Unmanned aerial vehicle path planning method and system in obstacle avoidance process

Abstract

The invention provides an unmanned aerial vehicle path planning method and system in an obstacle avoidance process, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining an obstacle region on a navigation path of an unmanned aerial vehicle; representing the obstacle region as a closed differential form for describing a three-dimensional structure of the obstacle region by combining an algebraic topology algorithm; establishing an unmanned aerial vehicle safety potential field under the constraint of the minimum turning radius of the unmanned aerial vehicle in combination with a closed differential form and an unmanned aerial vehicle navigation path; defining a Riemannian metric tensor for describing a flight path of the unmanned aerial vehicle in a three-dimensional space by combining a safety potential field of the unmanned aerial vehicle; determining the flight path constraint of the unmanned aerial vehicle by combining the closed differential form and the unmanned aerial vehicle navigation path; and under the constraint of the flight path constraint of the unmanned aerial vehicle, carrying out shortest path optimization by combining a Riemannian metric tensor, and outputting an optimal flight path of the unmanned aerial vehicle. Through the shortest path optimization, the method can adapt to a complex flight environment in real time, effectively avoids the problem of circular obstacle avoidance, reduces the energy consumption, and improves the flight efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a method and system for planning a UAV path during an obstacle avoidance process. Background Art

[0002] Drone obstacle avoidance refers to the process of sensing obstacles in the environment and adjusting the flight path to avoid collisions. This typically requires sensors, computer vision, or radar technologies to identify surrounding obstacles in real time and use algorithms to plan a path to avoid them.

[0003] During drone flight, path planning is crucial to ensuring safe and efficient flight in complex environments. Especially during obstacle avoidance, drones must not only avoid static and dynamic obstacles but also consider dynamic constraints such as flight smoothness, speed, and turning radius. Inaccurate path planning can lead to collisions or path discontinuities, compromising flight safety and stability. Precise path planning helps drones navigate complex terrain, unexpected obstacles, and rapidly changing environments, ensuring successful mission completion.

[0004] However, existing drone obstacle avoidance systems typically focus solely on avoiding obstacles while ignoring their complex shapes, particularly recessed areas. This prevents them from quickly escaping complex areas, reducing flight efficiency and increasing energy consumption. Furthermore, after avoiding an obstacle, the navigation path often needs to be reloaded, increasing the computational and processing burden and potentially preventing smooth adjustments to the drone's flight path. This impacts flight efficiency and stability, and prevents real-time adaptation to complex flight environments. Summary of the Invention

[0005] In view of the above shortcomings of the existing technology, the purpose of the embodiments of the present invention is to provide a method for drone path planning during obstacle avoidance. This method can address the technical problem that existing drone obstacle avoidance processes typically focus solely on avoiding obstacles while ignoring the complex shapes of obstacles, particularly the presence of recessed areas. This prevents rapid escape from complex areas, reducing flight efficiency and increasing energy consumption. Furthermore, after obstacle avoidance, the navigation path often needs to be reloaded, which not only increases the computational and processing burden but can also result in less-than-smooth adjustments to the drone's flight path, affecting flight efficiency and stability, and preventing real-time adaptation to complex flight environments.

[0006] A first aspect of an embodiment of the present invention provides a method for planning a path for a drone during obstacle avoidance, comprising:

[0007] S1: Obtain the obstacle area on the drone’s navigation path;

[0008] S2: Combine algebraic topology algorithms to represent the obstacle area as a closed differential form describing the three-dimensional structure of the obstacle area;

[0009] S3: Combining the closed differential form and the UAV navigation path, the UAV safety potential field is established under the constraint of the UAV's minimum turning radius;

[0010] S4: Combined with the UAV safety potential field, define the Riemannian metric tensor describing the UAV's flight trajectory in three-dimensional space;

[0011] S5: Determine the UAV flight path constraints by combining the closed differential form and the UAV navigation path;

[0012] S6: Under the constraints of the UAV flight path, the shortest path optimization is performed in combination with the Riemann metric tensor to output the optimal flight path of the UAV.

[0013] A second aspect of an embodiment of the present invention provides a UAV path planning system during obstacle avoidance, comprising: a processor and a memory;

[0014] The memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the drone path planning method in the obstacle avoidance process as described in the first aspect are implemented.

[0015] According to a third aspect of an embodiment of the present invention, a readable storage medium is proposed, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the drone path planning method in the obstacle avoidance process as described in the first aspect are implemented.

