Path planning method of logistics unmanned aerial vehicle considering safety and energy conservation under influence of wind conditions

Abstract

The invention relates to unmanned aerial vehicle logistics distribution, in particular to a path planning method of a logistics unmanned aerial vehicle considering safety and energy conservation under the influence of wind conditions. According to the method, the collision risk of'ground-attached flight 'caused by only pursuing energy conservation is effectively avoided, and an efficient solution is provided for energy-saving path planning of the unmanned aerial vehicle in a complex environment. Comprising the following steps: S1, based on a velocity vector relationship of a multi-rotor logistics unmanned aerial vehicle, constructing an unmanned aerial vehicle horizontal and vertical flight energy consumption evaluation model under the influence of a wind condition; s2, for a flight corridor generated by expansion of the initial path, stripping a redundant non-connected space through a centripetal contraction mechanism; a communicated safe flight corridor is generated based on a merging criterion of the spatial geometrical relationship; s3, extracting a geometric center of a safe flight corridor overlapping area as a portal point, and mapping the portal point into a safe reference state point sequence with a timestamp through time discretization; and S4, constructing an energy consumption cost by using the energy consumption model, and solving to generate a smooth trajectory.

Description

Technical Field

[0001] This invention relates to unmanned aerial vehicle (UAV) logistics delivery, and more particularly to a path planning method for logistics UAVs under the influence of wind conditions, taking into account safety and energy conservation. Background Technology

[0002] Currently, logistics drones, with their significant advantages such as being unrestricted by ground transportation infrastructure and offering high delivery speeds, have become an important means of solving the last-mile delivery problem and responding to complex scenarios such as forest fires and earthquakes. However, limited by the capacity of onboard lithium batteries, the flight time and range of drones face severe challenges, and this problem is most significantly affected by wind conditions. In the path planning of logistics drones, how to achieve optimal energy consumption while ensuring flight safety in environments with obstacles and wind conditions, and avoid safety hazards arising from simply pursuing energy conservation, is of significant research importance.

[0003] Traditional path planning methods often focus on the generation of geometric paths, the smoothness of trajectories, and dynamic feasibility, neglecting the significant impact of wind conditions on energy consumption. While existing energy-optimal trajectory planning algorithms can reduce energy consumption, they often lead to a "ground-hugging" phenomenon, where paths are chosen extremely close to the edges of obstacles or the ground to minimize energy consumption. This lacks sufficient safety margin and makes it difficult to cope with sudden airflow disturbances. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by providing a path planning method for logistics drones under wind conditions that considers both safety and energy efficiency.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a safe path planning method for logistics drones that considers wind direction influence and energy saving, comprising the following steps:

[0006] S1. Based on the velocity vector relationship of multi-rotor logistics drones, construct an assessment model for the horizontal and vertical flight energy consumption of drones under the influence of wind conditions.

[0007] S2. For the flight corridor generated by the initial path expansion, redundant non-connected spaces are stripped away through a centripetal contraction mechanism; and a connected safe flight corridor is generated based on the merging criteria of spatial geometric relationships.

[0008] S3. Extract the geometric center of the overlapping area of ​​the safe flight corridor as the gateway point, generate a geometric safety reference path, and map it into a sequence of safety reference state points with timestamps through time discretization.

[0009] S4. Construct energy consumption cost using the energy consumption model, construct safety cost using the safety reference state point, and construct a composite objective function by combining dynamic constraints and flight corridor boundary constraints, and solve to generate a smooth trajectory.

[0010] Furthermore, step S1 specifically includes:

[0011] S1.1 Establish a three-dimensional velocity vector relationship model. Based on the velocity vector Vg of the UAV on the X-axis, Y-axis and Z-axis and the wind speed vector Vw, determine the airspeed vector Va of the UAV relative to the air, where Va = Vg - Vw.

