Regional environment detection method and device, storage medium and electronic device

Abstract

This disclosure relates to the field of environmental detection technology, specifically to a method and apparatus for regional environmental detection, a storage medium, and an electronic device. The method includes: identifying an image corresponding to the area to be detected, dividing it into obstacle sub-regions and marking their positions; deploying n detection robots at initial positions; where n is a positive integer and n is greater than or equal to the number of obstacle sub-regions; and assigning each detection robot a corresponding obstacle sub-region to be detected based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot. This solution enables collaborative detection of a target area by multiple robots.

Description

Technical Field

[0001] This disclosure relates to the field of environmental detection technology, specifically to a regional environmental detection method, a regional environmental detection device, a storage medium, and an electronic device. Background Technology

[0002] In some unknown environments, such as the surface of the moon, Mars, or parts of a region's surface, due to complex terrain, exploration robots or rovers are typically used for environmental exploration, terrain information recognition, and other data collection tasks. In these approaches, exploration robots or rovers generally operate individually. However, due to the limited payload and data processing capabilities of a single robot, it can only observe environmental information within its localized environment, failing to obtain sufficient information in complex environments; this may result in either insufficient or excessive capabilities on the part of the individual robot. Furthermore, limitations in communication environments may also present difficulties in robot control.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This disclosure provides a method for regional environmental detection, a device for regional environmental detection, a storage medium, and an electronic device, which can realize the collaborative detection of target areas by multiple robots and effectively overcome the defects existing in the prior art.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to a first aspect of this disclosure, a method for regional environmental detection is provided, the method comprising:

[0007] The image corresponding to the area to be detected is identified, and obstacle sub-regions are divided and their positions are marked.

[0008] Deploy the initial positions of n probe robots; where n is a positive integer and n is greater than or equal to the number of obstacle sub-regions;

[0009] Based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot, a corresponding obstacle sub-region to be detected is assigned to each detection robot.

[0010] In some exemplary embodiments, the process of dividing the obstacle sub-regions and marking their locations includes:

[0011] The minimum coverage circle is used to cover each obstacle in the area to be detected, thereby dividing the area into several obstacle sub-regions. The center coordinates of the minimum coverage circle are used to mark the positions of the corresponding obstacle sub-regions.

[0012] The area outside each of the obstacle sub-regions in the detection area is marked as a free region.

[0013] In some exemplary embodiments, the initial positions for deploying the n probe robots include:

[0014] Deploy the initial positions of n probe robots in a random manner; or,

[0015] The initial positions of n detection robots are deployed according to the distribution of the obstacle sub-regions.

[0016] In some exemplary embodiments, before assigning corresponding obstacle sub-regions to be detected to each of the detection robots, the method further includes:

[0017] A corresponding detection robot group is configured according to the area index of each of the obstacle sub-regions; wherein, the detection robot group includes at least one of the detection robots.

[0018] In some exemplary embodiments, the step of assigning a corresponding obstacle sub-region to each of the detection robots by combining the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot includes:

[0019] A first cost function is constructed by combining the division results of the detection robot group and the distance between the initial position of the detection robot and the obstacle sub-region;

[0020] Construct a second cost function to describe the detection robot's detection of the obstacle sub-region;

[0021] The total cost function is constructed based on the first cost function and the second cost function, and the constraints are constructed using the mobility of the probe robot.

[0022] The total cost function is optimized, and the optimization results are used to assign corresponding obstacle sub-regions to be detected to each of the detection robots.

[0023] In some exemplary embodiments, the method further includes:

[0024] Based on the initial position of each of the exploration robots and the corresponding obstacle sub-regions assigned to the exploration robots, a path is planned to determine the planned path; wherein, the planned path is used for the exploration robots to travel to the corresponding obstacle sub-regions.

[0025] In some exemplary embodiments, the method further includes:

[0026] An outer boundary is configured for each of the obstacle sub-regions, and the outer boundary is used as the detection trajectory of the obstacle sub-region; wherein the outer boundary and the minimum coverage circle of the obstacle sub-region are set as concentric circles, and the radius of the outer boundary is greater than or equal to the radius of the minimum coverage circle;

[0027] The planned path is calculated for the exploration robot based on the exploration trajectory.

[0028] In some exemplary embodiments, the method further includes:

[0029] The effective detection distance constraint is constructed based on the minimum effective detection distance and field of view of the detection robot, and the minimum coverage circle corresponding to the obstacle sub-region;

[0030] Based on the effective detection distance constraint, the detection configuration of the detection robot group is calculated using the Fisher information matrix;

[0031] Based on the uniformly distributed detection configuration, each obstacle sub-region is traversed to plan the detection configuration information of the detection robot group corresponding to each obstacle sub-region.

