Adaptive terrain crossing control method and system applied to search and rescue robot

Abstract

The invention discloses a self-adaptive terrain crossing control method and system applied to a search and rescue robot, and relates to the technical field of intelligent control, and the method comprises the steps: reading a target point of a target search and rescue robot, setting a path range area, building a center path based on a current position, activating a sensor to collect environment data, and transmitting the environment data to the search and rescue robot; generating an environment data set with a distance attenuation identifier; performing deviation analysis according to the central path, establishing a path deviation constraint, optimizing a path decision, and generating a free path decision result; and path passing compensation and terrain crossing control are carried out by reading associated environment data of the group search and rescue robot. The technical problem that the search and rescue efficiency and safety are affected due to the lack of dynamic path adjustment and real-time environment adaptability in the multi-robot cooperative search and rescue task in the prior art is solved, and the technical effects of multi-robot cooperative work, dynamic path adjustment, obstacle avoidance and improvement of the search and rescue efficiency and safety are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an adaptive terrain crossing control method and system applied to a search and rescue robot. Background Art

[0002] In complex disaster environments, search and rescue missions often need to be completed quickly, efficiently and safely. Traditional single robot or manual search and rescue methods have problems of low efficiency and slow response. Although group search and rescue robot systems can improve efficiency, existing technologies still face challenges in dynamic environment adaptation, path planning and multi-robot collaboration. In particular, there is a lack of effective solutions in real-time path adjustment, conflict avoidance and response to environmental changes. Summary of the invention

[0003] The present application provides an adaptive terrain traversal control method and system for search and rescue robots, which is used to solve the technical problem that the prior art lacks dynamic path adjustment and real-time environmental adaptability in multi-robot collaborative search and rescue missions, affecting the search and rescue efficiency and safety.

[0004] The first aspect of the present application provides an adaptive terrain crossing control method for a search and rescue robot, the method comprising: reading a search and rescue target point of a target search and rescue robot, and setting a path range area, wherein the path range area is a path range area allocated according to the search and rescue mission of the group search and rescue robot; establishing a central path based on the search and rescue target point and the current position point, activating the acquisition sensor of the target search and rescue robot to perform environmental data acquisition, and establishing an environmental data set, wherein the environmental data set has a distance attenuation mark; performing deviation analysis according to the central path, and establishing a path deviation constraint; making a path crossing decision based on the environmental data set within the path range area, and optimizing the decision through the path deviation constraint to establish a free path decision result; reading the associated environmental data of the group search and rescue robot, performing traffic compensation for the free path decision result according to the associated environmental data, and performing terrain crossing control of the target search and rescue robot according to the traffic compensation result.

[0005] According to a second aspect of the present application, an adaptive terrain crossing control system for a search and rescue robot is provided, the system comprising: a path range area setting module, the path range area setting module is used to read the search and rescue target point of the target search and rescue robot and set the path range area, the path range area being the path range area assigned according to the search and rescue mission of the group search and rescue robot; an environmental data acquisition module, the environmental data acquisition module is used to connect the positions based on the search and rescue target point and the current position point, establish a central path, activate the acquisition sensor of the target search and rescue robot to perform environmental data acquisition, and establish an environmental data set, the environmental data set having a distance attenuation mark; a path deviation analysis module, the path deviation analysis module is used to perform deviation analysis based on the central path and establish a path deviation constraint; a path decision optimization module, the path decision optimization module is used to perform a path crossing decision based on the environmental data set within the path range area, and perform decision optimization through the path deviation constraint to establish a free path decision result; a terrain crossing control module, the terrain crossing control module is used to read the associated environmental data of the group search and rescue robot, perform passage compensation for the free path decision result according to the associated environmental data, and perform terrain crossing control of the target search and rescue robot according to the passage compensation result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The adaptive terrain crossing control method and system for search and rescue robots provided in this application relate to the field of intelligent control technology. By setting the path range, establishing the central path and collecting environmental data, path deviation analysis and optimization decision are performed to generate free path decision results. In combination with the environmental data associated with the group search and rescue robot, path passage compensation is performed to optimize the terrain crossing control of the target search and rescue robot, ensuring efficient and safe execution of search and rescue tasks in a dynamic environment. The method solves the technical problem that the existing technology lacks dynamic path adjustment and real-time environmental adaptation capabilities in multi-robot collaborative search and rescue tasks, which affects the efficiency and safety of search and rescue. The method realizes the technical effect of improving the efficiency and safety of search and rescue by collecting environmental data in real time and optimizing path planning, realizing multi-robot collaborative work, dynamic path adjustment and obstacle avoidance, and improving search and rescue efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1A schematic flow chart of an adaptive terrain traversal control method for a search and rescue robot provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an adaptive terrain traversal control system applied to a search and rescue robot provided in an embodiment of the present application.

