Risk assessment and obstacle avoidance method for state uncertainty of low-altitude aircraft

Abstract

The invention discloses a risk assessment and obstacle avoidance method for state uncertainty of a low-altitude aircraft, and relates to the technical field of aircraft control, and the method comprises the steps: constructing surrounding Gaussian distribution for the state uncertainty of the aircraft according to the state of the aircraft and the historical deviation data of the state; generating field intensity distribution of each risk source in the space according to a Gaussian function; constructing a risk probability field according to the surrounding Gaussian distribution and the field intensity distribution corresponding to each risk source; the expected field intensity of the aircraft in the risk probability field is determined, and then the stress of the aircraft is determined; and adjusting the track of the aircraft according to the stress so as to avoid the obstacle. According to the method, a dynamic aircraft risk probability field suitable for a low-altitude aircraft is designed on the basis of an offset model surrounding Gaussian distribution, field intensity distribution of each risk source in space and an aircraft state probability distribution and dynamic risk field model constructed through fusion, decision support is provided for obstacle avoidance of the aircraft, and the risk probability of the aircraft is improved. And the safety of low-altitude flight is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of aircraft control technology, and in particular to a risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty. Background Art

[0002] As urban populations continue to grow and urban functions continue to improve, residents' demand for efficient travel continues to rise. However, traditional transportation systems that rely on ground roads have reached capacity bottlenecks and are unable to effectively cope with high-density travel demands. Urban low-altitude airspace, as a new type of space resource that has not yet been fully developed, is seen as an important supplement to future urban travel systems. However, compared with traditional high-altitude aviation operating environments, urban low-altitude airspace is characterized by narrow space, dense obstacles, and high flight density. Aircraft must frequently respond to potential threats from multiple risk sources during operation, such as buildings, other aircraft, and sudden weather events. Summary of the Invention

[0003] In view of this, an embodiment of the present application provides a risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty to improve the flight safety of the aircraft.

[0004] An aspect of an embodiment of the present application provides a risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty, the method comprising the following steps:

[0005] constructing a bounding Gaussian distribution for uncertainty in the state of the aircraft based on the state of the aircraft and historical deviation data of the state;

[0006] Generate the field intensity distribution of each risk source in space according to the Gaussian function;

[0007] Constructing a risk probability field according to the enclosing Gaussian distribution and the field intensity distribution corresponding to each of the risk sources;

[0008] Determining the expected field strength of the aircraft in the risk probability field and thus determining the force on the aircraft;

[0009] The trajectory of the aircraft is adjusted according to the force to avoid obstacles.

[0010] In some embodiments, constructing a bounding Gaussian distribution for the uncertainty of the aircraft state based on the aircraft state and the historical deviation data of the state includes the following steps:

[0011] Constructing a first enveloping Gaussian distribution model according to the position deviation of the aircraft;

[0012] Constructing a second enveloping Gaussian distribution model according to the speed deviation of the aircraft;

[0013] The state of the aircraft includes position and speed.

[0014] In some embodiments, constructing a first enclosing Gaussian distribution model based on the position deviation of the aircraft includes the following steps:

[0015] The probability density of the position deviation of the aircraft is modeled using multivariate Gaussian distribution:

[0016] ;

[0017] ;

[0018] in, represents the error probability distribution; is the covariance of different states of the aircraft in different directions, which is used to describe the correlation in the corresponding directions; is the covariance matrix, which is a diagonal matrix when the errors in all directions are independent, that is, the errors of the aircraft in the X, Y and Z directions are completely independent.

[0019] In some embodiments, generating the field intensity distribution of each risk source in space according to the Gaussian function includes the following steps:

[0020] Generating a risk field distribution in space for each of the risk sources according to a Gaussian function;

[0021] The field intensity distribution of the risk field distribution is:

[0022] ;

[0023] in, Represents the scaling factor, which is used to adjust the field intensity scaling ratio; is the distance from the specified location in the airspace to the field source; is the variance, which represents the gradient strength of the potential energy field;

[0024] The risk field distribution is changed into a dynamic risk field in combination with the dynamic properties of the aircraft;

[0025] The dynamic risk field is:

[0026] ;

[0027] ;

[0028] ;

[0029] in, is the field source; Indicates the mass of the obstacle; is the covariance matrix, describing the shape of the field strength distribution; The angle between the line connecting the aircraft and the obstacle and the direction of the obstacle's velocity; is the attenuation coefficient; and They represent the distribution of field strength in the velocity direction and the vertical velocity direction respectively.

[0030] In some embodiments, determining the expected field strength of the aircraft in the risk probability field includes the following steps:

[0031] The expected field strength at any position in space after considering the aircraft state offset distribution is determined as:

[0032] ;

[0033] in, represents the expected field strength, is the probability density function of the aircraft position; is the probability density function of the aircraft speed; Indicates that if the aircraft is in state In position Perceived risk field strength.

