Dense unmanned aerial vehicle obstacle avoidance and conflict resolution method and device

Abstract

The application discloses a dense unmanned aerial vehicle obstacle avoidance and conflict resolution method and device, wherein the method comprises the following steps: confirming initial information, wherein the initial information comprises unmanned aerial vehicle information and obstacle information; establishing a uniform space grid, and mapping the unmanned aerial vehicle information and the obstacle information into the uniform space grid; constructing an artificial potential field function, and obtaining an artificial potential field force according to the artificial potential field function; introducing a kernel function to perform weighted adjustment on the influence of the artificial potential field force; introducing an adjusting force acting on a normal direction of a speed of the unmanned aerial vehicle; introducing an adjusting coefficient when an unmanned aerial vehicle approaches a target, and calculating a resultant force by combining the weighted and adjusted artificial potential field force and the adjusting force. The application can ensure the flight safety of the unmanned aerial vehicles and the overall calculation efficiency under the premise that the number of the unmanned aerial vehicles is large enough, and can meet the respective flight task requirements. The application can be widely applied in the technical field of unmanned aerial vehicle cooperative control.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology for unmanned aerial vehicles (UAVs), and in particular to a method and apparatus for obstacle avoidance and conflict resolution among dense UAVs. Background Technology

[0002] Modern drones, characterized by their small size, flexibility, and ease of control, can explore areas inaccessible or dangerous to humans, playing a crucial role in the military field. The increased payload capacity of drones has also provided a new development path for the transportation and agriculture industries, with drone delivery services being a hot topic in recent years. Due to limitations in the endurance, field of view, and payload of individual drones, research has often focused on drone swarms. However, with the rapid development of drone technology in recent years, the number of drones simultaneously performing tasks in the same airspace is constantly increasing, making obstacle avoidance and conflict resolution among drones increasingly difficult. Currently, commonly used obstacle avoidance and conflict resolution algorithms include optimization theory, artificial potential field methods, and neural network methods. Compared to the high computational cost of optimization methods and the difficulty in training neural networks, artificial potential field methods are simple in principle, easy to understand, highly efficient, and produce smooth paths, often making them the preferred choice for solving such problems. However, traditional artificial potential field methods suffer from two major problems: they are prone to getting trapped in local optima and failing to reach the target. Furthermore, their computational efficiency decreases rapidly with the increase in the number of drones. Summary of the Invention

[0003] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide a method and apparatus for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles.

[0004] The technical solution adopted in this invention is:

[0005] A method for obstacle avoidance and conflict resolution for dense unmanned aerial vehicles (UAVs) includes the following steps:

[0006] Confirm the initial information, which includes information about each UAV and obstacle information;

[0007] Establish a spatially uniform grid and map UAV information and obstacle information onto the spatially uniform grid;

[0008] Construct an artificial potential field function, and obtain the artificial potential field force based on the artificial potential field function;

[0009] A kernel function is introduced to weight and adjust the influence of the artificial potential field force.

[0010] An adjustment force is introduced that acts on the direction of the drone's velocity normal;

[0011] An adjustment coefficient is introduced when the drone approaches the target, and the resultant force is calculated by combining the weighted adjusted artificial potential force and the adjustment force.

[0012] Furthermore, the step of establishing a spatially uniform grid and mapping UAV information and obstacle information onto the spatially uniform grid includes:

[0013] Construct a uniform spatial grid, dividing the entire flight airspace into N independent grid spaces, with the size of each grid space falling between the communication range and safe distance of the UAV;

[0014] The actual spatial coordinates of the UAV are transformed into grid coordinates, the index of the grid in which it is located is calculated, and stored in a set.

[0015] Furthermore, the construction of the artificial potential field function and the acquisition of the artificial potential field force based on the artificial potential field function include:

[0016] Construct the gravitational potential field function between the UAV and the target point;

[0017] Construct the repulsive potential field function between the drone and the obstacle;

[0018] Construct the repulsive potential field function between drones;

[0019] Based on the constructed potential field function, the negative gradient of the potential field function is calculated to obtain the corresponding potential force.