[0032] In some exemplary embodiments, calculating the detection configuration of the detection robot group using the Fisher information matrix based on the effective detection distance constraint includes:

[0033] The Fisher information gain of the detection robot group is constructed by combining the Jacobian matrix of the direction finding and ranging sensor detection model and the covariance of the sensor measurement error; wherein, the Fisher information matrix is ​​used to characterize the obstacle information acquired by the detection robot.

[0034] Concentric circles with the minimum effective detection distance of the detection robot as the radius are configured as the cooperative detection path of each detection robot in the detection robot group;

[0035] The Fisher information gain is optimized based on the cooperative detection path to determine the detection configuration of the detection robot group.

[0036] In some exemplary embodiments, the detection configuration of the detection robot group includes:

[0037] The detection phase difference between different detection robots and the detection distance of the detection robots.

[0038] In some exemplary embodiments, the method further includes:

[0039] The obstacle information collected by each of the detection robots is fused to obtain the area information corresponding to the area to be detected.

[0040] According to a second aspect of this disclosure, a regional environmental detection device is provided, comprising:

[0041] The obstacle sub-region segmentation module is used to identify the image corresponding to the area to be detected, segment the obstacle into sub-regions, and mark their positions.

[0042] An initial position deployment module is used to deploy the initial positions of n probe robots; where n is a positive integer and is greater than or equal to the number of obstacle sub-regions;

[0043] The detection robot allocation module is used to allocate a corresponding obstacle sub-region to be detected to each detection robot based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot.

[0044] According to a third aspect of this disclosure, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described area environment detection method.

[0045] According to a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0046] Processor; and

[0047] Memory for storing the executable instructions of the processor;

[0048] The processor is configured to implement the above-described regional environment detection method by executing the executable instructions.

[0049] One embodiment of this disclosure provides a regional environment detection method that identifies the area to be detected, determines obstacle sub-regions, deploys multiple detection robots within the area to be detected, and assigns the detection robots to each other, thereby achieving a distributed detection robot layout, enabling collaborative detection by multiple robots, and thus improving detection efficiency and accuracy.

[0050] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0052] Figure 1 The illustration shows a schematic diagram of a regional environmental detection method according to an exemplary embodiment of the present disclosure.

[0053] Figure 2 The illustration shows a schematic diagram of obstacle sub-region task allocation based on a detection robot group, which is an exemplary embodiment of the present disclosure.

[0054] Figure 3 This schematically illustrates an optimal allocation scheme for collaborative detection by a group of two detection robots, as an exemplary embodiment of the present disclosure.

[0055] Figure 4 This illustration schematically shows an optimal allocation scheme for collaborative detection by a group of three detection robots, as described in an exemplary embodiment of this disclosure.

[0056] Figure 5 This schematically illustrates another optimal allocation scheme for collaborative detection by a group of three probe robots, as described in an exemplary embodiment of this disclosure.

[0057] Figure 6 The illustration schematically shows a method for detecting and assigning sub-regions of obstacles in an exemplary embodiment of the present disclosure.

[0058] Figure 7 This schematic diagram illustrates the composition of a regional environmental detection device according to an exemplary embodiment of the present disclosure.

[0059] Figure 8 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0060] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0061] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0062] In related technologies, the lunar surface environment is complex and varied, containing a large amount of sand and dust, and is dotted with rocks and craters of varying diameters. These complex terrain factors affect the construction of lunar bases, and incomplete terrain information perception can also affect the safety and efficiency of lunar base site selection. Therefore, before the construction of a lunar base begins, it is necessary to use robots to conduct lunar surface environment exploration, extract terrain information around candidate areas, identify and detect obstacles, and obtain a large amount of scientific data such as the distribution of rocks and craters and geographical features. Currently, robots or rovers used for lunar surface exploration operate individually, such as my country's "Yutu" lunar rover and the US's LRV (Lunar Rover Vehicle). However, the payload and data processing capabilities of such individual lunar robots are limited, allowing them to observe only the environmental information of their local scene, making it impossible to obtain sufficient information about the selected site area in the complex environment. In addition, existing schemes for exploring the lunar surface environment generally adopt a collaborative operation mode between a lunar relay satellite and a lunar rover. Among them, the lunar relay satellite continuously provides the lunar rover with a wide range of exploration image information and communication relay from its perspective, assisting the lunar rover in safe navigation planning and completing the journey from the starting position to the target position; the lunar rover provides the lunar relay satellite with more accurate lunar surface environment information, and the lunar relay satellite can transmit this accurate terrain information back to the ground in real time, and use this accurate information to correct and optimize the lunar rover's forward path online. However, this cross-regional air-ground collaborative exploration operation mode has certain limitations, specifically: (1) The lunar relay satellite and the lunar rover have a large difference in operating speed, making it difficult to maintain the exploration perspective. The lunar rover travels only about 15 to 30 meters per lunar day, while the speed of the lunar relay satellite is about kilometers per second. The two cannot meet the requirement of continuous exploration of the same area at all times. Although the relay communication satellite is placed in the Earth-Moon L2 translational point orbit to achieve fixed-point observation of the designated area, a large amount of propellant is required to maintain the orbit. (2) Communication conditions are limited. The lunar surface environment is highly uncertain, and the communication environment is quite harsh, making it difficult to guarantee uninterrupted communication between the lunar relay satellite and the lunar rover. Long-distance communication also faces challenges such as bandwidth limitations, high packet loss rates, and long latency. Furthermore, a single lunar relay satellite cannot achieve continuous communication with invisible areas such as the far side of the moon. Launching multiple satellites to form a constellation would increase the system's development and construction costs, and also introduce complexities in telemetry, tracking, and command (TT&C).

