Path planning method and device oriented to ornithopter model constraint

Abstract

The invention provides a path planning method and device for flapping-wing aircraft model constraint, and relates to the technical field of bionic flapping-wing aircrafts. The method comprises the following steps: acquiring a path starting point and a path ending point of the ornithopter to be subjected to path planning; constructing a model constraint of the maneuvering characteristics of the ornithopter, and constructing a path planning model of the ornithopter according to the model constraint; and obtaining a path planning result according to the path starting point, the path end point and the ornithopter path planning model. The invention provides a path planning method combining # imgabs0 # and an artificial potential field based on model constraint. Comprising a dual-motor-driven X-wing flapping-wing aircraft and a path planning algorithm design thereof, and aims to overcome the defect that a traditional algorithm cannot meet specific requirements of the aircraft.

Description

Technical Field

[0001] The invention relates to the technical field of bionic flapping-wing aircraft, and in particular to a path planning method and device facing flapping-wing aircraft model constraints. Background Art

[0002] Bionic flapping-wing aircraft is a kind of bionic aircraft that imitates the flapping wings of birds and insects in nature to generate lift and thrust. In the long-term evolution of nature, many flying creatures have evolved flapping wings to fly, and many flying creatures have high flight efficiency. It can be seen that flapping-wing aircraft have unique advantages over rotary and fixed wings. In addition, flapping-wing aircraft has a very broad application prospect in military reconnaissance and other fields due to its bionic nature.

[0003] However, due to the slow development of flapping-wing aircraft, people's vague understanding of the aerodynamic mechanism of flapping-wing aircraft, the complexity of the flight mechanism of flapping-wing aircraft, and the difficulty in designing and manufacturing, flapping-wing aircraft are still in their infancy. There has been a lack of exploration in the planning and control methods of flapping-wing aircraft. At present, there are mature solutions for some common tasks on other aircraft, such as trajectory tracking, but there is a lack of mature and effective path planning and control strategies for flapping-wing aircraft. Summary of the invention

[0004] In order to solve the technical problems of the uncertainty in the aerodynamic mechanism research of existing flapping-wing aircraft, the complexity of flight control, and the lack of effective trajectory task algorithm, the embodiment of the present invention provides a path planning method and device for flapping-wing aircraft model constraints. The technical solution is as follows:

[0005] On the one hand, a path planning method for flapping-wing aircraft model constraints is provided, the method is implemented by a path planning device, and the method includes:

[0006] S1. Obtain a path starting point and a path end point of a flapping-wing aircraft to be path planned.

[0007] S2. Construct model constraints of the maneuverability of the flapping-wing aircraft, and construct a path planning model for the flapping-wing aircraft based on the model constraints.

[0008] S3. Obtain a path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model.

[0009] Optionally, the model constraints of the maneuverability of the flapping-wing aircraft in S2 include: a maximum flight distance constraint, a minimum turning radius constraint, and a minimum step length constraint.

[0010] Among them, the maximum flight distance constraint is as shown in the following formula (1):

[0011] (1)

[0012] In the formula, represents the path length, Indicates the preset maximum flight distance. Indicates the number of nodes, Represents the distance between each node.

[0013] The minimum turning radius constraint is as shown in the following formula (2):

[0014] (2)

[0015] In the formula, represents any turning radius, Indicates that the minimum turning radius is, Indicates the minimum flight speed, Indicates the tilt angle.

[0016] The minimum step length constraint is as shown in equation (3):

[0017] (3)

[0018] In the formula, represents any step length, Indicates the minimum step size.

[0019] Optionally, the path planning result is obtained according to the path starting point, the path end point and the flapping-wing aircraft path planning model in S3, including:

[0020] S31. Establish the first tree for the starting point of the path , build a second tree for the end point of the path , and set the cost function.

[0021] S32, determine whether the preset number of iterations has been reached; if so, execute step S38; if not, execute step S33.

[0022] S33. Generate a random node.

[0023] S34, according to the random node, for the first tree And the second tree Expand new nodes and update the cost function.

[0024] S35. Check the neighboring nodes near the expanded new node to obtain the optimized path.