[0012] S1.2, Construct the horizontal energy consumption model as follows:

[0013]

[0014] in, This refers to the energy consumption of the drone in the horizontal direction; The induced power in the horizontal direction, This refers to the type resistance power in the horizontal direction. The waste resistance power is in the horizontal direction;

[0015] S1.3, Construct the vertical energy consumption model as follows:

[0016]

[0017] This is a model of the vertical energy consumption of drones. It is the induced power in the vertical direction. It is the type resistance power in the vertical direction. It is the climbing power in the vertical direction.

[0018] Furthermore, the step S2, which involves stripping away redundant disconnected spaces through a centripetal contraction mechanism, includes:

[0019] Obtain a discrete path point sequence generated by a preset path search algorithm, and expand the path point sequence to generate an original corridor block sequence.

[0020] For each corridor block, a boundary evolution step size is set, and the boundary is driven to perform iterative shrinkage towards the center position of the path point in the three-dimensional coordinate axis direction;

[0021] During the boundary contraction process, the geometric connectivity features of the current corridor block and the adjacent corridor block in the overlapping area are monitored in real time. When the contraction operation causes the volume of the intersection of the current corridor block and the adjacent corridor block to be less than the initial intersection volume, or causes the current path point to overflow the boundary, the contraction termination mechanism is triggered.

[0022] Extract the minimum envelope space that satisfies the aforementioned topological connectivity conservation criterion, and use it as the final compact flight corridor.

[0023] Furthermore, the step S2, which generates a connected safe flight corridor based on the merging criteria of spatial geometric relationships, specifically includes:

[0024] Calculate the distribution characteristics of the spatial intersection of two adjacent flight corridors relative to their respective volumes, wherein the distribution characteristics include at least the intersection-union ratio and the degree of inclusion.

[0025] A preset threshold is set. When the intersection-union ratio of adjacent corridors exceeds the preset threshold, the two corridors are determined to satisfy the overlap consistency association; or, when the inclusion degree of adjacent corridors exceeds the preset threshold, the two corridors are determined to satisfy the nested inclusion relationship.

[0026] Merge adjacent corridor blocks that satisfy overlapping consistency association and / or nested containment relationships into a connected convex polyhedron; that is, merge them into a new corridor.

[0027] Traverse the flight corridor sequence and execute the merge mechanism.

[0028] Furthermore, the specific process of generating the safety reference point sequence in step S3 is as follows:

[0029] S3.1 Based on the safe flight corridor sequence generated in step S2, identify the overlapping areas of adjacent flight corridors in space, and extract the geometric center of the overlapping area as the gateway point;

[0030] S3.2 Connect the starting point, the ending point, and all the gateway points arranged in flight order to generate a geometric safety reference path sequence composed of discrete spatial location information;

[0031] S3.3. Perform arc length parameterization on the geometric safety reference path sequence to obtain the cumulative Euclidean distance from the starting point to each vertex and the total path length;

[0032] S3.4 Set the total task time and divide it into uniform step sizes. Map the cumulative length of the geometric path to the time axis and ensure logical consistency at the time starting point by introducing a numerical correction term.

[0033] S3.5 By identifying the interval affiliation of each discrete time point in the geometric path segment, linear interpolation is used to calculate the specific spatial coordinates corresponding to each time point. Finally, the geometric reference path is converted into a sequence of safe reference state points with timestamps, which serves as the input benchmark for trajectory optimization.

[0034] Furthermore, the specific process of solving for the smooth trajectory in step S4 is as follows:

[0035] The entire flight path of the UAV is divided into N local trajectory segments, which are the same as the number of compact flight corridors mentioned in step S2, and each trajectory segment is discretized into M time nodes.

[0036] At each discrete time node, the spatial coordinates at the current time are iteratively calculated by combining the velocity vector at the current time with the position state at the previous time using the kinematic recursive relationship; at the same time, the finite difference method is used to perform differential calculation on the rate of change of velocity between adjacent nodes to constrain the acceleration vector of the UAV in the wind field environment.