[0063] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a regional environment exploration method. This method can be applied to accurately explore the topography and landforms of a specified area, particularly the lunar surface, utilizing multi-robot collaborative work within the same spatial domain. The aforementioned regional environment exploration method can be used to achieve multi-robot collaborative exploration of the lunar surface environment. (Refer to...) Figure 1 As shown, it may specifically include:

[0064] Step S11: Identify the image corresponding to the area to be detected, divide the obstacle sub-regions, and mark their positions;

[0065] Step S12: Deploy the initial positions of n detection robots; where n is a positive integer and n is greater than or equal to the number of obstacle sub-regions;

[0066] Step S13: Based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot, assign a corresponding obstacle sub-region to be detected to each detection robot.

[0067] The regional environment detection method provided in this example implementation can be used to achieve multi-robot collaborative detection of the lunar surface environment. After identifying the area to be detected and determining obstacle sub-regions, multiple detection robots are deployed and assigned within the area to be detected, thereby achieving a distributed robot layout and enabling collaborative detection by multiple robots, thus improving detection efficiency and accuracy. Based on the distance between the initial position of each detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot, corresponding obstacle sub-regions to be detected are assigned to each detection robot, achieving reasonable and effective area division. By selecting appropriate cooperation strategies and through top-level design of task allocation principles, the detection time or energy consumption of the multi-robot system is minimized, and the total benefit of the detection task covering the specified area is maximized.

[0068] The steps of the regional environment detection method in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0069] In step S11, the image corresponding to the area to be detected is identified, and obstacle sub-regions are divided and their positions are marked.

[0070] In this example implementation, when exploring the lunar surface environment, a simplified topographic map of the area to be explored is obtained using a lunar orbiting satellite. This topographic map only contains basic obstacle distribution information. Based on the obstacle distribution information within the area to be explored, the area is divided into multiple sub-regions containing obstacles using a certain strategy.

[0071] Specifically, preliminary exploration of the lunar surface environment using lunar orbiting satellites can directly obtain lunar surface satellite images. These images are then used as the target area for exploration. Image recognition is performed to select the target area and determine the distribution of M large-scale obstacles within that area. These obstacles can be impact craters with diameters of 10-50 meters or rocks of a certain size.

[0072] In this example implementation, the step of dividing the obstacle sub-regions and marking their positions includes: using a minimum coverage circle to cover each obstacle in the area to be detected to divide it into several obstacle sub-regions, and using the center coordinates of the minimum coverage circle to mark the position of the corresponding obstacle sub-region; and marking the area outside each obstacle sub-region in the area to be detected as a free area.

[0073] Specifically, based on the distribution of obstacles throughout the area to be detected, the entire detection area can be divided into M obstacle circular regions and one free region using a minimum coverage circle containing irregular obstacles. The obstacles defined by the minimum coverage circle are considered obstacle sub-regions. The area outside each obstacle's minimum coverage circle is considered the free region. This region is relatively flat and has good accessibility, therefore requiring no refined collaborative detection; the terrain information in this region is considered known.

[0074] The area of ​​irregularly shaped obstacles is normalized using the minimum covering circle of a polygon. The area of ​​the minimum covering circle is used as the detection area index for the M obstacles. The area index of the j-th obstacle is:

[0075]

[0076] Where, r j Let represent the minimum coverage circle radius of the j-th obstacle. For M circular obstacle sub-regions, find the center of their minimum coverage circle, which serves as the coordinates of the center point of the j-th obstacle sub-region, and also the coordinates of the center point of the j-th obstacle:

[0077] O j =(x j y j )); j = 1, 2, ..., M.

[0078] In step S12, the initial positions of n detection robots are deployed; where n is a positive integer and is greater than or equal to the number of obstacle sub-regions.

[0079] In this example implementation, the initial positions for deploying the n probe robots include: deploying the n probe robots at initial positions in a random manner; or,

[0080] The initial positions of n detection robots are deployed according to the distribution of the obstacle sub-regions.

