort due to close proximity, thus violating social etiquette. Therefore, it is necessary to prioritize detours to directly avoid high-cost passage. If detours are not possible due to environmental constraints, then slow, deliberate movement should be used to reduce the risk of interaction interference and conflict.

[0063] If the area is a passageway, then acceleration mode will be activated and the current path direction will be maintained.

[0064] It should be explained that the core characteristics of the passageway area are that people are facing the same direction and the population density is relatively low. According to the social cost function assessment, the physical passage cost, social intrusion cost, and expected conflict cost of this area are also low. This low overall cost and highly predictable environment provides a safe foundation for initiating acceleration mode and maintaining path direction, without reducing efficiency due to frequent avoidance or direction adjustments.

[0065] Path correction and optimization: The navigation process data of the service robot executing the social navigation path is collected in real time using a multi-source sensor array. A navigation effect distribution map is generated by comparing the navigation process data before and after navigation. The social cost function generation parameters are adjusted based on the navigation effect distribution map.

[0066] In a preferred embodiment of the present invention, the specific process of using a multi-source sensor array to collect navigation process data in real time during the execution of a socialized navigation path by a service robot includes:

[0067] During navigation intervals, spatial reconstruction features updated by the lidar and attitude reconstruction features updated by the depth camera are acquired, and the spatial reconstruction features and attitude reconstruction features are combined into the navigation process data.

[0068] It should be explained that the navigation interval refers to the brief gap between when the robot completes a path planning and driving and has not yet started the decision-making for the next path. Data collected during this period and combined into navigation process data provides real-time environmental basis for subsequent dynamic path adjustment.

[0069] In a preferred embodiment of the present invention, the specific process of generating a navigation effect distribution map based on the comparison of navigation process data before and after navigation includes:

[0070] Extract the baseline spatial reconstruction features and baseline attitude reconstruction features before navigation in different partitions of the target area from the database, and extract the spatial reconstruction features and attitude reconstruction features after navigation in different partitions of the target area.

[0071] By comparing the baseline spatial features and spatial reconstruction features before and after navigation in different partitions of the target area, the spatial adaptation rate matrix of different partitions of the target area is calculated.

[0072] It should be explained that the spatial adaptation rate matrix is ​​a matrix formed by calculating the matching degree between the baseline spatial features before navigation and the spatial reconstruction features after navigation in the sub-partitions of the target area according to preset rules, and then arranging the adaptation rates of all partitions according to their position coordinates.

[0073] By comparing the baseline attitude features and attitude reconstruction features before and after navigation in different partitions of the target area, the attitude coordination rate matrix of different partitions of the target area is calculated.

[0074] It should be explained that the attitude coordination rate matrix is ​​a matrix formed by calculating the coordination degree between the reference attitude features before navigation and the attitude reconstruction features after navigation in the target area, which is divided into sub-regions according to preset rules, and then arranging them according to the spatial coordinates of the sub-regions.

[0075] By fusing the spatial adaptation rate matrix and the attitude coordination rate matrix, a navigation effect distribution map of different partitions of the target area is generated.

[0076] It should be noted that the spatial adaptation rate matrix focuses on the physical level, evaluating the robot's safety and efficiency in navigating within a zone; while the posture coordination rate matrix focuses on the social level, evaluating the robot's social friendliness and low-conflict behavior within a zone. Both have evaluation blind spots when used alone, therefore, weighted fusion is necessary to achieve dimensional complementarity, ensuring both unobstructed physical passage and interference-free social interaction, ultimately forming a quantitative basis reflecting the overall navigation effect.

[0077] In a preferred embodiment of the present invention, the specific process of adjusting the social cost function generation parameters based on the navigation effect distribution map includes:

[0078] Traverse the navigation effect distribution map, filter out the coordinates of weak areas with a spatial adaptation rate lower than a preset threshold, increase the path weight to the coordinates of the weak areas and increase the flexibility of path planning.

[0079] It needs further explanation that the problem with weakly effective areas is their low adaptability to physical space. Directly planning according to conventional paths can easily lead to impassable routes or a sharp drop in travel efficiency. By increasing the path weight, the algorithm can prioritize paths that adapt to the physical conditions of weakly effective areas during planning. Increasing path flexibility provides alternative solutions. If a path suddenly becomes infeasible due to changes in the spatial conditions of a weakly effective area, it can be quickly switched to other alternative paths to avoid navigation interruption.

[0080] The navigation effect distribution map is traversed, and the coordinates of the strong regions with attitude coordination rates higher than a preset threshold are filtered out. The path weights of the strong regions are reduced and the redundancy of the path planning is reduced.

