Unmanned aerial vehicle on-load ground-hugging flight altitude planning method and system based on deep learning

Abstract

The application discloses a kind of unmanned aerial vehicle load based on deep learning Ground Flight Altitude Planning Method, to solve the problem of model uncertainty driven adaptive clearance and verifiable hard constraint Ground Altitude Planning, the present application is through multi-source perception alignment and evidence type uncertainty prediction, based on the weighted quantile calibration of forward speed, total time delay and effective look-ahead distance, the confidence upper bound of terrain height is obtained, adaptive clearance is generated using monotonicity mapping, and the hard safety constraint of altitude reference trajectory is constructed by combining time delay compensation and online adaptive conservatism with safety model predictive control containing control barrier function constraint, which realizes the technical effect of improving clearance safety, considering trajectory tracking and energy consumption under complex terrain and dynamic working conditions.

Description

Technical Field

[0001] This invention relates to the field of drone flight altitude planning, and in particular to a method and system for drone flight altitude planning with payload and terrain-following capability based on deep learning. Background Technology

[0002] In low-altitude terrain-following flight missions, UAVs need to rely on forward-looking perception (cameras, lidar, millimeter-wave altimeters, etc.) and navigation fusion to plan their flight altitude in real time under complex terrain, load disturbances and wind fields, so as to ensure obstacle clearance and trajectory tracking performance at the same time.

[0003] With the maturity of multi-source sensor time synchronization and extrinsic parameter registration technologies, deep learning-based environment understanding and uncertainty estimation are gradually being used for terrain height prediction. In terms of optimization and control, model predictive control is widely adopted. Some studies introduce probabilistic constraints, risk metrics, or control barrier functions to improve safety, and consider state extrapolation and compensation based on the perceived execution link delay.

[0004] However, existing technologies still have the following shortcomings when facing complex and rapidly changing operating conditions for loaded, terrain-following flight:

[0005] 1. Uncertainty calibration and safety upper bound lack verifiability: The confidence of most prediction models comes from experience or offline global calibration, which is difficult to conditionally adjust with forward speed, total delay and effective forward look distance, which can easily lead to overly conservative airspace boundaries or distortion in high-risk conditions.

[0006] 2. Rigid and unadaptive headroom design: Common practices use fixed or simplified headrooms, without systematically incorporating monotonic mappings of factors such as terrain variance, local slope, speed and time delay, making it difficult to dynamically balance safety and performance.

[0007] 3. Lack of integrated processing of verifiable hard constraints and time delays in the discrete time domain at the planning level: Model predictive control based on probability or soft constraints is difficult to provide time-by-time hard safety guarantees, the integration of control barrier functions and flight altitude planning is insufficient, and the constraint satisfaction and feasibility maintenance mechanisms are imperfect when time delay exists.

[0008] Therefore, a method and system for flight altitude planning that can overcome the shortcomings of the existing technology is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0009] One objective of this invention is to propose a deep learning-based method and system for altitude planning of UAVs carrying terrain-following flight. Addressing the problems in existing technologies, such as difficulty in conditionally calibrating prediction uncertainties, rigid and fixed airspace margins, and the lack of verifiable hard constraints at the planning layer while failing to adequately consider perceived execution delays, this invention proposes a multi-source observation time synchronization and extrinsic parameter registration method, evidence-based deep learning terrain prediction, weighted quantile (conditional conformal) calibration combining forward velocity, total delay, and effective forward look distance to obtain a high-confidence upper bound for terrain and a monotonic adaptive airspace margin mapping, and a safety model predictive control that introduces control barrier function constraints and performs delay compensation. Furthermore, it employs a technical solution of online index-driven closed-loop adaptive adjustment of quantile levels and mapping parameters. This invention achieves the technical effects of time-by-time hard safety constraints, improved airspace maintenance and obstacle clearance success rates, reduced violation rates, and balanced energy consumption and trajectory tracking performance under complex terrain and dynamic operating conditions.

[0010] A deep learning-based method for altitude planning of UAVs carrying payloads and mimicking terrain, according to an embodiment of the present invention, is characterized by comprising:

[0011] S1. Collect observations from multiple sensor sources, perform time synchronization and spatial extrinsic parameter registration, unify them to the reference coordinate system, and obtain the synchronized and registered data sequence;

[0012] S2. Using the data sequence as input, calculate the forward velocity, the total time delay from perception to execution, and the effective forward look-ahead distance, and arrange them according to multiple discrete moments within the forward time window to form a condition variable sequence.

[0013] S3. Using the data sequence as input, the evidence-based deep learning model is used to predict the terrain height at discrete moments in the forward time window, resulting in a terrain height mean sequence, variance sequence, and a local slope absolute value sequence calculated from the mean sequence, forming a prediction set.

[0014] S4. Using the predicted set as input, perform weighted quantile calibration on the residual between the historical true terrain height and the predicted mean within the sliding time window, based on forward velocity, total delay and effective forward sight distance, to obtain the upper bound of the high confidence quantile of terrain height at each discrete time and form a sequence. Combine this sequence with the terrain height mean sequence, variance sequence, local slope absolute value sequence and forward velocity, total delay and effective forward sight distance to form a calibration set.

[0015] S5. Using the calibration set as input, the adaptive airspace margin at each discrete time is calculated using a mapping function to form an adaptive airspace margin sequence. The sequence is then combined with the upper bound sequence of the high confidence quantile of terrain height to form an airspace set.

[0016] S6. Using the aforementioned clearance set as input, construct and solve an optimization problem that includes flight dynamics constraints and hard safety inequalities to obtain the flight altitude reference trajectory.