[0081] Specifically, the initial positions of n probe robots can be randomly deployed.

[0082] Alternatively, based on the distribution of obstacle sub-regions, at least one probe robot can be deployed around each obstacle sub-region. Furthermore, when determining the initial positions for deploying n probe robots, the size of each obstacle sub-region can be considered. Multiple probe robots can be deployed in areas with large and densely distributed obstacle sub-regions. These probe robots can be wheeled or tracked lunar rovers equipped with imaging sensors and other devices, enabling them to collect data such as photos and videos.

[0083] In this example embodiment, before assigning a corresponding obstacle sub-region to be detected to each of the detection robots, the method further includes: configuring a corresponding detection robot group according to the area index of each obstacle sub-region; wherein the detection robot group includes at least one of the detection robots.

[0084] Specifically, for obstacle sub-regions with large area indices, multiple detection robots can be configured and deployed in groups to detect the obstacle sub-region. Specifically, M obstacle sub-regions are sorted sequentially according to their area indices. Based on the ratio of the area indices of each obstacle sub-region, N detection robots are divided into M groups (N≥M), and each group detects the corresponding obstacle sub-region. That is, the number of detection robots Q in each detection robot group is... j Area index R of obstacle sub-region j The relationship is: Q1∶Q2∶Q3∶...∶Q M-1 ∶Q M ≈R1∶R2∶R3∶...∶R M-1 ∶R M The constraint relationship is: Q1+Q2+Q2+:...:Q M-1 +Q M =N and R1≤R2≤R3≤…≤R M-1 ≤R M .

[0085] In step S13, based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot, a corresponding obstacle sub-region to be detected is assigned to each detection robot.

[0086] In this example embodiment, step S13 described above may include:

[0087] Step S131: Construct a first cost function by combining the division results of the detection robot group and the distance between the initial position of the detection robot and the obstacle sub-region;

[0088] Step S132: Construct a second cost function to describe the detection robot's detection of the obstacle sub-region;

[0089] Step S133: Construct a total cost function based on the first cost function and the second cost function, and construct constraints using the mobility of the probe robot;

[0090] Step S134: Optimize the total cost function and use the optimization results to assign corresponding obstacle sub-regions to be detected to each of the detection robots.

[0091] Specifically, when allocating obstacle sub-regions to the detection robots, a distance-based cost function can be constructed first, namely the first cost function mentioned above. Specifically, N detection robots are deployed at random initial positions, and the Manhattan distance from the geographical coordinates of each obstacle sub-region to the starting point of each detection robot is calculated. Let the position coordinates of the starting point of the i-th detection robot be (x... i y i The coordinates of the center point of the j-th obstacle sub-region are (x, y). j y j If ), then the Manhattan distance between the two can be expressed as:

[0092] d ij =|x i -x j |+|y i -y j |

[0093] Based on the Manhattan distance of the center point of the obstacle sub-region, the cost for the i-th probe robot to travel to the j-th obstacle region is:

[0094] P ij =d ij / d max

[0095] Where, d max To detect the maximum Manhattan distance from the robot to the obstacle sub-region.

[0096] Secondly, the cost of the detection robot group detecting the obstacle sub-region, i.e., the second cost function mentioned above, can be expressed by the following formula:

[0097] Specifically,

[0098]

[0099] Among them, V j Let L be the cost function for the j-th detection robot group when detecting obstacles in the j-th obstacle sub-region; j The optimal detection circle radius is θ; the phase difference is θ. kl .

[0100] Based on the above-mentioned settings for the detection robot group, and under the constraint of satisfying the mobility of each detection robot (mainly referring to the capacity of the battery carried by each robot, which limits the maximum distance the robot can travel, so it is necessary to ensure that the robot can reach the obstacle area), the task cost incurred by the multi-detection robot system when detecting the obstacle sub-regions within the entire area to be detected should be minimized. The total task cost function for obstacle sub-region task allocation is constructed through linear weighting, and is expressed as:

[0101]

[0102] Among them, P ij V represents the cost for the i-th probe robot to travel to the j-th obstacle sub-region; j Let be the cost function for the j-th detection robot group when detecting obstacles in the j-th obstacle sub-region.

[0103] refer to Figure 2 As shown, after the task area is centrally allocated to multiple probe robots, each probe robot forms a robot group in the allocated obstacle sub-region, realizing the external optimal resource allocation decision for the regional probe task.

[0104] In this example implementation, after assigning a group of detection robots to an obstacle sub-region, the travel path of the detection robots to the corresponding obstacle sub-region can be planned, as well as the detection configuration of the detection robot group within the obstacle sub-region can be planned.

