Rescue robot target tracking method and system based on satellite navigation

Abstract

The invention relates to the technical field of robot target tracking, and discloses a rescue robot target tracking method and system based on satellite navigation, and the method comprises the steps: receiving the last satellite positioning data of a target, recording a receiving timestamp, and calculating the historical data reliability of the last satellite positioning data; when an intermittent auxiliary signal sent by a target is received, calculating the reliability of the auxiliary signal according to the intermittent auxiliary signal; adjusting the contribution ratio of the latest satellite positioning data of the target and the intermittent auxiliary signal in target position estimation, and calculating to obtain a real-time target position estimation result in combination with navigation information of the robot; according to the real-time target position estimation result, the real-time energy reserve of the robot and task urgency, path planning and energy consumption are optimized. According to the invention, the target tracking capability and rescue efficiency of the rescue robot in a complex disaster environment are significantly improved.

Description

Technical Field

[0001] This application relates to the field of robot target tracking technology, and more specifically, to a satellite navigation-based target tracking method and system for rescue robots. Background Technology

[0002] In vast disaster areas, such as large-scale flooded regions or remote wildfire sites, human rescue teams often face difficulties in reaching them quickly or encounter extremely high risks. In this context, autonomous rescue robots are seen as a crucial pioneering force. These robots are typically equipped with advanced satellite navigation systems, enabling them to accurately determine their real-time location. Meanwhile, trapped individuals often carry personal location beacons or emergency communication devices, which periodically send data containing their satellite positioning information to the command center. Upon receiving this data, the command center forwards the target coordinates to the rescue robots on-site. Based on their precise satellite positioning and the received target coordinates, the robots plan a path to the target location, aiming to achieve rapid search, location, and initial assistance for the trapped individuals. This constitutes the ideal target-tracking workflow for rescue robots.

[0003] However, actual disaster environments are far more complex than imagined, and their variability poses a severe challenge to the aforementioned idealized working model. First, disaster scenarios are not static. For example, flood-affected areas have complex ocean currents, eddies, and constantly changing flow velocities; in wildfires, the fire line spreads rapidly with wind direction and terrain. This variability directly means that the location of trapped individuals is not fixed. The location information transmitted by their personal location beacons is often outdated historical data by the time the robot receives it and begins to act. The robot plans a path based on coordinates sent minutes or even earlier; by the time it arrives at that point, the survivor may have drifted to a considerably distant location, creating a situation of "chasing shadows."

[0004] Secondly, driven by their survival instincts, trapped individuals are not entirely passively drifting with the current. They may actively influence their drift path by paddling, grabbing onto shoreline vegetation, or altering the attitude of floating objects. This proactive behavior makes the target's trajectory more complex and unpredictable. Furthermore, when they sense a rescue robot approaching, they may activate short-range auxiliary distress signals, such as handheld radio transmitters or acoustic beacons. While these auxiliary signals provide closer-range guidance, their signal strength is typically weak, their transmission distance is limited, and they may be directional, unable to provide precise coordinates like satellite positioning.

[0005] In this situation, the robot faces a decision-making dilemma: it possesses precise satellite positioning capabilities, but the coordinates of its primary target are outdated and inaccurate. Simultaneously, it receives short-range, discontinuous, and low-precision auxiliary signals. The robot needs to determine the authenticity of these auxiliary signals and decide whether to continue along the predicted path based on outdated satellite data, or adjust its strategy to prioritize responding to these nearby but ambiguous signals. Blindly following every short-range signal could cause the robot to deviate from its main course, wasting valuable energy and time. Completely ignoring them could mean missing the opportunity to establish effective contact with survivors.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application provides a satellite navigation-based target tracking method and system for rescue robots, which solves the problems of outdated and inaccurate target location information and insufficient utilization of auxiliary signals faced by existing target tracking technologies for rescue robots in complex and ever-changing disaster environments.

[0008] In a first aspect, this application provides a satellite navigation-based target tracking method for rescue robots, comprising: Receive the target’s most recent satellite positioning data and record the receiving timestamp. Based on the time difference between the current time and the receiving timestamp, the real-time acquired surrounding environmental force field information, and the preset target active behavior pattern, calculate the historical data reliability of the most recent satellite positioning data. When an intermittent auxiliary signal is received from the target, the reliability of the auxiliary signal is calculated based on the intermittent auxiliary signal. Based on the reliability of the historical data and the reliability of the auxiliary signal, the contribution ratio of the most recent satellite positioning data of the target and the intermittent auxiliary signal in the target position estimation is adjusted, and combined with the robot's own navigation information, the real-time target position estimation result is calculated. Based on the real-time target location estimation, the robot's real-time energy reserves, and the urgency of the task, the path planning and energy consumption are optimized to achieve target tracking and rescue.

[0009] This technical solution effectively addresses the problem of outdated and inaccurate target location information in disaster environments. By comprehensively evaluating the reliability of historical satellite data and intermittent auxiliary signals, its weight in target location estimation is dynamically adjusted. Combined with the robot's own navigation information, real-time and accurate target location estimation is achieved, while optimizing path planning and energy consumption to ensure the effective execution of rescue missions.

[0010] Furthermore, the steps for calculating the historical data reliability of the most recent satellite positioning data include: Based on the time difference between the current moment and the received timestamp, the real-time acquired surrounding environmental force field information, and the preset target active behavior pattern, a short-term drift trend prediction of the target is generated. Based on the short-term drift trend prediction and the time difference, the target's current predicted position is obtained by forward extrapolating from the most recent satellite positioning data. The reliability of the historical data is calculated by comparing the target's current predicted location with the most recent satellite positioning data.

[0011] This technical solution enables a more accurate assessment of the timeliness and accuracy of historical satellite positioning data. By considering environmental force fields and the active behavior of the target, it allows for forward extrapolation of the target's location, thus providing a more reliable historical data reference for subsequent target location estimation.

[0012] Furthermore, when receiving intermittent auxiliary signals from the target, the step of calculating the reliability of the auxiliary signal based on these intermittent auxiliary signals includes: The signal strength, duration, directionality, and frequency characteristics of the intermittent auxiliary signal are obtained, and the reliability of the auxiliary signal is calculated based on the signal strength, duration, directionality, and frequency characteristics.

[0013] This technical solution enables a comprehensive evaluation of the quality and availability of intermittent auxiliary signals. By taking into account various characteristics of the signals, it provides a quantitative basis for the contribution of auxiliary signals in target location estimation.

