Dynamic path planning and pollution source positioning method and system for environment service robot

Abstract

The invention relates to an environment service robot-oriented dynamic path planning and pollution source positioning method and system. The method comprises the following steps of S1, acquiring multi-modal data; s2, pollutant concentration filtering and gradient construction; s3, determining the target orientation of the pollution source; s4, modeling a behavior semantic weighted path; s5, dynamic path planning; and S6, executing feedback and path correction. The system comprises a multi-modal data acquisition module, a pollutant concentration filtering and gradient construction module, a pollution source target orientation determination module, a behavior semantic weighted path modeling module, a dynamic path planning module and an execution feedback and path correction module. According to the method, the pollutant concentration data is filtered, the concentration gradient field is constructed, and meanwhile, the user behavior state is mapped into the semantic resistance parameter in the path planning, so that efficient source searching, stable movement and user-friendly service of the robot in a complex indoor environment can be realized.

Description

Technical Field

[0001] This invention belongs to the field of environmental service robot technology, and in particular relates to a dynamic path planning and pollution source localization method and system for environmental service robots. Background Technology

[0002] With increasingly stringent requirements for indoor environmental quality, environmental service robots (such as mobile air-purifying robots and environmental control robots) are gradually being applied in homes and public indoor settings. These robots typically detect and purify pollutants by moving within indoor spaces.

[0003] In existing technologies, environmental service robots still have the following shortcomings in path planning and pollution treatment: 1. Low efficiency in locating pollution sources Existing robots mostly acquire environmental parameters by using preset cruising paths or random movement, failing to form active guidance based on the spatial distribution characteristics of pollutants, resulting in low efficiency in locating pollution sources.

[0004] 2. Sensor data is susceptible to environmental disturbances. Indoor air circulation and personnel movement can cause instantaneous fluctuations in the data collected by pollutant sensors. If decisions are made directly based on the original concentration values, it can easily lead to frequent adjustments to the robot's movement path, affecting stability.

[0005] 3. Lack of semantic path planning capabilities for user behavior Existing path planning methods typically only consider spatial obstacles and fail to translate user behavior into technical parameters in path planning, making it difficult to balance user comfort and privacy needs while ensuring environmental adjustment effectiveness.

[0006] 4. Insufficient scheduling capabilities in multi-tasking scenarios In environments with multiple rooms and multiple sources of pollution, existing technologies struggle to rationally allocate robot task order based on the degree of pollution and the urgency of user behavior, resulting in low overall response efficiency.

[0007] Therefore, it is necessary to propose a dynamic path planning and pollution source localization method that can integrate the distribution characteristics of environmental pollutants with semantic information of user behavior in order to improve the intelligence level of environmental service robots. Summary of the Invention

[0008] The purpose of this invention is to provide a dynamic path planning and pollution source localization method and system for environmental service robots. By filtering pollutant concentration data and constructing a concentration gradient field, and mapping user behavior state to semantic resistance parameters in path planning, the robot can achieve efficient source finding, smooth movement, and user-friendly service in complex indoor environments.

[0009] This invention provides a dynamic path planning and pollution source localization method for environmental service robots, comprising the following steps: S1. By setting up an environmental sensor group on the robot body, the concentration data of at least one indoor pollutant is collected in real time, and non-image-based perception data characterizing the user's behavior status is collected simultaneously. S2. Filter the pollutant concentration data to obtain stable concentration state values ​​for path decision-making, and construct a pollutant concentration gradient field based on the stable concentration state values ​​of multiple spatial sampling points. S3. Based on the pollutant concentration gradient field, determine the target location of the pollution source and generate a guiding direction pointing to the target location; S4. Based on the user's behavior state, map the user's activity area to the corresponding semantic resistance parameter, and superimpose the semantic resistance parameter onto the path cost map in the robot's workspace; the semantic resistance parameter is used to characterize the magnitude of the robot's passage cost in the corresponding spatial area; S5. In the path cost map, plan the movement path with the minimum cost, and control the robot to move along the movement path toward the target location of the pollution source. S6. During the robot's movement, the target location and movement path of the pollution source are dynamically corrected based on the real-time updated pollutant concentration data.