[0105] Specifically, the above method may further include: performing path planning based on the initial position of each of the exploration robots and the obstacle sub-regions assigned to the exploration robots to determine a planned path; wherein the planned path is used for the exploration robots to travel to the corresponding obstacle sub-regions.

[0106] In this example embodiment, the method further includes: configuring an outer boundary for each of the obstacle sub-regions, and using the outer boundary as the detection trajectory of the obstacle sub-region; wherein the outer boundary and the minimum coverage circle of the obstacle sub-region are set as concentric circles, and the radius of the outer boundary is greater than or equal to the radius of the minimum coverage circle; and calculating the planned path for the detection robot based on the detection trajectory.

[0107] Specifically, a radius of L is set around the minimum coverage circle of the irregular obstacle. j Concentric circles serve as the outer boundaries of the obstacle sub-regions, and L j ≥r j ;r j Let represent the minimum radius of the coverage circle for the j-th obstacle. The detection trajectories of each robot in the detection robot group are configured based on this outer boundary. Each robot travels to the boundary of the circular obstacle sub-region according to the externally optimized planned path, thus forming a robot group.

[0108] In this example implementation, the method may further include the following for detection allocation within the obstacle sub-region:

[0109] Step S141: Construct effective detection distance constraints based on the minimum effective detection distance and field of view of the detection robot and the minimum coverage circle corresponding to the obstacle sub-region;

[0110] Step S142: Calculate the detection configuration of the detection robot group using the Fisher information matrix based on the effective detection distance constraint.

[0111] Step S143: Based on the uniformly distributed detection configuration, traverse each obstacle sub-region to plan the detection configuration information of the detection robot group corresponding to each obstacle sub-region.

[0112] Specifically, in step S141 above, considering that the field of view of the camera carried by the detection robot should cover the smallest coverage circle as much as possible, and there is also a limitation on the minimum effective detection distance, it can be expressed as:

[0113]

[0114] In the formula: L jmin To determine the minimum effective observation distance of the robot's camera to the j-th obstacle; r j A is the minimum coverage circle radius of the j-th obstacle sub-region; Fov To detect the field of view of the robot's camera; λ≈1.

[0115] Specifically, in step S141 above, the calculation of the detection configuration of the detection robot group based on the effective detection distance constraint using the Fisher information matrix includes:

[0116] The Fisher information gain of the detection robot group is constructed by combining the Jacobian matrix of the direction finding and ranging sensor detection model and the covariance of the sensor measurement error; wherein, the Fisher information matrix is ​​used to characterize the obstacle information acquired by the detection robot.

[0117] Concentric circles with the minimum effective detection distance of the detection robot as the radius are configured as the cooperative detection path of each detection robot in the detection robot group;

[0118] The Fisher information gain is optimized based on the cooperative detection path to determine the detection configuration of the detection robot group.

[0119] Specifically, for the optimal configuration problem of multiple probe robots, the Fisher Information Matrix (FIM) is used to represent the acquired obstacle information. The relative positional relationship between the probe robot group and the obstacle sub-region is calculated to obtain the probe robot group configuration that maximizes the FIM determinant, thus minimizing the uncertainty of its detection of the obstacle sub-region. When a probe robot group consisting of K probe robots collaboratively detects a fixed obstacle sub-region, the total FIM information gain of the system is:

[0120]

[0121] Among them, H k The Jacobian matrix represents the simplified detection model of the imaging sensor (camera) to the direction-finding and range-finding sensor detection model, containing the detection distance L from each robot in the robot group to the center of the obstacle. j and the phase difference θ between them k ; k = 1, 2, ..., K; R k Let be the covariance of the sensor measurement error. If a D-optimal design is adopted, and the FIM determinant scalar is selected as the performance index, its form is as follows:

[0122] J2 = max(det(J FIM ))

[0123] The necessary condition for the FIM determinant corresponding to the detection configuration of the detection robot group to reach its maximum value is:

[0124] L j =L jmin

[0125] Specifically, in step S142 above, the detection configuration of the detection robot group includes: the detection phase difference between different detection robots and the detection distance of the detection robots.

[0126] Based on this, assuming each detection robot carries the same detection equipment, the optimal trajectory for collaborative detection by the robot group is a concentric circle with the minimum effective detection distance as its radius, called the optimal detection circle, i.e., the aforementioned outer boundary. Based on the optimal detection circle, by maximizing the FIM determinant, the phase difference θ between the k-th and l-th detection robots is obtained. klWhen the detection distance is the minimum effective observation distance, the optimal detection phase difference θ of a detection robot group consisting of K (K≥3) detection robots is... kl It can be represented as:

[0127]

[0128] Where k, l∈{1,...,K}. That is, the K probe robots in the probe robot group are evenly distributed on the optimal probe circle. Common optimal probe configurations for two-robot and three-robot groups are referenced. Figures 3-5 As shown.