[0037] The compact flight corridor generated in step S2 is used to perform spatial position boundary constraints; and position state coincidence constraints are set for the intersection of adjacent trajectory segments to ensure trajectory continuity. At the same time, inter-segment transition acceleration constraints are set based on the average value of the preceding and following time steps.

[0038] Based on the model constructed in step S1, the instantaneous power of each discrete node under the corresponding wind direction component is calculated in real time, and the energy consumption is accumulated through the time step of the whole process to generate the energy consumption assessment cost.

[0039] Introduce the safety reference state point sequence generated in step S3, and construct the safety deviation cost of guiding the trajectory away from the obstacle edge by calculating the spatial deviation norm of the actual trajectory discrete points relative to the corresponding reference points.

[0040] By setting a balance weight coefficient, the energy consumption assessment cost and the safety deviation cost are linearly weighted and combined to construct an overall objective function; by minimizing the overall objective function, the optimal position state at each discrete time node is solved, thereby generating the smooth trajectory.

[0041] Compared with the prior art, the present invention has the following advantages.

[0042] This invention constructs an energy consumption model that comprehensively considers the influence of wind conditions, improving the accuracy of energy consumption assessment in actual flight environments; it also removes redundant space, improving space utilization efficiency; and to address the safety risks that energy consumption optimization may bring, it proposes a safety reference path planning method based on flight corridors. By guiding the trajectory to the center of the corridor overlap area and cleverly solving the balance problem between energy consumption optimization and safe flight through the adjustment of safety parameters, it effectively avoids the "ground-hugging flight" collision risk caused by simply pursuing energy saving, and provides an efficient solution for UAV energy-saving path planning in complex environments. Attached Figure Description

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The scope of protection of the present invention is not limited to the following description.

[0044] Figure 1 This is a block diagram of the unmanned aerial vehicle (UAV) safe path planning structure of the present invention;

[0045] Figure 2 This is a vector diagram showing the relationship between the speed of the quadcopter UAV and the ambient wind speed of the present invention.

[0046] Figure 3 This is the first safe flight corridor block of the present invention. A three-dimensional coordinate graph;

[0047] Figure 4 This is a two-dimensional plan view of the overall unprocessed safe flight corridor of the present invention;

[0048] Figure 5 This is a two-dimensional plan view of the safety flight corridor after overall trimming according to the present invention;

[0049] Figure 6 This is a two-dimensional planar schematic diagram of the overall and combined safe flight corridor of the present invention;

[0050] Figure 7 This is a two-dimensional planar schematic diagram of the geometric safety reference path point sequence of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0052] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. The singular forms “a,” “the,” and “the” as used in the embodiments of this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] Depending on the context, words such as “if” or “suppose” used here can be interpreted as “when”, “in response to determination”, or “in response to detection”.

[0054] For ease of understanding, the embodiments of this disclosure will be described in detail first.

[0055] like Figure 1-7 As shown in the figure, this embodiment provides a safe path planning method for logistics drones that considers the influence of wind direction and energy saving. The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] The overall process of the method in this embodiment includes the following four main steps:

[0057] S1. Based on the velocity vector relationship of multi-rotor logistics drones, construct an assessment model for the horizontal and vertical flight energy consumption of drones under the influence of wind conditions; where the influence of wind conditions includes the combined effects of wind direction and wind speed.

[0058] S2. For the flight corridor generated by the initial path expansion, redundant non-connected spaces are stripped away through a centripetal contraction mechanism; and connected safe flight corridors are generated based on the merging criteria of spatial geometric relationships.

[0059] S3. Extract the geometric center of the overlapping area of ​​the safe flight corridor as the gateway point, generate a geometric safety reference path, and map it into a sequence of safety reference state points with timestamps through time discretization.

[0060] S4. Construct energy consumption cost using the energy consumption model, construct safety cost using the safety reference state point, and construct a composite objective function by combining dynamic constraints and flight corridor boundary constraints, and solve to generate a smooth trajectory.

[0061] The following provides a detailed explanation of each step.