[0014] Based on the above, this application further proposes that the method also includes: A three-dimensional topology map of the robot's surrounding environment is constructed in real time. When the intermittent auxiliary signal emitted by the target is received, the direction information of the intermittent auxiliary signal is extracted. The propagation prediction is performed by combining the robot's current position and the three-dimensional topology map. The propagation prediction is to predict the propagation path of the intermittent auxiliary signal in the environment by using a preset propagation model. By comparing the direction information with the predicted propagation path, the direction of the intermittent auxiliary signal is reverse-calibrated and a confidence level is assigned. The reliability of the auxiliary signal is adjusted based on this confidence level.

[0015] Preferably, the steps for optimizing path planning and energy consumption based on the real-time target location estimation result, the robot's real-time energy reserves, and the urgency of the task, to achieve target tracking and rescue, include: Based on the robot's real-time energy reserves, an energy risk coefficient is calculated, which increases as the robot's real-time energy reserves decrease. Based on the energy risk coefficient, the real-time target location estimation result, and the urgency of the mission, the weights of energy consumption, time cost, and interception success probability are dynamically adjusted, and the more energy-efficient path is prioritized to achieve target tracking and rescue.

[0016] In some preferred embodiments, the method further includes: When the energy risk coefficient reaches a preset risk threshold, the low power mode is activated. In this low-power mode, the weight of this energy consumption is set to the highest priority, and a path to the nearest supply point is planned.

[0017] More specifically, in some implementation schemes, the steps for planning a route to the nearest supply point include: Obtain the location information of multiple preset supply points in the 3D topology map of the robot's surrounding environment, and calculate the estimated energy consumption from the robot's current position to each preset supply point; Based on the projected energy consumption, the supply point with the lowest projected energy consumption is selected as the target supply point, and a path to the target supply point is generated.

[0018] Based on the above, this application further proposes a step of adjusting the contribution ratio of the most recent satellite positioning data of the target and the intermittent auxiliary signal in the target position estimation according to the reliability of the historical data and the reliability of the auxiliary signal, including: When the reliability of the auxiliary signal is higher than the first preset threshold and the reliability of the historical data is lower than the second preset threshold, the intermittent auxiliary signal is preferentially adopted as the main basis for target position estimation. When the reliability of the historical data is higher than the second preset threshold and the reliability of the auxiliary signal is lower than the first preset threshold, the most recent satellite positioning data shall be adopted as the main basis for estimating the target position. When the reliability of the historical data and the reliability of the auxiliary signal do not meet the above conditions, the reliability of the historical data and the reliability of the auxiliary signal are compared, and a higher contribution ratio is allocated to the information source with higher reliability.

[0019] In addition, the steps for calculating the real-time target position estimation result by combining the robot's own navigation information include: The robot obtains real-time navigation and positioning information, compensates for environmental interference, and obtains its own compensated trajectory. Based on the compensated trajectory of the target, the reliability of the historical data, and the reliability of the auxiliary signal, the current position and future movement trend of the target are comprehensively calculated, and the real-time target position estimation result including the center position and error range is output.

[0020] Secondly, this application also discloses a satellite navigation-based rescue robot target tracking system for performing the aforementioned satellite navigation-based rescue robot target tracking method. The system includes: The historical data reliability assessment module is used to receive the target's most recent satellite positioning data and record the receiving timestamp. Based on the time difference between the current time and the receiving timestamp, the real-time acquired surrounding environmental force field information, and the preset target active behavior pattern, it calculates the historical data reliability of the most recent satellite positioning data. The auxiliary signal reliability assessment module is used to calculate the reliability of the auxiliary signal based on the intermittent auxiliary signal when it receives the intermittent auxiliary signal emitted by the target. The target position estimation module is used to adjust the contribution ratio of the target's most recent satellite positioning data and the intermittent auxiliary signal in the target position estimation based on the reliability of the historical data and the reliability of the auxiliary signal, and to calculate the real-time target position estimation result by combining the robot's own navigation information. The path planning and energy optimization module is used to optimize path planning and energy consumption based on the real-time target location estimation results, the robot's real-time energy reserves, and the urgency of the task, so as to achieve target tracking and rescue.

[0021] In summary, this application provides a satellite navigation-based target tracking method and system for rescue robots. The method receives the target's most recent satellite positioning data and records the reception timestamp. Based on the time difference between the current moment and the reception timestamp, real-time acquired surrounding environmental force field information, and a preset target active behavior pattern, it calculates the historical data reliability of the most recent satellite positioning data, effectively solving the problem of outdated target location information in disaster environments. Simultaneously, when intermittent auxiliary signals emitted by the target are received, the reliability of the auxiliary signals is calculated, fully utilizing near-range auxiliary information. Based on this, the contribution ratio of the target's most recent satellite positioning data and intermittent auxiliary signals in target location estimation is dynamically adjusted according to the historical data reliability and auxiliary signal reliability. Combined with the robot's own navigation information, a real-time target location estimation result is calculated, overcoming the limitations of a single information source and improving the accuracy and real-time performance of target location estimation. Finally, based on the real-time target location estimation result, the robot's real-time energy reserves, and the urgency of the task, path planning and energy consumption are optimized to achieve target tracking and rescue, effectively solving the problems of energy waste and low rescue efficiency caused by robots blindly chasing outdated coordinates in existing technologies. Through the above technical solution, this application can significantly improve the target tracking ability and rescue efficiency of rescue robots in complex disaster environments, avoid the dilemma of "chasing shadows", and ensure the effective execution of rescue missions. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a satellite navigation-based target tracking method for rescue robots, as provided in an embodiment of this application.

[0023] Figure 2 This is a schematic diagram of a satellite navigation-based target tracking system for a rescue robot, provided as an embodiment of this application.

[0024] Labeling Explanation: 210, Historical Data Reliability Assessment Module; 220, Auxiliary Signal Reliability Assessment Module; 230, Target Location Estimation Module; 240, Route Planning and Energy Optimization Module. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] In vast disaster areas, such as large-scale flooded regions or remote wildfire sites, human rescue teams often face difficulties in reaching them quickly or encounter extremely high risks. In this context, autonomous rescue robots are seen as a crucial pioneering force. These robots are typically equipped with advanced satellite navigation systems, enabling them to accurately determine their real-time location. Meanwhile, trapped individuals often carry personal location beacons or emergency communication devices, which periodically send data containing their satellite positioning information to the command center. Upon receiving this data, the command center forwards the target coordinates to the rescue robots on-site. Based on their precise satellite positioning and the received target coordinates, the robots plan a path to the target location, aiming to achieve rapid search, location, and initial assistance for the trapped individuals. This constitutes the ideal target-tracking workflow for rescue robots.