[0009] Explanation of reference numerals: path range area setting module 11 , environmental data acquisition module 12 , path deviation analysis module 13 , path decision optimization module 14 , terrain crossing control module 15 . DETAILED DESCRIPTION

[0010] The present application provides an adaptive terrain traversal control method and system for search and rescue robots, which is used to solve the technical problem that the prior art lacks dynamic path adjustment and real-time environmental adaptability in multi-robot collaborative search and rescue missions, affecting the search and rescue efficiency and safety.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides an adaptive terrain crossing control method applied to a search and rescue robot, the method comprising: P10: Read the search and rescue target point of the target search and rescue robot, and set a path range area, wherein the path range area is a path range area allocated according to the search and rescue mission of the group search and rescue robot.

[0014] Optionally, it is first necessary to read the search and rescue target point of the target search and rescue robot and set a path range area for it to ensure that the robot can complete the task within a reasonable area and eventually reach the designated target.

[0015] Specifically, at the beginning of the mission, the search and rescue target points of the target search and rescue robot are read. These target points are usually determined based on the on-site search and rescue needs and the results of the robot's task assignment. The target point may be the location of trapped people, key areas of the disaster site, or other key targets. The current position of the robot can be obtained in real time through a positioning system (such as GPS, IMU or indoor positioning technology), and the coordinates of the target point are sent to the robot through the task scheduling system. The accuracy of this process directly affects the accuracy of subsequent path planning.

[0016] Then, the path range area is set. The path range area is the range within which the target search and rescue robot can walk. It is dynamically delineated based on the area allocated for group search and rescue tasks. The boundary of this area is determined by the task scheduling system during global task allocation, and usually includes the search area of ​​the task and possible obstacles. The setting of the path range area should not only consider the location of the target point, but also the robot's operating capabilities, such as the maximum drivable distance, load capacity, speed, etc. Environmental modeling technology uses sensor data collection (such as lidar, visual sensors, etc.) to perceive the environment, generate dynamic maps, and calibrate obstacles and terrain changes in real time, thereby ensuring that the path range area can cover the area that actually needs search and rescue.

[0017] In actual tasks, multiple robots usually perform tasks at the same time, so group task allocation is required for search and rescue targets. For example, the scheduling system will reasonably allocate the search and rescue targets of each robot based on the current position, performance and task requirements of each robot. Group search and rescue task allocation helps to achieve parallel processing of tasks and improve the efficiency of task execution. During the task allocation process, the current power, load and task priority of the robot are dynamically evaluated to ensure that each robot can complete the task within its capabilities and work efficiently within the path range.

[0018] Once the target point and path range area are set, the system also needs to ensure that the target point is within the set path range or can be reached through a reasonable path. If the target point is outside the robot's path range area, the path range area is adjusted as needed, or the task is reallocated to other robots for processing. The path range area is not just a static boundary, it may be adjusted as the environment changes. For example, when dynamic obstacles or emergencies occur, the robot needs to adjust the path in real time to ensure the completion of the task. To achieve this, the system constantly monitors the robot's position and environmental data, and dynamically adjusts the boundaries of the path range area based on the path planning algorithm to ensure that the target search and rescue robot can accurately obtain the search and rescue target and perform the task in a suitable area. This not only improves the efficiency of task execution, but also ensures the robot's operational safety and high accuracy of task completion.

[0019] P20: Connect the positions based on the search and rescue target point and the current position point to establish a central path, activate the acquisition sensor of the target search and rescue robot to perform environmental data acquisition, and establish an environmental data set, wherein the environmental data set has a distance attenuation mark.

[0020] It should be understood that the target search and rescue robot establishes a central path by connecting the current position point with the search and rescue target point, and at the same time activates the robot's collection sensor to start collecting environmental data, thereby providing basic data support for subsequent path optimization and environmental adaptation, ensuring that the robot can make dynamic decisions based on real-time environmental information.

[0021] Exemplarily, a central path is first calculated based on the current position of the target search and rescue robot and the coordinates of the search and rescue target point. This path is a straight line connecting the current position of the robot and the target point, and is usually used as a preliminary navigation reference for the robot. The central path provides a basic framework for subsequent path planning and adjustment. It is an idealized path without any obstacles or other environmental factors. When calculating the central path, basic path planning algorithms (such as A* algorithm, Dijkstra algorithm, etc.) can be used to obtain a shortest or optimal path, and the robot's action restrictions (such as turning radius, maximum speed, etc.) can be considered.

[0022] After the central path is determined, the robot needs to perceive its route and surrounding environment in real time. At this time, the sensor system of the target search and rescue robot is activated and begins to collect environmental data. Common sensors include laser radar (LIDAR), infrared sensors, cameras, ultrasonic sensors, etc. These sensors can capture surrounding obstacles, terrain changes, ambient temperature and other information in real time. By integrating data from multiple sensors, the robot can obtain a full range of perception of the surrounding environment.