[0062] Step S1 specifically includes:

[0063] S1.1: Establish a three-dimensional velocity vector relationship model for horizontal and vertical flight under wind conditions. Introduce the absolute velocity of the UAV. ,in This is expressed as the velocity vector of the drone on the X, Y, and Z axes under ideal conditions. It is represented as the velocity vector of wind speed on the X, Y, and Z axes.

[0064] when Time indicates in Downwind on the axis Time indicates in Headwinds on the axis. Different wind conditions directly change the drone's absolute airspeed relative to the air, thus affecting its aerodynamic characteristics during flight.

[0065] S1.2: Calculate the induced power, form drag power, and exhaust drag power of the UAV in the horizontal direction, taking into account the influence of wind conditions, and construct an energy consumption model in the horizontal direction:

[0066]

[0067] in, This refers to the energy consumption of the drone in the horizontal direction; The induced power in the horizontal direction is the power loss caused by the downward exhaust airflow forming induced velocity when the rotor generates lift. The form drag power in the horizontal direction is the power loss caused by the form drag of the rotor blades themselves. The drag power in the horizontal direction is the power loss caused by the drag of non-rotor parts such as the drone fuselage.

[0068] Considering that the vertical velocity of the drone does not affect the power calculation when it is flying horizontally, a vector is introduced. and These represent the horizontal components of absolute velocity and wind speed, respectively. The horizontal absolute velocity of the drone is determined by whether the wind is tailwind or headwind. This change affects the homing speed of the drone during operation. And the ratio of horizontal absolute velocity to tip velocity. This causes changes in induced power and type resistance power. Among these, the induced velocity... Represented as:

[0069]

[0070] The induced power can then be obtained. With type power as follows:

[0071]

[0072]

[0073] in, Indicates the lift generated by the rotor, Expressed as air density, The area of ​​the rotor disk is expressed as... This refers to the induced speed of the drone during hovering. Expressed as the average value of the resistance, The rotor solidity, expressed as the rotor blade For the angular velocity of the rotor, It is expressed as the radius of the rotor.

[0074] Since wind conditions significantly affect the absolute speed of a drone, and since drag power has a cubic relationship with horizontal absolute speed, drag power is most significantly affected by wind conditions. Therefore, the influence of wind direction and speed on drag power must be considered in the modeling of drag power for horizontal flight.

[0075] (1) That is, in axis, When the wind is against the axis, the waste resistance power is expressed as:

[0076]

[0077] (2) That is, in Downwind in the axial direction When there is a headwind along the axial direction, the drag power of the drone can be divided into two cases, namely, the drone in... The speed in the axial direction is greater than the wind speed. and speed less than wind speed :

[0078]

[0079] Among them, when In this situation, there is drag, and the tailwind factor is equivalent to the wind helping the drone reduce drag, thus reducing drag power; when At this time, the air is not blocking the drone, but rather "pushing" it. In reality, the air is doing positive work on the drone. The waste resistance power in the axial direction is 0.

[0080] (3) When That is, in shaft and With a tailwind along the axial direction, the drone's drag power can be categorized into four cases: the drone in... The speed in the axial direction is greater than the wind speed. And in The speed in the axial direction is less than the wind speed. Drones in The speed in the axial direction is greater than the wind speed. And in The speed in the axial direction is greater than the wind speed. Drones in The speed in the axial direction is less than the wind speed. And in The speed in the axial direction is less than the wind speed. Drones in The speed in the axial direction is less than the wind speed. And in The speed in the axial direction is greater than the wind speed. .

[0081]

[0082] (4) When That is, in Headwind along the axis With the wind at the axial direction, the drone's drag power can be divided into two cases, namely, the drone in... The speed in the axial direction is greater than the wind speed. and speed less than wind speed :

[0083]

[0084] S1.3: Calculate the induced power, shape drag power, and climb power of the UAV in the vertical direction, taking into account wind conditions, and construct an energy consumption model in the vertical direction:

[0085]

[0086] in, This is a model of the vertical energy consumption of drones. It is the induced power in the vertical direction. It is the type resistance power in the vertical direction. It is the vertical climbing power. Expressed as the drone's climb rate, Represented as the vertical velocity component of the wind. Correction factors to account for tip loss and eddy current loss.