[0034] In some embodiments, determining the forces on the aircraft in the risk probability field includes the following steps:

[0035] Determine the field strength force that the aircraft is subject to within the range of the field strength generated by the obstacle:

[0036] ;

[0037] in, The speed of the aircraft to perform the obstacle avoidance maneuver; The mass of the aircraft to perform the obstacle avoidance maneuver;

[0038] Determine the attraction of the aircraft at the target point as:

[0039] ;

[0040] in, The current position of the aircraft; is the location of the target point; is the scaling factor.

[0041] In some embodiments, after adjusting the trajectory of the aircraft according to the force to avoid obstacles, the method further includes the following steps:

[0042] The state of the aircraft is updated, and then the process returns to the step of constructing a bounding Gaussian distribution for the uncertainty of the aircraft state according to the aircraft state and the historical deviation data of the state.

[0043] Another aspect of the present application further provides a risk assessment and obstacle avoidance device for low-altitude aircraft state uncertainty, the device comprising:

[0044] an uncertainty modeling unit, configured to construct a bounding Gaussian distribution for the uncertainty of the aircraft state based on the aircraft state and historical deviation data of the state;

[0045] A risk field strength generating unit, configured to generate the field strength distribution of each risk source in space according to a Gaussian function;

[0046] a risk probability field construction unit, configured to construct a risk probability field according to the enclosing Gaussian distribution and the field intensity distribution corresponding to each of the risk sources;

[0047] a force determination unit, configured to determine an expected field strength of the aircraft in the risk probability field and thereby determine the force on the aircraft;

[0048] The obstacle avoidance unit is used to adjust the trajectory of the aircraft according to the force to avoid obstacles.

[0049] Another aspect of the embodiments of the present application further provides an electronic device, including a processor and a memory;

[0050] The memory is used to store programs;

[0051] The processor executes the program to implement any of the above methods.

[0052] Another aspect of the embodiments of the present application further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement any of the above methods.

[0053] This application has at least the following beneficial effects:

[0054] This application can construct an enclosing Gaussian distribution for the uncertainty of the aircraft state based on the state of the aircraft and the historical deviation data of the state; generate the field strength distribution of each risk source in space based on the Gaussian function; construct a risk probability field based on the enclosing Gaussian distribution and the field strength distribution corresponding to each risk source; determine the expected field strength of the aircraft in the risk probability field and then determine the force on the aircraft; adjust the trajectory of the aircraft according to the force to avoid obstacles. This application describes the actual operating deviation of the aircraft under the influence of observation errors based on the offset model of the enclosing Gaussian distribution, which serves as an important basis for the construction of the risk field. Secondly, a dynamic field strength design method based on the operating state of the aircraft is proposed, and the field strength distribution of each risk source in space is generated according to the Gaussian function to improve the adaptability of the model in a dynamic environment. Finally, the constructed aircraft state probability distribution and the dynamic risk field model are integrated to design a dynamic aircraft risk probability field suitable for low-altitude aircraft, which provides decision support for aircraft obstacle avoidance and effectively improves the safety of low-altitude flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flowchart of a risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty provided in an embodiment of the present application;

[0057] Figure 2 A flowchart of an obstacle avoidance risk probability field model based on aircraft state uncertainty provided in an embodiment of the present application;

[0058] Figure 3 An example diagram of the enclosed Gaussian distribution provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of aircraft position offset provided in an embodiment of the present application;

[0060] Figure 5 An example diagram of the spatial field intensity distribution of the dynamic risk field provided in the embodiment of the present application;

[0061] Figure 6 A structural block diagram of a risk assessment and obstacle avoidance device for low-altitude aircraft state uncertainty provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] Before describing the embodiments of the present application in detail, some of the related technologies involved in the embodiments of the present application are first described as follows:

[0064] Given the limitations of existing technologies, accurately modeling and real-time sensing of potential risks in complex airspace, and effectively incorporating these risks into flight path planning and obstacle avoidance strategies, is a key issue in ensuring the safe operation of urban low-altitude transportation systems. Establishing a risk assessment mechanism that dynamically responds to changes in the airspace environment can provide aircraft with safer path decision support, reduce the probability of conflicts and collisions at the source, and lay the foundation for the sustainable operation of urban low-altitude transportation.

[0065] A variety of risk assessment and obstacle avoidance methods have been proposed, such as A*, RRT, geometric obstacle avoidance, and speed barrier methods. While effective in structured or static environments, these methods typically require high computational resources and struggle to adapt to the complex, dynamic, and real-time low-altitude environments. Furthermore, aircraft are subject to errors during actual operation, causing deviations between their observed state (speed, position) and true state, leading to deviations from the planned path and entry into potentially high-risk areas. Therefore, traditional methods lack safety and robustness in dynamic and uncertain flight environments, making it difficult to meet the dual requirements of real-time and reliability for urban low-altitude airspace risk assessment.