[0020] Further, the step of calculating the negative gradient of the potential field function to obtain the corresponding potential force based on the constructed potential field function includes:

[0021] Calculate the gravitational potential field U of the target point on the UAV. att And the attractiveness of the target point to the drone F att :

[0022]

[0023]

[0024] In the formula, k att Let q be the gravitational coefficient, and q be the current position of the drone. goal Indicates the target location of the drone, d * The effective distance that increases the attractiveness of the target point to the drone;

[0025] Calculate the repulsive potential field U of the obstacle on the drone. rep And the repulsive force F of the obstacle on the drone rep :

[0026]

[0027]

[0028] In the formula, k rep Let q be the repulsive force coefficient, and q be the current position of the drone. obs Indicates the location of the obstacle, d r The effective distance at which obstacles exert a repulsive force on the drone;

[0029] Calculate the repulsive potential field U between drones i and the corresponding repulsive force F i :

[0030]

[0031]

[0032] In the formula, k c Let q be the repulsive force coefficient between drones. i Let q be the current position of the drone under stress. j This indicates the current location of the neighboring drone exerting force, and T is the maximum influence distance set for the drone.

[0033] Furthermore, the weighted adjustment of the influence of the introduced kernel function on the artificial potential field force includes:

[0034] A kernel function is introduced to weight the repulsive forces between drones, and the resulting weighted repulsive force F on the drone is calculated. c for:

[0035]

[0036] In the formula, α i Let α be the density coefficient of the current drone location. j q is the density coefficient of the location of the drone exerting force on the neighboring drone. i Let q be the current position of the drone under stress. j This indicates the current position of the neighboring drone exerting force, R is the maximum influence distance set for the drone, and W... spiky (r,h) represents the kernel function used in the calculation.

[0037] Furthermore, a function of the form W(r,h) is a weighted kernel function. The first input is the distance from the input to the center of the kernel function, and the second input is the radius used by the kernel function, the size of which should be equal to the maximum influence distance between the UAVs. The kernel function W... spiky (r,h) and W poly6 (r, h) are respectively:

[0038]

[0039]

[0040] Where r is the distance from the input to the center of the kernel function, and h is the radius used by the kernel function.

[0041] Furthermore, the expression for the density coefficient α is:

[0042]

[0043] In the formula, q is the target location for calculating the density coefficient. i This indicates the current position of all drones in the current grid and its neighboring grids, where R is the maximum influence distance set for the drones, and W... poly6 (r,h) represents the kernel function used in the calculation.

[0044] Furthermore, the introduced adjustment force acting on the normal direction of the UAV velocity includes:

[0045] When the attractive and repulsive forces acting on the drone are in equilibrium, and the speed decreases to zero, the system falls into a local optimum. Therefore, an adjustment force is introduced along the normal direction of the original velocity to help the drone escape from the local optimum.

[0046] The adjusting force F adj The size is:

[0047]

[0048] In the formula, θ represents the attractive force F. att With repulsive force F rep The angle between F and F, at this time rep For the resultant repulsive force, k a To adjust the scaling factor of the force, k s To adjust the sensitivity coefficient to force changes, ε is a preset constant;

[0049] In this case, the Z-axis direction of the UAV ballistic coordinate system is taken as the adjustment force F. adj The direction in which it is located.

[0050] Furthermore, the process of introducing an adjustment coefficient when the drone approaches the target to obtain the resultant force includes:

[0051] When the distance between the drone's target location and the obstacle is very small, the drone will never reach the target point due to the repulsive force. Therefore, an adjustment coefficient μ is introduced to scale the repulsive force, thereby enabling the drone to overcome the influence of the obstacle and reach the target location.

[0052] The adjustment coefficient μ is:

[0053]

[0054] In the formula, dμ To adjust the effective distance of the coefficient μ, where q is the current position of the UAV, q goal Indicates the target location of the drone

[0055] Based on the adjustment coefficient μ, the final resultant force F applied to the drone is obtained:

[0056] F = F att +F c +μ(F rep +F adj )

[0057] In the formula, F att For the attraction of the target point to the drone, F c The repulsive force between drones is weighted, μ is the adjustment coefficient, and F is the repulsive force between drones. rep F represents the repulsive force exerted by the obstacle on the drone. adj The force is the adjusting force in the direction of the velocity normal.

[0058] A method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles (UAVs) according to claim 1, characterized in that the initial information specifically includes: the total number of UAVs n, the initial position q of each UAV, the initial velocity v0 of each UAV, and the target position q of each UAV. goal The position coordinates q of all obstacles obs and its scope of application d r .