[0023] The environmental data continuously collected by the sensor is processed and stored in real time to form an environmental data set. This data set contains various types of information obtained from the sensor, including but not limited to the location of obstacles, ground type, slope, humidity, temperature and other environmental characteristics. These data will provide the necessary basis for path optimization and decision-making. It is worth noting that the environmental data set will be marked with a distance attenuation mark, because different types of sensors have different perception capabilities at different distances. For example, the accuracy of lidar will decay with increasing distance, and the recognition ability of infrared sensors at long distances is generally weaker. Therefore, the distance attenuation mark is an important supplementary information used to identify the credibility and effective range of the perception data, helping the subsequent path decision system to determine which data needs to be given priority.

[0024] The distance attenuation mark not only reflects the physical characteristics of different sensors, but also helps the path planning system to weight the reliability of environmental data. According to the characteristics of sensor distance attenuation, the obstacle information collected at a long distance can be properly corrected to avoid inaccurate path planning due to data errors. For example, if the data attenuation of the lidar at a long distance is more serious, the weight of this data on the path decision can be reduced to ensure that the path selected by the robot is safer and more reliable.

[0025] P30: Perform deviation analysis based on the central path and establish path deviation constraints.

[0026] Specifically, the target search and rescue robot needs to analyze the deviation between its current position and the pre-set central path, and establish path deviation constraints based on this. The core of this process is to detect whether the robot has deviated from the original predetermined path, and apply corresponding constraints according to the deviation to ensure that the robot can effectively correct the path and continue to move forward.

[0027] First, the robot continuously monitors its current position and compares it with the predetermined center path. The center path is the ideal route for the robot to get from its current position to the target point, but in actual execution, the robot may deviate from this path due to obstacles in the environment, terrain undulations, or other factors. Therefore, it is necessary to calculate the deviation between the robot's current position and the center path in real time, usually expressed as a deviation distance or angle.

[0028] In deviation analysis, the robot can rely on its positioning system (such as GPS, inertial navigation system, visual positioning system, etc.) to determine the current position, and continuously calculate the difference between the current path and the center path through the path tracking algorithm. The system will perform multiple deviation calculations within a specified time interval and feed back the deviation data to the control system for timely adjustment.

[0029] Once the deviation analysis results indicate that the robot has deviated, the next step is to establish path deviation constraints based on the degree and direction of the deviation. The purpose of these constraints is to limit the deviation range of the robot and ensure that it does not leave the predetermined feasible path, thereby ensuring that the task can be successfully completed. Among them, path deviation constraints can include: position constraints, that is, by calculating the distance between the current position of the robot and the center path, a maximum allowable deviation distance is set. If the distance that the robot deviates from the center path exceeds this threshold, the system will force the path correction mechanism to start, or guide the robot back to the predetermined path. This constraint can be dynamically adjusted according to environmental conditions. For example, smaller deviations can be allowed in some narrow areas, while larger deviations can be allowed in open areas. Behavioral constraints, that is, when deviation occurs, the robot may need to make specific behavioral adjustments, such as slowing down, detouring, or automatically correcting the direction. These behavioral constraints can help robots respond flexibly in complex environments, ensuring that deviations are corrected in a timely manner, and not causing mission failures due to excessive deviations.

[0030] Path deviation constraints are not fixed. In a dynamic environment, the robot may encounter various sudden obstacles, terrain changes, or other unpredictable factors, so the path deviation constraint needs to be able to adjust dynamically. When the robot detects a new obstacle or changes the driving direction, the parameters of the deviation constraint can be adjusted in real time. For example, the robot may need stricter deviation constraints in areas with dense obstacles, while the constraints can be relaxed in open areas to improve search efficiency. In addition, the adjustment of the deviation constraint needs to be combined with the current state of the robot, such as battery power, load, and speed. These factors may affect the robot's tolerance for path deviation, thereby affecting the setting of the deviation constraint.

[0031] After the path deviation constraints are established and adjusted, these constraints can be enforced through the control algorithm. When the deviation exceeds the set tolerance range, the robot will adjust according to the instructions of the control algorithm, which not only improves the robot's adaptability in dynamic environments, but also ensures the efficient completion of the task.

[0032] P40: Perform path crossing decisions based on the environmental data set within the path range area, and optimize the decision through the path deviation constraint to establish a free path decision result.

[0033] Furthermore, step P40 of the embodiment of the present application also includes: P41: Use the environmental data set to construct the environmental terrain, and identify the abnormal area based on the distance attenuation mark, the abnormal area includes a stable abnormal area and a dangerous abnormal area; P42: Read the robot characteristics of the target search and rescue robot, and construct a decision fitness channel; P43: Input the robot characteristics, the environmental terrain, and the abnormal area into the decision fitness channel to generate a path crossing decision result; P44: Optimize the path crossing decision result through the path deviation constraint to establish a free path decision result.

[0034] Optionally, the target search and rescue robot needs to make path crossing decisions based on the environmental data set, and optimize the decision by combining the path deviation constraints to generate the final free path decision result. The purpose is to select an optimal path to complete the search and rescue mission within a given path range, taking into account environmental factors, robot characteristics and task requirements.