[0081] It's important to further explain that high-efficiency regions inherently possess characteristics of low conflict and high adaptability. Over-allocating path weights or retaining a large number of redundant paths can lead to a waste of algorithmic resources and actually reduce navigation response speed. By reducing weights, the algorithm can allocate more computational resources to path optimization in low-efficiency regions; reducing redundant paths simplifies the decision-making process, allowing the robot to quickly select the optimal path in high-efficiency regions, ensuring both traffic efficiency and avoiding resource waste.

[0082] Please see Figure 2As shown, a second aspect of the present invention provides a dynamic navigation planning system for service robots, including an environment perception module, an intent inference module, a path planning module, a path correction module, and a database, wherein the environment perception module is connected to the intent inference module, the intent inference module is connected to the path planning module, the path planning module is connected to the path correction module, and the database is connected to the path correction module.

[0083] The environmental perception module defines the service robot's operating area as the target area, acquires real-time perception data from the multi-source sensor array deployed within the target area, and performs environmental state fusion analysis to generate a dynamic social heat map and extract individual behavioral characteristics.

[0084] The intent deduction module, based on the dynamic social heatmap and the individual behavioral characteristics, analyzes the behavioral tendencies of individual behavioral characteristics under the environmental constraints represented by the dynamic social heatmap, and deduces the dynamic intention data of the group that represents the future trend of the group.

[0085] The path planning module generates a social cost function based on the group's dynamic intention data. The social cost function is used to quantitatively evaluate social etiquette and traffic efficiency, and plans a socialized navigation path based on the social cost function.

[0086] The path correction module uses a multi-source sensor array to collect navigation process data in real time as the service robot executes the socialized navigation path. It generates a navigation effect distribution map by comparing the navigation process data before and after navigation, and adjusts the social cost function generation parameters based on the navigation effect distribution map.

[0087] The database stores the baseline spatial reconstruction features and baseline attitude reconstruction features of different partitions of the target area before navigation.

[0088] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A dynamic navigation planning method for service robots, characterized in that: include: Environmental perception analysis: The service robot's operating area is defined as the target area. Real-time perception data from the multi-source sensor array deployed within the target area is obtained, and environmental state fusion analysis is performed based on this data to generate a dynamic social heat map and extract individual behavioral characteristics. Intent inference generation: Based on the dynamic social heat map and the individual behavioral characteristics, by analyzing the behavioral tendencies of individual behavioral characteristics under the environmental constraints represented by the dynamic social heat map, dynamic intention data representing the future trend of the group is inferred. The specific process of inferring the dynamic intention data of the group to represent the future trend of the group includes: Extract the orientation and temperature field features of the target area from real-time sensing data; The rate of change of the motion trajectory of the target area within the time window is calculated in real time. If there is a significant consistency in direction within a short period of time, the target area is identified as having a group movement trend point, thereby extracting the group movement trend point from the posture orientation feature. Calculate the temperature fluctuation slope of the target area and detect the slope change of the temperature fluctuation slope at the corresponding coordinates; When the group movement trend point coincides with the area where the temperature fluctuation slope changes significantly, group dynamic intention data representing the group's future movement is generated; Path planning generation: A social cost function is generated based on the group's dynamic intent data. The social cost function is used to quantitatively evaluate social etiquette and traffic efficiency, and a socialized navigation path is planned based on the social cost function. Path correction and optimization: The navigation process data of the service robot executing the social navigation path is collected in real time using a multi-source sensor array. A navigation effect distribution map is generated by comparing the navigation process data before and after navigation. The social cost function generation parameters are adjusted based on the navigation effect distribution map.

2. The dynamic navigation planning method for a service robot according to claim 1, characterized in that: The multi-source sensor array includes a lidar, a depth camera, an ultrasonic sensor, and an infrared sensor; The specific process of acquiring real-time sensing data from a multi-source sensor array deployed within the target area includes: synchronously acquiring spatial geometric features output by lidar, attitude and orientation features output by depth camera, distance distribution features output by ultrasonic sensor, and temperature field features output by infrared sensor, and integrating the spatial geometric features, attitude and orientation features, distance distribution features, and temperature field features into the real-time sensing data.

3. The dynamic navigation planning method for a service robot according to claim 2, characterized in that: The specific process of generating a dynamic social heatmap through environmental state fusion analysis includes: Extract spatial geometric features, orientation features, and distance distribution features of the target area from real-time sensing data; Based on the mapping relationship between spatial geometric features and distance distribution features, functional areas are classified into different levels. By superimposing the posture orientation features, the functional region level is semantically corrected, and a dynamic social heatmap labeled with channel area, entrance area and gathering area is output. The passage area is a region with a uniform orientation and extends along a fixed direction; the entrance area is a region with multiple orientations pointing in different directions and a specific point within that region; and the gathering area is a region with chaotic and irregular orientations and no uniform direction.