[0010] Furthermore, in step S2: The filtering process includes establishing a state prediction model for pollutant concentration changes and correcting the pollutant concentration data in real time based on the deviation between the predicted and measured values, so as to suppress instantaneous fluctuations caused by airflow disturbances. When constructing a pollutant concentration gradient field, the robot collects pollutant concentration state values ​​at multiple spatial locations within a local space according to a preset sampling trajectory, and calculates the spatial change direction of pollutant concentration based on the multiple state values.

[0011] Furthermore, in step S3, the robot's movement direction is iteratively updated along the direction of increasing pollutant concentration to gradually approach the extreme value region of pollutant concentration, and the extreme value region is determined as the location of the pollution source.

[0012] Further, step S4 includes: When the user's behavior is identified as sleep behavior, the semantic resistance parameter corresponding to a preset range area centered on the user's location is set to a higher value than that of other areas, in order to restrict the robot from entering the area or reduce the robot's movement speed and working power in the area.

[0013] Further, step S5 includes: When there are multiple pollution treatment tasks, a task priority queue is generated based on the pollutant concentration level, the distance between the pollution source and the robot, and the urgency of the corresponding user behavior status. The robot's movement path is then planned sequentially according to the task priority queue. The task priority queue is dynamically updated when the robot completes the current task or detects a new pollution event.

[0014] Furthermore, step S6 also includes: After the robot performs environmental adjustment operations, it records the corresponding environmental parameters, user behavior status, and adjustment results, and updates the semantic resistance parameters or path planning strategy based on the user's subsequent feedback or changes in physiological state.

[0015] Furthermore, the user's physiological state changes include at least one of respiratory rate changes and heart rate changes, and these physiological state changes are used to assess the effectiveness of the current path planning and environmental regulation strategies.

[0016] This invention also provides a dynamic path planning and pollution source localization system for environmental service robots, comprising: The multimodal data acquisition module includes an environmental sensor group mounted on the robot body, used to collect concentration data of at least one indoor pollutant in real time, and simultaneously collect non-image-based sensory data characterizing the user's behavioral state. The pollutant concentration filtering and gradient construction module is used to filter the pollutant concentration data to obtain stable concentration state values ​​for path decision-making, and to construct a pollutant concentration gradient field based on the stable concentration state values ​​of multiple spatial sampling points. The pollution source target orientation determination module is used to determine the target orientation of the pollution source based on the pollutant concentration gradient field, and generate a guiding direction pointing to the target orientation; The behavior semantic weighted path modeling module is used to map the user's activity area to the corresponding semantic resistance parameter based on the user's behavior state, and to superimpose the semantic resistance parameter onto the path cost map in the robot's workspace; the semantic resistance parameter is used to characterize the magnitude of the robot's passage cost in the corresponding spatial area; The dynamic path planning module is used to plan the movement path with the minimum cost in the path cost map and control the robot to move along the movement path toward the target location of the pollution source. The execution feedback and path correction module is used to dynamically correct the target location and movement path of the pollution source based on real-time updated pollutant concentration data during the robot's movement.

[0017] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the electronic device performs the method described thereon.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method described thereon.

[0019] By employing the above-mentioned scheme, the dynamic path planning and pollution source localization method and system for environmental service robots achieve the following technical effects: (1) By constructing a pollutant concentration gradient field, the robot is guided to actively approach the pollution source, which significantly improves the efficiency of pollution source location.

[0020] (2) By filtering the pollutant concentration data, transient noise interference is suppressed, and the stability of path planning is improved.

[0021] (3) Map user behavior state to semantic resistance parameters and introduce them into the path planning model to achieve flexible avoidance of user areas.

[0022] (4) In a multi-tasking environment, task priorities can be dynamically adjusted according to the degree of pollution and the urgency of user behavior to improve overall response efficiency.

[0023] (5) Support execution feedback and strategy updates, enabling the robot to continuously optimize its service behavior.

[0024] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0025] Figure 1 This is a flowchart of the dynamic path planning and pollution source localization method for environmental service robots according to the present invention. Figure 2 This is a schematic diagram of the control closed loop of the dynamic path planning and pollution source location method in one embodiment of the present invention; Figure 3 This is a schematic diagram of the pollutant concentration gradient field and the robot's source-finding trajectory in one embodiment of the present invention; Figure 4 This is a schematic diagram of dynamic cost map construction based on user behavior semantics in one embodiment of the present invention. Detailed Implementation

[0026] The following will further describe in detail the specific implementation manners of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0027] As shown Figure 1 in the figure, this embodiment provides a dynamic path planning and pollution source location method for an environmental service robot, including the following steps: S1. Multi-modal data acquisition: Through an environmental sensor group arranged on the robot body, the concentration data of at least one indoor pollutant is collected in real time, and non-image perception data representing the user's behavior state is synchronously collected.