[0017] S7. Using the flight altitude reference trajectory as input, issue and execute altitude or vertical speed commands, statistically analyze online indicators, obtain the adjustment amount of quantile level and mapping function parameters based on the threshold, and use them as inputs to S4 and S5 in the next loop to achieve adaptive conservatism.

[0018] Optionally, step S1 specifically includes:

[0019] The data collection includes multi-source sensor observations such as forward-looking camera image data, lidar point cloud data, millimeter-wave radar altitude measurement data, inertial measurement unit data, global navigation satellite system positioning data, and barometer altitude data.

[0020] The observations are time-synchronized to align the data from each sensor to a unified time reference. The time synchronization is achieved by hardware-triggered synchronization or software timestamp alignment. In the event of inconsistent sampling rates or transmission or processing delays, alignment is achieved through resampling, interpolation, or buffer queues.

[0021] Spatial extrinsic parameter registration is performed on the observations to determine the rigid body pose transformation parameters of each sensor relative to a unified reference coordinate system. The pose transformation parameters include rotation and translation. The unified reference coordinate system is a body coordinate system, an inertial coordinate system, or a navigation coordinate system. The pose transformation parameters are obtained through offline calibration or online estimation.

[0022] Accordingly, each observation is mapped to the unified reference coordinate system and arranged in chronological order to obtain a synchronized and registered data sequence as input for subsequent steps.

[0023] Optionally, step S2 specifically includes:

[0024] Using a data sequence that has been time-synchronized and spatially registered with extrinsic parameters as input, the forward velocity of the UAV along the heading, the total latency of sensing and execution, and the effective forward-looking distance are calculated.

[0025] The forward velocity is obtained by estimating the component of the UAV's velocity in the heading direction, which is determined based on the attitude and heading calculation results. The velocity estimate is obtained from the navigation fusion results of the data sequence.

[0026] The total delay is calculated by statistically analyzing the timestamps of the sensing processing, fusion calculation, prediction and planning, control command issuance and execution, etc., to obtain the time difference between the generation of the corresponding observation and the start of the execution of the control command, and is updated over time.

[0027] The effective forward-looking distance is defined as the maximum forward distance that can reliably image or reliably measure distance in the heading direction. It is determined by the field of view of the forward-looking sensor and the current flight altitude, and is calculated based on geometric relationships under the condition that it does not exceed the nominal detection range of the forward-looking sensor.

[0028] The forward velocity, the total delay, and the effective forward look-ahead distance are arranged at multiple discrete moments within the forward time window to form a sequence of conditional variables.

[0029] Optionally, step S3 specifically includes:

[0030] Using the data sequence as input, an evidence-based deep learning model is used to predict terrain height at multiple discrete moments within a forward time window, and the mean and variance of terrain height at each discrete moment are output.

[0031] The mean elevation values ​​at each discrete time point are arranged into a mean elevation value sequence, and the variance of elevation at each discrete time point is arranged into a variance elevation value sequence. The absolute value of the local slope is calculated based on the mean elevation value sequence through adjacent difference or local fitting.

[0032] The mean sequence of terrain height, the variance sequence of terrain height, and the absolute value sequence of local slope are combined to form a prediction set.

[0033] Optionally, step S4 specifically includes:

[0034] Using the predicted set as input, a calibration sample set is constructed within a set sliding time window. The calibration sample set consists of the residual between the actual terrain height at historical moments and the corresponding mean terrain height. The residual is defined as the actual terrain height minus the corresponding mean terrain height. The actual terrain height is obtained by the fusion calculation of a millimeter-wave radar altimeter, a global navigation satellite system, and a barometer.

[0035] For each discrete moment of the forward time window, weights are assigned to the calibration samples based on the forward velocity, total delay and effective forward look distance in the condition variable sequence, and the weights are non-negative values.

[0036] The residuals are quantile statistics are performed according to the weights to obtain the quantile calibration value at the discrete time. The quantile calibration value is then added to the mean terrain height at the discrete time to obtain the upper bound of the high confidence quantile of the terrain height at the discrete time.

[0037] The upper bounds of the high confidence quantiles of terrain height at each discrete time are arranged in order to form a sequence of upper bounds of the high confidence quantiles of terrain height. This sequence, together with the mean sequence of terrain height, the variance sequence of terrain height, the absolute value sequence of local slope, and the forward velocity, total delay, and effective forward sight distance, forms a calibration set.

[0038] Optionally, step S5 specifically includes:

[0039] Using the calibration set as input, for each discrete moment within the forward time window, an adaptive clearance margin is calculated using a mapping function based on the terrain height variance, local slope absolute value, forward velocity, total delay, and effective forward look distance at that moment. The mapping function is monotonically non-decreasing with respect to terrain height variance, local slope absolute value, forward velocity, and total delay, and monotonically non-increasing with respect to effective forward look distance, and its parameters are adjustable.

[0040] Set lower and upper limits for the adaptive clearance margin, so that its value is between the lower and upper limits;

[0041] The adaptive net margins at each discrete time point are sequentially arranged into an adaptive net margin sequence, which is then combined with the upper bound sequence of the high confidence quantile of terrain height to form a net set.

[0042] Optionally, step S6 specifically includes:

[0043] Using the aforementioned airspace set as input, an optimization problem is constructed within a forward time window. The cost function of the optimization problem includes at least an energy consumption term and a reference track deviation term. The constraints include at least load-bearing flight dynamics model constraint parameters and a hard safety inequality. The load-bearing flight dynamics model constraint parameters include maximum climb rate, maximum descent rate, upper limit of attitude angle, lower limit of thrust margin, and upper limit of acceleration change rate. The hard safety inequality states that the flight altitude reference trajectory at each discrete time is not less than the sum of the upper limit of the high confidence quantile of terrain altitude at that time and the adaptive airspace margin.