[0035] First, the environmental terrain is constructed using environmental data sets collected from the robot's sensors. These data include obstacles, ground type, slope and other environmental features. The system integrates data from multiple sensors through data fusion technology to generate a three-dimensional environmental model of the robot. This model not only describes the physical environment around the robot, but also helps the subsequent path planning system identify possible obstacles and complex terrain.

[0036] At the same time, the distance attenuation markers in the environmental data set are crucial for identifying abnormal areas. Since different sensors have different working ranges, the distance attenuation markers can reflect the sensor's perception ability and accuracy at different distances. Therefore, the definition of abnormal areas will be determined based on the attenuation markers. These abnormal areas can be further divided into: stable abnormal areas, which have persistent obstacles or dangers but usually do not change, such as deep trenches, cliffs, etc. Dangerous abnormal areas, which may be unstable, such as areas where collapse or landslides may occur at any time, and have higher risks. In this way, it can be clear which areas are safe and which areas need to be avoided or paid special attention to, providing an important basis for subsequent path planning.

[0037] In the process of path decision-making, in addition to environmental factors, the robot characteristics of the target search and rescue robot also need to be considered. These characteristics include the robot's maximum speed, load capacity, movement mode (such as all-terrain wheels, tracks or quadruped robots, etc.) and battery life. Different robots have different abilities to adapt to the environment, which directly affects their performance in complex terrain.

[0038] The system reads various performance parameters of the robot and builds a decision fitness channel, which combines the characteristics of the robot with the environmental data to provide a fitness evaluation framework for path selection. The role of the decision fitness channel is to ensure that the selected path is not only feasible but also the most suitable for the robot to complete the task by matching the robot performance with the environmental conditions. For example, some robots may be good at navigating narrow passages, while others may perform better at high speeds in open areas.

[0039] Furthermore, the previously constructed environmental terrain, abnormal areas, and robot features are used as inputs to the decision fitness channel. This channel uses a multi-level decision algorithm to comprehensively consider various factors in the environment and the characteristics of the robot to generate a path crossing decision result. The path crossing decision result refers to the optimal path selection for the robot to reach the target point from the current position. The decision process will weigh factors such as the complexity of the environment, the feasibility of the path, the robot's movement ability, and safety. For example, if a path passes through a dangerous abnormal area, the decision system will tend to choose a path that avoids the area, or use other methods (such as detours, climbing, etc.) to enable the robot to pass safely.

[0040] Finally, the path crossing decision result is combined with the path deviation constraint to perform decision optimization. The path deviation constraint is established in the previous step, which aims to limit the deviation range of the robot when executing the path to avoid excessive deviation that may cause mission failure or enter the infeasible area. The decision optimization process can fine-tune the existing path decision according to the deviation constraint to ensure that the final selected path not only meets the environmental adaptability requirements but also minimizes the deviation. For example, if a path is too far from the center path, the system will automatically adjust the path to make it more consistent with the preset constraint range. The result of the optimization process is to generate a free path decision result, which is the path that best meets the robot characteristics, environmental requirements and mission objectives, ensuring that the robot can efficiently and safely complete path planning and execution in complex search and rescue missions.

[0041] Furthermore, step P43 of the embodiment of the present application also includes: P43-1: Call the terrain fitness layer of the decision fitness channel, and after receiving the robot characteristics and the environmental terrain with the terrain fitness layer, perform terrain access adaptation analysis of the position point and establish the position access value; P43-2: Synchronize the position access value to the path fitting layer of the decision fitness channel, perform path fitting, and establish a basic path fitting set; P43-3: Generate a path crossing decision result based on the basic path fitting set.

[0042] Specifically, the target search and rescue robot needs to generate the most suitable path crossing decision result through multiple levels of analysis and optimization in the decision fitness channel.

[0043] First, the terrain fitness layer in the decision fitness channel is called. The task of the terrain fitness layer is to analyze the match between the robot's characteristics and the environmental terrain and evaluate the passability of each location point. Robot characteristics such as maximum climbing angle, load capacity, and ground friction coefficient are combined with factors such as terrain slope, obstacle location, and ground type for comprehensive analysis. The output of this process is the position passability value, which reflects the feasibility of the robot passing through a specific location point. If the position passability value is low, it means that the point is difficult to pass, and it may be necessary to replan the path or find a detour. This analysis ensures that path planning can be dynamically adjusted according to real-time terrain changes.

[0044] Next, the position pass value of each position point is passed to the path fitting layer in the decision fitness channel. The main task of the path fitting layer is to optimize different paths based on the known environmental terrain and position pass values ​​to generate multiple candidate paths. These candidate paths will be further adjusted and optimized on the basis of meeting the passability requirements to ensure the smoothness, effectiveness and safety of the path. The path fitting layer will take into account robot characteristics such as turning radius, acceleration ability, etc. in order to generate a path combination suitable for the robot. The generated basic path fitting set is a number of optimized paths that provide multiple feasible options, each of which can effectively avoid obstacles and adapt to environmental changes.