[0059] Another technical solution adopted in this invention is:

[0060] A dense unmanned aerial vehicle obstacle avoidance and conflict resolution device includes:

[0061] At least one processor;

[0062] At least one memory for storing at least one program;

[0063] When the at least one program is executed by the at least one processor, the at least one processor implements the method described above.

[0064] The beneficial effects of this invention are as follows: By using a spatially uniform grid, this invention ensures the computational efficiency of the system in dense UAV airspace; by designing and utilizing the potential force provided by an artificial potential field, it ensures that each UAV can effectively avoid obstacles and other UAVs and reach its destination to perform its mission; by using a kernel function to weight the repulsive forces between UAVs, it can alleviate the instability of the artificial potential field method trajectory when UAVs are too dense, achieving a smoother and more stable effect; by introducing an adjustment force acting on the velocity normal direction, and an adjustment coefficient acting on the repulsive force and the adjustment force, it solves the problems of local optima and target unreachability in the artificial potential field method. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating the steps of a dense unmanned aerial vehicle (UAV) obstacle avoidance and conflict resolution method in an embodiment of the present invention.

[0067] Figure 2 This is a schematic diagram of the spatial uniform grid method in an embodiment of the present invention;

[0068] Figure 3 This is a schematic diagram illustrating the principle of the artificial potential field method in an embodiment of the present invention;

[0069] Figure 4 This is a schematic diagram of the Poly6 kernel function and the Spiky kernel function in an embodiment of the present invention;

[0070] Figure 5 This is a schematic diagram illustrating the local optima and unreachable target of the artificial potential field method in an embodiment of the present invention;

[0071] Figure 6 This is a schematic diagram of the drone's operating trajectory in an embodiment of the present invention;

[0072] Figure 7 This is a minimum distance-time curve between the drone and the obstacle in an embodiment of the present invention;

[0073] Figure 8 This is a minimum distance-time curve between drones in an embodiment of the present invention. Detailed Implementation

[0074] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0075] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0076] In the description of this invention, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0077] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0078] like Figure 1 As shown, this embodiment provides a method for obstacle avoidance and conflict resolution among dense unmanned aerial vehicles (UAVs), which can ensure flight safety and overall computational efficiency while meeting the flight mission requirements of each UAV, even when the number of UAVs is sufficiently large. The method specifically includes the following steps:

[0079] S1. Confirm initial information, which includes information about each drone and obstacles.

[0080] Confirm initial information, including the initial information of each drone and obstacle information. Specific initial information includes: the total number of drones n, the initial position q of each drone, the initial velocity v0 of each drone, and the target position q of each drone. goal The position coordinates q of all obstacles obs and its scope of application d r Establish a set of unmanned aerial vehicles {A1, A2, ..., A}. n}, each drone A i It should include the corresponding real-time location, real-time speed, and target location of the drone's mission, and then write the initial information of all drones into the drone collection.

[0081] S2. Establish a spatially uniform grid and map UAV information and obstacle information onto the spatially uniform grid.

[0082] Establish a uniform spatial grid, dividing the entire flight airspace into N independent grid spaces. The size of each independent grid space must not exceed the communication range of the UAV, nor be less than the safe distance between UAVs. For example... Figure 2 As shown, when the UAV in the central area performs query calculations, although its communication range can cover the entire captured area, due to the existence of the spatial grid, the query scope only needs to be the entire neighboring grid. This can significantly improve the system's computational efficiency in dense airspace conditions where UAVs fly. At the beginning of each round, the actual spatial coordinates of each UAV are converted to grid coordinates, the index of the grid it is in is calculated, and stored in the corresponding set, completing one update of the grid space.

[0083] S3. Construct the artificial potential field function and obtain the artificial potential force based on the artificial potential field function.

[0084] like Figure 3 As shown, the basic idea of ​​the artificial potential field method is to transform the complex environment into an imaginary potential field, where the attractive force F generated by the target point... att The repulsive force F generated by the obstacle rep The combined forces acting on the drone result in a net external force F controlling its motion. Under the attraction of the target point, the drone moves towards the target; however, upon approaching an obstacle, the repulsive force causes the drone to avoid it, achieving obstacle avoidance. Simultaneously, a potential field exists between the drones. When the distance between the drones is less than the repulsive force's influence distance, the drones generate a repulsive force to move away from each other, thus resolving the conflict. In this embodiment, it is necessary to design the potential field function U between the target point, the obstacle, and the drones, based on the formula for calculating the potential field force. Calculate the negative gradient of its potential field to obtain the corresponding potential force F.