[0025] S36. Determine the first tree and the second tree whether the extended nodes meet; if so, a connection path is generated and step S37 is executed; if not, step S32 is executed.

[0026] S37: Determine whether the total length of the connection path is less than the preset maximum flight distance ; If so, record the cost function of the connection path, save the connection path, and go to step S32; if not, go to step S32.

[0027] S38, determine whether the preset time interval has been reached; if so, smooth the connection path with the minimum cost and output the path planning result; if not, go to step S32.

[0028] Optionally, the cost function in S31 is as shown in the following formula (4):

[0029] (4)

[0030] In the formula, represents the path cost, represents the weight coefficient, represents the turning angle of the path, represents the weight coefficient, Indicates the path length.

[0031] Optionally, generating a random node in S33 includes:

[0032] Generate random nodes in the workspace , applying artificial potential field to random nodes Correction is performed to obtain the corrected random nodes.

[0033] Among them, the artificial potential field is applied to random nodes Correction is performed as shown in the following formula (5):

[0034] (5)

[0035] In the formula, Represents a random node The repulsive force of the obstacle, represents the set of obstacles, represents the repulsion coefficient, Represents a random node Location, Indicates the location of the obstacle. Indicates the maximum range of repulsive force.

[0036] Optionally, in S34, according to the random node, the first tree And the second tree New node expansion, including:

[0037] In the first tree and the second tree In the example, find the node closest to the corrected random node. , calculate from the node Extension paths to corrected random nodes.

[0038] Optionally, checking neighboring nodes near the expanded new node in S35 to obtain an optimized path includes:

[0039] Obtain the neighboring nodes near the expanded new node, reconnect the neighboring nodes, and obtain a path that meets the minimum turning radius constraint, the longest flight distance constraint and has a lower cost, that is, the optimized path.

[0040] On the other hand, a path planning device for flapping-wing aircraft model constraints is provided, and the device is applied to a path planning method for flapping-wing aircraft model constraints, and the device comprises:

[0041] The acquisition module is used to acquire the path starting point and path ending point of the flapping-wing aircraft to be path planned.

[0042] The construction module is used to construct the model constraints of the maneuverability performance of the flapping-wing aircraft, and to construct the path planning model of the flapping-wing aircraft according to the model constraints.

[0043] The output module is used to obtain the path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model.

[0044] Optionally, the model constraints of the maneuverability of the flapping-wing aircraft include: a maximum flight distance constraint, a minimum turning radius constraint, and a minimum step length constraint.

[0045] Among them, the maximum flight distance constraint is as shown in the following formula (1):

[0046] (1)

[0047] In the formula, represents the path length, Indicates the preset maximum flight distance. Indicates the number of nodes, Represents the distance between each node.

[0048] The minimum turning radius constraint is as shown in the following formula (2):

[0049] (2)

[0050] In the formula, represents any turning radius, Indicates that the minimum turning radius is, Indicates the minimum flight speed, Indicates the tilt angle.

[0051] The minimum step length constraint is as shown in equation (3):

[0052] (3)

[0053] In the formula, represents any step length, Indicates the minimum step size.

[0054] Optionally, the output module is further configured to:

[0055] S31. Establish the first tree for the starting point of the path , build a second tree for the end point of the path , and set the cost function.

[0056] S32, determine whether the preset number of iterations has been reached; if so, execute step S38; if not, execute step S33.

[0057] S33. Generate a random node.

[0058] S34, according to the random node, for the first tree And the second tree Expand new nodes and update the cost function.

[0059] S35. Check the neighboring nodes near the expanded new node to obtain the optimized path.

[0060] S36. Determine the first tree and the second tree whether the extended nodes meet; if so, a connection path is generated and step S37 is executed; if not, step S32 is executed.

[0061] S37: Determine whether the total length of the connection path is less than the preset maximum flight distance ; If so, record the cost function of the connection path, save the connection path, and go to step S32; if not, go to step S32.

[0062] S38, determine whether the preset time interval has been reached; if so, smooth the connection path with the minimum cost and output the path planning result; if not, go to step S32.