[0044] Solve the optimization problem to generate an altitude reference trajectory that satisfies the constraints;

[0045] To compensate for the total time delay, the UAV state is extrapolated according to the time offset corresponding to the total time delay based on the loaded flight dynamics model to obtain the predicted state. The upper bound of the high confidence quantile of the terrain height at the time corresponding to the predicted state is then added to the optimization problem as the terrain height constraint after time delay compensation.

[0046] Construct a look-ahead barrier function such that the value of the barrier function at each discrete time step is equal to the flight altitude reference trajectory minus the upper bound of the corresponding terrain height high confidence quantile minus the corresponding adaptive airspace margin, and require that the weighted sum of the derivative of the barrier function and the gain function is not less than zero. Incorporate this barrier constraint into the optimization problem to ensure that the hard safety inequality is satisfied in time, and obtain the flight altitude reference trajectory.

[0047] Optionally, step S7 specifically includes:

[0048] Using the flight altitude reference trajectory as input, send altitude or vertical speed commands to the flight control interface and execute them;

[0049] During execution, online indicators are calculated based on the flight altitude reference trajectory and real-time observations, including minimum airspace hold rate, obstacle clearance success rate and violation rate, and the online indicators are statistically obtained within the sliding time window or the current execution cycle.

[0050] The online indicators are compared with preset thresholds, and the adjustment amount of the quantile level and the adjustment amount of the mapping function parameters are determined based on the comparison results. The adjustment amount is a numerical value and is limited to the preset upper and lower limits to ensure stability.

[0051] The adjustment amount of the quantile level is used to update the quantile level setting of the next cycle, and the adjustment amount of the mapping function parameter is used to update the mapping function parameter setting of the next cycle, so as to improve the conservatism under dangerous conditions and maintain performance under safe conditions.

[0052] In the next cycle, the updated quantile level and mapping function parameters are used as part of the inputs for steps S4 and S5, respectively, to achieve adaptive conservative closed-loop adjustment.

[0053] A deep learning-based UAV altitude planning system for terrain-following flight includes:

[0054] The perception alignment module is used to acquire multi-source observations, synchronize time and register extrinsic parameters, and output data sequences.

[0055] The condition variable module is used to calculate the forward velocity, total delay, and effective forward look distance of the data sequence, and output the condition variable sequence.

[0056] The prediction module is used to output a prediction set using evidence-based deep learning;

[0057] The calibration module is used to weight the residuals based on the condition variables within a sliding window, output the upper bound sequence of the high confidence quantile of terrain height, and form a calibration set with the prediction set and the condition variable sequence.

[0058] The margin module is used to map and generate an adaptive net margin sequence, which is combined with the upper bound sequence to form a net set;

[0059] The safety planning module is used to solve the flight altitude reference trajectory based on the aforementioned clearance set and load dynamic constraints, and to perform time delay compensation and look-ahead barrier constraints.

[0060] The adaptive module is used to issue height or vertical speed commands, collect online metrics, and output the adjustment amounts of the percentile level and mapping parameters for the next loop.

[0061] The beneficial effects of this invention are:

[0062] 1. Provide verifiable time-by-time hard safety assurance: Obtain a high confidence upper bound for terrain by using evidence-based deep learning prediction and weighted quantile calibration based on forward velocity, total delay, and effective forward look distance. Combined with monotonic adaptive clearance margin, control barrier function constraints, and delay compensation, apply a hard safety inequality to the flight altitude reference trajectory at the planning layer, significantly improving the minimum clearance hold-up rate and obstacle clearance success rate, and reducing the violation rate.

[0063] 2. Achieving an adaptive trade-off between safety and performance: The clearance margin mapping remains monotonically unchanged for terrain variance, local slope, forward speed, and total delay, and monotonically unchanged for effective forward sight distance. It also adjusts the quantile level and mapping parameters through online closed-loop adjustment of indicators, automatically increasing conservatism in hazardous conditions and reducing redundancy margin in safe conditions, thus balancing energy consumption and trajectory tracking accuracy.

[0064] 3. Improve coverage reliability and robustness under different working conditions: The time-sequential and conditional quantization calibration within the sliding time window effectively avoids the distortion of global unified calibration, enabling the terrain upper boundary to maintain stable coverage under conditions of speed changes, time delay fluctuations and limited line of sight, while also having stronger robustness to multi-source sensing alignment errors and load disturbances. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0066] Figure 1 This is a flowchart of a deep learning-based method and system for planning the altitude of a drone carrying a terrain-following flight, as proposed in this invention. Detailed Implementation

[0067] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0068] refer to Figure 1 A deep learning-based method for altitude planning of UAVs carrying terrain-following flight, characterized by comprising:

[0069] S1. Collect observations from multiple sensor sources, perform time synchronization and spatial extrinsic parameter registration, unify them to the reference coordinate system, and obtain the synchronized and registered data sequence;

[0070] S2. Using the data sequence as input, calculate the forward velocity, the total time delay from perception to execution, and the effective forward look-ahead distance, and arrange them according to multiple discrete moments within the forward time window to form a condition variable sequence.

[0071] S3. Using the data sequence as input, the evidence-based deep learning model is used to predict the terrain height at discrete moments in the forward time window, resulting in a terrain height mean sequence, variance sequence, and a local slope absolute value sequence calculated from the mean sequence, forming a prediction set.

