An Internet of Things-based UAV flight path control system

Abstract

This invention discloses an IoT-based UAV flight path control system, relating to the field of UAV control technology. It includes: an electromagnetic environment and flight status joint sensing module for collecting channel data and flight status data; a channel-flight control coupling risk assessment module for calculating a navigability index based on channel data and flight status data; a navigability map construction and prediction module for constructing a navigability map based on the electromagnetic environment navigability index and predicting the navigability index; a predictive path adaptive planning module for generating a safe path based on the predicted navigability index; and a flight control command generation and execution module for converting the safe path into flight control commands and executing them. This invention predicts coupling instability risks through the electromagnetic environment navigability index, employs a dual-threshold hierarchical response strategy, and combines multi-dimensional data fusion decision-making to improve the safety and mission execution efficiency of UAVs in complex electromagnetic environments.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and more specifically to an Internet of Things (IoT)-based UAV flight path control system. Background Technology

[0002] In the field of drone control technology, drone flight control in complex electromagnetic environments is crucial. With the development of Internet of Things (IoT) technology, drones are continuously improving their capabilities in communication and sensing. However, the complex electromagnetic environment and the diversity of flight states bring new challenges to drone flight path control.

[0003] In existing technologies, UAV path control systems struggle to effectively assess the coupling risks between communication links and flight control, cannot accurately distinguish between benign disconnections and malicious interference in communication links, lack the ability to predict the risk of channel-flight control coupling instability, and often misattribute flight accidents, resulting in low safety and mission execution efficiency of UAVs in complex electromagnetic environments. Summary of the Invention

[0004] The purpose of this invention is to provide an Internet of Things-based drone flight path control system, which solves the problems existing in the background technology.

[0005] To address the aforementioned technical problems, this invention provides an IoT-based drone flight path control system, comprising: an electromagnetic environment and flight status joint sensing module, used to collect channel data of the drone's communication link and its own flight status data;

[0006] The channel-flight control coupling risk assessment module is used to calculate the electromagnetic environment navigability index, which characterizes flight risk, based on the channel data and flight status data.

[0007] The navigability map construction and prediction module is used to construct a navigability map representing the spatial risk distribution based on the historical and real-time electromagnetic environment navigability index, and to predict the navigability index of future waypoints on the predetermined route.

[0008] A predictive path adaptive planning module is used to adaptively adjust the predetermined route based on the predicted navigability index to generate a safe route.

[0009] The flight control command generation and execution module is used to convert the safety path into specific flight control commands and execute them.

[0010] Preferably, the channel data includes channel state information, signal-to-noise ratio, and bit error rate; the flight state data includes three-dimensional position, velocity, linear acceleration, and angular velocity.

[0011] Preferably, the predictive path adaptive planning module adaptively adjusts the predetermined route in the following manner:

[0012] The predicted navigability index is compared with preset critical thresholds and warning thresholds;

[0013] If the predicted navigability index is lower than the critical threshold, route replanning is initiated based on the navigability map.

[0014] If the predicted navigability index falls between the critical threshold and the warning threshold, a risk downgrade operation is performed by reducing the target speed command or decreasing the upper limit of path curvature.

[0015] Preferably, the critical threshold is determined by calibrating the electromagnetic environment navigability index that causes control divergence in an experiment and taking the safe statistical lower bound of the electromagnetic environment navigability index data distribution; the warning threshold is a value set based on the critical threshold by a preset multiple.

[0016] Preferably, the path replanning adopts a heuristic path search algorithm. When searching for the optimal path in the navigability map, the algorithm makes a decision by combining the actual travel cost from the starting point to the current point and the estimated travel cost from the current point to the destination. The travel cost of each point is inversely proportional to the electromagnetic environment navigability index.

[0017] Preferably, the channel-flight control coupling risk assessment module calculates the electromagnetic environment navigability index in the following manner:

[0018] Based on the channel data and the flight status data, the comprehensive communication link quality index, the communication link volatility index, and the flight control command sensitivity index are calculated respectively.

[0019] Multiply the communication link volatility index by the flight control command sensitivity index, then multiply the product by the preset channel-flight control coupling coefficient and sum it with the value 1 to obtain the risk denominator.

[0020] The electromagnetic environment navigability index is obtained by dividing the comprehensive communication link quality index as the numerator by the risk denominator.

[0021] Preferably, the calculation method of the comprehensive communication link quality index is as follows: the signal-to-noise ratio, channel state information and bit error rate in the channel data are normalized, and then the processed values ​​are weighted and summed based on their respective preset weight coefficients.