[0028] However, actual disaster environments are far more complex than imagined, and their variability poses a severe challenge to the aforementioned idealized working model. First, disaster scenarios are not static. For example, flood-affected areas have complex ocean currents, eddies, and constantly changing flow velocities; in wildfires, the fire line spreads rapidly with wind direction and terrain. This variability directly means that the location of trapped individuals is not fixed. The location information transmitted by their personal location beacons is often outdated historical data by the time the robot receives it and begins to act. The robot plans a path based on coordinates sent minutes or even earlier; by the time it arrives at that point, the survivor may have drifted to a considerably distant location, creating a situation of "chasing shadows."

[0029] Secondly, driven by their survival instincts, trapped individuals are not entirely passively drifting with the current. They may actively influence their drift path by paddling, grabbing onto shoreline vegetation, or altering the attitude of floating objects. This proactive behavior makes the target's trajectory more complex and unpredictable. Furthermore, when they sense a rescue robot approaching, they may activate short-range auxiliary distress signals, such as handheld radio transmitters or acoustic beacons. While these auxiliary signals provide closer-range guidance, their signal strength is typically weak, their transmission distance is limited, and they may be directional, unable to provide precise coordinates like satellite positioning.

[0030] In this situation, the robot faces a decision-making dilemma: it possesses precise satellite positioning capabilities, but the coordinates of its primary target are outdated and inaccurate. Simultaneously, it receives short-range, discontinuous, and low-precision auxiliary signals. The robot needs to determine the authenticity of these auxiliary signals and decide whether to continue along the predicted path based on outdated satellite data, or adjust its strategy to prioritize responding to these nearby but ambiguous signals. Blindly following every short-range signal could cause the robot to deviate from its main course, wasting valuable energy and time. Completely ignoring them could mean missing the opportunity to establish effective contact with survivors.

[0031] In this regard, firstly, referring to Figure 1 This application proposes a target tracking method for rescue robots based on satellite navigation, including: S1. Receive the target's most recent satellite positioning data and record the receiving timestamp. Based on the time difference between the current time and the receiving timestamp, the real-time acquired surrounding environmental force field information, and the preset target active behavior pattern, calculate the historical data reliability of the most recent satellite positioning data. S2. When an intermittent auxiliary signal is received from the target, the reliability of the auxiliary signal is calculated based on the intermittent auxiliary signal. S3. Based on the reliability of historical data and the reliability of auxiliary signals, adjust the contribution ratio of the target's most recent satellite positioning data and intermittent auxiliary signals in the target position estimation, and combine it with the robot's own navigation information to calculate the real-time target position estimation result. S4. Based on the real-time target location estimation results, the robot's real-time energy reserves, and the urgency of the task, optimize path planning and energy consumption to achieve target tracking and rescue.

[0032] This application overcomes the problems of outdated target location information and uncertain auxiliary signals in traditional methods by comprehensively evaluating the reliability of historical satellite positioning data and the effectiveness of intermittent auxiliary signals, and dynamically adjusting their weights in target location estimation. Furthermore, this application optimizes path planning and energy consumption by incorporating the robot's own navigation information, energy reserves, and mission urgency, ensuring the efficiency and success rate of the rescue mission.

[0033] The method proposed in this application is mainly applied to target tracking and rescue scenarios using rescue robots. Satellite positioning data refers to target location information acquired through Global Navigation Satellite Systems (GNSS), such as GPS and BeiDou, typically including coordinate data such as longitude, latitude, and altitude. The reception timestamp records the specific time the satellite positioning data was received. Surrounding environmental force field information refers to external physical forces affecting target movement, such as water flow, wind, and terrain slope. This force field information can be acquired in real-time by sensors onboard the robot or estimated using a pre-set model. Target active behavior patterns refer to the autonomous movement or self-rescue behavior of the target in specific environments, such as paddling in floods or searching for shelter in a fire. These patterns can be pre-set empirical models or predictive models obtained through machine learning. Intermittent auxiliary signals refer to discontinuous distress signals emitted by the target under specific circumstances, such as short-range radio signals, acoustic signals, and visual signals. These signals typically have certain directional and intensity characteristics. Historical data reliability and auxiliary signal reliability are indicators that measure the credibility of different information sources in target location estimation. The robot's own navigation information includes its precise current position, velocity, and attitude, typically provided by its internal inertial navigation system and visual odometry. Real-time target position estimation is the best estimate of the target's current position obtained by integrating various information sources, usually including a center position and a corresponding error range. The robot's real-time energy reserves refer to its currently available electrical or fuel power, directly affecting its endurance. Mission urgency refers to the urgency of the rescue mission, such as the target's life-threatening condition or the rate of environmental degradation, which influences path planning and energy consumption priorities.

[0034] In the embodiments of this application, firstly, the rescue robot receives the target's most recent satellite positioning data and records the reception timestamp. For example, when the trapped person's personal positioning beacon transmits satellite positioning data every 5 minutes, the robot receives this data and records the specific time of reception. Subsequently, the robot calculates the historical data reliability of the most recent satellite positioning data based on the time difference between the current moment and the reception timestamp, real-time acquired surrounding environmental force field information, and preset target active behavior patterns. Specifically, the larger the time difference, the lower the historical data reliability generally is. Surrounding environmental force field information, such as water flow speed or wind speed, can be used to predict possible drift of the target. Preset target active behavior patterns, such as the target attempting to move towards the shore in the water, can also be used to correct drift predictions. By combining these factors, the validity of historical satellite positioning data can be evaluated. For example, if the target is in a strong current area and satellite positioning data has been received for 10 minutes, its historical data reliability will be significantly reduced.

[0035] When a robot receives intermittent auxiliary signals from a target, it calculates the reliability of the auxiliary signal based on the intermittent nature of the signal. For example, a trapped person might send a short distress signal via a handheld radio transmitter. Upon receiving this signal, the robot analyzes its signal strength, duration, directionality, and frequency characteristics. The stronger the signal, the longer its duration, the clearer its directionality, and the more stable its frequency characteristics, the higher the reliability of the auxiliary signal. For instance, an auxiliary signal with a short duration, weak signal, and ambiguous directionality will have lower reliability than an auxiliary signal with a long duration, strong signal, and clear directionality.

