Mars seismic exploration method and system based on drone deployment and recovery of seismometers

Abstract

The present invention discloses a Mars seismic exploration method and system based on the deployment and recovery of seismometers by unmanned aerial vehicles, which belongs to the field of Mars seismic exploration and includes the following steps: S1, generating a flight path with deployment points; S2, under the coordination of a distributed algorithm, using a navigation module to correct the flight path in real time, so that the unmanned aerial vehicle body carrying the seismometer arrives at the deployment point on the flight path; S3, the unmanned aerial vehicle body descends until the seismometer is inserted into the deployment point to complete the deployment of the seismometer; S4, using the seismometer to collect Mars waveform data, and transmitting the waveform data back to the ground control terminal; S5, the task is completed, and the seismometer at the deployment point is recovered. The present invention adopts the above-mentioned Mars seismic exploration method and system based on the deployment and recovery of seismometers by unmanned aerial vehicles, and realizes efficient deployment and recovery of seismometers through autonomous flight, navigation and operation of unmanned aerial vehicles, ensuring stable operation in the extreme environment of Mars and obtaining high-quality Mars seismic data.

Description

Technical Field

[0001] The present invention relates to the field of Mars seismic exploration, and in particular to a Mars seismic exploration method and system based on deploying and recovering seismometers using unmanned aerial vehicles. Background Art

[0002] Mars is one of the planets in the solar system that is most similar to the Earth. Its geological history, climate environment and potential signs of life have attracted widespread attention from scientists and exploration agencies. Since the 1960s, many countries and agencies have carried out dozens of Mars exploration missions, accumulating a large amount of information about the Martian atmosphere, surface and climate. However, the internal structure and geological activities of Mars are still an important area of ​​scientific research, and the application of geophysical exploration technology to Mars exploration is a key means to reveal these issues.

[0003] In the field of geophysical exploration, the main exploration methods include seismic exploration, electromagnetic exploration, gravity and magnetic measurement, etc. Among these methods, seismic exploration is an effective means to study underground structures and geological activities, that is, by placing seismographs on the surface to receive the reflection and refraction signals of seismic waves caused by artificial seismic sources, the structure and properties of underground rock formations can be analyzed.

[0004] However, implementing the above seismic exploration on Mars still faces the following challenges:

[0005] First, the atmosphere on Mars is very thin, with a density of only 1% of that on Earth, making it impossible for humans to conduct artificial source seismic detection; second, the environment on Mars is very extreme, with huge surface temperature differences, ranging from 20 degrees Celsius during the day to minus 130 degrees Celsius at night, which requires seismographs to be resistant to high and low temperatures; in addition, there are a large number of sand dunes, rocks and volcanic landforms on the surface of Mars, and traditional ground vehicles find it difficult to deploy seismographs in these complex terrains; moreover, the vast surface area of ​​Mars and the fact that humans have not yet landed on Mars make it extremely difficult and impractical to manually deploy and recover seismographs, that is, with existing technical means, it is difficult to achieve human exploration operations in a short period of time. Summary of the invention

[0006] To solve the above problems, the present invention provides a Mars seismic exploration method and system based on the deployment and recovery of seismometers by unmanned aerial vehicles. Through the autonomous flight, navigation and operation of the unmanned aerial vehicles, the efficient deployment and recovery of seismometers can be achieved, ensuring stable operation in the extreme environment of Mars and obtaining high-quality Mars seismic data.

[0007] To achieve the above object, the present invention provides a Mars seismic exploration method based on deploying and recovering seismometers by drones, comprising the following steps:

[0008] S1. Generate a flight path with deployment points using a ground control terminal deployed on Earth, and transmit the flight path to the drone cluster on Mars;

[0009] S2. Under the coordination of the distributed algorithm, the flight path is corrected in real time using the navigation module so that the drone carrying the seismometer reaches the deployment point on the flight path;

[0010] S3, the drone body descends until the seismometer is inserted into the deployment point, completing the deployment of the seismometer;

[0011] S4, using a seismograph to collect Mars waveform data, and transmitting the waveform data back to a ground control terminal;

[0012] S5. Mission accomplished, recover the seismograph at the deployment point.

[0013] Preferably, step S1 specifically includes the following steps:

[0014] S11. Obtaining topographic data of the area to be explored on Mars from remote sensing satellites in the orbit of Mars;

[0015] S12, performing data cleaning, geometric correction and image matching processing on the acquired terrain data in sequence;

[0016] S13, performing stereo matching, parallax calculation and elevation calculation on the terrain data processed in step S12 in sequence, so as to generate a preliminary DEM model;

[0017] S14, fusing the historical seismic data of the area to be explored with the preliminary DEM model to obtain a final DEM model;

[0018] S15. Determine the spacing of the deployment points in the final DEM model based on the exploration purpose and geological characteristics;

[0019] S16, based on the location of the deployment point, the flight path is generated by using the A* algorithm, the Dijkstra algorithm or the dynamic programming algorithm taking into account the flight time, energy consumption and path safety;