[0063] Optionally, the cost function is as shown in the following formula (4):

[0064] (4)

[0065] In the formula, represents the path cost, represents the weight coefficient, represents the turning angle of the path, represents the weight coefficient, Indicates the path length.

[0066] Optionally, the output module is further configured to:

[0067] Generate random nodes in the workspace , applying artificial potential field to random nodes Correction is performed to obtain the corrected random nodes.

[0068] Among them, the artificial potential field is applied to random nodes Correction is performed as shown in the following formula (5):

[0069] (5)

[0070] In the formula, Represents a random node The repulsive force of the obstacle, represents the set of obstacles, represents the repulsion coefficient, Represents a random node Location, Indicates the location of the obstacle. Indicates the maximum range of repulsive force.

[0071] Optionally, the output module is further configured to:

[0072] In the first tree and the second tree In the example, find the node closest to the corrected random node. , calculate from the node Extension paths to corrected random nodes.

[0073] Optionally, the output module is further configured to:

[0074] Obtain the neighboring nodes near the expanded new node, reconnect the neighboring nodes, and obtain a path that meets the minimum turning radius constraint, the longest flight distance constraint and has a lower cost, that is, the optimized path.

[0075] On the other hand, a path planning device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned path planning methods for flapping-wing aircraft model constraints is implemented.

[0076] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned path planning methods for flapping-wing aircraft model constraints.

[0077] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0078] In the present invention, the classic RRT (Rapidly-Exploring Random Tree) path planning algorithm is improved, and angle constraints are introduced to ensure that the generated path angle is more suitable for the tracking requirements of the flapping-wing prototype. In order to improve the utilization efficiency of random points in the RRT algorithm, the present invention adopts a dual-tree bidirectional expansion strategy, and combines the artificial potential field algorithm to correct the random points so that the path is as far away from obstacles as possible. In addition, in order to find a path that is closer to the optimal path, the algorithm will not stop immediately after finding a path that meets the angle constraint, but will continue to optimize, and finally obtain a result in which the angle is more in line with the constraint and the path distance is shorter. Finally, the path is smoothed by a B-spline curve, which further improves the feasibility and execution efficiency of the path. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0080] Figure 1 It is a flow chart of a path planning method for flapping-wing aircraft model constraints provided by an embodiment of the present invention;

[0081] Figure 2 is a structural diagram of a tailless X-wing flapping-wing aircraft provided by an embodiment of the present invention;

[0082] Figure 3 It is an overall flow chart of a path planning algorithm and tracking control system for flapping-wing aircraft model constraints provided by an embodiment of the present invention;

[0083] Figure 4 is a path schematic diagram provided by an embodiment of the present invention;

[0084] Figure 5 is a flow chart of path planning for a flapping-wing aircraft provided by an embodiment of the present invention;

[0085] Figure 6It is a block diagram of a path planning device for flapping-wing aircraft model constraints provided by an embodiment of the present invention;

[0086] Figure 7 It is a structural schematic diagram of a path planning device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0087] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0088] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0089] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0090] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0091] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0092] The embodiment of the present invention provides a path planning method for flapping-wing aircraft model constraints, which can be implemented by a path planning device, which can be a terminal or a server. Figure 1 The flow chart of the path planning method for flapping-wing aircraft model constraints is shown, and the processing flow of the method may include the following steps:

[0093] S1. Obtain a path starting point and a path end point of a flapping-wing aircraft to be path planned.

[0094] In a feasible implementation, the flapping-wing aircraft maintains a large angle of attack during flight, and is able to achieve slow forward movement while maintaining stable flight. The direction of the downstream airflow generated by the wings is controlled by the swing of the tail rudder, thereby changing the aerodynamic torque, giving the aircraft a higher steering sensitivity. Based on this, the present invention proposes a path planning algorithm that is more suitable for such aircraft, while ensuring stable flight, maintaining a slow forward speed, and smooth steering. Because the prototype has a certain forward speed, the path turning radius and smoothness requirements are high, so the path generated by the traditional unconstrained path planning algorithm may not be suitable for the tracking task of the prototype. Therefore, a fusion path planning method that considers angle constraints is more needed.