[0036] After acquiring the reliability of historical data and auxiliary signals, the robot adjusts the contribution ratio of the most recent satellite positioning data and intermittent auxiliary signals in target location estimation based on these two reliability levels. For example, if the historical data reliability is low (indicating that the satellite positioning data may be outdated or inaccurate) while the auxiliary signal reliability is high (indicating that the auxiliary signal provides more reliable near-range information), the weight of the auxiliary signal in target location estimation will be increased. Conversely, if the historical data reliability is high and the auxiliary signal reliability is low, the weight of the satellite positioning data will be increased. When both reliability levels are at a moderate level, the contribution ratio can be allocated according to their relative magnitudes, with the more reliable information source receiving a higher contribution ratio.

[0037] After adjusting the contribution ratio, the robot combines its own navigation information to calculate a real-time target position estimate. The robot's own navigation information, such as its precise position and trajectory obtained through an inertial measurement unit (IMU) and visual odometry, can be used to correct and optimize the target position estimate. For example, the robot can use its own trajectory to predict the target's relative position change, thereby improving the accuracy of the target position estimate. Ultimately, the output real-time target position estimate will include a center position and a corresponding error range, providing a basis for subsequent path planning.

[0038] Finally, based on the real-time target location estimation, the robot's real-time energy reserves, and the urgency of the task, the robot optimizes its path planning and energy consumption to achieve target tracking and rescue. For example, if the real-time target location estimation indicates a long distance, sufficient robot energy reserves, and moderate task urgency, the robot may choose a relatively smooth but time-consuming path to conserve energy. Conversely, if the target is close but the task is extremely urgent (e.g., the target is in critical condition), even with higher energy consumption, the robot may choose the shortest path to reach the target as quickly as possible.

[0039] Specifically, in disaster scenarios, traditional rescue robots often rely on periodically transmitted satellite positioning data from trapped individuals for target tracking. However, due to the dynamic nature of disaster environments (such as floods and wind) and the active behavior of trapped individuals, this satellite positioning data is often outdated by the time the robot receives it and begins its operation, resulting in the robot "chasing shadows." Furthermore, intermittent auxiliary signals emitted by trapped individuals, while providing short-range guidance, are weak, have limited transmission distance, and may be directional, failing to provide precise coordinates and making it difficult for the robot to determine their authenticity and validity. Without addressing these issues, rescue robots will struggle to accurately and efficiently locate and track targets, thus delaying rescue efforts and even endangering the lives of trapped individuals.

[0040] This application significantly improves the accuracy and real-time performance of target position estimation by introducing the concepts of historical data reliability and auxiliary signal reliability, and dynamically adjusting the contribution ratio of different information sources in target position estimation based on these reliability levels. For example, when the reliability of satellite positioning data decreases due to excessive time difference, this application can prioritize the use of intermittent auxiliary signals with higher reliability, thereby avoiding the problem of blindly pursuing outdated coordinates in traditional methods. Simultaneously, this application also incorporates the robot's own navigation information, further improving the accuracy of target position estimation.

[0041] Furthermore, this application also considers the robot's real-time energy reserves and mission urgency, optimizing path planning and energy consumption. This allows the rescue robot to maximize its operating time while ensuring rescue efficiency, avoiding mission interruptions due to energy depletion. For example, when energy reserves are low but mission urgency is not high, the robot can prioritize more energy-efficient paths to ensure continuous tracking of the target until the rescue is successful.

[0042] In summary, the satellite navigation-based target tracking method for rescue robots proposed in this application overcomes the problems of outdated target location information, uncertain auxiliary signals, and improper energy management in existing technologies through multi-source information fusion, dynamic weight adjustment, intelligent path planning, and energy optimization. It provides strong technical support for the efficient and accurate rescue of rescue robots in complex disaster environments and has significant progressive and practical value.

[0043] In some embodiments of this application, in order to more accurately assess the validity of the target's most recent satellite positioning data, it is necessary to calculate the reliability of its historical data.

[0044] Specifically, the steps for calculating the historical data reliability of the most recent satellite positioning data include: Based on the time difference between the current moment and the received timestamp, real-time acquired surrounding environmental force field information, and preset target active behavior patterns, a short-term drift trend prediction of the target is generated. The current predicted position of the target is obtained by forward extrapolating from the most recent satellite positioning data based on short-term drift trend prediction and time difference. The reliability of historical data is calculated by comparing the target's current predicted location with the most recent satellite positioning data.

[0045] The short-term drift trend prediction of the generated target refers to predicting the possible direction and speed of the target's movement in a short period of time, taking into account the potential movement of the target after receiving satellite positioning data. This is achieved by analyzing the time difference between the current moment and the receiving timestamp, the surrounding environmental force field information acquired by the robot in real time (such as wind, water flow, terrain slope, etc.), and the preset active behavior pattern of the target (such as the target being stationary, moving at a constant speed, avoiding obstacles, etc.). For example, if the target is trapped in a river, the water flow speed and direction in the environmental force field information will be important predictive factors; if the target is an injured person, its active behavior pattern may be slow movement or waiting in place.

[0046] Furthermore, based on short-term drift trend prediction and time difference, forward extrapolation is performed on the most recent satellite positioning data to obtain the target's current predicted position. Forward extrapolation can be understood as using the predicted short-term drift trend to extrapolate the target's most recent satellite positioning data from its reception timestamp back to the present moment, thereby estimating the target's possible position at the current time. Its purpose is to compensate for the time lag in satellite positioning data and provide a reference point that is closer to the target's actual current position.

[0047] Therefore, the reliability of historical data is calculated by comparing the target's current predicted location with the most recent satellite positioning data. Specifically, reliability can be assessed by calculating the spatial distance or positional deviation between the target's current predicted location and the most recent satellite positioning data. For example, if the distance between the two is small, it indicates that the most recent satellite positioning data still has high reference value over time, and its historical data reliability is high; conversely, if the distance is large, it indicates that the data may have deviated significantly from the target's actual location, and its historical data reliability is low.

[0048] In some embodiments of this application described above, when an intermittent auxiliary signal is received from the target, it is necessary to calculate the reliability of the auxiliary signal based on the intermittent auxiliary signal. Specifically, the step of calculating the reliability of the auxiliary signal includes: The signal strength, duration, directionality, and frequency characteristics of the intermittent auxiliary signal are obtained, and the reliability of the auxiliary signal is calculated based on the signal strength, duration, directionality, and frequency characteristics.