[0020] Among them, the A* algorithm is used to generate the path, and the calculation formula of the A* algorithm is as follows:

[0021] ;

[0022] In the formula, Indicates the basis for selecting the path; Indicates the actual cost from the starting point to the current node; Represents the heuristic estimated cost from the current node to the target node;

[0023] The Dijkstra algorithm is used to calculate the global shortest path in the static planning stage to obtain the optimal path, and the Dijkstra algorithm formula is as follows:

[0024] ;

[0025] In the formula, From the starting point to the node The shortest distance; Representation and Node The set of directly adjacent nodes; From the starting point to the node The shortest distance currently known; Represents a slave node To Node The path cost of

[0026] Under the premise of considering the influencing factors, the dynamic programming algorithm is used to optimize the multi-stage decision-making problem, and the dynamic programming algorithm formula is as follows:

[0027] ;

[0028] In the formula, From the starting point to the stage The minimum cost; From the starting point to the stage The minimum cost; Indicates that from the stage -1 to stage Costs;

[0029] S17, Simulation verification: Use simulation software to simulate and verify the flight path to determine the effectiveness of the flight path.

[0030] Preferably, step S2 specifically includes the following steps:

[0031] S21, initial task allocation: based on the current position, energy status and task priority of the UAV body, the predefined task pool or central control node obtains the initial task;

[0032] S22, Task exchange and reallocation: During the task execution process, the UAV can exchange tasks or request assistance with other adjacent UAVs according to their working status, thereby dynamically adjusting task allocation;

[0033] S23, path planning:

[0034] Local path planning: Each drone uses the navigation module to perform real-time path planning based on its current target mission and location, and builds a point cloud model based on the planning results to identify and mark obstacles in the environment. It then uses a distributed path planning algorithm to search for the shortest path while taking obstacle avoidance into consideration.

[0035] Global path coordination: information exchange between drones is realized through the communication network, path information is shared, and whether path conflicts occur is determined. If path conflicts occur, path planning is performed again;

[0036] S24, position adjustment:

[0037] Autonomous position correction: Use the navigation module to correct the flight attitude and path of each drone so that each drone is located on the preset flight path;

[0038] Group position adjustment: When a drone in the drone cluster is detected to have a position deviation, adjust its flight position, or malfunction, the other drones will be notified through the communication network to make overall position adjustments and generate a new flight path for each drone. During this process, the navigation module will be used to monitor the adjustment process in real time and feedback the current status through the communication network.

[0039] Preferably, step S23 specifically includes the following steps:

[0040] S231, Image acquisition and preprocessing: Collect images of the Martian surface, and perform image correction and image enhancement on the images;

[0041] S232, Image Recognition:

[0042] S2321, extracting feature points: extracting fixed points in the image as key points, and generating a feature descriptor for each key point, which is used to describe the local feature vector of the key point;

[0043] S2322, image matching: matching the feature descriptor of the current image with the known feature descriptors in the database to obtain matching points;

[0044] S233, pose estimation: remove wrong matches from the matching points, use the remaining multiple matching points to calculate the position information of the drone body relative to the known landmarks, and use the inertial sensor or starlight sensor to estimate the relative pose of the drone body;

[0045] S234, Fusion: During the day, the visual navigation results are fused with the UAV attitude data collected by the inertial navigation unit through the fusion algorithm to obtain real-time path planning;

[0046] At night or when the light intensity is lower than the set value, the star navigation unit is used for navigation to capture the star image in the Martian night sky, the star map matching algorithm is used to calculate the attitude data of the drone body, and the fusion algorithm is used to combine the attitude data of the drone body and the visual navigation results to obtain real-time path planning;

[0047] S235. Use the posture data determined by the starlight navigation unit to update the posture data collected by the inertial navigation unit.

[0048] Preferably, in step S2321, key points are extracted using SIFT, SURF, or ORB algorithms, and the feature descriptor is a SIFT descriptor, a SURF descriptor, or a BRIEF descriptor;

[0049] In step S2322, the closest matching point is obtained by calculating the Euclidean distance or Hamming distance between the feature points using the BFMatcher algorithm or the FLANN algorithm;

[0050] In step S233, the RANSAC algorithm is used to eliminate false matches, and the position information of the drone body relative to the known landmark is calculated by triangulation or PnP algorithm;

[0051] In step S234, a Kalman filter is used to perform data fusion.