[0066] To improve the operational safety of low-altitude aircraft in complex environments, existing research on obstacle avoidance and risk assessment in urban low-altitude airspace has proposed a variety of path planning and risk modeling methods. Common path planning methods include graph search algorithms such as A*, Dijkstra, D*, Rapid Random Trees (RRT), and improved versions of RRT*, as well as geometric obstacle avoidance methods. These methods offer significant effectiveness and ease of implementation in structured or static environments, particularly in environments with known maps or predictable static obstacles, enabling effective obstacle avoidance paths. However, these traditional methods often face two major challenges: First, the path planning process requires high computational resources, making it difficult to meet real-time requirements in the dynamic and unstructured urban low-altitude airspace. Second, most of these methods assume deterministic aircraft states for modeling and planning, ignoring the uncertainty in the aircraft's state during actual operation due to sensor errors, wind disturbances, and environmental interference. This, in turn, impacts the reliability of risk assessment and the rationality of obstacle avoidance.

[0067] To address the problem of modeling the real-time interaction risks of aircraft during low-altitude operations, some recent studies have introduced "risk field" or "potential field" models. These methods, from a field theory perspective, characterize the impact of potential threat sources (such as obstacles and other aircraft) in the airspace on aircraft operational safety. These methods, often inspired by artificial potential field theory, abstract risk sources as repulsive sources and target points as attractive sources, forming a distributed potential field or risk field in space. Aircraft adjust their paths based on the forces acting in the field to avoid obstacles or approach their targets. For example, some researchers have constructed three-dimensional risk field models that incorporate dynamic obstacles, taking into account the distance between the aircraft and the obstacles. Other studies have attempted to use multi-source fusion strategies to incorporate static and dynamic obstacles, as well as airspace rules, into the potential field modeling framework to reflect a more realistic risk distribution. These models overcome the limitations of traditional obstacle avoidance algorithms, which struggle to handle complex distributions in continuous space, and provide intuitive and continuous risk information for aircraft obstacle avoidance.

[0068] However, existing risk field models still have certain flaws, which affect the accuracy of risk assessment for aircraft in complex and dynamic airspace. First, existing risk field models assume that the aircraft's state is fully observable and deterministic, meaning that the aircraft's actual position is highly consistent with its observed position. This assumption is reasonable under ideal conditions, but in real-world low-altitude urban airspace, aircraft states are often affected by a variety of factors, resulting in uncertainty. For example, navigation systems such as GNSS have accuracy limitations and are susceptible to interference from factors such as tall buildings and weather, leading to position estimation errors. Furthermore, aircraft are susceptible to external disturbances such as airflow disturbances and execution system errors during flight, potentially causing slight but significant deviations in their position or velocity. If risk assessment models fail to account for this uncertainty, risk assessment results may deviate significantly from actual conditions, resulting in two risks: first, an aircraft operating in a presumed safe zone may enter a high-risk zone due to actual deviations; second, the system may overestimate the risk in certain areas, limiting the flexibility of aircraft path planning and reducing airspace utilization efficiency. In addition, some risk field models consider relatively limited factors during the field strength design process. They generally calculate the repulsive force field based on the distance between the aircraft and the obstacle, ignoring the influence of dynamic characteristics such as aircraft speed, type, mass, and direction of movement, making it difficult to accurately reflect the risk of aircraft interaction in the airspace.

[0069] Explanation of relevant terms:

[0070] (1) Risk field model.

[0071] The risk field is the core model used in this application to represent the spatial risk distribution. Its essence is a spatial mapping function based on the idea of ​​potential field, which is used to describe the risk impact of obstacles or other aircraft in the surrounding area. The intensity of the risk field is usually related to the spatial distance between the aircraft and the obstacle. The closer the distance, the higher the risk. In addition, the risk field in this technology also introduces dynamic properties such as the speed and mass of the obstacle, so that the risk field has directionality and dynamic perception capabilities, which can more realistically reflect the potential threat of obstacles to aircraft.

[0072] (2) Modeling of the probability of aircraft state deviation.

[0073] During actual operation, the aircraft is affected by a variety of interference factors, such as satellite positioning errors, sensor noise, wind disturbances, etc., which lead to differences between its actual state and the observed state. In order to characterize this offset characteristic, this application adopts a probabilistic modeling method to establish independent probability distribution models for the position and speed of the aircraft respectively. In this way, the state of the aircraft is no longer regarded as a definite value, but obeys a certain probability distribution. In addition, this "enclosed modeling" idea does not pursue an accurate fit to the actual error distribution, but adopts a relatively more conservative modeling method to cover all areas where the aircraft may appear, thereby improving the system's tolerance to errors and the safety redundancy of the modeling.