[0129] Under the optimal detection configuration with uniform distribution, the cost function V of the j-th detection robot group consisting of K detection robots when detecting obstacles in the j-th sub-region is... j With phase difference θ kl and the optimal detection circle radius L j Related. Specifically, it can be described as:

[0130]

[0131] In this example embodiment, the method further includes: performing data fusion processing on the obstacle information collected by each of the detection robots to obtain the area information corresponding to the area to be detected.

[0132] Specifically, each detection robot group, based on its planned optimal detection configuration, collaboratively detects the obstacles in its assigned sub-region. Once all obstacles within the sub-region have been detected, each robot can be controlled to return to a unified rendezvous point to process and fuse the obtained detailed obstacle information, such as the depth of the crater, the height of the rocks, and the slope of the convex humps, thus completing the entire detection mission.

[0133] The regional environment detection method provided in this disclosure can be applied to multi-robot collaborative detection of the lunar surface environment. A simplified topographic map of the area to be detected is obtained using a lunar orbiting satellite; this map contains only basic obstacle distribution information. Based on the obstacle distribution information, the area to be detected is divided into multiple obstacle sub-regions containing obstacles using a certain strategy, thus achieving the division of the area to be detected. After dividing the area to be detected into obstacle sub-regions, the sub-regions can be allocated to each detection robot according to the area of ​​the sub-region, the distance from the sub-region to the initial position of the detection robot, and the mobility of a single detection robot, using a certain strategy. These sub-regions serve as the task areas for precise exploration by each detection robot, thus achieving task area allocation for the detection robots. After the task area allocation is completed, the travel paths of each detection robot are planned. Each detection robot arrives at the obstacle sub-region to be precisely detected according to its planned path. For the allocated obstacle sub-region, one or more detection robots use a certain strategic travel method to detect the precise information of the obstacles. This scheme, within the same lunar orbital region, only requires initial satellite images from a lunar relay satellite, eliminating the need for continuous transit and orbit maintenance. This avoids the instability of the detection perspective, inaccurate detection range, and limited communication conditions encountered in other lunar orbits. Furthermore, the exploration robots only need to carry simple terrain equipment, resulting in small size, low power consumption, low cost, and ease of implementation. By allocating multiple exploration robots into groups and planning detection within obstacle sub-regions, the scheme effectively solves the technical problems of unreasonable task allocation, low group utilization, and insufficient or excessive capabilities of individual robots in lunar exploration environments. Moreover, by setting up exploration robot groups and implementing area detection on a group basis, employing an internal and external dual-layer optimization strategy, greater detection efficiency and higher detection accuracy can be achieved, helping to overcome uncertainties in sensors and the environment; enabling precise detection of the lunar surface topography.

[0134] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0135] Further reference Figure 7 As shown, this example embodiment also provides a regional environment detection device 70, which includes: an obstacle sub-region division module 701, an initial position deployment module 702, and a detection robot allocation module 703. Wherein,

[0136] The obstacle sub-region division module 701 can be used to identify the image corresponding to the area to be detected, divide the obstacle sub-regions, and mark their positions.

[0137] The initial position deployment module 702 can be used to deploy the initial positions of n exploration robots; where n is a positive integer and is greater than or equal to the number of obstacle sub-regions.

[0138] The detection robot allocation module 703 can be used to allocate a corresponding obstacle sub-region to be detected to each detection robot based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of ​​the obstacle sub-region, and the mobility of each detection robot.

[0139] In some exemplary embodiments, the obstacle sub-region division module 701 can be used to cover each obstacle in the area to be detected using a minimum coverage circle to divide it into several obstacle sub-regions, and use the center coordinates of the minimum coverage circle to mark the position of the corresponding obstacle sub-region; and mark the area outside each obstacle sub-region in the area to be detected as a free area.

[0140] In some exemplary embodiments, the initial positions for deploying the n probe robots include: deploying the initial positions of the n probe robots in a random manner; or, deploying the initial positions of the n probe robots according to the distribution of the obstacle sub-regions.

[0141] In some exemplary embodiments, the area environment detection device 70 further includes: a robot group division module, configured to configure corresponding detection robot groups according to the area index of each obstacle sub-region; wherein the detection robot group includes at least one of the detection robots.

[0142] In some exemplary embodiments, the detection robot allocation module 703 may include: constructing a first cost function by combining the division result of the detection robot group and the distance between the initial position of the detection robot and the obstacle sub-region; constructing a second cost function to describe the detection robot's detection of the obstacle sub-region; constructing a total cost function based on the first cost function and the second cost function, and constructing constraints using the movement capability of the detection robot; optimizing the total cost function, and using the optimization result to allocate a corresponding obstacle sub-region to be detected to each of the detection robots.