[0049] Specifically, signal strength refers to the intensity of the intermittent auxiliary signal at the robot's receiver, reflecting the degree of signal attenuation and environmental interference during propagation. Higher signal strength generally indicates a closer signal source or better propagation conditions, thus increasing the reliability of the auxiliary signal. Duration refers to the length of time from the start to the end of reception of the intermittent auxiliary signal. A longer duration usually indicates better signal source stability or more time for the robot to process and analyze the signal, thereby improving the reliability of the auxiliary signal. Directional information refers to the direction of arrival of the intermittent auxiliary signal, obtained, for example, through an antenna array or directional sensor. Accurate directional information helps narrow the search range for the target location and improves positioning accuracy; therefore, the clearer the directional information, the higher the reliability of the auxiliary signal. Frequency characteristics refer to the frequency or spectral characteristics of the intermittent auxiliary signal, such as the signal's center frequency, bandwidth, and modulation method. By analyzing frequency characteristics, the source of the signal can be identified, different signals can be distinguished, and the propagation characteristics of the signal in a specific environment can be evaluated, thus affecting the reliability of the auxiliary signal.

[0050] In some embodiments described above, when an intermittent auxiliary signal is received from a target, the reliability of the auxiliary signal is calculated based on its signal strength, duration, directionality, and frequency characteristics. However, in actual rescue scenarios, especially in complex and variable environments, the directionality of the intermittent auxiliary signal is easily interfered with by obstacles, reflections, multipath effects, and other factors, leading to reduced accuracy. If this problem is not addressed, the calculation of the auxiliary signal reliability will be biased, thus affecting the accuracy of target position estimation. Therefore, this application further proposes a method to optimize the calculation of auxiliary signal reliability by calibrating the direction of the intermittent auxiliary signal in conjunction with environmental information, thereby improving the accuracy of the auxiliary signal reliability.

[0051] The above methods also include: A 3D topology map of the robot's surrounding environment is constructed in real time. When an intermittent auxiliary signal is received from the target, the direction information of the intermittent auxiliary signal is extracted. The propagation prediction is performed by combining the robot's current position and the 3D topology map. The propagation prediction is to predict the propagation path of the intermittent auxiliary signal in the environment by using a preset propagation model. By comparing the direction information with the predicted propagation path, the direction of the intermittent auxiliary signal is reverse-calibrated and a confidence level is assigned. The reliability of the auxiliary signal is adjusted based on the confidence level.

[0052] Specifically, real-time construction of a 3D topological map of the robot's surrounding environment refers to continuously scanning the surrounding environment using sensors onboard the robot (such as LiDAR, depth cameras, sonar, etc.) and combining this with Simultaneous Localization and Mapping (SLAM) technology to generate a high-precision 3D spatial model. This 3D topological map includes geometric information such as obstacles, reflective surfaces, and passages in the environment. When receiving intermittent auxiliary signals from a target, the robot extracts the original direction information of the signal, for example, through angle of arrival (AoA) estimation techniques.

[0053] Furthermore, the extracted directional information, combined with the robot's current precise position and a 3D topology map, is input into a pre-defined propagation model for propagation prediction. The propagation model can be a physical model based on ray tracing or wave equations, used to simulate the propagation paths that intermittent auxiliary signals might follow in the environment represented by the 3D topology map, including direct, reflected, and diffracted paths. By comparing the original directional information with the predicted propagation path, potential deviations in the original directional information can be identified. For example, if the original direction points to an area obscured by tall buildings, while the propagation prediction shows the signal is more likely to arrive via a reflected path, the original direction can be reverse-calibrated to better conform to actual propagation physics. During calibration, a confidence level is assigned based on the reliability of the calibration results; for example, a higher confidence level is indicated when the calibrated direction is highly consistent with multiple predicted paths. Finally, the reliability of the auxiliary signal is adjusted based on the confidence level. For example, if the calibrated direction has a high confidence level, the weight of the auxiliary signal reliability in the overall reliability calculation can be increased; conversely, if the confidence level is low, its weight may be reduced, or it may indicate significant uncertainty in the directional information.

[0054] In some preferred embodiments, it is assumed that a rescue robot is operating in the ruins of an earthquake-ravaged city, with the target trapped inside a collapsed building. The robot is equipped with lidar and ultrasonic sensors, continuously scanning the surrounding environment to construct a real-time 3D topological map, accurately depicting the distribution of rubble, broken walls, and narrow passages within the ruins. When the target emits an intermittent assist signal, the robot makes an initial estimate of the signal's direction using its directional antenna array. However, because the signal may reflect multiple times before reaching the robot, the initially estimated direction may be significantly inaccurate.

[0055] At this point, the robot will utilize its current location information and the constructed 3D topology map, combined with a ray-tracing-based propagation model, to predict the possible propagation paths of intermittent auxiliary signals in the ruins environment. For example, the model might predict that after the signal is emitted from the target location, it will first hit a sloping wall and then reflect back to the robot's location. By comparing the originally estimated direction with the model-predicted reflection path, the robot finds that the original direction does not match the direct path but closely matches a certain reflection path. Based on this, the robot reverse-calibrates the original direction, adjusting it to a direction that better matches the reflection path, and assigns a high confidence level, such as 0.9, to the calibrated direction based on the degree of matching between the predicted path and the characteristics of the actual received signal. Subsequently, when calculating the reliability of the auxiliary signal, in addition to considering signal strength, duration, and frequency characteristics, the calibrated direction information and its 0.9 confidence level will be taken into account. Because the direction information has been environmentally calibrated and has a high confidence level, the reliability of the auxiliary signal will be correspondingly improved, thus giving it a higher contribution ratio in the target location estimation. This allows the robot to more accurately determine the approximate location of the target, and to effectively track the target even in extremely complex signal propagation environments.

[0056] In some of the embodiments described above in this application, schemes for optimizing path planning and energy consumption based on real-time target location estimation results, robot real-time energy reserves, and task urgency have been proposed. However, in actual rescue missions, the robot's energy reserves are limited and constantly being consumed. If only general optimization is performed, it may not be able to adequately cope with the risk of energy depletion, leading to mission failure or inefficiency. Especially when energy reserves are low, traditional optimization strategies may not be able to adjust priorities in a timely manner, thus failing to effectively ensure the robot's continuous operation and mission completion. To address this, this application further proposes a more refined path planning and energy consumption optimization scheme, aiming to enable the robot to manage its energy more intelligently by introducing an energy risk assessment mechanism, thereby making optimal decisions under different energy conditions.