[0074] (3) Ideas for constructing the risk probability field.

[0075] Traditional risk potential fields only consider the spatial relationship between the aircraft's current position and obstacles, making it difficult to reflect the overall risk it faces under different possible states. Based on the uncertainty distribution of aircraft states, this application proposes a method for constructing a risk probability field. This method weights and integrates the perceived risk field strengths at each possible aircraft position and speed to form a more reasonable risk space map, thereby achieving a safer and more proactive obstacle avoidance control strategy.

[0076] (4) Repulsive force modeling and navigation guidance mechanism.

[0077] After the risk probability field is constructed, this application calculates the repulsive force on the aircraft through the relationship between the risk field strength and the current state of the aircraft, which is used to guide it to avoid high-risk areas. At the same time, the aircraft is also attracted by the target point. The two force fields act together on the aircraft dynamics model to drive it to complete obstacle avoidance and navigation control. Compared with traditional methods based on hard constraints or heuristic planning, this technology achieves more natural and continuous path adjustment through potential field force guidance, and has good responsiveness.

[0078] (5) Overall modeling and system implementation process.

[0079] The application process includes, from bottom to top, the following: aircraft state uncertainty modeling, risk field modeling, risk probability field generation, and repulsion and attraction calculations, forming a closed-loop control system that can be iterated in real time. During operation, the system generates a probability distribution based on the aircraft's current observed state, and uses this to assess its risk level in the airspace, thereby driving the dynamic adjustment of the obstacle avoidance path. This method is suitable for a variety of application scenarios, including low-altitude urban areas, logistics routes, and dense traffic. It is particularly robust and practical in environments with a large amount of uncertainty and interference factors.

[0080] Reference Figure 1 The embodiment of the present application provides a risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty, which specifically includes the following steps S100~S140:

[0081] S100: constructing a bounding Gaussian distribution for uncertainty of the aircraft state based on the aircraft state and historical deviation data of the state;

[0082] S110: generating the field intensity distribution of each risk source in space according to the Gaussian function;

[0083] S120: Constructing a risk probability field according to the enclosing Gaussian distribution and the field intensity distribution corresponding to each risk source;

[0084] S130: Determine the expected field strength of the aircraft in the risk probability field and thereby determine the force on the aircraft;

[0085] S140: Adjusting the trajectory of the aircraft according to the force to avoid obstacles.

[0086] In some embodiments, constructing a bounding Gaussian distribution for the uncertainty of the aircraft state based on the aircraft state and the historical deviation data of the state includes the following steps:

[0087] Constructing a first enveloping Gaussian distribution model according to the position deviation of the aircraft;

[0088] Constructing a second enveloping Gaussian distribution model according to the speed deviation of the aircraft;

[0089] The state of the aircraft includes position and speed.

[0090] In some embodiments, constructing a first enclosing Gaussian distribution model based on the position deviation of the aircraft includes the following steps:

[0091] The probability density of the position deviation of the aircraft is modeled using multivariate Gaussian distribution:

[0092] ;

[0093] ;

[0094] in, represents the error probability distribution; is the covariance of different states of the aircraft in different directions, which is used to describe the correlation in the corresponding directions; is the covariance matrix, which is a diagonal matrix when the errors in all directions are independent, that is, the errors of the aircraft in the X, Y and Z directions are completely independent.

[0095] In some embodiments, generating the field intensity distribution of each risk source in space according to the Gaussian function includes the following steps:

[0096] Generating a risk field distribution in space for each of the risk sources according to a Gaussian function;

[0097] The field intensity distribution of the risk field distribution is:

[0098] ;

[0099] in, Represents the scaling factor, which is used to adjust the field intensity scaling ratio; is the distance from the specified location in the airspace to the field source; is the variance, which represents the gradient strength of the potential energy field;

[0100] The risk field distribution is changed into a dynamic risk field in combination with the dynamic properties of the aircraft;

[0101] The dynamic risk field is:

[0102] ;

[0103] ;

[0104] ;

[0105] in, is the field source; Indicates the mass of the obstacle; is the covariance matrix, describing the shape of the field strength distribution; The angle between the line connecting the aircraft and the obstacle and the direction of the obstacle's velocity; is the attenuation coefficient; and They represent the distribution of field strength in the velocity direction and the vertical velocity direction respectively.

[0106] In some embodiments, determining the expected field strength of the aircraft in the risk probability field includes the following steps:

[0107] The expected field strength at any position in space after considering the aircraft state offset distribution is determined as:

[0108] ;

[0109] in, represents the expected field strength, is the probability density function of the aircraft position; is the probability density function of the aircraft speed; Indicates that if the aircraft is in state In position Perceived risk field strength.