[0045] Finally, after the path fitting is completed, the final path crossing decision result will be generated based on the basic path fitting set. This decision result is the optimal path selection for the robot to reach the target point from the current position. It not only considers terrain adaptability, robot performance and environmental complexity, but also optimizes based on path deviation constraints. Each path in the fitting set is evaluated, and a path that best suits the current environment and robot characteristics is selected as the final action route. The final generated path crossing decision result can provide the robot with clear route planning to ensure that the task can be completed efficiently and safely.

[0046] Furthermore, step P43-3 of the embodiment of the present application also includes: P43-31: Synchronize the abnormal area to the abnormal accumulation layer of the decision fitness channel, perform abnormal accumulation analysis on the basic path fitting set through the abnormal accumulation layer, and generate abnormal accumulation results; P43-32: Screen the basic path fitting set according to the abnormal accumulation results to generate a path crossing decision result.

[0047] In a possible embodiment of the present application, the path fitting set can be further optimized to ensure that path planning not only takes into account the environmental terrain and robot characteristics, but also effectively avoids potential dangerous areas.

[0048] First, the pre-identified abnormal areas (such as dangerous abnormal areas or stable abnormal areas) are synchronized to the abnormal accumulation layer in the decision fitness channel. The main task of the abnormal accumulation layer is to analyze the situation of each path passing through these abnormal areas. It analyzes factors such as the frequency, type, and length of each abnormal area on the path to generate a comprehensive abnormal accumulation result. This result can help the system evaluate the safety of each path. The more abnormal areas a path passes through, the greater the risk.

[0049] Next, the basic path fitting set is screened according to the abnormal accumulation results generated above. Paths that cross more abnormal areas or have higher risks will be excluded, and the system will give priority to those paths that pass through a small number of abnormal areas or through low-risk areas. The screened path set will represent the safest and most suitable path options for the current environment and robot. Finally, a path crossing decision result is generated from these screened paths to provide the target search and rescue robot with an optimal path that meets safety standards and adapts to its performance requirements, to ensure that path planning not only considers terrain adaptability and robot characteristics, but also minimizes possible dangerous areas in the path, thereby providing the robot with an efficient and safe route.

[0050] Furthermore, step P43-31 of the embodiment of the present application also includes: P43-311: Perform distance identification of abnormal areas for each path in the basic path fitting set, and establish distance identification results; P43-312: Construct a distance impact attenuation coefficient for abnormal areas, perform abnormal area impact analysis based on the distance impact attenuation coefficient and the distance identification results, and establish independent distance impact analysis results; P43-313: Complete abnormal accumulation analysis based on the independent distance impact analysis results.

[0051] It should be understood that a more detailed abnormal area analysis can be performed on each path to ensure that path planning can effectively avoid potential risk areas.

[0052] First, the distance between each path in the basic path fitting set and the surrounding abnormal areas is identified. The distance between the path and the abnormal area is one of the important factors in judging the safety of the path. The distance from each path to each abnormal area (such as dangerous area, stable abnormal area, etc.) can be calculated. The output of this process is the distance identification result, which provides a basis for the specific distance information between each path and the abnormal area. Paths with shorter distances usually mean greater potential risks, while paths with longer distances may be safer.

[0053] Next, based on the distance information between the path and the abnormal area, a distance impact attenuation coefficient is constructed, which describes how the distance between the path and the abnormal area affects the safety of the path. The abnormal area with a closer distance has a greater impact on the path, while the impact gradually decreases for the path far away from the abnormal area. The abnormal area impact analysis can be performed by combining the distance identification result and the attenuation coefficient to evaluate the degree of impact of the abnormal area on each path. The result of this analysis is an independent distance impact analysis result, which reflects the potential risk between each path and the abnormal area.

[0054] Finally, based on the above-mentioned independent distance impact analysis results, the abnormal accumulation analysis of each path is completed. The overall risk value of each path is calculated by accumulating the impact of all abnormal areas that the path passes through. Paths that pass through abnormal areas at a closer distance will accumulate more risks and are therefore considered to have higher risks. Through this process, the safety of the path can be comprehensively evaluated, thereby ensuring that the path planning can avoid potential high-risk areas and provide reliable data support for subsequent path screening and optimization.

[0055] Furthermore, step P43-313 of the embodiment of the present application also includes: P43-3131: Obtain the order of passing through the abnormal area, perform abnormal correlation superposition identification based on the order of passing through, and compensate the independent distance impact analysis result based on the abnormal correlation superposition identification result; P43-3132: Complete the abnormal cumulative analysis based on the compensated independent distance impact analysis result.