[0072] S4. Using the predicted set as input, perform weighted quantile calibration on the residual between the historical true terrain height and the predicted mean within the sliding time window, based on forward velocity, total delay and effective forward sight distance, to obtain the upper bound of the high confidence quantile of terrain height at each discrete time and form a sequence. Combine this sequence with the terrain height mean sequence, variance sequence, local slope absolute value sequence and forward velocity, total delay and effective forward sight distance to form a calibration set.

[0073] S5. Using the calibration set as input, the adaptive airspace margin at each discrete time is calculated using a mapping function to form an adaptive airspace margin sequence. The sequence is then combined with the upper bound sequence of the high confidence quantile of terrain height to form an airspace set.

[0074] S6. Using the aforementioned clearance set as input, construct and solve an optimization problem that includes flight dynamics constraints and hard safety inequalities to obtain the flight altitude reference trajectory.

[0075] S7. Using the flight altitude reference trajectory as input, issue and execute altitude or vertical speed commands, statistically analyze online indicators, obtain the adjustment amount of quantile level and mapping function parameters based on the threshold, and use them as inputs to S4 and S5 in the next loop to achieve adaptive conservatism.

[0076] In this specific embodiment, S1 specifically refers to:

[0077] The system acquires multi-source observations from a forward-looking camera, lidar point clouds, millimeter-wave radar altimeter, inertial measurement unit, global navigation satellite system, and barometer. A unified reference time axis and a unified reference coordinate system are established as alignment benchmarks. Time synchronization and spatial extrinsic parameter registration are performed separately to form a data sequence for subsequent calculations. Time synchronization employs affine correction of the clocks of each sensor, mapping the original timestamps to the reference time axis. The correction model is written as follows:

[0078] ;

[0079] in For sensor index and Indicates camera, Indicates lidar, Indicates millimeter-wave radar altimeter, Indicating inertial measurement unit, Indicates Global Navigation Satellite System, Indicates barometer, For sensors The original timestamp, For sensors timestamp mapped to the reference timeline For sensors Clock drift coefficient, For sensors Time offset;

[0080] To unify the sampling rhythm, an equally spaced sampling grid is established on the reference time axis to drive resampling and interpolation processing. The sampling grid is written as:

[0081] ;

[0082] in For the first One reference sampling time, For the start time of the reference timeline, For reference sampling interval, The sample number is the index and its value ranges from 0 to... This refers to the total number of reference sampling points;

[0083] Spatial extrinsic parameter registration to unify the reference coordinate system For each sensor coordinate system, the target is... Estimate the rigid body pose and perform consistency verification. The mapping of 3D points from the sensor coordinate system to the reference coordinate system is written as:

[0084] ;

[0085] in In the sensor coordinate system 3D point vectors in To map to the reference coordinate system 3D point vectors From arrive of Rotation matrix From arrive of Translation vector;

[0086] In practice, hardware triggering is used to constrain initial alignment, while software timestamps and buffer queues are used to compensate for sampling rate inconsistencies and link latency, employing linear interpolation or hold-and-hold strategies. For filling in missing measurements, extrinsic parameters can be obtained through offline calibration and fine-tuned through small-range recursive estimation during the online phase. Finally, the observations from each path, synchronized with time and registered with spatial extrinsic parameters, are combined according to... The data is sequentially integrated into a unified data sequence to serve as input for subsequent steps.

[0087] In this specific embodiment, S2 specifically refers to:

[0088] Taking an aligned data sequence as input, discrete time points are set at equal intervals within a given forward time window, and the required condition variables are calculated time-by-time. The discrete time points are written as:

[0089] ;

[0090] in For the first discrete time, For the start time of the time window, For time step, For time indexing, End index;

[0091] The forward velocity is given by the projection of the velocity obtained from navigation fusion onto the forward direction, written as:

[0092] ;

[0093] in For the first The forward velocity at that moment, For the first The heading unit vector at each moment, Reference coordinate system Next The velocity vector at each moment Indicates transpose, This is the unified reference coordinate system established in step S1;

[0094] The total perceived execution latency is calculated as the difference between the command execution time and the observation generation time, written as:

[0095] ;

[0096] in For the first The total delay at each moment The timestamp at which the control command begins execution at that moment. The timestamp generated for observations related to that moment;

[0097] The smaller of the effective forward-looking distance, nominal detection range, and geometric visibility is taken as:

[0098] ;

[0099] in For the first Effective forward sight distance at any given moment The nominal detection range of the forward-looking sensor, The first is calculated from the relationship between flight altitude, sensor attitude, and field of view geometry. Geometric forward distance at each moment It is a binary minimum value operator;

[0100] Finally, the above quantities are arranged in chronological order to form a sequence of condition variables:

[0101] and ;

[0102] in For condition variable sequences, For the first The condition variable triples at each time point are used to drive subsequent uncertainty calibration and flight altitude planning calculations.

[0103] In this specific embodiment, S3 specifically refers to:

[0104] Using the aligned data sequence as input, the uncertainty of terrain elevation is predicted for each discrete time point within the forward time window, generating a prediction set for subsequent calibration and planning. Specifically, for the ... discrete time ;

[0105] in For time index and For the first time window At that moment, End index;

[0106] Constructing feature blocks from multi-source observations ;

[0107] in To integrate cameras, lidar, millimeter-wave radar altimeters, inertial measurement units, global navigation satellite systems, and barometers, etc. The feature vector or tensor obtained by fusing observations at a location;

[0108] And with evidence-based deep learning models Perform parametric prediction of terrain height, and write the model output as follows:

[0109] ;

[0110] in In order to be in The predicted average terrain height at the location In order to be in Variance of terrain height prediction at location For parameters Evidence-based deep learning models It is a set of trainable parameters;

[0111] The predicted mean and predicted variance at each time point are arranged into a sequence:

[0112] and ;

[0113] The absolute value of the local slope is calculated based on the change in the adjacent predicted mean to characterize the sensitivity of the terrain to undulation. The adjacent difference form is written as... ;

[0114] in In order to be in The absolute value of the local slope at the location, for and The discrete spacing along the flight direction (a positive number determined by both platform motion and sampling step size) is used to combine the three types of quantities into a prediction set:

[0115] ;

[0116] in This is the input set used for subsequent weighted quantile calibration and net-space mapping.