[0143] In some exemplary embodiments, the area environment detection device 70 further includes a travel path planning module.

[0144] The travel path planning module can be used to determine a planned path based on the initial position of each of the exploration robots and the obstacle sub-regions assigned to the exploration robots; wherein the planned path is used for the exploration robots to travel to the corresponding obstacle sub-regions.

[0145] In some exemplary embodiments, the area environment detection device 70 further includes an outer cabinet boundary setting module.

[0146] The outer cabinet boundary setting module can be used to configure an outer boundary for each of the obstacle sub-regions and use the outer boundary as the detection trajectory of the obstacle sub-region; wherein the outer boundary and the minimum coverage circle of the obstacle sub-region are set as concentric circles, and the radius of the outer boundary is greater than or equal to the radius of the minimum coverage circle; the planned path is calculated for the detection robot based on the detection trajectory.

[0147] In some exemplary embodiments, the area environment detection device 70 further includes a detection area configuration module.

[0148] The detection area configuration module can be used to construct effective detection distance constraints based on the minimum effective detection distance and field of view of the detection robot and the minimum coverage circle corresponding to the obstacle sub-region; calculate the detection configuration of the detection robot group using the Fisher information matrix based on the effective detection distance constraints; and traverse each obstacle sub-region based on the uniformly distributed detection configuration to plan the detection configuration information of the detection robot group corresponding to each obstacle sub-region.

[0149] In some exemplary embodiments, the detection area configuration module can be used to construct the Fisher information gain of the detection robot group by combining the Jacobian matrix of the direction-finding and ranging sensor detection model and the covariance of the sensor measurement error; wherein, the Fisher information matrix is ​​used to characterize the obstacle information acquired by the detection robot; concentric circles with the minimum effective detection distance of the detection robot as the radius are configured as the cooperative detection path of each detection robot in the detection robot group; and the Fisher information gain is optimized based on the cooperative detection path to determine the detection configuration of the detection robot group.

[0150] In some exemplary embodiments, the detection configuration of the detection robot group includes: the detection phase difference between different detection robots and the detection distance of the detection robots.

[0151] In some exemplary embodiments, the regional environment detection device 70 further includes a data fusion module.

[0152] The data fusion module can be used to perform data fusion processing on the obstacle information collected by each of the detection robots to obtain the area information corresponding to the area to be detected.

[0153] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0154] Figure 8 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0155] It should be noted that, Figure 8 The illustrated electronic device 1000 is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0156] like Figure 8 As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from Storage Unit 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004.

[0157] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0158] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0159] Specifically, the aforementioned electronic devices can be smart mobile electronic devices such as mobile phones, tablets, or laptops. Alternatively, the aforementioned electronic devices can also be smart electronic devices such as desktop computers.

[0160] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0162] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0163] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps shown.

[0164] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0165] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0166] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method of area environmental detection, characterized by, The method comprises: recognizing an image corresponding to a to-be-detected region, dividing obstacle sub-regions and marking positions; deploying initial positions of n detection robots; wherein n is a positive integer, and n is greater than or equal to the number of the obstacle sub-regions; configuring a corresponding detection robot group for each obstacle sub-region according to an area index of the obstacle sub-region; wherein the detection robot group comprises at least one detection robot; allocating a corresponding to-be-detected obstacle sub-region for each detection robot based on distances between the initial positions of the detection robots and the obstacle sub-regions, areas of the obstacle sub-regions, and movement capabilities of the detection robots, comprising: constructing a first cost function in combination with the distances between the initial positions of the detection robots and the obstacle sub-regions; constructing a second cost function for describing detection of the obstacle sub-regions by the detection robots; constructing a total cost function according to the first cost function and the second cost function, and constructing a constraint condition by using the movement capabilities of the detection robots; optimizing the total cost function, and allocating a corresponding to-be-detected obstacle sub-region for each detection robot by using an optimization result; and configuring a peripheral boundary for each obstacle sub-region, and taking the peripheral boundary as a detection track of the obstacle sub-region; wherein the peripheral boundary and a minimum covering circle of the obstacle sub-region are arranged as concentric circles, and a radius of the peripheral boundary is greater than or equal to a radius of the minimum covering circle; calculating a planning path for the detection robot based on the detection track; wherein the first cost function is: ， wherein, to detect the maximum value in the Manhattan distance of the robot to the obstacle sub-region, is the Manhattan distance from the geographic location coordinate point of each obstacle sub-region to the starting point of each detection robot; the second cost function is: wherein, V j is the cost function for the kth detection robot group to detect the obstacles in the lth obstacle sub-region; j j is the cost function for the kth detection robot group to detect the obstacles in the lth obstacle sub-region; L j is the optimal detection circle radius; and is the phase difference between the kth detection robot and the lth detection robot.​​ 2. The regional environmental survey method according to claim 1, characterized by, the dividing of the obstacle sub-regions and the marking of positions comprise: covering each obstacle in the to-be-detected region by a minimum covering circle to divide a plurality of obstacle sub-regions, and marking a position of a corresponding obstacle sub-region by using a center coordinate of the minimum covering circle; and marking a region outside each obstacle sub-region in the to-be-detected region as a free region.