[0028] S2. Pollutant concentration filtering and gradient construction: Filter the pollutant concentration data to obtain a stable concentration state value for path decision-making, and construct a pollutant concentration gradient field based on the stable concentration state values of multiple spatial sampling points. As shown Figure 3 in the figure.

[0029] S3. Determination of the target orientation of the pollution source: According to the pollutant concentration gradient field, determine the target orientation of the pollution source and generate a guiding direction pointing to the target orientation.

[0030] S4. Behavior semantic weighted path modeling: Based on the user's behavior state, map the user's activity area to the corresponding semantic resistance parameter, and superimpose the semantic resistance parameter onto the path cost map in the robot's working space; the user's behavior state is mapped to at least one semantic resistance parameter, and the semantic resistance parameter is used to represent the magnitude of the passing cost of the robot in the corresponding spatial area.

[0031] S5. Dynamic path planning: In the path cost map, plan the moving path with the minimum cost, and control the robot to move along the moving path towards the target orientation of the pollution source; S6. Execution feedback and path correction: During the movement of the robot, dynamically correct the target orientation of the pollution source and the moving path according to the real-time updated pollutant concentration data.

[0032] In this embodiment, in step S2: The filtering process includes establishing a state prediction model for the change of pollutant concentration, and performing real-time correction on the pollutant concentration data based on the deviation between the predicted value and the measured value to suppress the instantaneous fluctuation caused by air flow disturbance; When constructing the pollutant concentration gradient field, the robot collects the pollutant concentration state values at multiple spatial positions according to a preset sampling trajectory in the local space, and calculates the spatial change direction of the pollutant concentration based on the multiple state values.

[0033] In this embodiment, in step S3, the moving direction of the robot is iteratively updated along the direction of increasing pollutant concentration to gradually approach the extreme value region of the pollutant concentration, and the extreme value region is determined as the position of the pollution source.

[0034] In this embodiment, step S4 includes: When the user behavior state is recognized as a sleep behavior, the semantic resistance parameter corresponding to the preset range area centered on the user's location is set to be higher than the resistance values of other areas to restrict the robot from entering this area or reduce the movement speed and working power of the robot in this area.

[0035] In this embodiment, step S5 includes: When there are multiple pollution treatment tasks, a task priority queue is generated according to the pollutant concentration level, the distance between the pollution source and the robot, and the urgency of the corresponding user behavior state, and the robot movement path is planned in sequence according to the task priority queue; the task priority queue is dynamically updated when the robot completes the current task or detects a new pollution event.

[0036] In this embodiment, step S6 further includes: After the robot performs the environmental adjustment operation, the corresponding environmental parameters, user behavior state, and adjustment results are recorded, and the semantic resistance parameter or path planning strategy is updated based on the subsequent feedback of the user or the change in the physiological state.

[0037] In this embodiment, the change in the physiological state of the user includes at least one of the change in breathing frequency and the change in heart rate, and the change in the physiological state is used to evaluate the effectiveness of the current path planning and environmental adjustment strategy.

[0038] The present invention will be further described in detail below through specific examples.

[0039] Embodiment 1: Closed-loop control of dynamic path planning and pollution source location As shown in the figure Figure 2 The robot collects pollutant concentration data in real time through the environmental sensor group, and filters the collected concentration data to obtain a stable concentration state value.

[0040] The robot collects concentration state values at multiple spatial positions during the movement, constructs a pollutant concentration gradient field based on the state values, and determines the target orientation of the pollution source according to the gradient direction.

[0041] At the same time, the robot obtains the user behavior state through the non-imaging perception module, maps the user behavior state to a semantic resistance parameter, and superimposes it on the path cost map.

[0042] The robot plans the moving path with the minimum cost in the comprehensive cost map and continuously corrects the path according to the latest concentration data during the execution process to achieve dynamic closed-loop control.

[0043] Example of concentration data: The collected indoor pollutant concentration data includes, but is not limited to, the real-time mass concentrations of PM2.5, formaldehyde (HCHO), total volatile organic compounds (TVOC), carbon dioxide (CO2), and carbon monoxide (CO).