[0056] Specifically, by analyzing the correlation impact of abnormal areas, we can ensure that the risk assessment of the path is more accurate. By considering the order and correlation of abnormal areas, we can compensate and optimize the risk assessment of the path, and finally complete a more accurate abnormal accumulation analysis.

[0057] First, the order of abnormal areas passed on the path is obtained, because the relative position and order of the abnormal areas have an impact on the path risk that is interrelated. On the path, some abnormal areas may have a greater impact on the robot, especially when multiple abnormal areas appear in succession. By analyzing the associated superposition effects of these areas, the mutual relationship between the abnormal areas is identified. Based on this associated superposition identification result, the independent distance impact analysis result obtained in the previous step is compensated, that is, the risk assessment of the path is adjusted to ensure that the continuity and mutual influence of the abnormal areas are reasonably reflected. For example, if the path passes through multiple dangerous areas in succession, the risk assessment is aggravated according to the relationship between them, so as to more accurately assess the safety of the path.

[0058] Next, based on the compensated independent distance impact analysis results, an abnormal accumulation analysis is performed on each path. This analysis not only considers the risk of the abnormal areas that each path passes through, but also includes the superposition effect between abnormal areas. By incorporating the compensated impact results into the path evaluation, the overall risk value of each path can be calculated more accurately. For example, when a path passes through multiple abnormal areas, the system will calculate the overall risk value of each path based on the correlation order and impact attenuation coefficient of these abnormal areas. The more abnormal areas a path passes through and the more influential they are, the higher its risk value will be, which will help the system ultimately select the safest and most appropriate path.

[0059] Through these two sub-steps, the order and correlation of abnormal areas can be fully considered in path planning, the risk assessment of the path can be further optimized, and more accurate data support can be provided for subsequent path selection.

[0060] P50: Read the associated environmental data of the group search and rescue robot, perform traffic compensation for the free path decision result according to the associated environmental data, and perform terrain crossing control of the target search and rescue robot according to the traffic compensation result.

[0061] It should be understood that the associated environmental data of the group search and rescue robot is read, and this data contains real-time information about the search and rescue robot and its surrounding environment, such as terrain, obstacles, weather conditions, and the location of other robots. Through this data, the system can more accurately understand the current search environment and make traffic compensation for the previously generated free path decision results based on this. Specifically, the path planning may need to be adjusted due to new obstacles or environmental changes. The path is corrected according to these environmental data to ensure that the target search and rescue robot can avoid areas that are not suitable for passage.

[0062] The compensated path will be used to control the target search and rescue robot's terrain traversal by adjusting the robot's motion strategy, such as adjusting the robot's movement mode, speed, steering and other control parameters, to ensure that the robot can smoothly traverse complex terrain and avoid obstacles, and achieve optimal path planning, thereby improving the efficiency and safety of mission execution.

[0063] Furthermore, the embodiment of the present application further includes step P60, and step P60 further includes: P61: During the terrain traversal process of the target search and rescue robot, environmental data is continuously collected to generate updated environmental data; P62: The updated environmental data and the environmental data set are verified for consistency; P63: When the consistency verification deviation exceeds a preset threshold, a path adjustment instruction is generated, the travel path is reconstructed according to the path adjustment instruction, and terrain traversal control is performed according to the reconstructed travel path.

[0064] Optionally, the target search and rescue robot can be further refined to dynamically adjust its path through the collection and verification of real-time environmental data during the mission to adapt to the complex search environment. This ensures that the robot can adjust its path according to the latest environmental changes during the terrain traversal process, thereby improving the flexibility and accuracy of its mission execution.

[0065] Specifically, when the target search and rescue robot is performing terrain traversal, it continuously collects data about the surrounding environment. This data includes information such as changes in obstacles, ground conditions, and weather conditions. Through continuous environmental monitoring, the system can obtain updated environmental data in real time to ensure that path planning takes into account the most accurate current environmental status. This process is critical for responding to dynamically changing environments, allowing the robot to adjust its behavior at any time to avoid potential obstacles or dangerous areas.

[0066] After acquiring the updated environmental data, it is necessary to verify its consistency with the previous environmental data set. The purpose of consistency verification is to check whether there are significant differences between the newly acquired environmental data and the existing data set. For example, whether new obstacles or environmental changes are consistent with previous data, or data deviations caused by environmental changes. If there is a large deviation, it may mean that the environment has changed significantly and the system needs to make path adjustments.

[0067] If the consistency verification finds that the deviation between the new data and the original data set exceeds the preset threshold, a path adjustment instruction is generated, which means that the existing path planning may no longer be applicable or may pose a safety hazard. Based on these instructions, the system will recalculate and reconstruct the robot's path to ensure that it avoids new obstacles or adapts to new environmental conditions. The reconstructed path will be used to guide the target search and rescue robot to perform terrain traversal control, ensuring that it can still perform search and rescue missions smoothly and efficiently in new environments. This mechanism enables the robot to maintain efficiency and safety when performing tasks in complex and dynamic environments, thereby better completing search and rescue missions.