[0087] II. Step S2 is implemented as follows:

[0088] For the initial flight corridor generated by the A* algorithm with pathpoint inflation, a corridor block trimming and merging algorithm is designed to remove redundant space. Based on the "intersection-union ratio criterion" and the "inclusion ratio criterion", adjacent corridor blocks are trimmed and merged to finally generate a compact and connected safe flight corridor.

[0089] S2.1: The trimming design for the flight corridor blocks aims to eliminate redundant space outside the path direction, thereby reducing the volume of the flight corridor. The unprocessed first... Each flight corridor block is denoted as ,in The minimum coordinates of the zoomed-in flight corridor block. This is the maximum side length of the enlarged flight corridor block. Design the trimming step size. Iterate along each coordinate axis direction with a step size, thereby shrinking the corridor boundary. (In the negative direction...) Taking axis cutting as an example, the first a corridor block of and The updated formula is:

[0090]

[0091] The trimming process must satisfy the following two hard constraints: First, all original path points must remain within the trimmed corridor; second, the connectivity between adjacent flight corridor blocks must not be destroyed, that is, the intersection of the trimmed flight corridor blocks is not less than the intersection of the untrimmed flight corridor blocks. The mathematical expressions for the above two constraints are as follows (10) and (11):

[0092]

[0093]

[0094] in For path points generated by the A* algorithm, The number of path points generated by the A* algorithm, The path point sequence generated by the A* algorithm. Indicates the volume after cutting. This indicates the untrimmed volume.

[0095] S2.2: Because the originally generated flight corridors are not continuous and adjacent corridor blocks overlap, a merging criterion is designed based on spatial geometric relationships to improve space utilization efficiency. When two adjacent flight corridors... and A new flight corridor can be merged when any of the following conditions are met:

[0096] (1) Intersection-Union Ratio Criterion: If the ratio of the intersection volume to the union volume of two flight corridors is greater than a set threshold, it indicates that the overlap between the two is extremely high.

[0097]

[0098] in, Indicates the first A corridor block, express and The intersection and union ratio, Indicates the intersection-union ratio threshold. This represents the volume of the intersection of two adjacent safe flight corridors. This represents the volume of the union of two adjacent safe flight corridors.

[0099] (2) Inclusion ratio criterion: If the ratio of the intersection volume of two adjacent flight corridors to the volume of either corridor exceeds a set threshold, it indicates that one corridor is largely contained within the other corridor.

[0100]

[0101] in, express and The volume of the intersection and The ratio of volumes This indicates that the threshold is included.

[0102] III. Step S3 is implemented as follows:

[0103] To address the issue of safe passage in complex environments, the center of the overlapping area of ​​the flight corridor generated by S2 is extracted as the gateway point. A safe reference path is constructed by connecting the starting point, each gateway point and the endpoint. A time discretization method is used to map the geometric reference path into a sequence of safe reference state points with timestamps.

[0104] S3.1: The collision-free safe flight corridor generated by S2 is composed of multiple convex polyhedra, represented as... and define two adjacent flight corridor blocks. Minimum corner point of spatial range and the largest corner point To obtain the maximum safety margin, the geometric center of the overlapping area of ​​adjacent corridors is selected as the gateway point, which is located at the geometric midpoint of the two shared feasible regions. The calculation formula is:

[0105]

[0106] in For the first and the The smallest corner point of the intersection space of the flight corridors, For the first and the The largest corner point of the intersection space of the flight corridors.

[0107] S3.2: The gateway point represents the optimal reference point connecting two adjacent flight corridors, ensuring that subsequently generated paths stay away from obstacle boundaries. (Set the starting point...) ,end All portal points 1. Concatenate them sequentially to generate a sequence of geometrically safe reference path points containing only spatial location information. :

[0108]

[0109] in, The number of safe flight corridors, .