[0110] In some embodiments, determining the forces on the aircraft in the risk probability field includes the following steps:

[0111] Determine the field strength force that the aircraft is subject to within the range of the field strength generated by the obstacle:

[0112] ;

[0113] in, The speed of the aircraft to perform the obstacle avoidance maneuver; The mass of the aircraft to perform the obstacle avoidance maneuver;

[0114] Determine the attraction of the aircraft at the target point as:

[0115] ;

[0116] in, The current position of the aircraft; is the location of the target point; is the scaling factor.

[0117] In some embodiments, after adjusting the trajectory of the aircraft according to the force to avoid obstacles, the method further includes the following steps:

[0118] The state of the aircraft is updated, and then the process returns to the step of constructing a bounding Gaussian distribution for the uncertainty of the aircraft state according to the aircraft state and the historical deviation data of the state.

[0119] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0120] In order to overcome the problem that the existing technology fails to fully consider the uncertainty of aircraft state and the risk field modeling is imperfect, this embodiment proposes a risk assessment and obstacle avoidance scheme for low-altitude aircraft state uncertainty. This scheme constructs a risk probability field modeling framework that integrates the uncertainty of aircraft state. First, the actual operation deviation of the aircraft under the influence of observation error is described based on the offset model of the surrounding Gaussian distribution, which serves as an important basis for risk field construction. Secondly, the influence of the dynamic attributes of the aircraft such as speed, heading, and mass on the risk field strength is further considered, and a dynamic field strength design method based on the aircraft operating state is proposed to improve the adaptability of the model in a dynamic environment. Finally, the constructed aircraft state probability distribution and the dynamic risk field model are integrated to design a dynamic aircraft risk probability field suitable for low-altitude aircraft, which provides decision support for aircraft obstacle avoidance and effectively improves the safety of low-altitude flight. The obstacle avoidance process based on the risk probability field model of aircraft state uncertainty is as follows: Figure 2 shown.

[0121] Specifically, this embodiment may include the following technical solutions:

[0122] 1. Construction of aircraft state uncertainty distribution model.

[0123] During actual low-altitude aircraft operations, their observed positions are inevitably affected by various error sources, such as GNSS positioning errors, sensor noise, environmental occlusion, and signal multipath. These errors can lead to deviations between the aircraft's actual operating state (position, velocity) and its observed state. To accurately assess the safety of an aircraft's state in airspace, this embodiment establishes an uncertainty model for the aircraft's operating state and, based on this model, constructs a risk probability field.

[0124] This model mainly considers the dynamic state characteristics of the three-dimensional position and velocity of the aircraft, combines the uncertainty of the aircraft's motion, and infers the state distribution that may appear in the airspace based on the aircraft state deviation distribution modeling. However, building a high-precision aircraft offset probability distribution model usually requires a large amount of historical observation data, and the high computational cost makes it difficult to meet the requirements of real-time and generalization capabilities in actual systems. Therefore, this embodiment adopts the idea of ​​surrounding Gaussian distribution to model the aircraft state deviation probability, and constructs a Gaussian distribution with larger variance and wider coverage, which is used to "surround" the state area that the aircraft may appear in a certain period of time, such as Figure 3 This approach not only effectively simplifies the modeling process but also significantly reduces reliance on the scale and quality of historical data. Under the bracketing strategy, even if the true deviation distribution exhibits a non-Gaussian shape, it can still be approximated and contained by a conservative Gaussian model, ensuring the security and robustness of risk assessment.

[0125] Under this modeling assumption, when the deviations of the aircraft in the three orthogonal directions (X, Y, Z) are independent of each other, the position error and velocity error of the aircraft can be modeled as independent three-dimensional Gaussian distributions, such as Figure 4 As shown. Its probability obeys the following function form:

[0126] (1)

[0127] (2)

[0128] (3)

[0129] in, is the mean value of trajectory deviation, is the variance, which describes the uncertainty of the trajectory deviation.

[0130] However, due to signal obstruction caused by buildings or the influence of meteorological factors such as wind on the aircraft, the position and velocity distribution of the aircraft often have a certain correlation in various directions. Therefore, in order to accurately model the correlation of the aircraft's offset in different directions, this embodiment uses a multivariate Gaussian distribution to model the correlation of the aircraft's position deviation. Its probability density is:

[0131] (4)

[0132] (5)

[0133] in represents the error probability distribution; is the covariance of different aircraft states in different directions, which is used to describe their correlation in each direction; is the covariance matrix, which is a diagonal matrix when the errors in each direction are independent, meaning that the errors in the X, Y, and Z directions are completely independent. Furthermore, since position error and velocity error are usually independent of each other, a Gaussian distribution model based on the position and velocity errors of the aircraft should be constructed separately during modeling.