[0052] Preferably, step S3 specifically includes the following steps:

[0053] S31. Positioning the predetermined deployment point:

[0054] S311, using the visual navigation unit to collect images of the points to be deployed, and extracting feature points in the images, and matching them with pre-stored reference images, so as to identify the current position of the drone body;

[0055] S312, using an inertial navigation unit to measure the acceleration and rotation rate of the drone body, and calculating the speed, position and attitude change of the drone body by integration;

[0056] The speed calculation formula is as follows:

[0057] ;

[0058] In the formula, Indicates the drone itself at time The velocity vector of represents the initial velocity vector, Indicates the current time; Indicates the initial time; Indicates the drone itself at time The linear acceleration vector of

[0059] The position calculation formula is as follows:

[0060] ;

[0061] In the formula, Indicates the drone itself at time The position vector of represents the initial position vector; Indicates the drone itself at time The velocity vector of

[0062] The posture change calculation formula is as follows:

[0063] ;

[0064] In the formula, Indicates the drone itself at time The attitude angle; represents the initial attitude angle; Indicates the drone itself at time Angular velocity vector;

[0065] S313, fusing the position information obtained by the visual navigation unit and the inertial navigation unit to correct the positioning error;

[0066] S32. The drone body descends to the set height, driving the seismometer to be inserted into the deployment point.

[0067] Preferably, in step S4, the drone body enters a sleep mode, the seismograph starts to collect seismic wave data and transmits it to the ground control terminal in real time, the ground control terminal stores the collected seismic wave data for a second time, and transmits it to the orbiting satellite, which then transmits it to the receiving station on Earth.

[0068] Preferably, step S5 specifically includes the following steps:

[0069] S51, generating a recovery path: according to the current geographical location of the drone cluster, confirm the current location of each drone body, obtain the current mission status information of the drone body, and confirm the location of the base station to which the drone is to return. In the case of dynamic planning taking into account obstacles and the remaining flight distance, use the Dijkstra algorithm to generate a recovery path;

[0070] S52, the drone body carrying the seismometer performs a return flight according to the recovery path, and during the return flight, when the drone body encounters an obstacle or a temporary flight restriction, sends position information and deviation reports to other drone bodies, and other drone bodies integrate the received position information and deviation reports, update the overall spatial layout of the drone cluster and the position of each drone body, and generate a new target position or recovery path for each drone body according to the adjustment strategy;

[0071] At the same time, during the flight, the navigation module is used to track the recovery path in real time and correct the flight trajectory so that the drone body flies according to the predetermined recovery path;

[0072] S53. Land after reaching the base station.

[0073] A system for Mars seismic exploration based on a method of deploying and recovering seismometers by drones, comprising a receiving station arranged on the surface of the earth, a ground control terminal arranged on the surface of Mars, and a drone cluster consisting of a plurality of drone bodies, each drone body being equipped with a seismometer at the bottom, and the seismometer and the drone body communicating with the receiving station via a data acquisition module, a ground control terminal, and an orbiting satellite in sequence;

[0074] The drone body is equipped with an airborne terminal and a sensor system that communicates with the airborne terminal. The sensor system includes a navigation module and an environmental perception module. The navigation module includes a visual navigation unit, an inertial navigation unit and a starlight navigation unit. The visual navigation unit is used to collect images of the surface of Mars and transmit them to the airborne terminal to generate a real-time planned path. The inertial navigation unit and the starlight navigation unit are used to realize starlight inertial combined navigation to correct the planned path. The environmental perception module includes a magnetometer and a barometer.

[0075] Preferably, the drone cluster communicates with each other via MANET, and the drone cluster adopts a distributed computing architecture for information exchange between multiple drone entities;

[0076] A seismograph is arranged at the bottom of the drone body, and a contact sensor and an acceleration sensor are embedded at the bottom of the seismograph. Both the contact sensor and the acceleration sensor communicate with the airborne terminal to determine whether the seismograph is inserted into the deployment point;

[0077] A solar panel is mounted on the top of the drone body, the solar panel is electrically connected to a battery pack placed inside the drone body, the battery pack is electrically connected to a driving mechanism of the drone body, an airborne terminal and a seismograph, the driving mechanism includes a brushless motor placed inside the drone body and a propeller rotatably arranged on the drone body, and the output end of the brushless motor is connected to a rotating shaft of the propeller;

[0078] The visual navigation unit includes a high-definition camera and a visual processor embedded in an airborne terminal, and the high-definition camera communicates with the visual processor.

[0079] The present invention has the following beneficial effects:

[0080] 1. Applying drone technology to Mars exploration can significantly improve exploration efficiency, reduce the difficulty of human operation, and improve data quality and coverage;

[0081] 2. Accurate and efficient deployment: The seismometers are collected at a well-arranged position spacing, which can effectively provide good data for subsequent data processing. At the same time, the inertial measurement unit, lidar, and visual navigation system are used to assist the drone in locating the target position, so as to achieve accurate and efficient deployment of seismometers;

[0082] 3. High degree of automation: It can automatically complete the deployment of seismographs, data collection, transmission and recovery without human intervention, greatly improving work efficiency;

[0083] 4. Applicable to complex terrain: The use of drones can overcome the difficulty of deploying traditional equipment in complex terrain and adapt to the complex terrain conditions of Mars;

[0084] 5. High reliability: Redundant design is implemented in drone positioning, data transmission, data storage, etc., so that the drone can fly stably to the predetermined position, while ensuring the accuracy of data during data transmission and storage.