[0016] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0017] In an embodiment of the present invention, an algebraic topology algorithm is used to accurately describe the complex shapes of obstacles, especially complex areas such as concave areas, thereby avoiding the defects of traditional methods that only focus on avoiding obstacles while ignoring the tendency to fall into complex areas and cause cyclic obstacle avoidance. A closed differential form is used to accurately describe the three-dimensional structure of the obstacle area, and the Riemann metric tensor is combined to define the metric method of the flight path. This method constructs a safe potential field while considering the minimum turning radius and flight constraints, ensuring the smoothness and continuity of the flight path. By optimizing the shortest path, it can adapt to complex flight environments in real time and effectively avoid the problem of cyclic obstacle avoidance. This not only reduces energy consumption, but also improves flight efficiency. At the same time, the method introduces drone navigation as a constraint in the obstacle avoidance process, avoiding the computational burden of reloading the navigation path, and can quickly avoid obstacles in the minimum yaw state, thereby improving flight stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments of the present invention. It is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0019] Figure 1 1 is a flow chart of a method for planning a path for a drone during obstacle avoidance provided by an embodiment of the present invention;

[0020] Figure 2 Schematic diagram of the structure of a UAV path planning system during obstacle avoidance provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the scope of protection of the present invention.

[0022] The following describes in detail the path planning method for a drone during obstacle avoidance provided by an embodiment of the present invention through specific embodiments and application scenarios in conjunction with the accompanying drawings.

[0023] Reference Manual Figure 1 , shows a flow chart of a method for planning a drone path during an obstacle avoidance process provided by an embodiment of the present invention.

[0024] An embodiment of the present invention provides a method for planning a path for a drone during obstacle avoidance, which may include the following steps:

[0025] S1: Get the obstacle area on the drone’s navigation path.

[0026] The drone's navigation path refers to the drone's planned target flight route, the predetermined trajectory from its starting point to its destination. Obstacle areas refer to potential obstacles along the drone's flight path. These obstacles can be static or dynamic, located in areas of the flight volume that could interfere with the drone's flight. The goal of this step is to identify and demarcate these obstacle areas, providing a basis for subsequent obstacle avoidance path planning.

[0027] In one possible implementation, the obstacle area includes convex obstacles and concave obstacles.

[0028] A convex obstacle is one in three-dimensional space where all points on a line drawn from any two points on the boundary of the obstacle are located inside the obstacle, such as a mountain peak. A concave obstacle is one with a concave area, such as a wall with potholes or a valley.

[0029] S2: Combined with algebraic topology algorithms, the obstacle area is represented as a closed differential form describing the three-dimensional structure of the obstacle area.

[0030] Among them, algebraic topology algorithms utilize mathematical tools from algebraic topology to analyze the morphological and structural characteristics of objects in space, particularly their connectivity and basic topological properties of morphology. Algebraic topology can obtain an abstract description of obstacle regions by analyzing the boundaries and shapes of obstacles. In this process, algebraic topology algorithms are primarily used to determine the topological properties of obstacle regions (such as loops and holes) to more accurately describe and avoid these regions. Closed differential forms are mathematical tools used to describe the relationships between different points on a manifold, particularly for quantitatively representing space in geometry and topology. The closed nature of closed differential forms indicates that there are no "sources" or "sinks" between different parts of the manifold, effectively describing the overall structure and flow properties of obstacle regions. Using algebraic topology and closed differential forms, the three-dimensional geometry of obstacles can be converted into a precise mathematical representation, facilitating subsequent path planning and obstacle avoidance.

[0031] It's important to note that the combination of algebraic topology algorithms and closed differential forms enables precise modeling of obstacle areas from a topological and geometric perspective. In particular, it can identify complex obstacle shapes, such as depressions or holes. This precise mathematical modeling provides greater accuracy for subsequent path planning, enabling drones to better avoid these complex obstacle areas and ensure the safety and reliability of their flight paths.

[0032] In a possible implementation, S2 specifically includes:

[0033] S201: Determine a compact subset of the obstacle area and an obstacle boundary under the compact subset.

[0034] Among them, a compact subset refers to a sub-region within the obstacle area that meets certain conditions. Compactness is an important concept in topology, which usually means that a space is finite and does not "extend infinitely" under certain conditions.

[0035] S202: Determine the one-dimensional homology group of the obstacle boundary, and perform direct sum decomposition on the one-dimensional homology group to obtain multiple topological trap generators in the form of non-contractible closed chains on the obstacle boundary.

[0036] One-dimensional homology groups are mathematical tools used in algebraic topology to describe the one-dimensional loop characteristics of topological spaces (such as obstacle boundaries). Simply put, they are used to capture the "ring" structure in space, representing independent loops on obstacle boundaries. These loops cannot be contracted to a single point through continuous deformation and are therefore considered the "fundamental structure" of space. Non-contractible closed chain forms are closed paths (chains) on obstacle boundaries that cannot be contracted to a single point through continuous deformation. These closed chains represent the topological properties of obstacles, such as ring structures or immutable geometric forms. Because they are "incontractible," they are considered important elements in topological spaces. Topological trap generators are the basic building blocks of these non-contractible closed chains and play a generative role in describing the topological characteristics of obstacles. Each topological trap generator corresponds to a topological structure that represents an immutable ring feature on the obstacle boundary. These generators are key areas to avoid during path planning.