[0110] This path connects the centers of all safe zones and represents the theoretically safest route.

[0111] S3.3: To incorporate a geometric path containing only spatial information into a time-domain-based trajectory optimization model, it must be mapped to a sequence of reference state points with timestamps. First, the arc length of the safety reference path is parameterized, and the safety reference path is calculated from the starting point to each vertex. Cumulative Euclidean distance and total path length .

[0112]

[0113] S3.4: Define the timeline, including the total task time. Classified by uniform step size Part. Definition of Part The time interval of the segment trajectory is The first in this section Each moment can be represented as , The corresponding cumulative path length along the reference path The calculation formula is as follows:

[0114]

[0115] in ,but .use replace The purpose is to avoid when When the equation (17) has no solution.

[0116] S3.5: To determine the path length To pinpoint a specific location on the safety reference path, it is necessary to identify the corresponding path segment or interval. To satisfy the following inequality constraints:

[0117]

[0118] In determining The interval to which it belongs Then, its internal local normalization parameters are calculated. :

[0119]

[0120] Finally, the first value is obtained through linear interpolation. Duan Di Specific safety reference location at any time .

[0121] This safety reference point The objective function in the optimization problem of S4 is formed to guide the drone to always stay in a safer area of ​​the corridor and prevent the drone from flying close to the edge of the obstacle, thereby maximizing safety in trajectory optimization.

[0122] IV. Step S4 is implemented as follows:

[0123] The energy cost of the optimization problem is constructed using the energy consumption model of S1, the boundary of the optimization problem is constructed using the safe flight corridor generated by S2, and the safety cost of the optimization problem is constructed using the timestamped safety reference points generated by S3. A composite objective function including energy cost, safety cost, and safety parameters is designed to realize the energy cost and safety cost balance mechanism of parameter adjustment, transforming the continuous trajectory optimization problem into a discrete time point optimization problem, and solving for a smooth trajectory that takes into account both energy efficiency and flight safety.

[0124] S4.1: To transform the trajectory optimization problem into a solvable mathematical programming problem, the overall flight trajectory of the UAV is divided into... The local trajectory segment, of which the first... The segment trajectory strictly corresponds to the first segment generated in step S2. The number of safe flight corridor blocks ensures that the number of trajectory segments matches the number of corridors. To achieve refined planning, the first... The segment trajectory is further discretized into Each time point.

[0125] For ease of description, the speed of the drone in S1 exist The value at time is denoted as The location of the quadcopter drone is denoted as .

[0126] S4.2: Design constraints, including: trajectory start and end boundary point constraints; velocity constraints, acceleration constraints, and position constraints (flight corridor boundaries) at discrete time nodes; connection constraints at trajectory segment intersections and acceleration constraints at trajectory segment intersections.

[0127] (1) In order to ensure the integrity of the flight mission, the trajectory needs to be constrained by the first and last boundary points, that is, the first state point of the trajectory sequence is set as the mission start point. The last state point marks the end of the task. .

[0128]

[0129] (2) In order to ensure the dynamic feasibility of the UAV, a hard constraint is imposed on the velocity at all discrete time points to ensure that the flight speed at any time is maintained at the maximum allowable speed. Within the range.

[0130]

[0131] (3) Considering the stability and power limitations during UAV flight, the finite difference method is used to constrain the acceleration at discrete time nodes. By limiting the amplitude of the rate of change of velocity between adjacent time nodes, the maximum acceleration threshold is satisfied during both acceleration and deceleration of the UAV. and maximum deceleration threshold The design constraints are as follows:

[0132]

[0133] A discrete position model is established based on kinematic recursion. This model utilizes the current time interval... velocity vector Calculate the displacement increment, and then recursively calculate the current UAV position coordinates based on the previous position state:

[0134]

[0135] (4) Use the flight corridor boundary generated in step S2 as a position constraint. For the first... All discrete points of the segment trajectory It must be strictly limited to the first Within each flight corridor, the formula is expressed as:

[0136]

[0137] (5) To ensure the continuity of the UAV's trajectory when traversing different flight corridors, connection constraints between trajectory segments must be applied. Mandatory requirement #1 The end position state of the segment trajectory and the first The initial position states of the segment trajectories strictly coincide to achieve a smooth transition.