[0057] In this regard, this application further proposes the following steps for optimizing route planning and energy consumption: Based on the robot's real-time energy reserves, the energy risk coefficient is calculated. The energy risk coefficient increases as the robot's real-time energy reserves decrease. Based on the energy risk coefficient, real-time target location estimation results, and mission urgency, the weights of energy consumption, time cost, and interception success probability are dynamically adjusted, prioritizing more energy-efficient paths to track and rescue the target.

[0058] Specifically, the energy risk coefficient is an indicator used to quantify the impact of a robot's current energy status on task execution risk. This coefficient is calculated based on the robot's real-time energy reserves, and its design principle is to increase as the robot's real-time energy reserves decrease. For example, when the robot's energy reserves are sufficient, the energy risk coefficient is low, indicating that energy has less of a constraint on the task; while when the energy reserves are nearly depleted, the energy risk coefficient will increase significantly to warn of an energy crisis in the system. Its purpose is to provide a key risk assessment basis for subsequent path planning and energy consumption decisions. Dynamically adjusting the weights of energy consumption, time cost, and interception success probability refers to adjusting the importance of different optimization objectives in path planning in real time based on the current energy risk coefficient, real-time target position estimation results, and task urgency. For example, when the energy risk coefficient is high, the weight of energy consumption will be set to the highest priority, prompting the system to prioritize paths that maximize energy savings, even if this may mean increased time costs or a slight decrease in the interception success probability. Conversely, when energy is sufficient and task urgency is extremely high, the weights of time cost or interception success probability may be increased. The aim is to enable robots to flexibly balance various task indicators according to the actual situation, so as to ensure that rescue missions can still be completed efficiently even when energy is limited.

[0059] However, in actual rescue missions, if the robot's energy reserves continue to decrease, even if energy-saving paths are prioritized, there is still a risk of energy depletion, which may lead to mission interruption or failure, affecting rescue efficiency and success rate.

[0060] In this regard, this application further proposes that the method also includes: When the energy risk coefficient reaches the preset risk threshold, the low power consumption mode is activated; In low-power mode, energy consumption is given the highest priority, and a path to the nearest supply point is planned.

[0061] Specifically, the preset risk threshold is a critical value. When the robot's real-time energy reserves fall below this value, the robot is considered to face a serious risk of energy depletion, requiring immediate emergency measures. This threshold can be preset or dynamically adjusted based on factors such as the robot's model, battery capacity, task type, and environmental conditions. Activating low-power mode means the system adjusts the robot's operating parameters, such as reducing sensor sampling frequency, decreasing power supply to unnecessary modules, and optimizing processor operating frequency, to minimize energy consumption. In practical applications, setting energy consumption as the highest priority means that in path planning and behavioral decision-making, energy consumption considerations take precedence over time costs and the probability of successful interception, even if this may mean longer processing time or slight deviation from the target, to ensure the robot can continue operating and reach the resupply point. Therefore, planning the path to the nearest resupply point aims to ensure that the robot can replenish its energy in a timely manner when energy is extremely scarce, avoiding task interruption.

[0062] In practical applications, accurately defining and selecting the nearest resupply point and generating the optimal path to ensure successful resupply for robots under extremely limited energy conditions is a problem that requires further refinement. Defining the nearest point solely based on geographical distance may not adequately consider the impact of terrain, obstacles, and the robot's own motion characteristics on energy consumption, resulting in a planned path that is not truly the most energy-efficient, or even failing to reach the resupply point due to energy depletion.

[0063] In this regard, this application further proposes the following steps for planning the path to the nearest supply point: Obtain the location information of multiple preset supply points in the 3D topology map of the robot's surrounding environment, and calculate the estimated energy consumption from the robot's current position to each preset supply point; Based on the projected energy consumption, select the supply point with the lowest projected energy consumption as the target supply point, and generate a path to the target supply point.

[0064] Specifically, acquiring the location information of multiple pre-set resupply points in a 3D topological map of the robot's surrounding environment refers to using sensors onboard the robot (such as LiDAR, depth cameras, etc.) to construct or update a 3D topological map of its surrounding environment in real time. This 3D topological map not only contains geometric information about the environment but may also include semantic information such as terrain undulations, obstacle distribution, and traversable areas. Pre-set resupply points are locations where the robot can resupply energy, pre-defined during the mission planning phase or based on the characteristics of the actual rescue area; their location information is accurately recorded in the 3D topological map.

[0065] The calculation of the estimated energy consumption for reaching each preset resupply point from the robot's current position can be understood as follows: for each preset resupply point, the system comprehensively considers the robot's current position, the location of the preset resupply point, path information in the 3D topology map (such as distance, slope, ground friction, etc.), the robot's own motion model (such as speed, load, drive efficiency, etc.), and environmental factors (such as wind resistance, temperature, etc.), and estimates it using a preset energy consumption model. The purpose is to provide a more accurate indicator of actual reachability than simply considering distance.

[0066] In practical applications, selecting the resupply point with the lowest expected energy consumption as the target resupply point means that after calculating the expected energy consumption of all reachable preset resupply points, the system compares them and selects the resupply point with the lowest expected energy consumption as the robot's primary resupply target. This aims to maximize the robot's survivability and task execution capabilities during energy crises.

[0067] Furthermore, generating a path to the target supply point means that once the target supply point is determined, the path planning module will use the existing 3D topology map and robot kinematics model, and employ path planning techniques such as A* algorithm, Dijkstra algorithm or RRT (fast random tree) algorithm, to generate a feasible path from the robot's current position to the target supply point, avoiding obstacles and conforming to the robot's motion constraints.

[0068] In response, this application further proposes a more intelligent and dynamic contribution ratio adjustment mechanism to ensure that the target location can be estimated more accurately under different information reliability scenarios.

[0069] The steps described above, which adjust the contribution ratio of the most recent satellite positioning data and intermittent auxiliary signals to the target position estimation based on historical data reliability and auxiliary signal reliability, include: When the reliability of the auxiliary signal is higher than the first preset threshold and the reliability of the historical data is lower than the second preset threshold, the intermittent auxiliary signal is adopted as the main basis for target position estimation. When the reliability of historical data is higher than the second preset threshold and the reliability of auxiliary signals is lower than the first preset threshold, the most recent satellite positioning data shall be adopted as the main basis for target position estimation. When the reliability of historical data and the reliability of auxiliary signals do not meet the above conditions, the reliability of historical data and the reliability of auxiliary signals are compared, and a higher contribution ratio is allocated to the information source with higher reliability.