[0117] In this specific embodiment, S4 specifically refers to:

[0118] Using the prediction set and the condition variable sequence as input, conditional weighted quantile calibration of historical residuals is performed around each forward discrete time step within a set sliding time window to obtain a high-confidence upper bound for terrain height and form a calibration set for subsequent mapping and planning; specifically, for the ... At that moment ;

[0119] in For time indexing, For the first time window discrete time, End index and ;

[0120] Building a sliding window index set Used to collect recent historical samples, for each Calculate the residuals:

[0121] ;

[0122] in For the first The residuals of a historical sample For the first The actual terrain elevation at any given time (obtained by fusing millimeter-wave radar altimeter, global navigation satellite system, and barometer data). For the first The average predicted terrain height at each time point;

[0123] To achieve conditional weighting based on forward velocity, total delay, and effective forward look distance, weights are defined based on similarity:

[0124] ;

[0125] in For the first The sample relative to the first Non-negative weights at each moment Forward velocity difference, For total delay difference, For effective forward sight distance difference, Forward speed, To perceive the total execution latency, For effective forward sight distance, The weighting scale hyperparameter is used to adjust the sensitivity to differences;

[0126] Define quantile calibration values ​​on the weighted samples:

[0127] ;

[0128] in For the first Quantile calibration value at each moment For the infimum operator, For the real number field, For indicator functions, Quantitative level;

[0129] The upper bound of the high-confidence quantile for terrain height is obtained by superimposing the quantile calibration value with the predicted mean:

[0130] ;

[0131] in For the first The upper bound of the high confidence quantile for terrain elevation at each moment, For the first The average predicted terrain height at each time point;

[0132] Ultimately and as well as according to Combined to form a calibration set:

[0133] Adaptive headroom mapping and safety planning to drive the next step.

[0134] In this specific embodiment, S5 specifically includes:

[0135] Using the calibration set as input, the adaptive airspace margin is calculated time-by-time within the forward time window based on terrain uncertainty and working conditions, and combined with the high confidence upper bound of terrain height to form the airspace set.

[0136] Specifically, for the first discrete time ;

[0137] in For the first time window discrete time, For time indexing;

[0138] Select the terrain height variance at this moment absolute value of local slope Forward speed Perceive the total execution latency and effective forward sight distance As input to the mapping, a monotonic, untruncated margin mapping is constructed, and saturation is performed using upper and lower bounds to obtain the final margin. The untruncated form is written as:

[0139] ;

[0140] in For the first Uncut margin at each moment, Non-negative adjustable weight parameters are used to control the contribution of each input to the margin. For the square root operator, Variance for predicting terrain height For the absolute value of the local slope, Forward speed, For total latency, For effective forward sight distance, To prevent small positive quantities with a denominator of zero;

[0141] when When the structure is relative to and Monotonous and undiminished The monotonicity does not increase with increasing volume, thus satisfying the monotonicity constraint in the claims;

[0142] The uncrunted result is then saturated to obtain the final adaptive net clearance margin:

[0143] ;

[0144] in For the first Adaptive headroom at any given moment and These are the lower and upper margin constants, respectively. For saturation operators, and These are the binary minimum and binary maximum operators, respectively;

[0145] In chronological order Composition of adaptive net clearance sequence and the upper bound sequence of high confidence quantiles for terrain height. Combined to form a set of net spaces To facilitate subsequent hard safety constraints, a safe terrain baseline can also be defined. and its sequence ;

[0146] in For the first The upper bound of the high confidence quantile for terrain elevation at each moment, The weighting parameter is the sum of the upper bound and the margin, used to constrain the flight altitude to not be lower than the baseline. and upper and lower limits It is adjustable within a preset range to allow for an online trade-off between safety and performance.

[0147] In this specific embodiment, S6 specifically refers to:

[0148] Using the clearance set as the constraint input, a discrete time-domain optimization is established within the forward time window, while simultaneously considering energy consumption and reference tracking, longitudinal dynamics with load, envelope constraints, hard safety inequalities after time delay compensation, and look-ahead barrier function constraints to generate the flight altitude reference trajectory.

[0149] First, we plan on discrete time points, and write the time scale as follows: ;

[0150] in For the first discrete time, For the start time of the time window, For discrete time step, For time indexing, End index;

[0151] The decision variable is selected as And using quadratic cost to measure tracking and energy consumption, the objective function is written as follows:

[0152] ;

[0153] in To optimize costs, For time indexing Summation operator To track weights, For energy consumption weight, For the first At that moment, the flight altitude reference For the first Expected altitude at that moment For the first Vertical acceleration at each moment;

[0154] The longitudinal discrete dynamics adopts a minimum-order linear model:

[0155] ;

[0156] in For the first At that moment, the flight altitude reference For the first Vertical velocity at each moment For the first Vertical velocity at each moment;

[0157] The load envelope is primarily constrained by rate and jerk constraints.

[0158] ;

[0159] in For maximum climb rate, For the maximum rate of decline, For absolute value operators, The upper limit of vertical acceleration, attitude angle and thrust margin constraints are also incorporated into the inequality, but for the sake of simplicity, they are expressed in words here.