[0095] Specifically, the control object of the present invention is a tailless X-wing flapping wing aircraft. Figure 2 , wherein the left wing 1 and the right wing 2 are respectively composed of a wing membrane and its wing veins, the reduction gear set 3 is mainly composed of three gears, the left and right top gears are connected to the wings through rockers, the middle lower gear is connected to the upper two gears and the output shaft gear of the brushless motor 4, the left wing 1, the right wing 2, the reduction gear set 3 and the brushless motor 4 constitute the driving part that provides the lift of the prototype flight, the pitch servo 5 provides the pitch torque, the yaw servo 6 provides the yaw torque, and the differential control of the left and right brushless motors 4 provides the yaw torque, so as to realize the multi-degree-of-freedom flight control of the prototype. The wireless communication module 7 is used to receive the control instructions sent by the computer, and the flight controller 8 is used to parse and execute the computer instructions.

[0096] Furthermore, the motion capture system is mainly composed of 14 OptiTrack infrared cameras, switches and Motive motion capture software. 14 infrared cameras are installed on the bracket above the rectangular field, 6 on each long side and the other two in the middle of the short side. The frame rate of the camera is 240Hz, ensuring that the prototype can capture the position and attitude information in time during flight. The camera is connected to the switch via a network cable, and the switch is connected to the computer for transmitting infrared images. The captured image will be processed in the Motive software to solve the position of the prototype marker point. Through the pre-calibrated prototype rigid body data, the position and attitude quaternion of the prototype rigid body can be further solved.

[0097] Furthermore, the host computer system is mainly responsible for determining the next path point according to the current position of the prototype and performing PID calculation. The host computer is written in C++, and the tracking path needs to be loaded into the program, and the position and posture information of the prototype rigid body needs to be obtained from the Motive software through inter-process communication.

[0098] The present invention adopts a vector field control method. Specifically, the host computer system obtains the real-time position and attitude information of the prototype and maps it to a pre-designed vector field. The vector field defines the direction and speed that the prototype should follow at different spatial positions to ensure that it flies along the predetermined path. When the prototype deviates from the path, the vector field controller calculates the expected direction (i.e., the vector direction) of the current position and compares it with the current actual flight direction. Based on this comparison result, the vector field controller generates an adjustment signal through a PID algorithm, which is used to control the yaw servo 6 so that the prototype is oriented toward the expected path. In addition, a weaker PID control is performed on the roll. When the difference between the prototype position and the expected path is large, the roll of the prototype is controlled to make it approach the expected path faster.

[0099] The motor PWM and servo PWM values ​​calculated on the host computer are connected to the communication module on the prototype through the serial port connection Zigbee communication module, thereby realizing communication between the host computer and the prototype. Figure 3 shown.

[0100] S2. Construct model constraints of the maneuverability of the flapping-wing aircraft, and construct a path planning model for the flapping-wing aircraft based on the model constraints.

[0101] Optionally, the model constraints of the maneuverability of the flapping-wing aircraft in S2 may include: a maximum flight distance constraint, a minimum turning radius constraint, and a minimum step length constraint.

[0102] Among them, aircraft are usually limited by energy or time when performing tasks. Just like traditional aircraft are limited by fuel due to load-bearing reasons, there is a maximum flight distance. The prototype used in the design of the present invention also has a maximum flight distance due to the limitations of battery quality and flight speed. Assume that there are N nodes in the aircraft path, and the distance between each node is set to , satisfying the following expression:

[0103] (1)

[0104] In the formula, represents the path length, Indicates the preset maximum flight distance. Indicates the number of nodes, Represents the distance between each node.

[0105] Specifically, the flapping-wing micro-aircraft in the present invention is small in size and light in weight. The battery energy it carries stores relatively little power, which can maintain the flapping-wing micro-aircraft for about 5 minutes of continuous and stable flight. The average speed is calculated as 5 / ms, and its maximum flight distance is about 1.5km.