[0085] Specifically, step S3 includes steps S31-S33:

[0086] S31, The constructed drone is subject to a gravitational potential field U from the target point. att The definition is as follows:

[0087]

[0088] Where, k att Let q be the gravitational coefficient, and q be the current position of the drone. goal Indicates the target location of the drone, d * The effective distance that increases the attractiveness of the target point to the drone.

[0089] Based on the negative gradient formula, the attractive force F of the target point on the UAV is calculated. att as follows:

[0090]

[0091] When the distance between the drone and the target point is greater than or equal to the effective distance d * At this time, the attractive force on the drone is directly proportional to the distance between the drone and the target point; the greater the distance, the greater the attractive force. When the distance between the drone and the target point is greater than the effective distance d... * At this time, the force acting on the drone is a constant, which can effectively avoid the problem of excessive attraction when the drone is far from the target.

[0092] S32, The constructed drone is subjected to a repulsive potential field U from the obstacle. rep The definition is as follows:

[0093]

[0094] Where, k rep Let q be the repulsive force coefficient, and q be the current position of the drone. obs Indicates the location of the obstacle, d r The effective distance at which obstacles exert a repulsive force on the drone.

[0095] Based on the negative gradient formula, the repulsive force F exerted by the obstacle on the drone is calculated. rep as follows:

[0096]

[0097] When the drone enters the range of influence of the obstacle's repulsive force d r When the distance between them is closer, the repulsive force on the drone will be greater. In extreme cases, if the drone collides with an obstacle, the repulsive force it experiences will become infinite.

[0098] S33, the repulsive potential field U constructed between drones i The definition is as follows:

[0099]

[0100] Where, k c Let q be the repulsive force coefficient between drones. i Let q be the current position of the drone under stress. j This indicates the current location of the neighboring drone exerting force, and R is the maximum influence distance set for the drone.

[0101] Due to the existence of a uniform grid in S2 space, q j Only a search is needed within the neighboring grid; there are 9 grids in 2D space and 27 in 3D. The repulsive potential field between drones is roughly the same as the repulsive potential field of obstacles on drones. The repulsive force F between drones can be calculated using the negative gradient formula.i as follows:

[0102]

[0103] The smaller the distance between drones, the greater the repulsive force they experience; when two drones get infinitely close or collide, the repulsive force they experience also becomes infinite.

[0104] S4. Introduce a kernel function to weight and adjust the influence of the artificial potential field force.

[0105] To achieve a smoother and more stable resolution of drone conflicts in densely populated areas, a kernel function is needed to weight and adjust the repulsive forces between drones. The weighted repulsive force F experienced by the drone is then calculated. c for:

[0106]

[0107] Where, α i Let α be the density coefficient of the current drone location. j q is the density coefficient of the location of the drone exerting force on the neighboring drone. i Let q be the current position of the drone under stress. j This indicates the current location of the neighboring drone exerting force, and R is the maximum influence distance set for the drone. W spiky (r,h) represents the kernel function used in the calculation.

[0108] The density coefficient α is specifically calculated as follows:

[0109]

[0110] Where q is the target location for calculating the density coefficient, q i This indicates the current position of all drones in the current grid and its neighboring grids, where R is the maximum influence distance set for the drones, and W... poly6 (r,h) represents the kernel function used in the calculation.

[0111] Unlike the repulsion calculation between drones, which only requires searching for neighboring drones, the density coefficient represents the density of the current area, so drones in the center of the area must also be included in the calculation. The higher the calculated density coefficient, the greater the density of drones in that area, and the higher the safety risk for drones continuing to travel to that area.

[0112] The weighted F c It consists of three parts. The first part is the repulsive force F that needs to be weighted. i The second part consists of weighted terms composed of dense coefficients. If the density coefficient α of drone j within the safe range jIt should be smaller than the density coefficient α of the drones in the computing center. i Then, the area where drone j is located is relatively safe compared to the area where the central drone is located, meaning there is a possibility of conflict resolution in the direction of drone j. Conversely, if the density coefficient α of drone j... j It must be greater than the density coefficient α of the drones in the computing center. i Therefore, continuing to the area where drone j is located is discouraged, so it is necessary to amplify the repulsive force between them; the third part This represents the specific weight value applied by the kernel function. Combining these three parts yields the F value. c .