[0160] The hard safety inequality for headroom uses the sum of the high-confidence terrain upper bound and the adaptive headroom margin as a baseline and considers the total link delay for timing extrapolation. First, the discrete delay step number and compensation index are defined:

[0161] ;

[0162] in For the first The number of discrete time delay steps at each time point, For rounding operators, For the first The total latency from perception to execution at any given moment For the constraint time index after delay compensation, It is a binary minimum value operator;

[0163] Based on this, write out the hard safety constraints. ;

[0164] in For compensation index The corresponding terrain height high confidence quantile upper bound, For the first Adaptive headroom margin at any given moment;

[0165] To enhance the verifiability of time series, a look-ahead barrier function is introduced:

[0166] It also requires that its discrete "derivative plus gain" be non-negative to suppress security boundary intrusion, written as ;

[0167] in For the first The barrier function value at each time point, Forward gain coefficient, For the first Upper bound of the high confidence quantile for terrain elevation at each moment;

[0168] In summary, this constitutes "minimization" Furthermore, it satisfies the hard safety inequalities after dynamics, envelope, and time delay compensation, as well as the look-ahead barrier constraint, using a convex quadratic programming problem or a nonlinear quadratic programming problem with linear constraints, and is solved by a numerical optimizer in each programming cycle. As a reference trajectory for flight altitude.

[0169] In this specific embodiment, S7 specifically refers to:

[0170] Using the flight altitude reference trajectory as the execution input, the flight control interface selects to issue altitude or vertical speed commands. During real-time execution, a sliding window is used to statistically analyze online safety and performance indicators, and based on this, the amplitude limit update of the quantile level and mapping parameters is generated for the setting of steps S4 and S5 in the next cycle.

[0171] To standardize the measurement of clearance boundaries, clearance margin is defined as:

[0172] ;

[0173] in For the first The clearance margin at any given moment For the first At that moment, the flight altitude reference For the first The upper bound of the high confidence quantile for terrain elevation at each moment, For the first Adaptive headroom margin at any given moment;

[0174] In length sliding window The minimum clearance rate, obstacle clearance success rate, and violation rate are statistically analyzed and written as follows:

[0175] and and ;

[0176] in For minimum headroom retention rate, To improve the success rate of obstacle crossing, For the rate of violations, For the number of samples in the window, For the set of time indexes within the window, For sets Internal index Summation operator For indicator functions, The number of times an obstacle can be overcome within the window while maintaining clearance. This represents the total number of obstacle crossing events within the window.

[0177] The above indicators are compared with the threshold to form a non-negative risk indicator:

[0178] ;

[0179] in For risk indicators, For weight, For preset threshold, It is a positive part operator and It is a binary maximum value operator;

[0180] Quantile levels and mapping parameters are updated using amplitude-limited additive updates to ensure robustness. The update law is as follows:

[0181] and ;

[0182] in For the next cycle, the quantile level used in step S4, For the current quantile level, For quantile gain, For quantile upper and lower limits, For the mapping parameter vector used in step S5 of the next loop, For the current mapping parameter vector, For parameter gain, To and Same-dimensional all-one vectors For the parameter, element-wise upper and lower bound vectors, For amplitude limiting operator and It is a binary minimum value operator;

[0183] Through the above closed loop or Below the threshold or When the value exceeds a threshold, the conservatism of the quantile level and mapping parameters is automatically increased, while maintaining parameter stability under safe operating conditions. and These will be used as inputs for the next loop.

[0184] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0185] This application addresses the technical challenge of simultaneously satisfying safety and performance requirements at the airspace boundary in low-altitude terrain-following flight due to prediction uncertainties, link delays, and limited forward look-ahead distances. It constructs a time-by-time verifiable hard safety constraint by employing a combination of calibration, margin, planning, and evaluation. Specifically, starting with multi-source perception alignment, evidence-based deep learning outputs the mean and variance of terrain height and calculates the local slope. Within a sliding time window, weighted quantile calibration is performed based on forward velocity, total delay, and effective forward look-ahead distance to obtain the upper bound of the high-confidence quantile for terrain height. An adaptive airspace margin is then generated using a monotonic mapping, and delay compensation is incorporated into the safety model predictive control with control barrier function constraints. The "upper bound + margin" is used as a time-by-time hard inequality to plan the flight altitude reference trajectory. This combination improves the minimum airspace hold rate and obstacle clearance success rate, reduces the violation rate, and balances energy consumption and trajectory tracking. During the execution phase, closed-loop adjustment based on online indicators and thresholds automatically increases conservatism in hazardous conditions and reduces redundancy in safe conditions, demonstrating stable and verifiable technical effects.

[0186] In terms of algorithm structure, this application makes targeted improvements to the core links to better address the above-mentioned technical problems and enhance the technical effect: (1) The sliding window calibration with conditional weighted quantiles is used to replace the offline global calibration, so that the terrain upper bound is dynamically updated with speed, time delay and forward look distance and the coverage reliability is maintained, avoiding global calibration distortion; (2) A margin mapping with uniform variance, slope, forward speed and total time delay, uniform effective forward look distance and upper and lower limit saturation is designed to avoid parameter adjustment direction errors and improve the adaptive trade-off between engineering controllability and performance-safety; (3) The time delay compensation and control barrier function are incorporated into the discrete time domain constraints of MPC to ensure the timing satisfaction and feasibility maintenance of hard safety inequalities. The limit update of quantile level and mapping parameters by the execution adaptive module forms a closed-loop robust mechanism, which further consolidates the safety of the airspace and improves the overall planning performance under complex terrain and dynamic working conditions.