[0138]

[0139] (6) To ensure a smooth transition between multiple trajectory segments for the UAV, the acceleration at the intersection of the trajectory segments is designed as follows:

[0140]

[0141] Among them, the time interval at the junction of trajectory segments This is the average of two adjacent time steps, and the inter-segment transition acceleration is calculated based on this. To limit it to the permitted range: , For the minimum inter-segment transition acceleration, This represents the maximum inter-segment transition acceleration.

[0142] S4.3: Calculate the first... Duan Di Instantaneous power at a given moment And calculate the energy consumption at that moment by combining the time step. :

[0143]

[0144] S4.4: Define the energy cost of the entire process The sum of energy consumption at all times:

[0145]

[0146] To prevent drones from getting too close to obstacles in pursuit of minimum energy consumption, trajectory points need to be optimized. This brings it closer to the safe reference path; by introducing a safe reference point generated by S3. Define security costs :

[0147]

[0148] S4.5: Taking into account both energy consumption and safety, define the overall objective function. The problem is then transformed into an optimization problem for solution:

[0149]

[0150] in , which is a weighting coefficient used to balance the competition between energy consumption optimization and safe flight.

[0151] This invention addresses the energy consumption and safety issues of multi-rotor logistics drones under wind conditions and obstacle conditions, proposing a trajectory planning method. This method constructs accurate energy consumption models for different wind conditions, generates compact and connected flight corridors using pruning and merging algorithms, extracts the centers of overlapping corridor areas to construct safe reference paths as soft constraints, and finally, based on discrete time points, transforms the trajectory planning problem into a mathematical optimization problem by constructing energy consumption and safety as the overall objective function, generating a trajectory that balances energy efficiency and flight safety.

[0152] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "preferred embodiment," "detailed description," or "preferred embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Therefore, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A safe path planning method for logistics drones that considers wind direction influence and energy saving, characterized in that, Includes the following steps: S1. Based on the velocity vector relationship of multi-rotor logistics drones, construct an assessment model for the horizontal and vertical flight energy consumption of drones under the influence of wind conditions. S2. For the flight corridor generated by the initial path expansion, redundant non-connected spaces are stripped away through a centripetal contraction mechanism; and a connected safe flight corridor is generated based on the merging criteria of spatial geometric relationships. S3. Extract the geometric center of the overlapping area of ​​the safe flight corridor as the gateway point, generate a geometric safety reference path, and map it into a sequence of safety reference state points with timestamps through time discretization. S4. Construct energy consumption cost using the energy consumption model, construct safety cost using the safety reference state point, and construct a composite objective function by combining dynamic constraints and flight corridor boundary constraints, and solve to generate a smooth trajectory.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: S1.1 Establish a three-dimensional velocity vector relationship model. Based on the velocity vector Vg of the UAV on the X-axis, Y-axis and Z-axis and the wind speed vector Vw, determine the airspeed vector Va of the UAV relative to the air, where Va = Vg - Vw. S1.2, Construct the horizontal energy consumption model as follows: in, This refers to the energy consumption of the drone in the horizontal direction; The induced power in the horizontal direction, This refers to the type resistance power in the horizontal direction. The waste resistance power is in the horizontal direction; S1.3, Construct the vertical energy consumption model as follows: This is a model of the vertical energy consumption of drones. It is the induced power in the vertical direction. It is the type resistance power in the vertical direction. It is the climbing power in the vertical direction.