[0113] A function of the form W(r,h) is a weighted kernel function. The first input is the distance from the input to the center of the kernel function, and the second input is the radius used by the kernel function, the size of which should be equal to the maximum influence distance between the drones. The kernel function W... spiky (r,h) and W koly6 (r, h) are respectively:

[0114]

[0115]

[0116] Where r is the distance from the input to the center of the kernel function, and h is the radius used by the kernel function.

[0117] W s9iky (r,h) and W poly6 The graph of the function (r,h) is as follows Figure 4 As shown, it is equivalent to an intermediate bulge probability density function. Kernel function W poly6 (r,h) compared to W spiky (r,h) does not require additional square root calculations, and its gradient is smoother. It is widely used in smooth particle fluid dynamics (excluding pressure and viscous forces), hence its density coefficient in the computational domain is used. However, due to W... poly6 The gradient at the apex of (r,h) is 0. If this gradient is used to weight the pressure, the drones will aggregate. Therefore, the weighting of the repulsive force uses W. spiky The (r,h) kernel function increases the weight of drones the closer they are to each other, and the higher their priority for conflict resolution.

[0118] S5. Introduce an adjustment force acting on the direction of the drone's velocity normal.

[0119] Traditional artificial potential field methods suffer from problems such as local optima and unreachable objectives. Figure 5As shown. When the attractive and repulsive forces acting on the drone are in equilibrium, and the speed decreases to 0, the system falls into a local optimum. Similarly, when the distance between the drone's target position and the obstacle is very small, the drone will never reach the target point due to the repulsive force. To solve these two problems, this invention first introduces an adjustment force along the original velocity normal, enabling the drone to rotate and escape the original local optimum. The adjustment force F... adj The size is:

[0120]

[0121] Where θ is the attraction F att With repulsive force F rep The angle between F and F, at this time rep For the resultant repulsive force, k a To adjust the scaling factor of the force, k s ε is a set minimum constant to adjust the sensitivity coefficient to force changes.

[0122] Meanwhile, the Z-axis direction of the UAV ballistic coordinate system is taken as the adjustment force F. adj The direction in which it is located.

[0123] Because escaping the local optimum requires a sufficiently large adjustment force, and in order to escape the concave local optimum, the scaling factor k of the adjustment force is... a It needs to be greater than 1, adjusting the magnitude of the force and the net repulsive force F. rep Proportional. Meanwhile, to prevent the adjustment force from affecting the drone's normal trajectory and thus impacting mission efficiency, a... The range of the adjusting force is limited; the adjusting force will only take effect when the angle between the repulsive and attractive forces is close to 180° and the repulsive force is large enough.

[0124] S6. Introduce an adjustment coefficient when the drone approaches the target, and calculate the resultant force by combining the weighted adjusted artificial potential force and the adjustment force.

[0125] To address the target unreachability problem, an adjustment coefficient μ is introduced to scale the repulsive force, enabling the UAV to overcome the obstacle's influence and reach the target location. The adjustment coefficient μ is:

[0126]

[0127] Where, d μ To adjust the effective distance of the coefficient μ, where q is the current position of the UAV, q goal This indicates the target location of the drone.

[0128] Because of the added adjusting force F adjMagnitude and repulsive force F rep Similarly, when encountering the problem of an unreachable target, an adjustment coefficient μ is also needed to adjust it. Thus, the final resultant force F applied to the drone is:

[0129] F = F att +F c +μ(F rep +F adj )

[0130] Where F att For the attraction of the target point to the drone, F c The repulsive force between drones is weighted, μ is the adjustment coefficient, and F is the repulsive force between drones. rep F represents the repulsive force exerted by the obstacle on the drone. adj The adjusting force is the force acting in the direction of the velocity normal.

[0131] Apply the resulting resultant force F to the drone to complete the update for this round. Repeat the above steps until the drone reaches the target point.

[0132] Based on the above, the following is a representative embodiment to further illustrate the relevant design of the technical solution of this embodiment.