[0106] Furthermore, assuming that the minimum turning radius of the aircraft is , the aircraft can turn at any radius The expression must be satisfied:

[0107] (2)

[0108] Minimum turning radius It can be calculated using a simple formula:

[0109] (3)

[0110] In the formula, represents any turning radius, Indicates that the minimum turning radius is, Indicates the minimum flight speed of the aircraft. Indicates the bank angle (i.e. the bank angle of the aircraft when turning).

[0111] Specifically, the minimum flight speed of the flapping-wing aircraft in the present invention is approximately , the maximum safe tilt angle is 30°, and the minimum turning radius of the flapping-wing aircraft is calculated to be about 0.7 m. In order to ensure that the accuracy of real-time tracking is controlled within ±0.3 meters, considering the minimum turning radius limit of the aircraft, the minimum angle between two consecutive path segments is Should be set to 120 degrees Figure 4 shown.

[0112] Furthermore, the aircraft needs to adjust its attitude at any time during the actual flight process to control the aircraft's forward flight, backward flight, climb, turn, etc. The execution of this series of instructions takes a certain amount of time. In order to ensure the aircraft's stable flight and avoid collisions during flight, a buffer flight distance must be reserved. This distance is the minimum step length. Suppose the minimum step length is , then any step length in the path planning process Need to satisfy the formula:

[0113] (4)

[0114] Generally, the flight distance of the research object in 1 second is selected as the minimum step length. The average speed of the flapping-wing micro-aircraft in the present invention is about , the minimum speed can also meet the demand, so the minimum speed is taken After estimation, the minimum step length is 2m.

[0115] S3. Obtain a path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model.

[0116] In one possible implementation, the entire system includes a set of model constraints A flapping-wing aircraft path planning with an artificial potential field fusion algorithm, an X-wing flapping-wing aircraft as a control object, a motion capture system for feeding back the real-time position and posture of the flapping-wing aircraft, and a host computer system for tracking the desired angle and altitude and heading angle PID output and sending control signals.

[0117] Optionally, the present invention provides a model constraint-based method according to the model constraint analysis of the maneuverability of flapping-wing aircraft. Path planning of flapping-wing aircraft using artificial potential field fusion algorithm, such as Figure 5 As shown, the above step S3 may include the following steps S31-S38:

[0118] S31. Establish the first tree for the starting point of the path , build a second tree for the end point of the path , and set the cost function.

[0119] In a feasible implementation, initialization is first performed, including:

[0120] Defining the start and end points: Setting the start point and end point .

[0121] Create two trees.

[0122] Tree From the starting point Start growing.

[0123] Tree From the end point Start growing.

[0124] Setting the cost function: path cost It consists of the turning angle cost of the path and the path length cost, namely:

[0125] (5)

[0126] In the formula, represents the path cost, represents the weight coefficient, represents the turning angle of the path, represents the weight coefficient, Indicates the path length.

[0127] S32, determine whether the preset number of iterations has been reached; if so, execute step S38; if not, execute step S33.

[0128] S33. Generate a random node.

[0129] In one possible implementation, random nodes are generated in the workspace. , applying artificial potential field to random nodes Correction is performed to obtain the corrected random nodes.

[0130] Specifically, generate random points in the workspace .

[0131] Applying artificial potential field to random points The influence of the artificial potential field can be expressed by the following formula:

[0132] (6)

[0133] In the formula, Represents a random node The repulsive force of the obstacle, represents the set of obstacles, represents the repulsion coefficient, Represents a random node Location, Indicates the location of the obstacle. Indicates the maximum range of repulsive force.

[0134] S34, according to the random node, for the first tree And the second tree Expand new nodes and update the cost function.

[0135] In one possible implementation, in the first tree and the second tree In the example, find the node closest to the corrected random node. , calculate from the node Extension paths to corrected random nodes.

[0136] Specifically, in the tree and Find the closest random point after correction Node .

[0137] Calculate from Towards The extended path and update the cost of the path .

[0138] The path cost update formula is:

[0139] (7)

[0140] In the formula, The turning angle of the newly generated path is required to be greater than 120°. The path length requirement is greater than .

[0141] S35. Check the neighboring nodes near the expanded new node to obtain the optimized path.