[0070] Specifically, the reliability of auxiliary signals refers to the confidence level obtained by comprehensively evaluating the signal strength, duration, directional information, and frequency characteristics of intermittent auxiliary signals emitted by the target. A higher value indicates higher accuracy of the location information provided by the auxiliary signal. Historical data reliability refers to the indicator that evaluates the validity and accuracy of the historical positioning data at the current moment by forward extrapolating from the target's most recent satellite positioning data and comparing it with the predicted position. A higher value indicates greater reference value of the historical positioning data at the current moment. The first and second preset thresholds are values ​​pre-set based on the actual application scenario, environmental complexity, and the required accuracy of target position estimation. For example, the first preset threshold can be set to 0.7, indicating that when the confidence level of the auxiliary signal reaches 70% or higher, it is considered to have high reference value; the second preset threshold can be set to 0.6, indicating that when the validity of the historical positioning data is below 60%, its reference value is considered low. These thresholds can be dynamically adjusted based on empirical data, simulation tests, or machine learning methods to adapt to different rescue environments. Prioritizing a particular information source as the primary basis for target location estimation means that this source will be given higher weight when calculating real-time target location estimates. In some extreme cases, the estimation can even be entirely based on this source, while the other source serves as an auxiliary or corrective measure. When neither source meets the criteria for priority, the higher contribution ratio is allocated to the more reliable source by directly comparing their values, thus achieving a more refined weight allocation.

[0071] In some embodiments of this application, in the process of combining the robot's own navigation information to calculate the real-time target position estimation result, it is necessary to more accurately fuse multi-source information to improve estimation accuracy and robustness.

[0072] In this regard, this application further proposes the following steps for calculating the real-time target position estimation result by combining the robot's own navigation information: The robot obtains real-time navigation and positioning information, compensates for environmental interference, and obtains its own compensated trajectory. Based on the compensated self-movement trajectory, historical data reliability, and auxiliary signal reliability, the target's current position and future movement trend are comprehensively calculated, and the real-time target position estimation result including the center position and error range is output.

[0073] Specifically, real-time navigation and positioning information for a robot can be understood as data about its own position, attitude, and velocity acquired by various sensors onboard the robot (such as inertial measurement units (IMUs), odometry, vision sensors, and lidar). This information is the foundation for the robot's autonomous navigation and positioning. Compensation for environmental interference refers to eliminating or reducing errors in sensor data caused by environmental factors (such as vibration, noise, changes in lighting, and terrain irregularities) through various filtering and calibration techniques. For example, Kalman filtering, extended Kalman filtering, particle filtering, or sensor fusion algorithms can be used to fuse information from different sensors and suppress noise, thereby obtaining a more accurate and reliable robot trajectory. The compensated trajectory is the robot's precise movement path relative to its starting point or a known reference point.

[0074] In practical applications, the comprehensive calculation of a target's current position and future movement trend based on its compensated self-motion trajectory, historical data reliability, and auxiliary signal reliability refers to the effective fusion of the robot's precise motion trajectory information with historical satellite positioning data from the target (whose reliability has been assessed through historical data reliability evaluation) and intermittent auxiliary signals (whose reliability has been assessed through auxiliary signal reliability evaluation). For example, a Bayesian filtering framework (such as extended Kalman filtering or particle filtering) can be used, treating the robot's own motion as part of the observation model, while treating historical satellite data and auxiliary signals as independent observations of the target position, and assigning weights according to their respective reliability. Through this comprehensive calculation, not only can the target's current position be estimated, but its short-term future movement trend can also be predicted based on the target's preset behavioral patterns and environmental force field information. Thus, the output includes a real-time target position estimation result containing the center position and error range. This means that the final estimation result not only provides the most probable target location point (center position) but also provides an error range representing the uncertainty of the estimation. This error range can be a confidence ellipse or confidence interval, used to quantify the accuracy and reliability of the estimation result, providing more comprehensive information for subsequent path planning and decision-making.

[0075] The above embodiments propose a satellite navigation-based target tracking method for rescue robots, which can effectively estimate the target location and optimize the rescue path. However, in order to apply this method to actual rescue scenarios and achieve automated operation, a specific system is needed to carry out and execute the method to ensure its efficient and reliable operation.

[0076] Regarding this, secondly, refer to Figure 2 This application proposes a satellite navigation-based target tracking system for rescue robots, used to execute the aforementioned satellite navigation-based target tracking method for rescue robots. The system includes: The historical data reliability assessment module 210 is used to receive the target's most recent satellite positioning data and record the receiving timestamp, and calculate the historical data reliability of the most recent satellite positioning data based on the time difference between the current time and the receiving timestamp, the real-time acquired surrounding environmental force field information, and the preset target active behavior pattern. The auxiliary signal reliability assessment module 220 is used to calculate the reliability of the auxiliary signal based on the intermittent auxiliary signal when the intermittent auxiliary signal emitted by the target is received. The target position estimation module 230 is used to adjust the contribution ratio of the target's most recent satellite positioning data and intermittent auxiliary signals in the target position estimation based on the reliability of historical data and the reliability of auxiliary signals, and to calculate the real-time target position estimation result by combining the robot's own navigation information. The path planning and energy optimization module 240 is used to optimize path planning and energy consumption based on the real-time target location estimation results, the robot's real-time energy reserves, and the urgency of the task, so as to achieve target tracking and rescue.

[0077] Specifically, the historical data reliability assessment module 210 is configured to continuously monitor and analyze the target's historical positioning information. By considering factors such as time drift, environmental interference, and the target's active behavior patterns, it quantifies the validity of the historical positioning data. For example, when the time difference between the current moment and the received timestamp is large, or when the surrounding environmental force field information is complex, the historical data reliability will be lowered accordingly to reflect its uncertainty. The auxiliary signal reliability assessment module 220 is configured to listen to and process intermittent auxiliary signals emitted by the target in real time, such as radio signals and sonar signals. This module analyzes the signal strength, duration, directionality, and frequency characteristics of the intermittent auxiliary signals to comprehensively evaluate the quality and reliability of the auxiliary signals. Furthermore, the target position estimation module 230 is the core of the system. It dynamically adjusts the weights of different information sources based on the two reliability levels to fuse the target's most recent satellite positioning data and intermittent auxiliary signals. Simultaneously, this module also integrates the robot's own real-time navigation and positioning information, such as inertial navigation system or visual odometry data, to improve estimation accuracy and robustness. In practical applications, the path planning and energy optimization module 240 is responsible for intelligently planning the robot's movement path based on the currently estimated real-time target location, the robot's real-time energy reserves, and the task priority (task urgency). This module comprehensively considers factors such as energy consumption, time efficiency, and interception success rate, dynamically adjusting strategies to ensure maximum rescue efficiency with limited energy.