[0133] There are 6 drones in a 10×10 space, with initial positions of (1,1), (1,9), (9,9), (9,1), (0,5), and (10,5), and target positions of (8,8), (8,2), (2,2), (2,8), (9,5), and (1,5), respectively. There is also a circular obstacle with a radius of 0.5 at position (5,5). The drones are controlled by a resultant force F. The specific calculation steps are as follows:

[0134] Step 1: Determine the initial information about the drone and obstacles.

[0135] Step 2: Construct a spatially uniform grid.

[0136] Step 3: Calculate the potential force.

[0137] Step 3 specifically includes steps 3.1-3.3:

[0138] Step 3.1: Calculate the attractive force F at the target point. att , where k att =1,d * It is 5.

[0139] Step 3.2: Calculate the repulsive force F of the obstacle. rep , where k rep 10, d r The value is 3.

[0140] Step 3.3: Calculate the repulsive force F between the drones i , where k c The value is 30, and the influence distance R is set to 3.

[0141] Step 4: Calculate the weighted repulsive force F between the drones using a kernel function. c .

[0142] Step 5: Calculate the adjusting force F in the direction of the velocity normal. adj The scaling factor k for adjusting the force magnitude a The sensitivity coefficient k for adjusting force changes is 2. s The value is 1.

[0143] Step 6: Calculate the adjustment factor μ, where d μ The value is 2.

[0144] Step 7: Calculate the resultant force F and apply it to the drone. Repeat the above steps until the drone reaches the target point.

[0145] Based on the above algorithm steps, a simulation experiment was designed and run. The final trajectory of the UAV is as follows: Figure 6 As shown, since the lines connecting the starting point and the target point of each drone are symmetrical, when the drone moves to the central obstacle area, if the traditional artificial potential field method is used, the system will fall into a local optimum. However, due to the adjustment force F... adj The presence of obstacles allowed the drone to resolve conflicts and avoid them, ultimately reaching the target point. During its movement, the minimum distance between the drone and the obstacle, and the minimum distance between drones, were as follows: Figure 7 , Figure 8 As shown.

[0146] In summary, the method of this embodiment has at least the following advantages and beneficial effects compared to the prior art:

[0147] (1) In this embodiment, a spatially uniform grid is used to divide the airspace. When calculating the potential field in a densely populated UAV area, it is only necessary to search for UAVs in the neighboring grid, which can greatly speed up the calculation efficiency.

[0148] (2) This embodiment applies the principle of smooth particle hydrodynamics. First, the Poly6 kernel function is used to calculate the density coefficient of the UAV at the current position. The larger the density coefficient, the denser the distribution of UAVs at that location, and the more likely the UAVs are to collide. After obtaining the density coefficient, the Spiky kernel function is used to weight the distribution between UAVs. The weighted result is combined with the density coefficient to obtain the processed interaction force between UAVs, thereby alleviating the instability of the artificial potential field trajectory when the UAVs are too dense, and achieving a smoother and more stable collision resolution effect.

[0149] (3) In this embodiment, the adjustment force applied to the drone's velocity normal direction when the drone is at a local optimum of the artificial potential field is used to enable the drone to escape the original dead zone tangentially, thereby effectively solving the local optimum problem of the traditional artificial potential field method.

[0150] (4) In this embodiment, the adjustment coefficient of the repulsive force on the obstacle when the UAV approaches the target point is used to reduce the influence of the obstacle on the UAV that is about to reach the target, thereby effectively solving the problem of target unreachability in the traditional artificial potential field method.

[0151] This embodiment also provides a dense drone obstacle avoidance and conflict resolution device, including:

[0152] At least one processor;

[0153] At least one memory for storing at least one program;

[0154] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 The method shown.

[0155] This embodiment of the dense UAV obstacle avoidance and conflict resolution device can execute the dense UAV obstacle avoidance and conflict resolution method provided in the method embodiment of the present invention, and can execute any combination of the implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0156] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0157] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0158] If the aforementioned functions are implemented as 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 this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0160] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0161] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0162] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, 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.