[0022] Preferably, the communication link volatility index is calculated by: calculating the sample standard deviation of the time series of the comprehensive communication link quality index within a preset time window.

[0023] Preferably, the flight control command sensitivity index is calculated as follows: the ratio of the current linear acceleration and angular velocity to the preset maximum values ​​of the current linear acceleration and angular velocity are calculated respectively, and the two ratios are weighted and summed based on their respective preset weights, and the result is ensured to be no greater than 1 by a limiting function.

[0024] The present invention has the following beneficial effects:

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. By calculating the electromagnetic environment airworthiness index, the risk of channel-flight control coupling instability can be predicted, and potential dangers can be avoided in advance, realizing the transformation from passive response to active prediction.

[0027] 2. By adopting a dual-threshold decision-making logic, an intelligent risk response strategy is provided, which takes different measures at different stages of risk, ensuring both safety and task efficiency.

[0028] 3. By using multi-dimensional data fusion for decision-making, and comprehensively collecting deep channel data and complete flight status data, the accuracy and reliability of decision-making are improved. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a logic block diagram of the system of the present invention. Detailed Implementation

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0032] Example 1:

[0033] Please see Figure 1 The present invention provides an Internet of Things-based unmanned aerial vehicle (UAV) flight path control system, comprising: an electromagnetic environment and flight status joint sensing module, used to collect channel data of the UAV communication link and its own flight status data;

[0034] The channel-flight control coupling risk assessment module is used to calculate the electromagnetic environment navigability index, which characterizes flight risk, based on the channel data and flight status data.

[0035] The navigability map construction and prediction module is used to construct a navigability map representing the spatial risk distribution based on the historical and real-time electromagnetic environment navigability index, and to predict the navigability index of future waypoints on the predetermined route.

[0036] A predictive path adaptive planning module is used to adaptively adjust the predetermined route based on the predicted navigability index to generate a safe route.

[0037] The flight control command generation and execution module is used to convert the safety path into specific flight control commands and execute them.

[0038] This embodiment provides an IoT-based UAV flight path control system, whose fundamental innovation lies in constructing a complete closed loop from data perception, risk quantification, situation prediction to decision execution. Through the collaborative work of an electromagnetic environment and flight status joint perception module, a channel-flight control coupling risk assessment module, a navigability map construction and prediction module, a predictive path adaptive planning module, and a flight control command generation and execution module, the system deeply couples and integrates the traditionally separate issues of communication link quality and flight control stability. The direct result of this design concept is that the system no longer passively responds to communication interruption events, but actively predicts and avoids the risk of channel-flight control coupling instability caused by the coupling of communication link fluctuations and violent flight maneuvers. This paradigm shift from passive response to active prediction eliminates a large number of potential flight accidents at their source, thereby greatly improving the operational safety and mission success rate of UAVs in complex electromagnetic environments.

[0039] Example 2:

[0040] Channel data includes channel state information, signal-to-noise ratio, and bit error rate; flight state data includes three-dimensional position, velocity, linear acceleration, and angular velocity.

[0041] In this embodiment, the electromagnetic environment and flight status joint sensing module collects data with unprecedented depth and breadth to achieve accurate quantification of flight risks. Specifically, the channel data is not limited to a single signal strength indicator, but encompasses channel state information (CSI), signal-to-noise ratio (SNR), and bit error rate (BER), which can deeply characterize the physical layer characteristics of the link. This set of data together constitutes a comprehensive description of the health status of the communication link. At the same time, the flight status data includes the three-dimensional position, velocity, linear acceleration, and angular velocity required for a complete kinematic description of the UAV. The ingenuity of this data acquisition strategy lies in the fact that it provides sufficiently rich and highly relevant channel and flight status data for the subsequent channel-flight control coupling risk assessment module, ensuring the accuracy and reliability of the risk assessment. This constitutes a solid data foundation for realizing the leap from passive response to active prediction and avoidance technology.

[0042] Example 3:

[0043] The predictive path adaptive planning module adaptively adjusts the predetermined route in the following ways:

[0044] The predicted navigability index is compared with preset critical thresholds and warning thresholds;

[0045] If the predicted navigability index is lower than the critical threshold, route replanning is initiated based on the navigability map.

[0046] If the predicted navigability index falls between the critical threshold and the warning threshold, a risk degrading operation is performed by reducing the target speed command or decreasing the upper limit of path curvature.