3. The regional environmental survey method according to claim 1, characterized by, the deploying of the initial positions of the n detection robots comprises: deploying the initial positions of the n detection robots in a random manner; or deploying the initial positions of the n detection robots according to a distribution of the obstacle sub-regions.

4. The regional environmental survey method according to claim 1, characterized by, The method further comprises: performing path planning according to the initial positions of each detection robot and the obstacle sub-region allocated to the detection robot to determine a planning path; wherein the planning path is used for the detection robot to travel to the corresponding obstacle sub-region.

5. The regional environmental survey method according to claim 1, wherein, The method further comprises: constructing an effective detection distance constraint condition according to a minimum effective detection distance and a field of view angle of the detection robot, and a minimum covering circle corresponding to the obstacle sub-region; calculating a detection configuration of a detection robot group by using a Fisher information matrix based on the effective detection distance constraint condition; based on the uniformly distributed detection configuration, traversing each obstacle sub-region to plan detection configuration information of the detection robot group corresponding to each obstacle sub-region.

6. The regional environmental survey method according to claim 5, wherein, The Fisher information matrix is used to calculate the detection configuration of the detection robot group based on the effective detection distance constraint condition, including: The Fisher information yield of the detection robot group is constructed by combining the Jacobian matrix of the direction-finding and distance-measuring sensor detection model and the covariance of the sensor measurement error; wherein the obstacle information obtained by the detection robot is represented by the Fisher information matrix; The concentric circle configuration with the minimum effective detection distance of the detection robot as the radius is configured as the cooperative detection path of each detection robot in the detection robot group; The Fisher information yield is optimized based on the cooperative detection path to determine the detection configuration of the detection robot group.

7. The regional environmental detection method according to claim 5 or 6, characterized by, The detection configuration of the detection robot group includes: The detection phase difference between different detection robots and the detection distance of the detection robot.

8. The regional environmental survey method according to claim 5, wherein, The method further includes: The obstacle information collected by each detection robot is subjected to data fusion processing to obtain the regional information corresponding to the to-be-detected region.

9. A regional environmental detection apparatus, characterized by, The device includes: An obstacle sub-region division module for identifying the image corresponding to the to-be-detected region, dividing the obstacle sub-region, and marking the position; An initial position deployment module for deploying the initial positions of n detection robots; wherein n is a positive integer and n is greater than or equal to the number of obstacle sub-regions; A robot group division module for configuring a corresponding detection robot group according to the area index of each obstacle sub-region; wherein the detection robot group includes at least one detection robot; A detection robot allocation module for allocating a corresponding to-be-detected obstacle sub-region to each detection robot based on the distance between the initial position of the detection robot and the obstacle sub-region, the area of the obstacle sub-region, and the movement ability of each detection robot, including: constructing a first cost function in combination with the distance between the initial position of the detection robot and the obstacle sub-region; constructing a second cost function for describing the detection of the obstacle sub-region by the detection robot; constructing a total cost function according to the first cost function and the second cost function, and constructing a constraint condition by using the movement ability of the detection robot; optimizing the total cost function, and allocating a corresponding to-be-detected obstacle sub-region to each detection robot by using the optimization result; An outer cabinet boundary setting module for configuring a peripheral boundary for each obstacle sub-region and taking the peripheral boundary as the detection trajectory of the obstacle sub-region; wherein the peripheral boundary and the minimum covering circle of the obstacle sub-region are concentrically arranged, and the radius of the peripheral boundary is greater than or equal to the radius of the minimum covering circle; and planning a path for the detection robot based on the detection trajectory; The first cost function is: ， wherein, to detect the maximum value in the Manhattan distance of the robot to the obstacle sub-region, is the Manhattan distance from the geographic location coordinate point of each obstacle sub-region to the starting point of each detection robot; The second cost function is: wherein, V j is the cost function for the kth detection robot group to detect the obstacles in the lth obstacle sub-region; j j is the cost function for the kth detection robot group to detect the obstacles in the lth obstacle sub-region; L j is the optimal detection circle radius; and is the phase difference between the kth detection robot and the lth detection robot.​​ 10. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the regional environment detection method of any one of claims 1 to 8.

11. An electronic device, comprising: including: a processor; and a memory for storing executable instructions of the processor; The processor is configured to execute the executable instructions to perform the area environment detection method of any one of claims 1-8.

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