3. The method according to claim 1, characterized in that, Step S2, which involves stripping away redundant disconnected spaces through a centripetal contraction mechanism, includes: Obtain a discrete path point sequence generated by a preset path search algorithm, and expand the path point sequence to generate an original corridor block sequence. For each corridor block, a boundary evolution step size is set, and the boundary is driven to perform iterative shrinkage towards the center position of the path point in the three-dimensional coordinate axis direction; During the boundary contraction process, the geometric connectivity features of the current corridor block and the adjacent corridor block in the overlapping area are monitored in real time. When the contraction operation causes the volume of the intersection of the current corridor block and the adjacent corridor block to be less than the initial intersection volume, or causes the current path point to overflow the boundary, the contraction termination mechanism is triggered. Extract the minimum envelope space that satisfies the aforementioned topological connectivity conservation criterion, and use it as the final compact flight corridor.

4. The method according to claim 1, characterized in that, Step S2, which generates a connected safe flight corridor based on the merging criteria of spatial geometric relationships, specifically includes: Calculate the distribution characteristics of the spatial intersection of two adjacent flight corridors relative to their respective volumes, wherein the distribution characteristics include at least the intersection-union ratio and the degree of inclusion. A preset threshold is set. When the intersection-union ratio of adjacent corridors exceeds the preset threshold, the two corridors are determined to satisfy the overlap consistency association; or, when the inclusion degree of adjacent corridors exceeds the preset threshold, the two corridors are determined to satisfy the nested inclusion relationship. Merge adjacent corridor blocks that satisfy overlapping consistency association and / or nested containment relationships into a connected convex polyhedron; Traverse the flight corridor sequence and execute the merge mechanism.

5. The method according to claim 1, characterized in that, The specific process of generating the safety reference point sequence in step S3 is as follows: S3.1 Based on the safe flight corridor sequence generated in step S2, identify the overlapping areas of adjacent flight corridors in space, and extract the geometric center of the overlapping area as the gateway point; S3.2 Connect the starting point, the ending point, and all the gateway points arranged in flight order to generate a geometric safety reference path sequence composed of discrete spatial location information; S3.

3. Perform arc length parameterization on the geometric safety reference path sequence to obtain the cumulative Euclidean distance from the starting point to each vertex and the total path length; S3.4 Set the total task time and divide it into uniform step sizes. Map the cumulative length of the geometric path to the time axis and ensure logical consistency at the time starting point by introducing a numerical correction term. S3.5 By identifying the interval affiliation of each discrete time point in the geometric path segment, linear interpolation is used to calculate the specific spatial coordinates corresponding to each time point. Finally, the geometric reference path is converted into a sequence of safe reference state points with timestamps, which serves as the input benchmark for trajectory optimization.

6. The method according to claim 3, characterized in that, The specific process of solving for the smooth trajectory in step S4 is as follows: The entire flight path of the UAV is divided into N local trajectory segments, which are the same as the number of compact flight corridors mentioned in step S2, and each trajectory segment is discretized into M time nodes. At each discrete time node, the spatial coordinates at the current time are iteratively calculated by combining the velocity vector at the current time with the position state at the previous time using the kinematic recursive relationship; at the same time, the finite difference method is used to perform differential calculation on the rate of change of velocity between adjacent nodes to constrain the acceleration vector of the UAV in the wind field environment. The compact flight corridor generated in step S2 is used to perform spatial position boundary constraints; and position state coincidence constraints are set for the intersection of adjacent trajectory segments to ensure trajectory continuity. At the same time, inter-segment transition acceleration constraints are set based on the average value of the preceding and following time steps. Based on the model constructed in step S1, the instantaneous power of each discrete node under the corresponding wind direction component is calculated in real time, and the energy consumption is accumulated through the time step of the whole process to generate the energy consumption assessment cost. Introduce the safety reference state point sequence generated in step S3, and construct the safety deviation cost of guiding the trajectory away from the obstacle edge by calculating the spatial deviation norm of the actual trajectory discrete points relative to the corresponding reference points. By setting a balance weight coefficient, the energy consumption assessment cost and the safety deviation cost are linearly weighted and combined to construct an overall objective function; by minimizing the overall objective function, the optimal position state at each discrete time node is solved, thereby generating the smooth trajectory.

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