[0134] In practical low-altitude airspace monitoring and management, accurately understanding and assessing the probability of an aircraft drifting into a specific area is a key issue in ensuring flight safety. Because aircraft state drift exhibits a continuous probability density, its uncertainty manifests as a probability distribution across three-dimensional space. This continuous drift probability density can be mapped to discrete airspace regions to better measure an aircraft's occupation of airspace resources. The probability of an aircraft drifting within a specified range grid can be calculated through multiple integrals in the x, y, and z dimensions.

[0135] 2. Dynamic risk field modeling for aircraft operation risks.

[0136] To accurately characterize the spatial distribution of multi-source, multi-type risks in complex air traffic scenarios, this example proposes a risk modeling method based on potential field theory. Based on potential field theory, this method models the interactions between aircraft and other risk factors and target points in the airspace as "force field effects" in space, describing the multiple risk pressures that aircraft may encounter during operation.

[0137] In a complex low-altitude environment, aircraft are often affected by a variety of factors, including operational interference from other aircraft, risks to their own operating status, etc. These influences do not exist in isolation, but are intertwined with each other and change dynamically, forming a highly coupled risk interaction system. In order to effectively assess the comprehensive interactive risks faced by aircraft in this system, it is difficult to meet the needs by relying solely on discrete point obstacles or static boundary models. This embodiment abstracts the above-mentioned various risk sources as "potential field sources" in space, and introduces a Gaussian function to describe the risk field distribution generated in space. The field strength distribution is shown below:

[0138] (6)

[0139] in Represents the scaling factor, which is used to adjust the field strength scaling ratio and can be determined based on the aircraft type; is the distance from the specified location in the airspace to the field source; is the variance, which represents the gradient strength of the potential energy field;

[0140] However, this type of risk potential field model still has certain limitations. The construction of the risk field is mainly based on the spatial distance between the aircraft and the risk source, and does not fully introduce the aircraft's own motion state factors, such as speed, heading and mass. In actual flight, the greater the speed of the aircraft, the greater its risk, and the more difficult it is to quickly adjust the path to avoid the risk; the difference in aircraft mass will also affect its ability to respond to external forces; and the heading directly determines the interaction between the direction of risk action and the direction of aircraft movement. Ignoring these dynamic properties may lead to a decrease in the risk field's ability to guide actual obstacle avoidance behavior, especially in high-speed or large-mass aircraft operation scenarios, where there may be deviations between its potential energy response model and the actual flight response. Therefore, this embodiment is improved on the basis of the Gaussian risk field to obtain the following dynamic risk field:

[0141] (7)

[0142] (8)

[0143] (9)

[0144] in is the source of the field, i.e. the speed of the obstacle; Indicates the mass of the obstacle; is the covariance matrix, describing the shape of the field strength distribution; The angle between the line connecting the aircraft and the obstacle and the direction of the obstacle's velocity; is the attenuation coefficient; and They represent the distribution of field strength in the velocity direction and the vertical velocity direction respectively. The dynamic field strength distribution generated by the aircraft during operation is shown in the vertical direction. Figure 5 As shown, its shape is highly correlated with the speed direction, thus reflecting the risk differences under different motion states.

[0145] When an aircraft is within the range of the field strength generated by an obstacle (other aircraft), it will be affected by the field strength force and will perform obstacle avoidance maneuvers. The force on the aircraft can be calculated using the following formula:

[0146] (10)

[0147] in The speed of the aircraft to perform the obstacle avoidance maneuver; is the mass of the aircraft.

[0148] In addition to the repulsive force generated by the field strength of the obstacle, the aircraft is also attracted by the target point. The magnitude of the attraction is related to the current distance between the aircraft and the target point. Its specific definition can be expressed as:

[0149] (11)

[0150] in is the current location of the UAV; is the location of the target point; is the scaling factor.

[0151] 3. Construction of risk probability field integrating uncertainty modeling.

[0152] In aircraft obstacle avoidance missions in complex low-altitude environments, traditional deterministic risk potential fields fail to accurately reflect an aircraft's actual exposure to potential risk sources because they ignore the uncertainty of the aircraft's state. This embodiment introduces a risk probability field modeling method that calculates the aircraft's expected perception of risk sources under uncertain position distribution from an overall probability perspective, significantly improving the foresight and robustness of obstacle avoidance strategies.

[0153] In the above scheme, the position and velocity of the aircraft have been modeled as random variables with probability distribution. Based on this, the expected risk field strength that it may perceive at each position in space can be inferred, thereby obtaining the risk probability field covering the entire three-dimensional airspace. Assume that the aircraft field strength at the mean is , then considering the state offset distribution at any position in the space The expected field strength is:

[0154] (12)

[0155] in is the probability density function of the aircraft position; is the probability density function of the aircraft speed; Indicates that if the aircraft is in state In position Perceived risk field strength.