4. The dynamic navigation planning method for a service robot according to claim 1, characterized in that: The specific process for generating the social cost function includes: Identify the coordinates of the channel areas marked in the dynamic social heat map and calculate the physical travel cost of alternative paths traversing the area; Identify the coordinates of clustered areas marked in the dynamic social heatmap and assess the social intrusion cost caused by a path traversing that area. Based on the group's dynamic intention data, the potential conflict risk between alternative paths and the future trajectory of personnel is predicted, and the expected conflict cost is obtained. The social cost function is constructed by combining the physical access costs, social intrusion costs, and anticipated conflict costs.

5. The dynamic navigation planning method for a service robot according to claim 4, characterized in that: The specific process of planning a socialized navigation path based on the social cost function includes: Locate the group movement trend point in the group dynamic intent data, and query the functional attributes of the location identifier area corresponding to the trend point in the dynamic social heat map; If the area is a clustered area, a low-speed slow-moving mode will be triggered and an alternative route will be selected first. If the area is a passageway, then acceleration mode will be activated and the current path direction will be maintained.

6. The dynamic navigation planning method for a service robot according to claim 1, characterized in that: The specific process of using a multi-source sensor array to collect navigation process data in real time during the execution of a socialized navigation path by a service robot includes: During navigation intervals, spatial reconstruction features updated by the lidar and attitude reconstruction features updated by the depth camera are acquired, and the spatial reconstruction features and attitude reconstruction features are combined into the navigation process data.

7. The dynamic navigation planning method for a service robot according to claim 1, characterized in that: The specific process of generating a navigation effect distribution map based on the comparison of navigation process data before and after navigation includes: Extract the baseline spatial reconstruction features and baseline attitude reconstruction features before navigation in different partitions of the target area from the database, and extract the spatial reconstruction features and attitude reconstruction features after navigation in different partitions of the target area. Compare the baseline spatial features and spatial reconstruction features before and after navigation in different partitions of the target area, and calculate the spatial adaptation rate matrix of different partitions of the target area. By comparing the baseline attitude features and attitude reconstruction features before and after navigation in different partitions of the target area, the attitude coordination rate matrix of different partitions of the target area is calculated. By fusing the spatial adaptation rate matrix and the attitude coordination rate matrix, a navigation effect distribution map of different partitions of the target area is generated.

8. The dynamic navigation planning method for a service robot according to claim 1, characterized in that: The specific process of adjusting the social cost function generation parameters based on the navigation effect distribution map includes: Traverse the navigation effect distribution map, filter out the coordinates of weak areas with a spatial adaptation rate lower than a preset threshold, increase the path weight to the coordinates of the weak areas and increase the flexibility of path planning. The navigation effect distribution map is traversed, and the coordinates of the strong regions with attitude coordination rates higher than a preset threshold are filtered out. The path weights of the strong regions are reduced and the redundancy of the path planning is reduced.

9. A dynamic navigation planning system for service robots, characterized in that: include: The environmental perception module defines the service robot's operating area as the target area, acquires real-time perception data from the multi-source sensor array deployed within the target area, and performs environmental state fusion analysis to generate a dynamic social heat map and extract individual behavioral characteristics. The intent deduction module, based on the dynamic social heatmap and the individual behavioral characteristics, analyzes the behavioral tendencies of individual behavioral characteristics under the environmental constraints represented by the dynamic social heatmap, and deduces the dynamic intention data of the group representing the future trend of the group. The specific process of inferring the dynamic intention data of the group to represent the future trend of the group includes: Extract the orientation and temperature field features of the target area from real-time sensing data; The rate of change of the motion trajectory of the target area within the time window is calculated in real time. If there is a significant consistency in direction within a short period of time, the target area is identified as having a group movement trend point, thereby extracting the group movement trend point from the posture orientation feature. Calculate the temperature fluctuation slope of the target area and detect the slope change of the temperature fluctuation slope at the corresponding coordinates; When the group movement trend point coincides with the area where the temperature fluctuation slope changes significantly, group dynamic intention data representing the group's future movement is generated; The path planning module generates a social cost function based on the group's dynamic intention data. The social cost function is used to quantitatively evaluate social etiquette and traffic efficiency, and plans a socialized navigation path based on the social cost function. The path correction module uses a multi-source sensor array to collect navigation process data in real time during the socialized navigation path execution process of the service robot. It generates a navigation effect distribution map by comparing the navigation process data before and after navigation, and adjusts the social cost function generation parameters based on the navigation effect distribution map. The database stores the baseline spatial reconstruction features and baseline attitude reconstruction features of different partitions of the target area before navigation.

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