[0047] The critical threshold is determined by calibrating the electromagnetic environment navigability index that causes control divergence in experiments and taking the safe statistical lower bound of the electromagnetic environment navigability index data distribution; the warning threshold is a value set by a preset multiple based on the critical threshold.

[0048] The predictive path adaptive planning module in this embodiment is the core of the entire system's decision-making process. Its operational logic demonstrates a high degree of intelligence and security. At its core is a dual-threshold decision-making mechanism, including key thresholds. With warning threshold The logic behind setting this threshold mechanism is a key step to ensure that those skilled in the art can reproduce and implement it.

[0049] Key threshold The calibration process is directly related to absolute flight safety, and its setting method is a repeatable experimental procedure: in a simulated or actual test environment, channel interference of different intensities is systematically injected into the UAV, while simultaneously instructing it to perform maneuvers of different intensities; the electromagnetic environment navigability index is comprehensively recorded each time that flight control divergence occurs, such as attitude angle error exceeding the preset safety range. The instantaneous value; finally, all the collected instability values. The values ​​constitute a dataset. This means that a safe statistical lower bound is set for the empirical cumulative distribution function of the dataset, for example, at the 5th percentile of that distribution; this method ensures that any value below this level is within a safe statistical bound. The predicted values ​​will all be regarded by the system as a high-risk state with a high probability of instability;

[0050] Warning threshold Then higher than This is used to define a potential risk zone; its numerical setting is based on historical experimental data showing phenomena that cause non-instability but significant performance degradation in drones, such as slight control jitter or significantly increased data transmission latency. Value range; one explicit way to define it is to set the value range. Set as A value between 1.5 and 2 times; this design provides a valuable buffer zone and intervention window before the system enters a truly dangerous state;

[0051] Based on this clear threshold logic, when the predictive path planning module detects the predictive navigability index of future waypoints on a predetermined route... Below When this happens, the system must immediately trigger path replanning; this is the highest level of safety response. Between and In between, the system prioritizes risk degradation operations, such as reducing the target speed command by a preset percentage, or imposing a stricter constraint in the path planner to forcibly reduce the upper limit of the path curvature; this hierarchical response mechanism achieves a delicate balance between ensuring absolute safety and maintaining task efficiency, demonstrating excellent engineering practicality.

[0052] Example 4:

[0053] The path replanning uses a heuristic path search algorithm. When searching for the optimal path in the navigability map, the algorithm makes a decision by combining the actual travel cost from the starting point to the current point and the estimated travel cost from the current point to the destination. The travel cost of each point is inversely proportional to the electromagnetic environment navigability index.

[0054] In this embodiment, when path replanning is triggered, the core heuristic path search algorithm, such as the A* algorithm, is used, and its decision-making is based on its unique cost function. A key innovation lies in the passage cost of each grid or node in the navigability map. It is set as the navigability index of the electromagnetic environment at that point. An inversely proportional function; a clear and feasible cost setting is ,because It is a normalized exponent, and the cost value will be between 0 and 1. The underlying logic of this design is that it cleverly transforms a complex physical concept, namely the airworthiness of the electromagnetic environment, into an intuitive mathematical quantity that can be directly optimized by the pathfinding algorithm: the passage cost. Therefore, it can be seen that the algorithm will instinctively avoid those... Areas with low electromagnetic fields and high travel costs, i.e., areas with harsh electromagnetic environments, tend to be traversed. The goal is to find a safe electromagnetic environment zone with high energy density and low travel costs. Ultimately, the algorithm not only finds the shortest path in terms of geometric distance, but also the safest and most reliable optimal flight path in terms of electromagnetic environment. This is the core technical effect pursued by this invention.

[0055] Example 5:

[0056] The channel-flight control coupling risk assessment module calculates the electromagnetic environment navigability index in the following manner:

[0057] Based on the channel data and the flight status data, the comprehensive communication link quality index, the communication link volatility index, and the flight control command sensitivity index are calculated respectively.

[0058] Multiply the communication link volatility index by the flight control command sensitivity index, then multiply the product by the preset channel-flight control coupling coefficient and sum it with the value 1 to obtain the risk denominator.

[0059] The electromagnetic environment navigability index is obtained by dividing the comprehensive communication link quality index as the numerator by the risk denominator.

[0060] The calculation method of the comprehensive communication link quality index is as follows: the signal-to-noise ratio, channel state information and bit error rate in the channel data are normalized, and then the processed values ​​are weighted and summed based on their respective preset weight coefficients.

[0061] The communication link volatility index is calculated as follows: within a preset time window, the sample standard deviation of the time series of the comprehensive communication link quality index is calculated.