[0156] This integral expression captures the expected effect of the risk field generated by an aircraft's perception of obstacles under any given spatial condition. Unlike traditional point-to-point risk perception, the risk probability field establishes a more realistic and continuous risk distribution model through a weighted assessment of the aircraft's position and velocity distribution.

[0157] In summary, this embodiment includes the following technical means:

[0158] (1) Modeling mechanism for offset distribution that integrates uncertainty in aircraft state: By constructing a probabilistic offset model based on disturbance effects and positioning errors, the degree of deviation between the aircraft in actual operation and its planned path is quantified, providing basic support for risk assessment.

[0159] (2) Dynamic risk field modeling method for aircraft operation risks: Based on the artificial potential field theory, the risk field strength function of the dynamic attributes of the aircraft, such as speed, heading, and mass, is considered to improve the response capability to changes in the aircraft state.

[0160] (3) Risk probability field construction method integrating uncertainty modeling: Based on the risk field, the probability distribution of aircraft offset is integrated, and the observation deviations of various risk sources are converted into probability expressions to form a risk probability field model, thereby realizing the probabilistic characterization of the uncertainty of the aircraft operating status in the potential area and improving the ability to assess the risk of aircraft operation in the airspace.

[0161] (4) Low-altitude aircraft obstacle avoidance path planning strategy based on risk field guidance: Based on the risk probability field model, a set of aircraft obstacle avoidance methods with real-time computing capabilities are designed to guide the aircraft to perform obstacle avoidance operations based on risks in real time in a dynamic airspace environment.

[0162] The beneficial effects of this embodiment include:

[0163] (1) Comprehensively consider the uncertainty of aircraft state and make modeling more accurate.

[0164] This embodiment addresses the issue of prior art that fails to fully account for aircraft observation errors by introducing aircraft state uncertainty and risk field modeling. Traditional risk field models typically assume accurate and error-free aircraft observations and employ static or simplified risk assessment methods. However, in complex and dynamic low-altitude environments, aircraft are not only affected by various obstacles and other aircraft, but also experience state deviations due to positioning errors, external meteorological interference, and unforeseen factors in dynamic path planning. These factors significantly impact risk assessments based on the real-time state of the aircraft and its surrounding airspace.

[0165] To address this issue, this embodiment innovatively proposes a risk probability field based on aircraft state offsets, building upon the general risk field. This field dynamically models the aircraft's positioning error and, by calculating the error distribution, establishes an uncertainty model for the aircraft's state. This innovation allows risk assessment to consider not only the aircraft's current state but also the distribution of its errors, enabling a more accurate analysis of the risk of interactions between aircraft and other aircraft in complex airspace environments. This avoids the risk assessment bias inherent in traditional methods that ignore aircraft uncertainty.

[0166] (2) Real-time dynamic risk field design to improve obstacle avoidance safety.

[0167] The risk field design in this embodiment further considers the dynamic properties of aircraft, such as speed and heading, enabling more accurate modeling of the interaction between the aircraft and other factors in the airspace. Compared to traditional static models, the dynamic risk field dynamically updates the risk distribution based on real-time changes in aircraft status, better aligning it with the actual flight environment and effectively improving the accuracy and timeliness of risk assessment. This helps aircraft proactively identify potential risks and implement more proactive obstacle avoidance strategies.

[0168] In addition, this embodiment also designs a force model for the aircraft in the field, taking into account the external forces acting on the aircraft in complex environments and using this force information to guide the aircraft in obstacle avoidance. By simulating the influence of the external risk field on the aircraft, the aircraft is guided to react to changes in the forces acting on it, thereby achieving obstacle avoidance and reducing potential collision risks. This design not only enhances the aircraft's obstacle avoidance capabilities in complex dynamic environments, but also enables it to fly safely under various external influences. Compared with traditional static models, this embodiment can more effectively adapt to sudden changes in the flight environment, providing a safer and more reliable obstacle avoidance solution.

[0169] Reference Figure 6The embodiment of the present application provides a risk assessment and obstacle avoidance device for low-altitude aircraft state uncertainty, including:

[0170] an uncertainty modeling unit, configured to construct a bounding Gaussian distribution for the uncertainty of the aircraft state based on the aircraft state and historical deviation data of the state;

[0171] A risk field strength generating unit, configured to generate the field strength distribution of each risk source in space according to a Gaussian function;

[0172] a risk probability field construction unit, configured to construct a risk probability field according to the enclosing Gaussian distribution and the field intensity distribution corresponding to each of the risk sources;

[0173] a force determination unit, configured to determine an expected field strength of the aircraft in the risk probability field and thereby determine the force on the aircraft;

[0174] The obstacle avoidance unit is used to adjust the trajectory of the aircraft according to the force to avoid obstacles.