Claims

1. A deep learning-based method for altitude planning during unmanned aerial vehicle (UAV) payload-based terrain-following flight, characterized in that, include: S1. Collect observations from multiple sensor sources, perform time synchronization and spatial extrinsic parameter registration, unify them to the reference coordinate system, and obtain the synchronized and registered data sequence; S2. Using the data sequence as input, calculate the forward velocity, the total time delay from perception to execution, and the effective forward look-ahead distance, and arrange them according to multiple discrete moments within the forward time window to form a condition variable sequence. S3. Using the data sequence as input, the evidence-based deep learning model is used to predict the terrain height at discrete moments in the forward time window, resulting in a terrain height mean sequence, variance sequence, and a local slope absolute value sequence calculated from the mean sequence, forming a prediction set. S4. Using the predicted set as input, perform weighted quantile calibration on the residual between the historical true terrain height and the predicted mean within the sliding time window, based on forward velocity, total delay and effective forward sight distance, to obtain the upper bound of the high confidence quantile of terrain height at each discrete time and form a sequence. Combine this sequence with the terrain height mean sequence, variance sequence, local slope absolute value sequence and forward velocity, total delay and effective forward sight distance to form a calibration set. S5. Using the calibration set as input, the adaptive airspace margin at each discrete time is calculated using a mapping function to form an adaptive airspace margin sequence. The sequence is then combined with the upper bound sequence of the high confidence quantile of terrain height to form an airspace set. S6. Using the aforementioned clearance set as input, construct and solve an optimization problem that includes flight dynamics constraints and hard safety inequalities to obtain the flight altitude reference trajectory. S7. Using the flight altitude reference trajectory as input, issue and execute altitude or vertical speed commands, statistically analyze online indicators, obtain the adjustment amount of quantile level and mapping function parameters based on the threshold, and use them as inputs to S4 and S5 in the next loop to achieve adaptive conservatism.

2. The method for altitude planning of UAVs with payload-based terrain-following flight based on deep learning according to claim 1, characterized in that, S1 specifically refers to: The data collection includes multi-source sensor observations such as forward-looking camera image data, lidar point cloud data, millimeter-wave radar altitude measurement data, inertial measurement unit data, global navigation satellite system positioning data, and barometer altitude data. The observations are time-synchronized to align the data from each sensor to a unified time reference. The time synchronization is achieved by hardware-triggered synchronization or software timestamp alignment. In the event of inconsistent sampling rates or transmission or processing delays, alignment is achieved through resampling, interpolation, or buffer queues. Spatial extrinsic parameter registration is performed on the observations to determine the rigid body pose transformation parameters of each sensor relative to a unified reference coordinate system. The pose transformation parameters include rotation and translation. The unified reference coordinate system is a body coordinate system, an inertial coordinate system, or a navigation coordinate system. The pose transformation parameters are obtained through offline calibration or online estimation. Accordingly, each observation is mapped to the unified reference coordinate system and arranged in chronological order to obtain a synchronized and registered data sequence as input for subsequent steps.

3. The method for altitude planning of UAV payload-based terrain-following flight based on deep learning according to claim 1, characterized in that, S2 specifically refers to: Using a time-synchronized and space extrinsic registration data sequence as input, calculate the forward velocity of the UAV along the heading, the total time delay from perception to execution, and the effective forward-looking distance; Wherein, the forward velocity is the projection component of the UAV's velocity estimation vector onto the unit vector in the heading direction, the unit vector in the heading direction is determined based on the attitude and heading calculation results, and the velocity estimation vector is obtained from the navigation fusion results of the data sequence; The total delay is calculated by statistically analyzing the timestamps of each stage, including perception processing, fusion calculation, prediction and planning, control command issuance, and the start of control command execution, to obtain the time difference between the occurrence of the corresponding observation and the start of control command execution, and then updating it over time. The effective forward-looking distance is defined as the maximum forward distance that can reliably image or reliably measure distance in the heading direction. It is determined by the field of view of the forward-looking sensor and the current flight altitude, and is calculated based on geometric relationships under the condition that it does not exceed the nominal detection range of the forward-looking sensor. The forward velocity, the total delay, and the effective forward look-ahead distance are arranged at multiple discrete moments within the forward time window to form a sequence of conditional variables.

4. The method for altitude planning of UAVs with payload-based terrain-following flight based on deep learning according to claim 1, characterized in that, S3 specifically refers to: Using the data sequence as input, an evidence-based deep learning model is used to predict terrain height at multiple discrete moments within a forward time window, and the mean and variance of terrain height at each discrete moment are output. The mean elevation values ​​at each discrete time point are arranged into a mean elevation value sequence, and the variance of elevation at each discrete time point is arranged into a variance elevation value sequence. The absolute value of the local slope is calculated based on the mean elevation value sequence through adjacent difference or local fitting. The mean sequence of terrain height, the variance sequence of terrain height, and the absolute value sequence of local slope are combined to form a prediction set.

5. The method for altitude planning of UAVs with payload-based terrain-following flight according to claim 1, characterized in that, S4 specifically refers to: Using the predicted set as input, a calibration sample set is constructed within a set sliding time window. The calibration sample set consists of the residual between the actual terrain height at historical moments and the corresponding mean terrain height. The residual is defined as the actual terrain height minus the corresponding mean terrain height. The actual terrain height is obtained by the fusion calculation of a millimeter-wave radar altimeter, a global navigation satellite system, and a barometer. For each discrete moment of the forward time window, weights are assigned to each calibration sample in the calibration sample set based on the forward velocity, total delay, and effective forward look distance in the condition variable sequence, and the weights are non-negative values. The residuals are quantile statistics are performed according to the weights to obtain the quantile calibration value at the discrete time. The quantile calibration value is then added to the mean terrain height at the discrete time to obtain the upper bound of the high confidence quantile of the terrain height at the discrete time. The upper bounds of the high confidence quantiles of terrain height at each discrete time are arranged in order to form a sequence of upper bounds of the high confidence quantiles of terrain height. This sequence, together with the mean sequence of terrain height, the variance sequence of terrain height, the absolute value sequence of local slope, and the forward velocity, total delay, and effective forward sight distance, forms a calibration set.