[0078] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A satellite navigation-based rescue robot target tracking method, characterized by, The method comprises the following steps: receiving satellite positioning data of a target at the last time and recording a receiving time stamp, and calculating a historical data reliability of the satellite positioning data at the last time based on a time difference between a current time and the receiving time stamp, real-time acquired surrounding environment force field information, and a preset active behavior mode of the target; when receiving an intermittent auxiliary signal sent by the target, calculating an auxiliary signal reliability according to the intermittent auxiliary signal; adjusting a contribution proportion of the satellite positioning data of the target at the last time and the intermittent auxiliary signal in target position estimation according to the historical data reliability and the auxiliary signal reliability, and combining robot self-navigation information to calculate a real-time target position estimation result; optimizing path planning and energy consumption according to the real-time target position estimation result, real-time energy reserve of the robot, and task urgency, so as to realize tracking and rescue of the target. 2.The satellite navigation-based target tracking method for a rescue robot according to claim 1, wherein, The step of calculating the historical data reliability of the satellite positioning data at the last time comprises the following steps: generating a short-time drift trend prediction of the target based on the time difference between the current time and the receiving time stamp, the real-time acquired surrounding environment force field information, and the preset active behavior mode of the target; performing forward calculation on the satellite positioning data at the last time based on the short-time drift trend prediction and the time difference to obtain a current predicted position of the target; calculating the historical data reliability by comparing the current predicted position of the target and the satellite positioning data at the last time. 3.The satellite navigation-based target tracking method for a rescue robot according to claim 1, wherein, The step of calculating the auxiliary signal reliability according to the intermittent auxiliary signal when receiving the intermittent auxiliary signal sent by the target comprises the following steps: acquiring signal strength, duration, directionality information, and frequency characteristics of the intermittent auxiliary signal, and calculating the auxiliary signal reliability according to the signal strength, duration, directionality information, and frequency characteristics.

4. The satellite navigation-based target tracking method for a rescue robot according to claim 3, characterized in that, The method further comprises the following steps: constructing a three-dimensional topological map of the surrounding environment of the robot in real time, extracting directionality information of the intermittent auxiliary signal when receiving the intermittent auxiliary signal sent by the target, and combining the current position of the robot and the three-dimensional topological map to perform propagation prediction, wherein the propagation prediction is to predict a propagation path of the intermittent auxiliary signal in the environment through a preset propagation model, and the direction of the intermittent auxiliary signal is inversely calibrated by comparing the directionality information with the predicted propagation path, and a confidence degree is given; correcting the auxiliary signal reliability according to the confidence degree. 5.The satellite navigation-based target tracking method for rescue robots according to claim 1, wherein, The step of optimizing path planning and energy consumption according to the real-time target position estimation result, real-time energy reserve of the robot, and task urgency to realize tracking and rescue of the target comprises the following steps: calculating an energy risk coefficient according to the real-time energy reserve of the robot, wherein the energy risk coefficient increases with the decrease of the real-time energy reserve of the robot; dynamically adjusting weights of energy consumption, time cost, and interception success probability according to the energy risk coefficient, the real-time target position estimation result, and the task urgency, and preferentially selecting a more energy-saving path to realize tracking and rescue of the target.

6. The satellite navigation-based target tracking method for a rescue robot according to claim 5, wherein, The method further comprises the following steps: When the energy risk coefficient reaches a preset risk threshold, a low-power mode is activated; In the low-power mode, the weight of the energy consumption is set as the highest priority, and a path to the nearest supply point is planned.

7. The satellite navigation-based target tracking method for a rescue robot according to claim 6, wherein, The step of planning the path to the nearest supply point comprises: acquiring position information of a plurality of preset supply points in a three-dimensional topological map of the surroundings of the robot, and calculating predicted energy consumption from the current position of the robot to each of the preset supply points; according to the predicted energy consumption, selecting a supply point with the least predicted energy consumption as a target supply point, and generating a path to the target supply point. 8.The satellite navigation-based target tracking method for rescue robots according to claim 1, wherein, The step of adjusting the contribution proportions of the target last satellite positioning data and the intermittent auxiliary signal in target position estimation according to the historical data reliability and the auxiliary signal reliability comprises: when the auxiliary signal reliability is higher than a first preset threshold and the historical data reliability is lower than a second preset threshold, the intermittent auxiliary signal is preferentially adopted as the main basis for target position estimation; when the historical data reliability is higher than the second preset threshold and the auxiliary signal reliability is lower than the first preset threshold, the last satellite positioning data is preferentially adopted as the main basis for target position estimation; when the historical data reliability and the auxiliary signal reliability do not satisfy the above conditions, the historical data reliability and the auxiliary signal reliability are compared, and a higher contribution proportion is allocated to the information source with higher reliability. 9.The satellite navigation-based target tracking method for rescue robots according to claim 1, wherein, The step of calculating a real-time target position estimation result in combination with the navigation information of the robot comprises: acquiring real-time navigation positioning information of the robot, and compensating for environmental interference to obtain a compensated self-motion trajectory; based on the compensated self-motion trajectory, the historical data reliability and the auxiliary signal reliability, comprehensively calculating a target current position and a future movement trend, and outputting the real-time target position estimation result containing a central position and an error range.

10. A satellite navigation-based rescue robot target tracking system for performing the satellite navigation-based rescue robot target tracking method according to any one of claims 1 to 9, characterized by The system comprises: a historical data reliability evaluation module configured to receive target last satellite positioning data and record a receiving time stamp, and calculate a historical data reliability of the last satellite positioning data based on a time difference between a current time and the receiving time stamp, real-time acquired surrounding environment force field information and a preset target active behavior mode; an auxiliary signal reliability evaluation module configured to calculate an auxiliary signal reliability according to an intermittent auxiliary signal emitted by a target when the intermittent auxiliary signal is received; a target position estimation module configured to adjust contribution proportions of the target last satellite positioning data and the intermittent auxiliary signal in target position estimation according to the historical data reliability and the auxiliary signal reliability, and calculate a real-time target position estimation result in combination with navigation information of the robot; a path planning and energy optimization module configured to optimize path planning and energy consumption according to the real-time target position estimation result, real-time energy reserves of the robot and task urgency, so as to realize tracking and rescue of the target.

Images