[0175] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0176] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0177] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0178] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0179] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0180] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0181] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0182] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0183] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0184] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present application, and these equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty, characterized by: The method comprises the following steps: constructing a bounding Gaussian distribution for uncertainty in the state of the aircraft based on the state of the aircraft and historical deviation data of the state; Generate the field intensity distribution of each risk source in space according to the Gaussian function; Constructing a risk probability field according to the enclosing Gaussian distribution and the field intensity distribution corresponding to each of the risk sources; Determining the expected field strength of the aircraft in the risk probability field and thus determining the force on the aircraft; The trajectory of the aircraft is adjusted according to the force to avoid obstacles.

2. The method for risk assessment and obstacle avoidance for low-altitude aircraft state uncertainty according to claim 1, characterized in that: The step of constructing a bounding Gaussian distribution for the uncertainty of the aircraft state based on the aircraft state and the historical deviation data of the state comprises the following steps: Constructing a first enveloping Gaussian distribution model according to the position deviation of the aircraft; Constructing a second enveloping Gaussian distribution model according to the speed deviation of the aircraft; The state of the aircraft includes position and speed.

3. The method for risk assessment and obstacle avoidance for low-altitude aircraft state uncertainty according to claim 2, characterized in that: The method of constructing a first enclosing Gaussian distribution model according to the position deviation of the aircraft includes the following steps: The probability density of the position deviation of the aircraft is modeled using multivariate Gaussian distribution: ； ； in, represents the error probability distribution; is the covariance of different states of the aircraft in different directions, which is used to describe the correlation in the corresponding directions; is the covariance matrix, which is a diagonal matrix when the errors in all directions are independent, that is, the errors of the aircraft in the X, Y and Z directions are completely independent.

4. The method for risk assessment and obstacle avoidance for low-altitude aircraft state uncertainty according to claim 1, characterized in that: Generating the field intensity distribution of each risk source in space according to the Gaussian function includes the following steps: Generating a risk field distribution in space for each of the risk sources according to a Gaussian function; The field intensity distribution of the risk field distribution is: ； in, Represents the scaling factor, which is used to adjust the field intensity scaling ratio; is the distance from the specified location in the airspace to the field source; is the variance, which represents the gradient strength of the potential energy field; The risk field distribution is changed into a dynamic risk field in combination with the dynamic properties of the aircraft; The dynamic risk field is: ； ； ； in, is the field source; Indicates the mass of the obstacle; is the covariance matrix, describing the shape of the field strength distribution; The angle between the line connecting the aircraft and the obstacle and the direction of the obstacle's velocity; is the attenuation coefficient; and They represent the distribution of field strength in the velocity direction and the vertical velocity direction respectively.

5. The method for risk assessment and obstacle avoidance for low-altitude aircraft state uncertainty according to claim 1, characterized in that: Determining the expected field strength of the aircraft in the risk probability field includes the following steps: The expected field strength at any position in space after considering the aircraft state offset distribution is determined as: ； in, represents the expected field strength, is the probability density function of the aircraft position; is the probability density function of the aircraft speed; Indicates that if the aircraft is in state In position Perceived risk field strength.

6. The method for risk assessment and obstacle avoidance for low-altitude aircraft state uncertainty according to claim 1, characterized in that: Determining the forces on the aircraft in the risk probability field includes the following steps: Determine the field strength force that the aircraft is subject to within the range of the field strength generated by the obstacle: ； in, The speed of the aircraft to perform the obstacle avoidance maneuver; The mass of the aircraft to perform the obstacle avoidance maneuver; Determine the attraction of the aircraft at the target point as: ； in, The current position of the aircraft; is the location of the target point; is the scaling factor.

7. A risk assessment and obstacle avoidance method for low-altitude aircraft state uncertainty according to any one of claims 1 to 6, characterized in that: After adjusting the trajectory of the aircraft according to the force to avoid obstacles, the method further includes the following steps: The state of the aircraft is updated, and then the process returns to the step of constructing a bounding Gaussian distribution for the uncertainty of the aircraft state according to the aircraft state and the historical deviation data of the state.

8. A risk assessment and obstacle avoidance device for low-altitude aircraft state uncertainty, characterized in that: The device comprises: an uncertainty modeling unit, configured to construct a bounding Gaussian distribution for the uncertainty of the aircraft state based on the aircraft state and historical deviation data of the state; A risk field strength generating unit, configured to generate the field strength distribution of each risk source in space according to a Gaussian function; a risk probability field construction unit, configured to construct a risk probability field according to the enclosing Gaussian distribution and the field intensity distribution corresponding to each of the risk sources; a force determination unit, configured to determine an expected field strength of the aircraft in the risk probability field and thereby determine the force on the aircraft; The obstacle avoidance unit is used to adjust the trajectory of the aircraft according to the force to avoid obstacles.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 7.

Images