[0085] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 The present invention is a flow chart of the Mars seismic exploration method based on the deployment and recovery of seismometers by unmanned aerial vehicles. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0088] It should be noted that the terms "include", "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or devices.

[0089] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0090] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0091] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0092] like Figure 1 As shown, the Mars seismic exploration method based on the deployment and recovery of seismometers by drones includes the following steps:

[0093] S1. Generate a flight path with deployment points using a ground control terminal deployed on Earth, and transmit the flight path to the drone cluster on Mars;

[0094] Step S1 specifically includes the following steps:

[0095] S11. Obtaining topographic data of the area to be explored on Mars from remote sensing satellites in the orbit of Mars;

[0096] S12, performing data cleaning, geometric correction and image matching processing on the acquired terrain data in sequence;

[0097] S13, performing stereo matching, parallax calculation and elevation calculation on the terrain data processed in step S12 in sequence, so as to generate a preliminary DEM model;

[0098] S14, fusing the historical seismic data of the area to be explored with the preliminary DEM model to obtain a final DEM model;

[0099] S15. Based on the exploration purpose and geological characteristics, the spacing of the layout points is determined in the final DEM model. In this embodiment, the spacing of the layout points is not greater than half of the shortest wavelength of the seismic wave to prevent aliasing effect.

[0100] S16, based on the location of the deployment point, the flight path is generated by using the A* algorithm, the Dijkstra algorithm or the dynamic programming algorithm taking into account the flight time, energy consumption and path safety;

[0101] Among them, the A* algorithm is used to generate the path (which is suitable for rapid response in the actual flight environment), and the A* algorithm calculation formula is as follows:

[0102] ;

[0103] In the formula, Indicates the basis for selecting the path; Represents the actual cost from the starting point to the current node (flight time, energy consumption, etc.); Represents the heuristic estimated cost from the current node to the target node, which is usually estimated using Euclidean distance or Manhattan distance;

[0104] The Dijkstra algorithm is used to calculate the global shortest path in the static planning stage to obtain the optimal path, and the Dijkstra algorithm formula is as follows:

[0105] ;

[0106] In the formula, From the starting point to the node The shortest distance; Representation and Node The set of directly adjacent nodes, i.e., the slave nodes The set of nodes that can be reached directly; From the starting point to the node The currently known shortest distance, which may be updated during the execution of the algorithm; Represents a slave node To Node The path cost of

[0107] Under the premise of considering influencing factors (such as energy, time, etc.), the dynamic programming algorithm is used to optimize the multi-stage decision-making problem (suitable for long-distance, multi-condition flight scenarios), and the dynamic programming algorithm formula is as follows:

[0108] ;

[0109] In the formula, From the starting point to the stage The minimum cost; From the starting point to the stage The minimum cost; Indicates that from the stage -1 to stage Costs;

[0110] S17, Simulation verification: Use simulation software to simulate and verify the flight path to determine the effectiveness of the flight path.

[0111] S2. Under the coordination of the distributed algorithm, the flight path is corrected in real time using the navigation module so that the drone carrying the seismometer reaches the deployment point on the flight path;

[0112] Step S2 specifically includes the following steps:

[0113] S21, initial task allocation: based on the current position, energy status and task priority of the UAV body, the predefined task pool or central control node obtains the initial task;

[0114] S22, Task exchange and reallocation: During the task execution process, the UAV can exchange tasks or request assistance with other adjacent UAVs according to their working status, thereby dynamically adjusting task allocation;

[0115] S23, path planning:

[0116] Local path planning: Each drone uses the navigation module to perform real-time path planning based on its current target mission and location, and builds a point cloud model based on the planning results to identify and mark obstacles in the environment. It then uses a distributed path planning algorithm to search for the shortest path while taking obstacle avoidance into consideration.

[0117] Global path coordination: information exchange between drones is realized through the communication network, path information is shared, and whether path conflicts occur is determined. If path conflicts occur, path planning is performed again;

[0118] Step S23 specifically includes the following steps:

[0119] S231, Image acquisition and preprocessing: Collect images of the Martian surface, and perform image correction and image enhancement on the images;

[0120] S232, Image Recognition:

[0121] S2321, extract feature points: use SIFT or ORB algorithm to extract fixed points in the image (such as rock edges, terrain protrusions, etc.) as key points, and generate feature descriptors for each key point to describe the local feature vector of the key point;

[0122] In step S2321, SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Feature), ORB algorithm (Orbital Rapid Robust Feature) is used to extract key points, and the feature descriptor is SIFT descriptor, SURF descriptor or BRIEF descriptor;

[0123] S2322, image matching: matching the feature descriptor of the current image with the known feature descriptors in the database to obtain matching points;

[0124] In step S2322, the closest matching point is obtained by calculating the Euclidean distance or Hamming distance between the feature points using the BFMatcher algorithm or the FLANN algorithm;