[0068] In summary, the embodiments of the present application have at least the following technical effects: This application establishes a central path based on the current position by reading the target point of the target search and rescue robot and setting the path range area, and activates the sensor to collect environmental data to generate an environmental data set with distance attenuation identification. Deviation analysis is performed based on the central path and path deviation constraints are established to optimize path decisions and generate free path decision results. By reading the associated environmental data of the group search and rescue robot and performing path passage compensation, the target search and rescue robot can finally achieve terrain crossing control and ensure the robot's efficient and safe operation in a dynamic environment.

[0069] The technical effect of achieving multi-robot collaborative work, dynamic path adjustment and obstacle avoidance through real-time collection of environmental data and optimized path planning, thereby improving search and rescue efficiency and safety, has been achieved.

[0070] Embodiment 2 is based on the same inventive concept as the adaptive terrain crossing control method applied to the search and rescue robot in the above embodiment. Figure 2 As shown, the present application provides an adaptive terrain crossing control system for a search and rescue robot, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The path range area setting module 11 is used to read the search and rescue target point of the target search and rescue robot and set the path range area. The path range area is the path range area allocated according to the search and rescue mission of the group search and rescue robot.

[0071] The environmental data acquisition module 12 is used to connect the positions based on the search and rescue target point and the current position point, establish a central path, activate the acquisition sensor of the target search and rescue robot to perform environmental data acquisition, and establish an environmental data set, wherein the environmental data set has a distance attenuation mark.

[0072] The path deviation analysis module 13 is used to perform deviation analysis according to the central path and establish path deviation constraints.

[0073] The path decision optimization module 14 is used to make a path crossing decision based on the environmental data set within the path range area, and to optimize the decision through the path deviation constraint to establish a free path decision result.

[0074] The terrain crossing control module 15 is used to read the associated environmental data of the group search and rescue robot, perform traffic compensation for the free path decision result according to the associated environmental data, and perform terrain crossing control of the target search and rescue robot according to the traffic compensation result.

[0075] Furthermore, the path decision optimization module 14 is also used to perform the following steps: The environmental terrain is constructed using the environmental data set, and abnormal areas are identified based on the distance attenuation mark, wherein the abnormal areas include stable abnormal areas and dangerous abnormal areas; the robot characteristics of the target search and rescue robot are read, and a decision fitness channel is constructed; the robot characteristics, the environmental terrain, and the abnormal areas are input into the decision fitness channel to generate a path crossing decision result; the path crossing decision result is optimized through the path deviation constraint to establish a free path decision result.

[0076] Furthermore, the path decision optimization module 14 is also used to perform the following steps: The terrain fitness layer of the decision fitness channel is called, and after receiving the robot characteristics and the environmental terrain through the terrain fitness layer, the terrain accessibility analysis of the position point is performed to establish a position accessibility value; the position accessibility value is synchronized to the path fitting layer of the decision fitness channel, path fitting is performed, and a basic path fitting set is established; and a path crossing decision result is generated according to the basic path fitting set.

[0077] Furthermore, the path decision optimization module 14 is also used to perform the following steps: The abnormal area is synchronized to the abnormal accumulation layer of the decision fitness channel, and the abnormal accumulation analysis of the basic path fitting set is performed through the abnormal accumulation layer to generate an abnormal accumulation result; the basic path fitting set is screened according to the abnormal accumulation result to generate a path crossing decision result.

[0078] Furthermore, the path decision optimization module 14 is also used to perform the following steps: For each path in the basic path fitting set, the distance of the abnormal area is identified respectively, and the distance identification result is established; the distance impact attenuation coefficient of the abnormal area is constructed, and the impact analysis of the abnormal area is performed according to the distance impact attenuation coefficient and the distance identification result, and an independent distance impact analysis result is established; and the abnormal accumulation analysis is completed according to the independent distance impact analysis result.

[0079] Furthermore, the path decision optimization module 14 is also used to perform the following steps: The passing order of the abnormal area is obtained, abnormal correlation superposition identification is performed according to the passing order, and the independent distance impact analysis result is compensated based on the abnormal correlation superposition identification result; and the abnormal accumulation analysis is completed according to the compensated independent distance impact analysis result.

[0080] Furthermore, the system also includes: An environmental data acquisition module, the environmental data acquisition module is used to continuously collect environmental data during the terrain traversal process of the target search and rescue robot to generate updated environmental data; a consistency verification module, the consistency verification module is used to verify the consistency of the updated environmental data and the environmental data set; a travel path reconstruction module, the travel path reconstruction module is used to generate a path adjustment instruction when the consistency verification deviation exceeds a preset threshold, reconstruct the travel path according to the path adjustment instruction, and perform terrain traversal control according to the reconstructed travel path.