[0062] The flight control command sensitivity index is calculated as follows: the ratio of the current linear acceleration and angular velocity to the preset maximum values ​​of the current linear acceleration and angular velocity are calculated respectively, and the two ratios are weighted and summed based on their respective preset weights, and the result is ensured to be no greater than 1 by a limiting function;

[0063] The core innovation of this embodiment, namely the channel-flight control coupling risk assessment module, achieves a quantitative assessment of flight risks through a series of sophisticated mathematical models; the module ultimately outputs an electromagnetic environment navigability index. Its calculation process reflects a profound insight into the sources of risk;

[0064] Calculate the electromagnetic environment navigability index The formula is:

[0065] ;

[0066] The electromagnetic environment navigability index represents the electromagnetic environment at spatial location p and time t. It is a dimensionless evaluation value and is the final output of this module.

[0067] It represents the overall quality of the communication link and is also a dimensionless value;

[0068] It represents the volatility of the communication link and is a dimensionless value.

[0069] This represents the sensitivity to flight control commands and is a dimensionless value.

[0070] The channel-flight control coupling coefficient is an adjustable dimensionless positive constant weight.

[0071] This formula is the core original of this invention. Its design concept draws upon the safe flight envelope concept from the aviation field and innovatively applies it to the electromagnetic space. Its fundamental motivation lies in the profound understanding that the real threat to UAV flight control systems is not simply poor communication quality, but rather the coupling effect between the severe fluctuations in the communication link and the sensitivity of the UAV's own control. (The denominator term is missing from the original text.) It is precisely this precise mathematical modeling of the coupling risk, where the constant 1 is introduced to ensure that there is no risk, i.e. or When the denominator is 0, the denominator is not zero, which is a standard robust design consideration;

[0072] This formula calculates The value is a key criterion for accurately distinguishing between benign disconnection and malicious interference in communication links; its output value will directly serve as the core input of the navigability map construction and prediction module and the predictive path adaptive planning module, driving subsequent situation prediction and path decision-making, and forming the cornerstone of the intelligent behavior of the entire system.

[0073] Coupling coefficient Calibration is crucial for model performance; its determination method is a standard experimental calibration procedure: through extensive flight experiments in a controlled electromagnetic environment, systematically recording data under different conditions. and Under these conditions, the critical data points that cause the UAV's attitude to become unstable or the control delay to exceed a preset threshold are identified. Using this experimental data, the optimal [data / mechanics] is fitted through regression analysis or machine learning methods. Value, making It can provide a stable and highly sensitive risk warning near the critical instability point;

[0074] Calculate the overall communication link quality The formula is:

[0075] ;

[0076] Let SNR, CSI and BER represent the dimensionless weighting coefficients, respectively. All three are positive constants and their sum is 1.

[0077] ^ indicates normalization, a well-known data preprocessing technique that aims to map raw physical quantities of different dimensions and ranges to a uniform interval of 0 to 1 for weighted summation, such as normalizing the signal-to-noise ratio. Through formula The calculation shows that, among which and These are reasonable upper and lower limits preset based on the characteristics of the communication system;

[0078] The original physical quantities are directly output by the physical layer of the communication receiver in the electromagnetic environment and flight status joint sensing module. They are directly obtainable measurement values, representing the signal-to-noise ratio, the error vector magnitude of the channel state information, and the bit error rate, respectively.

[0079] This formula originates from the concept of multi-index fusion evaluation of link quality in modern communication engineering. Practice has shown that a single index cannot fully reflect the true health status of the link. Therefore, this model integrates three key physical layer indices: signal-to-noise ratio, channel state information, and bit error rate, in order to obtain a more comprehensive and accurate evaluation result.

[0080] Next, calculate the communication link volatility. The formula is:

[0081] ;

[0082] The time window size used to calculate the standard deviation, i.e., the number of samples, is an adjustable positive integer parameter.

[0083] This represents the calculation result of Formula 2 at a past time point i;

[0084] ¯ represents the arithmetic mean. That is, in the case defined by parameter N, from arrive Within the time window, all The arithmetic mean of the values;

[0085] i represents the time step index in the summation operation, which is a standard mathematical notation;

[0086] N indicates that the average value is calculated based on a sample window of size N;

[0087] This formula is directly derived from the classic statistical method for measuring data dispersion, namely the sample standard deviation; its core idea is borrowed here to quantify the quality of communication links. Stability over a period of time; a highly volatile link, even if its average quality is acceptable, may harbor significant instantaneous risks;

[0088] Finally, the sensitivity to flight control commands is calculated. The formula is:

[0089] ;

[0090] represents the dimensionless weights of linear acceleration and angular velocity, respectively, both of which are positive constants and their sum is 1;

[0091] These represent the current linear acceleration vector and angular velocity vector of the UAV, respectively. These two vectors are the original inputs, which are measured and provided in real time by the inertial measurement unit (IMU) in the electromagnetic environment and flight status joint sensing module.