[0125] S233, pose estimation: Use the RANSAC (random sampling consensus) algorithm to remove false matches from the matching points, use the remaining multiple matching points to calculate the position information of the drone body relative to the known landmarks, and use the inertial sensor or starlight sensor to estimate the relative pose of the drone body;

[0126] In step S233, the RANSAC algorithm is used to eliminate false matches, and the position information of the drone body relative to the known landmark is calculated by triangulation or PnP (Perspective-n-Point) algorithm;

[0127] S234, Fusion: During the day, the visual navigation results are fused with the UAV attitude data collected by the inertial navigation unit through the fusion algorithm to obtain real-time path planning;

[0128] At night or when the light intensity is lower than the set value, the star navigation unit is used for navigation to capture the star image in the Martian night sky, the star map matching algorithm is used to calculate the attitude data of the drone body, and the fusion algorithm is used to combine the attitude data of the drone body and the visual navigation results to obtain real-time path planning;

[0129] In step S234, a Kalman filter is used to perform data fusion.

[0130] S235. Use the posture data determined by the starlight navigation unit to update the posture data collected by the inertial navigation unit.

[0131] S24, position adjustment:

[0132] Autonomous position correction: Use the navigation module to correct the flight attitude and path of each drone so that each drone is located on the preset flight path;

[0133] Group position adjustment: When a drone in the drone cluster is detected to have a position deviation, adjust its flight position, or malfunction, the other drones will be notified through the communication network to make overall position adjustments and generate a new flight path for each drone. During this process, the navigation module will be used to monitor the adjustment process in real time and feedback the current status through the communication network.

[0134] S3, the drone body descends until the seismometer is inserted into the deployment point, completing the deployment of the seismometer;

[0135] Step S3 specifically includes the following steps:

[0136] S31. Positioning the predetermined deployment point:

[0137] S311, using the visual navigation unit to collect images of the points to be deployed, and extracting feature points in the images, and matching them with pre-stored reference images, so as to identify the current position of the drone body;

[0138] S312, using an inertial navigation unit to measure the acceleration and rotation rate of the drone body, and calculating the speed, position and attitude change of the drone body by integration;

[0139] The speed calculation formula is as follows:

[0140] ;

[0141] In the formula, Indicates the drone itself at time The velocity vector of represents the initial velocity vector, Indicates the current time; Indicates the initial time; Indicates the drone itself at time The linear acceleration vector of

[0142] The position calculation formula is as follows:

[0143] ;

[0144] In the formula, Indicates the drone itself at time The position vector of represents the initial position vector; Indicates the drone itself at time The velocity vector of

[0145] The posture change calculation formula is as follows:

[0146] ;

[0147] In the formula, Indicates the drone itself at time attitude angles (which may include pitch angle, roll angle and yaw angle); represents the initial attitude angle; Indicates the drone itself at time The angular velocity vector is measured by the gyroscope;

[0148] S313, fusing the position information obtained by the visual navigation unit and the inertial navigation unit to correct the positioning error;

[0149] S32. The drone body descends to a set height, driving the seismometer to be inserted into the deployment point. During this process, the contact force collected by the contact sensor is used to determine whether the seismometer is effectively inserted into the deployment point.

[0150] S4, using a seismograph to collect Mars waveform data, and transmitting the waveform data back to a ground control terminal;

[0151] In step S4, the drone body enters sleep mode, and the seismometer starts collecting seismic wave data and transmits it to the ground control terminal in real time. The ground control terminal stores the collected seismic wave data for a second time and transmits it to the orbiting satellite, which then transmits it to the receiving station on Earth.

[0152] S5. Mission accomplished, recover the seismograph at the deployment point.

[0153] Step S5 specifically includes the following steps:

[0154] S51, generating a recovery path: according to the current geographical location of the drone cluster, confirm the current location of each drone body, and obtain the current mission status information of the drone body (such as whether the mission is completed and the remaining power), and confirm the location of the base station to which the drone is to return. In the case of dynamic planning taking into account obstacles and the remaining flight distance, the recovery path is generated using the Dijkstra algorithm. The recovery path is planned by taking into account factors such as flight distance, obstacles, aircraft status, and energy consumption to ensure that the drone can return to the base station safely in the shortest time;

[0155] S52, the drone body carrying the seismometer performs a return flight according to the recovery path, and during the return flight, when the drone body encounters an obstacle or a temporary flight restriction, sends position information and deviation reports to other drone bodies, and other drone bodies integrate the received position information and deviation reports, update the overall spatial layout of the drone cluster and the position of each drone body, and generate a new target position or recovery path for each drone body according to the adjustment strategy;

[0156] The adjustment strategy in this embodiment is as follows: First, status evaluation is performed: during flight, the drone will continuously monitor its own status, such as current position, energy status (battery power) and mission progress. When a drone in the drone cluster is detected to have a position deviation, insufficient energy or other abnormalities, the system will evaluate whether adjustments are needed;

[0157] Dynamic task redistribution after the evaluation is completed: Based on the results of the status evaluation, if a drone is unable to continue to perform the current task, the system will reallocate the task. Task redistribution takes into account the working status of the drone itself and the capabilities of adjacent drones, and uses the communication network to collaborate with other drones to complete the task.