[0142] In one feasible implementation, after each expansion of a new node, The new node's neighboring nodes are checked and the path is optimized by reconnecting them. If a lower-cost path can be formed through other nodes, the path is updated and ensured to meet the corner and distance requirements of the model constraints.

[0143] S36. Determine the first tree and the second tree whether the extended nodes meet; if so, a connection path is generated and step S37 is executed; if not, step S32 is executed.

[0144] In one possible implementation, when the tree and When the extended path nodes meet, connect the two trees to form a arrive The connecting path.

[0145] The total path length is less than Then record the total cost of the path , and save the path.

[0146] S37: Determine whether the total length of the connection path is less than the preset maximum flight distance ; If so, record the cost function of the connection path, save the connection path, and go to step S32; if not, go to step S32.

[0147] S38, determine whether the preset time interval has been reached; if so, smooth the connection path with the minimum cost and output the path planning result; if not, go to step S32.

[0148] In one possible implementation, the algorithm continuously iterates to generate new paths and records paths with lower costs.

[0149] If a path with a lower cost cannot be found within a set time (eg, several seconds), the current optimal path is smoothed.

[0150] Path smoothing can be achieved through B-Spline curves to further reduce the turning points and distance of the path.

[0151] The final output path is a smoothed path with the minimum cost, which is suitable for tracking flapping-wing aircraft.

[0152] In an embodiment of the present invention, the classic RRT (Rapidly-Exploring Random Tree) path planning algorithm is improved, and an angle constraint is introduced to ensure that the generated path angle is more suitable for the tracking requirements of the flapping-wing prototype. In order to improve the utilization efficiency of random points in the RRT algorithm, the present invention adopts a dual-tree bidirectional expansion strategy, and combines the artificial potential field algorithm to correct the random points so that the path is as far away from obstacles as possible. In addition, in order to find a path that is closer to the optimal path, the algorithm will not stop immediately after finding a path that meets the angle constraint, but will continue to optimize, and finally obtain a result in which the angle is more in line with the constraint and the path distance is shorter. Finally, the path is smoothed by a B-spline curve, which further improves the feasibility and execution efficiency of the path.

[0153] Figure 6 The block diagram of a path planning device for flapping-wing aircraft model constraints according to an exemplary embodiment is shown. The device is used for a path planning method for flapping-wing aircraft model constraints. Figure 6 The device includes an acquisition module 310, a construction module 320 and an output module 330. Among them:

[0154] The acquisition module 310 is used to acquire the path starting point and the path ending point of the flapping-wing aircraft to be path planned.

[0155] The construction module 320 is used to construct the model constraints of the maneuverability of the flapping-wing aircraft and to construct the path planning model of the flapping-wing aircraft according to the model constraints.

[0156] The output module 330 is used to obtain a path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model.

[0157] Optionally, the model constraints of the maneuverability of the flapping-wing aircraft include: a maximum flight distance constraint, a minimum turning radius constraint, and a minimum step length constraint.

[0158] Among them, the maximum flight distance constraint is as shown in the following formula (1):

[0159] (1)

[0160] In the formula, represents the path length, Indicates the preset maximum flight distance. Indicates the number of nodes, Represents the distance between each node.

[0161] The minimum turning radius constraint is as shown in the following formula (2):

[0162] (2)

[0163] In the formula, represents any turning radius, Indicates that the minimum turning radius is, Indicates the minimum flight speed, Indicates the tilt angle.

[0164] The minimum step length constraint is as shown in equation (3):

[0165] (3)

[0166] In the formula, represents any step length, Indicates the minimum step size.

[0167] Optionally, the output module 330 is further configured to:

[0168] S31. Establish the first tree for the starting point of the path , build a second tree for the end point of the path , and set the cost function.

[0169] S32, determine whether the preset number of iterations has been reached; if so, execute step S38; if not, execute step S33.

[0170] S33. Generate a random node.

[0171] S34, according to the random node, for the first tree And the second tree Expand new nodes and update the cost function.

[0172] S35. Check the neighboring nodes near the expanded new node to obtain the optimized path.

[0173] S36. Determine the first tree and the second tree whether the extended nodes meet; if so, a connection path is generated and step S37 is executed; if not, step S32 is executed.