[0044] Example of non-visual perception data: The user behavior data obtained through the non-visual module includes: the personnel movement heat source signal obtained by the pyroelectric infrared sensor, the environmental volume fluctuation obtained by the sound-sensitive sensor, and the user's spatial coordinates and limb movement frequency obtained by the millimeter-wave radar.

[0045] Example 2: Active positioning of pollution sources based on concentration gradients As shown in the figure Figure 3 The robot sequentially obtains the pollutant concentration status values of multiple sampling points in the local space according to the preset sampling trajectory. The system calculates the pollutant concentration gradient according to the concentration change direction between the sampling points and updates the moving direction of the robot along the direction of increasing concentration, so that the robot gradually approaches the extreme value region of the pollutant concentration, thereby determining the location of the pollution source.

[0046] Example 3: Path avoidance based on user behavior semantics As shown in the figure Figure 4 The robot maps the user behavior recognition result to the corresponding semantic resistance parameter on the basis of constructing the basic space cost map.

[0047] When it is recognized that the user is in a sleeping behavior, the semantic resistance parameter corresponding to the preset area centered on the user's location is set to a resistance value higher than that of other areas, so as to guide the robot to automatically bypass this area during the path planning process, or reduce the movement speed and working power of the robot in this area. The setting of the semantic resistance parameter is shown in Table 1: Table 1 Semantic resistance parameter setting table Example 4: Multi-task scheduling and policy update When there are multiple pollution treatment tasks, the system generates a task priority queue according to the pollutant concentration level, the distance between the pollution source and the robot, and the urgency of the corresponding user behavior state. An example of the task priority queue is shown in Table 2.

[0048] The robot sequentially plans the moving path according to the task priority queue and dynamically updates the task priority queue when the current task is completed or a new pollution event is detected.

[0049] After the robot performs environmental adjustment operations, the system records the corresponding environmental parameters, user behavior status, and execution results. Based on the user's subsequent feedback or changes in physiological state, the system updates the path planning strategy to improve the service matching degree during long-term use.

[0050] Table 2 Task Priority Queue Settings The system uses a weighted formula The urgency of actions is calculated and the queue is dynamically updated when the current task is completed or a new contamination event is detected.

[0051] Example 5: Closed-loop strategy optimization based on physiological feedback Scenario description: The robot performs a purification task near the user's sleeping area.

[0052] Feedback Acquisition: Real-time monitoring of the user's respiratory rate and heart rate using non-image sensors (such as millimeter-wave radar).

[0053] Logical judgment: If the user's heart rate fluctuation exceeds the preset threshold or the breathing rate increases, it is determined that the current robot's motion noise or airflow direction is interfering with the user.

[0054] Strategy Update: The system automatically increases the "semantic resistance parameter" of the user's area, further increases the avoidance distance in subsequent path planning, and automatically reduces the fan speed until the user's physiological signs return to stability.

[0055] The present invention also provides a dynamic path planning and pollution source localization system for environmental service robots, comprising a multimodal data acquisition module, a pollutant concentration filtering and gradient construction module, a pollution source target location determination module, a behavior semantic weighted path modeling module, a dynamic path planning module, and an execution feedback and path correction module, which are respectively used to perform the above steps S1 to S6.

[0056] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the electronic device performs the method described thereon.

[0057] The present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method described thereon.

[0058] This invention provides a method for dynamic path planning and automatic pollution source localization of environmental service robots based on environmental pollutant concentration gradients and user behavior semantics, which has the following technical effects: (1) By constructing a pollutant concentration gradient field, the robot is guided to actively approach the pollution source, which significantly improves the efficiency of pollution source location.

[0059] (2) By filtering the pollutant concentration data, transient noise interference is suppressed, and the stability of path planning is improved.

[0060] (3) Map user behavior state to semantic resistance parameters and introduce them into the path planning model to achieve flexible avoidance of user areas.

[0061] (4) In a multi-tasking environment, task priorities can be dynamically adjusted according to the degree of pollution and the urgency of user behavior to improve overall response efficiency.