[0158] Finally, the path is adjusted: when a path deviation is detected or an obstacle is encountered, the drone will correct the flight path in real time. If a path conflict occurs, the system will coordinate the path information within the cluster and re-plan the optimal path to avoid the conflict.

[0159] At the same time, during the flight, the navigation module is used to track the recovery path in real time and correct the flight trajectory so that the drone body flies according to the predetermined recovery path;

[0160] In this process, this embodiment also performs status monitoring and adjustment: that is, with the help of the status monitoring system equipped on the drone body, key parameters such as battery power, aircraft health (such as temperature, vibration) and navigation accuracy are tracked in real time. If the status monitoring system finds that the battery power is low or other faults occur, emergency measures are automatically implemented, such as adjusting the flight altitude, changing the recovery path, or activating the backup energy system to ensure that the drone can return safely.

[0161] S53. Land after reaching the base station.

[0162] When approaching the base station, the drone body executes the landing procedure. During the landing process, the drone uses high-definition cameras and radars to identify ground features to ensure a smooth landing in the designated area of ​​the base station. The drone body also accurately docks according to the receiving signs of the base station (such as signal lights and positioning marks) to ensure communication and physical docking with the base station. If the base station has automatic docking facilities, the drone body will automatically dock and perform data transmission and charging.

[0163] After the drone successfully lands, the ground control terminal will check the health status of the drone cluster, including system logs, flight data and battery status. At the same time, the drone cluster will upload data and records to the ground control terminal through the wireless communication system for subsequent analysis.

[0164] After the inspection is completed, the drone cluster and seismometer will undergo necessary maintenance and repair to ensure that the equipment is in good condition and ready for the next mission. The ground control terminal will conduct a comprehensive inspection, cleaning and calibration of all equipment to ensure that it can perform future missions stably and reliably.

[0165] A system for Mars seismic exploration based on a method of deploying and recovering seismometers using drones comprises a receiving station arranged on the surface of the earth, a ground control terminal arranged on the surface of Mars, and a drone cluster consisting of multiple drone bodies, wherein a seismometer is mounted on the bottom of each drone body, and the seismometer and the drone body communicate with the receiving station via a data acquisition module, a ground control terminal, and an orbiting satellite in sequence; an airborne terminal and a sensor system communicating with the airborne terminal are built into the drone body, and the sensor system comprises a navigation module and an environmental perception module, wherein the navigation module comprises a visual navigation unit, an inertial navigation unit, and a starlight navigation unit, wherein the visual navigation unit is used for collecting images of the surface of Mars and transmitting them to the airborne terminal to generate a real-time planned path, and the inertial navigation unit and the starlight navigation unit are used for realizing starlight inertial combined navigation to correct the planned path; and the environmental perception module comprises a magnetometer and a barometer.

[0166] The drone cluster communicates with each other through MANET (self-organizing network), and the drone cluster adopts a distributed computing architecture for information exchange between multiple drone entities;

[0167] A seismograph is arranged at the bottom of the drone body, and a contact sensor and an acceleration sensor are embedded at the bottom of the seismograph. Both the contact sensor and the acceleration sensor communicate with the airborne terminal to determine whether the seismograph is inserted into the deployment point;

[0168] The top of the drone body is equipped with a solar panel, which is electrically connected to a battery pack placed inside the drone body (the solar panel can continuously charge the battery under the sunlight of Mars, extending the drone's flight time). The battery pack is electrically connected to the drone body's drive mechanism, airborne terminal and seismometer. The drive mechanism includes a brushless motor placed inside the drone body and a propeller rotating on the drone body. The output end of the brushless motor is connected to the rotating shaft of the propeller to ensure the drone's flight stability in the thin atmosphere of Mars.

[0169] The visual navigation unit includes a high-definition camera and a visual processor embedded in an airborne terminal, and the high-definition camera communicates with the visual processor.

[0170] In this embodiment, the drone body is made of lightweight and high-strength materials, such as carbon fiber and polymer composite materials, to ensure its durability and reliability in the Martian environment. A closed structure is used or protective covers are added to key parts to reduce the impact of wind and sand on internal components.