6. The method for altitude planning of UAVs with payload-based terrain-following flight based on deep learning according to claim 1, characterized in that, S5 specifically refers to: Using the calibration set as input, for each discrete moment within the forward time window, an adaptive clearance margin is calculated using a mapping function based on the terrain height variance, local slope absolute value, forward velocity, total delay, and effective forward look distance at that moment. The mapping function is monotonically non-decreasing with respect to terrain height variance, local slope absolute value, forward velocity, and total delay, and monotonically non-increasing with respect to effective forward look distance, and its parameters are adjustable. Set lower and upper limits for the adaptive clearance margin, so that its value is between the lower and upper limits; The adaptive net margins at each discrete time point are sequentially arranged into an adaptive net margin sequence, which is then combined with the upper bound sequence of the high confidence quantile of terrain height to form a net set.

7. The method for altitude planning of UAV payload-based terrain-following flight based on deep learning according to claim 1, characterized in that, S6 specifically refers to: Using the aforementioned clearance set as input, an optimization problem is constructed within a forward time window. The cost function of the optimization problem includes an energy consumption term and a reference track deviation term. The constraints include load-bearing flight dynamics model constraint parameters and hard safety inequalities. The load-bearing flight dynamics model constraint parameters include maximum climb rate, maximum descent rate, upper limit of attitude angle, lower limit of thrust margin, and upper limit of acceleration change rate. The decision variables of the optimization problem include the flight altitude reference values ​​at each discrete moment within the forward time window, and the flight altitude reference trajectory is composed of the flight altitude reference values ​​at each discrete moment in chronological order. The hard safety inequality is: for any discrete moment within the forward time window, the flight altitude reference value at the discrete moment is not less than the sum of the upper bound of the terrain height high confidence quantile at that discrete moment and the adaptive airspace margin at that discrete moment. Solve the optimization problem to generate an altitude reference trajectory that satisfies the constraints; To compensate for the total time delay, the UAV state is extrapolated according to the time offset corresponding to the total time delay based on the loaded flight dynamics model to obtain the predicted state. The upper bound of the high confidence quantile of the terrain height at the discrete time corresponding to the predicted state is added to the optimization problem as the terrain height constraint after time delay compensation. A look-ahead barrier function is constructed such that the barrier function value at any discrete moment within the forward time window is equal to the flight altitude reference value at that discrete moment minus the upper bound of the high confidence quantile of the terrain altitude at that discrete moment minus the adaptive airspace margin at that discrete moment. A barrier constraint is applied to the barrier function such that the weighted sum of the difference between the barrier function and the preset gain function at adjacent discrete moments is not less than zero. This barrier constraint is incorporated into the optimization problem to ensure that the hard safety inequality is satisfied in time, thus obtaining the flight altitude reference trajectory.

8. The method for altitude planning of UAVs with payload-based terrain-following flight according to claim 1, characterized in that, S7 specifically refers to: Using the flight altitude reference trajectory as input, send altitude or vertical speed commands to the flight control interface and execute them; During execution, online indicators are calculated based on the flight altitude reference trajectory and real-time observation data. The real-time observation data includes the real-time flight altitude data of the UAV and the real-time terrain altitude data. The online indicators include minimum airspace hold rate, obstacle clearance success rate and violation rate. The online indicators are statistically obtained within a sliding time window or the current execution cycle. The online indicators are compared with preset thresholds, and the adjustment amount of the quantile level and the adjustment amount of the mapping function parameters are determined based on the comparison results. The adjustment amount is a numerical value and is limited to the preset upper and lower limits to ensure stability. The adjustment amount of the quantile level is used to update the quantile level setting of the next cycle, and the adjustment amount of the mapping function parameter is used to update the mapping function parameter setting of the next cycle. When the online indicator does not meet the corresponding preset threshold, the quantile level is increased and / or the mapping function parameter is increased to improve the conservatism. In the next cycle, the updated quantile level and mapping function parameters are used as part of the inputs to S4 and S5, respectively, to achieve adaptive conservative closed-loop regulation.

9. A deep learning-based UAV altitude planning system for payload-following terrain-following flight, used to execute the deep learning-based UAV altitude planning method for payload-following terrain-following flight as described in any one of claims 1 to 8, comprising: The perception alignment module is used to acquire multi-source observations, synchronize time and register extrinsic parameters, and output data sequences. The condition variable module is used to calculate the forward velocity, total delay, and effective forward look distance of the data sequence, and output the condition variable sequence. The prediction module is used to output a prediction set using evidence-based deep learning; The calibration module is used to weight the residuals based on the condition variables within a sliding time window, output the upper bound sequence of the high confidence quantile of terrain height, and form a calibration set with the prediction set and the condition variable sequence. The margin module is used to map and generate an adaptive net margin sequence, which is combined with the upper bound sequence to form a net set; The safety planning module is used to solve the flight altitude reference trajectory based on the aforementioned clearance set and load dynamic constraints, and to perform time delay compensation and look-ahead barrier constraints. The adaptive module is used to issue height or vertical speed commands, collect online metrics, and output the adjustment amounts of the percentile level and mapping parameters for the next loop.

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