[0174] S37: Determine whether the total length of the connection path is less than the preset maximum flight distance ; If so, record the cost function of the connection path, save the connection path, and go to step S32; if not, go to step S32.

[0175] S38, determine whether the preset time interval has been reached; if so, smooth the connection path with the minimum cost and output the path planning result; if not, go to step S32.

[0176] Optionally, the cost function is as shown in the following formula (4):

[0177] (4)

[0178] In the formula, represents the path cost, represents the weight coefficient, represents the turning angle of the path, represents the weight coefficient, Indicates the path length.

[0179] Optionally, the output module 330 is further configured to:

[0180] Generate random nodes in the workspace , applying artificial potential field to random nodes Correction is performed to obtain the corrected random nodes.

[0181] Among them, the artificial potential field is applied to random nodes Correction is performed as shown in the following formula (5):

[0182] (5)

[0183] In the formula, Represents a random node The repulsive force of the obstacle, represents the set of obstacles, represents the repulsion coefficient, Represents a random node Location, Indicates the location of the obstacle. Indicates the maximum range of repulsive force.

[0184] Optionally, the output module 330 is further configured to:

[0185] In the first tree and the second tree In the example, find the node closest to the corrected random node. , calculate from the node Extension paths to corrected random nodes.

[0186] Optionally, the output module 330 is further configured to:

[0187] Obtain the neighboring nodes near the expanded new node, reconnect the neighboring nodes, and obtain a path that meets the minimum turning radius constraint, the longest flight distance constraint and has a lower cost, that is, the optimized path.

[0188] In an embodiment of the present invention, the classic RRT (Rapidly-Exploring Random Tree) path planning algorithm is improved, and an angle constraint is introduced to ensure that the generated path angle is more suitable for the tracking requirements of the flapping-wing prototype. In order to improve the utilization efficiency of random points in the RRT algorithm, the present invention adopts a dual-tree bidirectional expansion strategy, and combines the artificial potential field algorithm to correct the random points so that the path is as far away from obstacles as possible. In addition, in order to find a path that is closer to the optimal path, the algorithm will not stop immediately after finding a path that meets the angle constraint, but will continue to optimize, and finally obtain a result in which the angle is more in line with the constraint and the path distance is shorter. Finally, the path is smoothed by a B-spline curve, which further improves the feasibility and execution efficiency of the path.

[0189] Figure 7 is a schematic diagram of the structure of a path planning device provided by an embodiment of the present invention, such as Figure 7 As shown, the path planning device may include the above Figure 6 The path planning device 410 shown is oriented to the constraints of the flapping-wing aircraft model. Optionally, the path planning device 410 may include a first processor 2001.

[0190] Optionally, the path planning device 410 may further include a memory 2002 and a transceiver 2003 .

[0191] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0192] Combine the following Figure 7 The components of the path planning device 410 are described in detail:

[0193] The first processor 2001 is the control center of the path planning device 410, and may be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be configured to implement one or more integrated circuits of the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0194] Optionally, the first processor 2001 may perform various functions of the path planning device 410 by running or executing a software program stored in the memory 2002 , and calling data stored in the memory 2002 .

[0195] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 7 CPU0 and CPU1 are shown in FIG.

[0196] In a specific implementation, as an embodiment, the path planning device 410 may also include multiple processors, such as Figure 7 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0197] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0198] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and access the first processor 2001 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0199] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0200] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 7 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0201] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0202] It should be noted that Figure 7 The structure of the path planning device 410 shown in the figure does not constitute a limitation on the router, and the actual knowledge structure recognition device may include more or fewer components than those shown in the figure, or combine certain components, or arrange the components differently.

[0203] In addition, the technical effects of the path planning device 410 can refer to the technical effects of the path planning method for flapping-wing aircraft model constraints described in the above method embodiment, which will not be repeated here.