[0171] Therefore, the present invention adopts the above-mentioned Mars seismic exploration method and system based on the deployment and recovery of seismometers by drones, and realizes the efficient deployment and recovery of seismometers through autonomous flight, navigation and operation of drones, ensuring stable operation in the extreme environment of Mars and obtaining high-quality Mars seismic data.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A Mars seismic exploration method based on the deployment and recovery of seismometers by drones, characterized in that: The following steps are involved: S1. Generate a flight path with deployment points using a ground control terminal deployed on Earth, and transmit the flight path to the drone cluster on Mars; Step S1 specifically includes the following steps: S11. Obtaining topographic data of the area to be explored on Mars from remote sensing satellites in the orbit of Mars; S12, performing data cleaning, geometric correction and image matching processing on the acquired terrain data in sequence; S13, performing stereo matching, parallax calculation and elevation calculation on the terrain data processed in step S12 in sequence, so as to generate a preliminary DEM model; S14, fusing the historical seismic data of the area to be explored with the preliminary DEM model to obtain a final DEM model; S15. Determine the spacing of the deployment points in the final DEM model based on the exploration purpose and geological characteristics; S16, based on the location of the deployment point, consider the flight time, energy consumption and path safety through Algorithm, Dijkstra algorithm or dynamic programming algorithm to generate flight paths; Among them, using The algorithm generates a path, and The algorithm calculation formula is as follows: ； In the formula, Indicates the basis for selecting the path; Indicates the actual cost from the starting point to the current node; Represents the heuristic estimated cost from the current node to the target node; The Dijkstra algorithm is used to calculate the global shortest path in the static planning stage to obtain the optimal path, and the Dijkstra algorithm formula is as follows: ； In the formula, From the starting point to the node The shortest distance; Representation and Node The set of directly adjacent nodes; From the starting point to the node The shortest distance currently known; Represents a slave node To Node The path cost of Under the premise of considering the influencing factors, the dynamic programming algorithm is used to optimize the multi-stage decision-making problem, and the dynamic programming algorithm formula is as follows: ； In the formula, From the starting point to the stage The minimum cost; From the starting point to the stage Costs; Indicates that from the stage To stage Costs; S17, simulation verification: Use simulation software to simulate and verify the flight path to determine the effectiveness of the flight path; S2. Under the coordination of the distributed algorithm, the flight path is corrected in real time using the navigation module so that the drone carrying the seismometer reaches the deployment point on the flight path; S3, the drone body descends until the seismometer is inserted into the deployment point, completing the deployment of the seismometer; S4, using a seismograph to collect Mars waveform data, and transmitting the waveform data back to a ground control terminal; S5. Mission accomplished, recover the seismograph at the deployment point.

2. The Mars seismic exploration method based on deploying and recovering seismometers by drone according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21, initial task allocation: based on the current position, energy status and task priority of the UAV body, the predefined task pool or central control node obtains the initial task; S22, Task exchange and reallocation: During the task execution process, the UAV can exchange tasks or request assistance with other adjacent UAVs according to their working status, thereby dynamically adjusting task allocation; S23, path planning: Local path planning: Each drone uses the navigation module to perform real-time path planning based on its current target mission and location, and builds a point cloud model based on the planning results to identify and mark obstacles in the environment. It then uses a distributed path planning algorithm to search for the shortest path while taking obstacle avoidance into consideration. Global path coordination: information exchange between drones is realized through the communication network, path information is shared, and whether path conflicts occur is determined. If path conflicts occur, path planning is performed again; S24, position adjustment: Autonomous position correction: Use the navigation module to correct the flight attitude and path of each drone so that each drone is located on the preset flight path; Group position adjustment: When a drone in the drone cluster is detected to have a position deviation, adjust its flight position, or malfunction, the other drones will be notified through the communication network to make overall position adjustments and generate a new flight path for each drone. During this process, the navigation module will be used to monitor the adjustment process in real time and feedback the current status through the communication network.

3. The Mars seismic exploration method based on deploying and recovering seismometers by drone according to claim 2, characterized in that: Step S23 specifically includes the following steps: S231, Image acquisition and preprocessing: Collect images of the Martian surface, and perform image correction and image enhancement on the images; S232, Image Recognition: S2321, extracting feature points: extracting fixed points in the image as key points, and generating a feature descriptor for each key point, which is used to describe the local feature vector of the key point; S2322, image matching: matching the feature descriptor of the current image with the known feature descriptors in the database to obtain matching points; S233, pose estimation: remove wrong matches from the matching points, use the remaining multiple matching points to calculate the position information of the drone body relative to the known landmarks, and use the inertial sensor or starlight sensor to estimate the relative pose of the drone body; S234, Fusion: During the day, the visual navigation results are fused with the UAV attitude data collected by the inertial navigation unit through the fusion algorithm to obtain real-time path planning; At night or when the light intensity is lower than the set value, the star navigation unit is used for navigation to capture the star image in the Martian night sky, the star map matching algorithm is used to calculate the attitude data of the drone body, and the fusion algorithm is used to combine the attitude data of the drone body and the visual navigation results to obtain real-time path planning; S235. Use the posture data determined by the starlight navigation unit to update the posture data collected by the inertial navigation unit.

4. The Mars seismic exploration method based on deploying and recovering seismometers by drone according to claim 3, characterized in that: In step S2321, key points are extracted using SIFT, SURF, or ORB algorithms, and the feature descriptor is a SIFT descriptor, a SURF descriptor, or a BRIEF descriptor; In step S2322, the closest matching point is obtained by calculating the Euclidean distance or Hamming distance between the feature points using the BFMatcher algorithm or the FLANN algorithm; In step S233, the RANSAC algorithm is used to eliminate false matches, and the position information of the drone body relative to the known landmark is calculated by triangulation or PnP algorithm; In step S234, a Kalman filter is used to perform data fusion.