[0092] The Euclidean norm of a vector is a well-known method in the field for calculating the size of a vector;

[0093] The maximum performance parameters of the drone body are derived from the product technical specifications or performance manuals provided by the drone manufacturer. They are inherent attributes of the equipment and are publicly available information that can be accessed by those skilled in the art.

[0094] It is a limiting function that ensures The final value will not exceed 1, so that its physical meaning conforms to a normalized sensitivity index with a value range between 0 and 1;

[0095] This formula originates from robotics and vehicle dynamics. The linear acceleration and angular velocity of an organism are the most direct and recognized physical indicators for measuring the intensity of its motion. The more intense the maneuvering of a drone, the higher its requirements for the real-time performance and accuracy of control commands, and the more sensitive it is to the stability of the communication link.

[0096] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A flight path control system for unmanned aerial vehicles (UAVs) based on the Internet of Things (IoT), characterized in that, include: The electromagnetic environment and flight status joint sensing module is used to collect channel data of the UAV's communication link and its own flight status data; The channel-flight control coupling risk assessment module is used to calculate the electromagnetic environment navigability index, which characterizes flight risk, based on the channel data and flight status data. The navigability map construction and prediction module is used to construct a navigability map representing the spatial risk distribution based on the historical and real-time electromagnetic environment navigability index, and to predict the navigability index of future waypoints on the predetermined route. A predictive path adaptive planning module is used to adaptively adjust the predetermined route based on the predicted navigability index to generate a safe route. The flight control command generation and execution module is used to convert the safety path into specific flight control commands and execute them. The channel-flight control coupling risk assessment module calculates the electromagnetic environment navigability index in the following manner: Based on the channel data and the flight status data, the comprehensive communication link quality index, the communication link volatility index, and the flight control command sensitivity index are calculated respectively. Multiply the communication link volatility index by the flight control command sensitivity index, then multiply the product by the preset channel-flight control coupling coefficient and sum it with the value 1 to obtain the risk denominator. The electromagnetic environment navigability index is obtained by dividing the comprehensive communication link quality index as the numerator by the risk denominator. The calculation method of the comprehensive communication link quality index is as follows: the signal-to-noise ratio, channel state information and bit error rate in the channel data are normalized, and then the processed values ​​are weighted and summed based on their respective preset weight coefficients. The communication link volatility index is calculated as follows: within a preset time window, the sample standard deviation of the time series of the comprehensive communication link quality index is calculated. The flight control command sensitivity index is calculated as follows: the ratio of the current linear acceleration and angular velocity to the preset maximum values ​​of the current linear acceleration and angular velocity are calculated respectively, and the two ratios are weighted and summed based on their respective preset weights, and the result is ensured to be no greater than 1 by a limiting function.

2. The IoT-based drone flight path control system according to claim 1, characterized in that, The channel data includes channel state information, signal-to-noise ratio, and bit error rate; the flight state data includes three-dimensional position, velocity, linear acceleration, and angular velocity.

3. The IoT-based drone flight path control system according to claim 1, characterized in that, The predictive path adaptive planning module adaptively adjusts the predetermined route in the following ways: The predicted navigability index is compared with preset critical thresholds and warning thresholds; If the predicted navigability index is lower than the critical threshold, route replanning is initiated based on the navigability map. If the predicted navigability index falls between the critical threshold and the warning threshold, a risk downgrade operation is performed by reducing the target speed command or decreasing the upper limit of path curvature.

4. The IoT-based drone flight path control system according to claim 3, characterized in that, The critical threshold is determined by calibrating the electromagnetic environment navigability index that causes control divergence in an experiment and taking the safe statistical lower bound of the electromagnetic environment navigability index data distribution; the warning threshold is a value set based on the critical threshold by a preset multiple.

5. The IoT-based drone flight path control system according to claim 3, characterized in that, The path replanning adopts a heuristic path search algorithm. When searching for the optimal path in the navigability map, the algorithm makes a decision by combining the actual travel cost from the starting point to the current point and the estimated travel cost from the current point to the destination. The travel cost of each point is inversely proportional to the electromagnetic environment navigability index.

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