[0204] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0205] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0206] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0207] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0208] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0209] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0210] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0212] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0213] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0214] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0215] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0216] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A path planning method for flapping-wing aircraft model constraints, characterized in that: The method comprises: S1, obtaining a path starting point and a path end point of a flapping-wing aircraft to be path planned; S2, constructing a model constraint of the maneuverability of the flapping-wing aircraft, and constructing a path planning model of the flapping-wing aircraft according to the model constraint; S3. Obtain a path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model.

2. The path planning method for flapping-wing aircraft model constraints according to claim 1, characterized in that: The model constraints of the flapping-wing aircraft maneuverability in S2 include: a maximum flight distance constraint, a minimum turning radius constraint, and a minimum step length constraint; The maximum flight distance constraint is as shown in the following formula (1): （1） In the formula, represents the path length, Indicates the preset maximum flight distance. Indicates the number of nodes, Represents the distance between each node; The minimum turning radius constraint is as shown in the following formula (2): （2） In the formula, represents any turning radius, Indicates that the minimum turning radius is, Indicates the minimum flight speed, represents the tilt angle; The minimum step length constraint is as shown in the following formula (3): （3） In the formula, represents any step length, Indicates the minimum step size.

3. The path planning method for flapping-wing aircraft model constraints according to claim 1, characterized in that: The step S3 obtains a path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model, including: S31, establishing the first tree for the starting point of the path , build a second tree for the end point of the path , and set the cost function; S32, determine whether the preset number of iterations has been reached; if so, execute step S38; if not, execute step S33; S33, generating a random node; S34: According to the random node, the first tree And the second tree Expand new nodes and update the cost function; S35, checking the neighboring nodes near the expanded new node to obtain the optimized path; S36, determine the first tree and the second tree whether the extended nodes meet; if so, generate a connection path and execute step S37; if not, go to step S32; S37: Determine whether the total length of the communication path is less than the preset maximum flight distance. ; If yes, then record the cost function of the connection path, save the connection path, and go to step S32; if no, then go to step S32; S38, determine whether the preset time interval has been reached; if so, smooth the connection path with the minimum cost and output the path planning result; if not, go to step S32.

4. The path planning method for flapping-wing aircraft model constraints according to claim 3 is characterized in that: The cost function in S31 is shown in the following formula (4): （4） In the formula, represents the path cost, represents the weight coefficient, represents the turning angle of the path, represents the weight coefficient, Indicates the path length.

5. The path planning method for flapping-wing aircraft model constraints according to claim 3, characterized in that: The generating of random nodes in S33 includes: Generate random nodes in the workspace , applying an artificial potential field to the random nodes Perform correction to obtain the corrected random nodes; Wherein, an artificial potential field is applied to the random nodes Correction is performed as shown in the following formula (5): （5） In the formula, Represents a random node The repulsive force of the obstacle, represents the set of obstacles, represents the repulsion coefficient, Represents a random node Location, Indicates the location of the obstacle. Indicates the maximum range of repulsive force.

6. The path planning method for flapping-wing aircraft model constraints according to claim 3, characterized in that: In the step S34, according to the random node, the first tree And the second tree New node expansion, including: In the first tree and the second tree In the example, find the node closest to the corrected random node. , calculate from the node Extension paths to corrected random nodes.

7. The path planning method for flapping-wing aircraft model constraints according to claim 4, characterized in that: The step S35 of checking neighboring nodes near the expanded new node to obtain an optimized path includes: Obtain neighboring nodes near the expanded new node, reconnect the neighboring nodes, and obtain a path that meets the minimum turning radius constraint, the longest flight distance constraint, and has a lower cost, that is, an optimized path.

8. A path planning device for flapping-wing aircraft model constraints, the path planning device for flapping-wing aircraft model constraints is used to implement the path planning method for flapping-wing aircraft model constraints as described in any one of claims 1 to 7, characterized in that: The device comprises: An acquisition module is used to acquire a path starting point and a path end point of a flapping-wing aircraft to be path planned; A construction module is used to construct a model constraint of the maneuverability of the flapping-wing aircraft, and to construct a path planning model of the flapping-wing aircraft according to the model constraint; The output module is used to obtain the path planning result according to the path starting point, the path end point and the flapping-wing aircraft path planning model.

9. A path planning device, characterized in that: The path planning device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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