5. The Mars seismic exploration method based on deploying and recovering seismometers by unmanned aerial vehicles according to claim 4, characterized in that: Step S3 specifically includes the following steps: S31. Positioning the predetermined deployment point: S311, using the visual navigation unit to collect images of the points to be deployed, and extracting feature points in the images, and matching them with pre-stored reference images, so as to identify the current position of the drone body; S312, using an inertial navigation unit to measure the acceleration and rotation rate of the drone body, and calculating the speed, position and attitude change of the drone body by integration; The speed calculation formula is as follows: ； In the formula, Indicates the drone body at time The velocity vector of represents the initial velocity vector, Indicates the current time; Indicates the initial time; Indicates the drone body at time The linear acceleration vector of The position calculation formula is as follows: ； In the formula, Indicates the drone itself at time The position vector of represents the initial position vector; Indicates the drone itself at time The velocity vector of The posture change calculation formula is as follows: ； In the formula, Indicates the drone itself at time The attitude angle; represents the initial attitude angle; Indicates the drone itself at time Angular velocity vector; S313, fusing the position information obtained by the visual navigation unit and the inertial navigation unit to correct the positioning error; S32. The drone body descends to the set height, driving the seismometer to be inserted into the deployment point.

6. The Mars seismic exploration method based on deploying and recovering seismometers by unmanned aerial vehicles according to claim 5, characterized in that: In step S4, the drone body enters sleep mode, and the seismometer starts collecting seismic wave data and transmits it to the ground control terminal in real time. The ground control terminal stores the collected seismic wave data for a second time and transmits it to the orbiting satellite, which then transmits it to the receiving station on Earth.

7. The Mars seismic exploration method based on deploying and recovering seismometers by drone according to claim 6, characterized in that: Step S5 specifically includes the following steps: S51, generating a recovery path: according to the current geographical location of the drone cluster, confirm the current location of each drone body, obtain the current mission status information of the drone body, and confirm the location of the base station to which the drone is to return. In the case of dynamic planning taking into account obstacles and the remaining flight distance, use the Dijkstra algorithm to generate a recovery path; S52, the drone body carrying the seismometer performs a return flight according to the recovery path, and during the return flight, when the drone body encounters an obstacle or a temporary flight restriction, sends position information and deviation reports to other drone bodies, and other drone bodies integrate the received position information and deviation reports, update the overall spatial layout of the drone cluster and the position of each drone body, and generate a new target position or recovery path for each drone body according to the adjustment strategy; At the same time, during the flight, the navigation module is used to track the recovery path in real time and correct the flight trajectory so that the drone body flies according to the predetermined recovery path; S53. Land after reaching the base station.

8. A system for executing the Mars seismic exploration method based on deploying and recovering seismometers by drones as described in any one of claims 1 to 7, characterized in that: It includes a receiving station arranged on the surface of the earth, a ground control terminal arranged on the surface of Mars, and a drone cluster composed of multiple drone bodies. The bottom of each drone body is equipped with a seismometer. The seismometer and the drone body communicate with the receiving station via a data acquisition module, a ground control terminal and an orbiting satellite in turn. The drone body is equipped with an airborne terminal and a sensor system that communicates with the airborne terminal. The sensor system includes a navigation module and an environmental perception module. The navigation module includes a visual navigation unit, an inertial navigation unit and a starlight navigation unit. The visual navigation unit is used to collect images of the surface of Mars and transmit them to the airborne terminal to generate a real-time planned path. The inertial navigation unit and the starlight navigation unit are used to realize starlight inertial combined navigation to correct the planned path. The environmental perception module includes a magnetometer and a barometer.

9. The system for Mars seismic exploration method based on the deployment and recovery of seismometers by drones as claimed in claim 8, characterized in that: The drone cluster communicates with each other through MANET, and the drone cluster adopts a distributed computing architecture for information exchange between multiple drone entities; A seismograph is arranged at the bottom of the drone body, and a contact sensor and an acceleration sensor are embedded at the bottom of the seismograph. Both the contact sensor and the acceleration sensor communicate with the airborne terminal to determine whether the seismograph is inserted into the deployment point; A solar panel is mounted on the top of the drone body, the solar panel is electrically connected to a battery pack placed inside the drone body, the battery pack is electrically connected to a driving mechanism of the drone body, an airborne terminal and a seismograph, the driving mechanism includes a brushless motor placed inside the drone body and a propeller rotatably arranged on the drone body, and the output end of the brushless motor is connected to a rotating shaft of the propeller; The visual navigation unit includes a high-definition camera and a visual processor embedded in an airborne terminal, and the high-definition camera communicates with the visual processor.

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