[0062] (5) Support execution feedback and strategy updates, enabling the robot to continuously optimize its service behavior.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic path planning and pollution source localization method for environmental service robots, characterized in that, Includes the following steps: S1. By setting up an environmental sensor group on the robot body, the concentration data of at least one indoor pollutant is collected in real time, and non-image-based perception data characterizing the user's behavior status is collected simultaneously. S2. Filter the pollutant concentration data to obtain stable concentration state values ​​for path decision-making, and construct a pollutant concentration gradient field based on the stable concentration state values ​​of multiple spatial sampling points. S3. Based on the pollutant concentration gradient field, determine the target location of the pollution source and generate a guiding direction pointing to the target location; S4. Based on the user's behavior state, map the user's activity area to the corresponding semantic resistance parameter, and superimpose the semantic resistance parameter onto the path cost map in the robot's workspace; The semantic resistance parameter is used to characterize the magnitude of the robot's passage cost within the corresponding spatial region; S5. In the path cost map, plan the movement path with the minimum cost, and control the robot to move along the movement path toward the target location of the pollution source. S6. During the robot's movement, the target location and movement path of the pollution source are dynamically corrected based on the real-time updated pollutant concentration data.

2. The dynamic path planning and pollution source localization method for environmental service robots according to claim 1, characterized in that, In step S2: The filtering process includes establishing a state prediction model for pollutant concentration changes and correcting the pollutant concentration data in real time based on the deviation between the predicted and measured values, so as to suppress instantaneous fluctuations caused by airflow disturbances. When constructing a pollutant concentration gradient field, the robot collects pollutant concentration state values ​​at multiple spatial locations within a local space according to a preset sampling trajectory, and calculates the spatial change direction of pollutant concentration based on the multiple state values.

3. The dynamic path planning and pollution source localization method for environmental service robots according to claim 3, characterized in that, In step S3, the robot's movement direction is iteratively updated along the direction of increasing pollutant concentration to gradually approach the extreme value region of pollutant concentration, and the extreme value region is determined as the location of the pollution source.

4. The dynamic path planning and pollution source localization method for environmental service robots according to claim 1, characterized in that, Step S4 includes: When the user's behavior is identified as sleep behavior, the semantic resistance parameter corresponding to a preset range area centered on the user's location is set to a higher value than that of other areas, in order to restrict the robot from entering the area or reduce the robot's movement speed and working power in the area.

5. The dynamic path planning and pollution source localization method for environmental service robots according to claim 1, characterized in that, Step S5 includes: When there are multiple pollution treatment tasks, a task priority queue is generated based on the pollutant concentration level, the distance between the pollution source and the robot, and the urgency of the corresponding user behavior status. The robot's movement path is then planned sequentially according to the task priority queue. The task priority queue is dynamically updated when the robot completes the current task or detects a new pollution event.

6. The dynamic path planning and pollution source localization method for environmental service robots according to claim 1, characterized in that, Step S6 further includes: After the robot performs environmental adjustment operations, it records the corresponding environmental parameters, user behavior status, and adjustment results, and updates the semantic resistance parameters or path planning strategy based on the user's subsequent feedback or changes in physiological state.

7. The dynamic path planning and pollution source localization method for environmental service robots according to claim 6, characterized in that, The user's physiological state changes include at least one of respiratory rate changes and heart rate changes, and these physiological state changes are used to assess the effectiveness of the current path planning and environmental regulation strategies.

8. A dynamic path planning and pollution source localization system for environmental service robots, characterized in that, include: The multimodal data acquisition module includes an environmental sensor group mounted on the robot body, used to collect concentration data of at least one indoor pollutant in real time, and simultaneously collect non-image-based sensory data characterizing the user's behavioral state. The pollutant concentration filtering and gradient construction module is used to filter the pollutant concentration data to obtain stable concentration state values ​​for path decision-making, and to construct a pollutant concentration gradient field based on the stable concentration state values ​​of multiple spatial sampling points. The pollution source target orientation determination module is used to determine the target orientation of the pollution source based on the pollutant concentration gradient field, and generate a guiding direction pointing to the target orientation; The behavior semantic weighted path modeling module is used to map the user's activity area to the corresponding semantic resistance parameter based on the user's behavior state, and to superimpose the semantic resistance parameter onto the path cost map in the robot's workspace; The semantic resistance parameter is used to characterize the magnitude of the robot's passage cost within the corresponding spatial region; The dynamic path planning module is used to plan the movement path with the minimum cost in the path cost map and control the robot to move along the movement path toward the target location of the pollution source. The execution feedback and path correction module is used to dynamically correct the target location and movement path of the pollution source based on real-time updated pollutant concentration data during the robot's movement.

9. An electronic device, characterized in that, The device includes a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, causes the electronic device to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method of any one of claims 1 to 7.

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