[0081] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0083] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An adaptive terrain crossing control method for a search and rescue robot, characterized in that: The method comprises: Reading the search and rescue target point of the target search and rescue robot, and setting a path range area, wherein the path range area is a path range area allocated according to the search and rescue mission of the group search and rescue robot; Connecting the search and rescue target point and the current position point to establish a central path, activating the acquisition sensor of the target search and rescue robot to perform environmental data acquisition, and establishing an environmental data set, wherein the environmental data set has a distance attenuation mark; Perform deviation analysis based on the central path and establish path deviation constraints; Performing a path crossing decision based on the environmental data set within the path range area, and performing decision optimization through the path deviation constraint to establish a free path decision result; The associated environment data of the group search and rescue robot is read, the free path decision result is compensated according to the associated environment data, and the terrain crossing control of the target search and rescue robot is performed according to the compensation result.

2. The adaptive terrain crossing control method for a search and rescue robot according to claim 1, characterized in that: The step of making a path crossing decision based on the environmental data set within the path range area, and performing decision optimization through the path deviation constraint to establish a free path decision result includes: Using the environmental data set to construct environmental terrain, and identifying abnormal areas based on the distance attenuation mark, the abnormal areas include stable abnormal areas and dangerous abnormal areas; Read the robot characteristics of the target search and rescue robot and build a decision fitness channel; Inputting the robot features, the environment terrain, and the abnormal area into the decision fitness channel to generate a path crossing decision result; The path crossing decision result is optimized through the path deviation constraint to establish a free path decision result.

3. The adaptive terrain crossing control method for a search and rescue robot according to claim 2, characterized in that: The step of inputting the robot features, the environment terrain, and the abnormal area into the decision fitness channel to generate a path crossing decision result includes: Calling the terrain fitness layer of the decision fitness channel, after receiving the robot characteristics and the environmental terrain through the terrain fitness layer, performing terrain pass adaptation analysis of the position point, and establishing a position pass value; Synchronizing the position pass value to the path fitting layer of the decision fitness channel, performing path fitting, and establishing a basic path fitting set; A path crossing decision result is generated according to the basic path fitting set.

4. The adaptive terrain crossing control method for a search and rescue robot as claimed in claim 3, characterized in that: The generating a path crossing decision result according to the basic path fitting set includes: Synchronizing the abnormal area to the abnormal accumulation layer of the decision fitness channel, performing abnormal accumulation analysis of the basic path fitting set through the abnormal accumulation layer, and generating an abnormal accumulation result; The basic path fitting set is screened according to the abnormal accumulation result to generate a path crossing decision result.

5. The adaptive terrain crossing control method for a search and rescue robot according to claim 4, characterized in that: The performing anomaly accumulation analysis of the basic path fitting set through the anomaly accumulation layer includes: Performing distance identification of abnormal areas on each path in the basic path fitting set to establish a distance identification result; Constructing a distance impact attenuation coefficient of the abnormal area, performing abnormal area impact analysis according to the distance impact attenuation coefficient and the distance identification result, and establishing an independent distance impact analysis result; The abnormal accumulation analysis is completed according to the independent distance impact analysis results.

6. The adaptive terrain crossing control method for a search and rescue robot according to claim 5, characterized in that: The abnormal accumulation analysis is completed according to the independent distance impact analysis result, including: Acquire a passing order of the abnormal area, perform abnormal correlation superposition identification according to the passing order, and compensate the independent distance impact analysis result based on the abnormal correlation superposition identification result; The abnormal accumulation analysis is completed based on the compensated independent distance impact analysis results.

7. The adaptive terrain crossing control method for a search and rescue robot according to claim 1, characterized in that: The method further comprises: Continuously collect environmental data during the terrain traversal process of the target search and rescue robot to generate updated environmental data; Performing consistency verification on the updated environment data and the environment data set; When the consistency verification deviation exceeds a preset threshold, a path adjustment instruction is generated, the travel path is reconstructed according to the path adjustment instruction, and terrain crossing control is performed according to the reconstructed travel path.

8. An adaptive terrain crossing control system applied to a search and rescue robot, characterized in that: The system comprises: A path range area setting module, the path range area setting module is used to read the search and rescue target point of the target search and rescue robot, and set the path range area, the path range area is the path range area allocated according to the search and rescue mission of the group search and rescue robot; An environmental data acquisition module, the environmental data acquisition module is used to connect the positions based on the search and rescue target point and the current position point, establish a central path, activate the acquisition sensor of the target search and rescue robot to perform environmental data acquisition, and establish an environmental data set, the environmental data set has a distance attenuation mark; A path deviation analysis module, the path deviation analysis module is used to perform deviation analysis according to the central path and establish path deviation constraints; A path decision optimization module, the path decision optimization module is used to make a path crossing decision based on the environmental data set within the path range area, and to make a decision optimization through the path deviation constraint to establish a free path decision result; A terrain crossing control module is used to read the associated environmental data of the group search and rescue robot, perform passage compensation for the free path decision result according to the associated environmental data, and perform terrain crossing control of the target search and rescue robot according to the passage compensation result.

Images