[0163] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0164] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Confirm the initial information, which includes information about each UAV and obstacle information; Establish a spatially uniform grid and map UAV information and obstacle information onto the spatially uniform grid; Construct an artificial potential field function, and obtain the artificial potential field force based on the artificial potential field function; A kernel function is introduced to weight and adjust the influence of the artificial potential field force. An adjustment force is introduced that acts on the direction of the drone's velocity normal; An adjustment coefficient is introduced when the drone approaches the target, and the resultant force is calculated by combining the weighted adjusted artificial potential force and the adjustment force. The weighted adjustment of the influence of the introduced kernel function on the artificial potential field force includes: A kernel function is introduced to weight the repulsive forces between drones, and the weighted repulsive forces obtained after weighting are then calculated. for: In the formula, This represents the density coefficient of the current drone location. The density coefficient of the location of the drone exerting force on the neighboring area. The current position of the drone under stress. Indicates the current location of the neighboring drone. The maximum range of influence for the drone is set. The kernel function used for the calculation; The introduced adjustment force acting on the UAV's velocity normal direction includes: Introduce an adjustment force along the original velocity normal to cause the UAV to deviate from its original local optimum. The adjusting force The size is: In the formula, For attraction Repulsive force The angle between them, at this time For the repulsive force, To adjust the scaling factor of the force, To adjust the sensitivity coefficient to force changes, This is a preset constant; In this case, the Z-axis direction of the UAV ballistic coordinate system is taken as the adjustment force. The direction in which it is located; The process of introducing an adjustment coefficient when the drone approaches the target to obtain the resultant force includes: Introduce an adjustment factor The repulsive force is scaled to allow the drone to escape the influence of obstacles and reach the target location. The adjustment coefficient for: In the formula, To adjust the coefficient The effective range of action, where q is the current position of the drone. Indicates the target location of the drone According to the adjustment coefficient To achieve the ultimate synergy applied to drones : In the formula, The attractiveness of the target point to the drone, The weighted repulsive force between drones To adjust the coefficient, The repulsive force exerted by the obstacle on the drone. The adjusting force is the force acting in the direction of the velocity normal.

2. The method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles according to claim 1, characterized in that, The process of establishing a spatially uniform grid, mapping UAV information and obstacle information onto the spatially uniform grid, includes: Construct a uniform spatial grid, dividing the entire flight airspace into N independent grid spaces, with the size of each grid space falling between the communication range and safe distance of the UAV; The actual spatial coordinates of the UAV are transformed into grid coordinates, the index of the grid in which it is located is calculated, and stored in a set.

3. The method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles according to claim 1, characterized in that, The construction of the artificial potential field function and the acquisition of the artificial potential field force based on the artificial potential field function include: Construct the gravitational potential field function between the UAV and the target point; Construct the repulsive potential field function between the drone and the obstacle; Construct the repulsive potential field function between drones; Based on the constructed potential field function, the negative gradient of the potential field function is calculated to obtain the corresponding potential force.

4. The method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles according to claim 3, characterized in that, The step of calculating the negative gradient of the potential field function to obtain the corresponding potential force based on the constructed potential field function includes: Calculate the gravitational potential field of the target point on the UAV. And the attractiveness of the target point to the drone : In the formula, Let be the gravitational coefficient, and q be the current position of the drone. Indicates the target location of the drone. The effective distance that increases the attractiveness of the target point to the drone; Calculate the repulsive potential field of obstacles on the drone. and the repulsive force of obstacles on drones : In the formula, Let be the repulsive force coefficient, and q be the current position of the drone. Indicates the location of the obstacle. The effective distance at which obstacles exert a repulsive force on the drone; Calculate the repulsive potential field between drones and the corresponding repulsive force : In the formula, The repulsive force coefficient between drones, The current position of the drone under stress. Indicates the current location of the neighboring drone. The maximum range of influence for the drone is set.

5. The method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles according to claim 1, characterized in that, Density coefficient The expression is: In the formula, To calculate the density coefficient of the force-bearing UAV at its current position, This indicates the current position of all drones in the current grid and its neighboring grids. The maximum range of influence for the drone is set. The kernel function used for the calculation.

6. The method for obstacle avoidance and conflict resolution of dense unmanned aerial vehicles according to claim 1, characterized in that, The initial information specifically includes: the total number of drones n, the initial position of each drone, and the initial velocity of each drone. The target location of each drone The position coordinates of all obstacles and its scope of application .

7. A dense unmanned aerial vehicle obstacle avoidance and conflict resolution device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1-6.

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