[0037] It's important to note that by analyzing one-dimensional homology groups and non-contractible closed chain forms, we can accurately identify important topological structures on obstacle boundaries, particularly complex loops or concave regions. This helps us better understand the geometry of obstacles and provides key topological information for subsequent obstacle avoidance path planning, enabling drones to avoid these complex areas and avoid cyclic obstacle avoidance problems.

[0038] The one-dimensional homology group is specifically:

[0039]

[0040] in, represents the group of integer coefficients, Indicates obstacles The obstacle boundary, Represents and The associated one-dimensional homology group, represents the kth non-contractible closed chain topological trap generator on the obstacle boundary, , represents the total number of non-contractible closed chains on the obstacle boundary, Represents direct sum decomposition symbol.

[0041] One-dimensional homology groups are mathematical tools used in algebraic topology to describe one-dimensional loop characteristics in topological spaces, such as obstacle boundaries. Simply put, they capture "ring" structures in space, representing independent loops on obstacle boundaries. These loops cannot be contracted to a single point through continuous deformation and are therefore considered the "fundamental structure" of space. Non-contractible closed chain forms are closed paths (chains) on obstacle boundaries that cannot be contracted to a single point through continuous deformation. These closed chains represent topological properties of obstacles, such as loops or immutable geometric forms. Because they are "incontractible," they are considered important elements in topological spaces. Topological trap generators are the building blocks of these non-contractible closed chains and play a generative role in describing the topological characteristics of obstacles. Each topological trap generator corresponds to a topological structure that represents an immutable ring feature on the obstacle boundary. These generators are key areas to avoid during path planning.

[0042] By analyzing one-dimensional homology groups and non-contractible closed chain forms, we can accurately identify important topological structures on obstacle boundaries, especially complex ring-shaped or concave regions. This helps to better understand the geometry of obstacles and provides key topological information for subsequent obstacle avoidance path planning, allowing drones to avoid these complex areas and avoid cyclic obstacle avoidance problems.

[0043] S203: According to the Hodge decomposition theorem, each topological trap generator is transformed into a harmonic form on the obstacle boundary as a mathematical marker of topological risk.

[0044] The Hodge decomposition theorem is a key theorem in algebraic topology and differential geometry. It provides a method for decomposing complex forms into simpler components. Specifically, the Hodge decomposition theorem decomposes a closed differential form (i.e., a differential form without sources or sinks) into three components: an exact form, a harmonic form, and a boundary form. The harmonic form is the most important of these components; it is a differential form that satisfies the Laplace equation, meaning it is "smooth" in a sense and contains no sources or sinks.

[0045] Harmonic forms are mathematical objects derived from the Hodge decomposition theorem, representing differential forms that have no sources, no sinks, and maintain smoothness in topological space. In describing obstacle boundaries, harmonic forms analyze the topological risk of the boundary, providing a mathematical means to mark potential danger zones within the obstacle area, aiding in the identification and avoidance of these areas. By converting topological trap generators into harmonic forms using the Hodge decomposition theorem, the complex topological characteristics of obstacles can be converted into mathematical markers that accurately describe the topological risk at the obstacle boundary. This enables path planning to be optimized based on the precise identification of potential risk areas, improving the efficiency and safety of obstacle avoidance while avoiding the drawbacks of traditional methods that may overlook complex topological structures.

[0046] S204: Based on the local topological properties of the harmonic form, the harmonic form is extended to three-dimensional space to obtain a closed differential form describing the three-dimensional structure of the obstacle area.

[0047] The solution formula of closed differential form is:

[0048]

[0049] in, Represents The related closed differential form describing the three-dimensional structure of the obstacle area is, represents the exterior differential operator, represents the lth topological trap generator, , represents the Kronecker function. When k=l, =1, otherwise, =0.

[0050] Among them, the local topological properties of the harmonic form specifically include closure and topological orthogonality.

[0051] Specifically, the entire process accurately describes the structure of the obstacle through algebraic topology and differential geometry methods. First, the compact subsets and boundaries of the obstacle region are determined, and topological trap generators are obtained through one-dimensional homology group decomposition to identify the complex shape of the obstacle. Next, these generators are converted into harmonic form using the Hodge decomposition theorem, and then expanded to three-dimensional space to form a closed differential form, accurately describing the three-dimensional structure of the obstacle. The advantage of this process is that it can deeply analyze and characterize the complex topological characteristics of the obstacle, especially the effective modeling of complex obstacles such as depressions and rings. This provides a more precise mathematical foundation for subsequent obstacle avoidance path planning, thereby improving the reliability and accuracy of path planning.

[0052] S3: Combining the closed differential form and the UAV navigation path, the UAV safety potential field is established under the constraint of the UAV's minimum turning radius.

[0053] The drone safety potential field uses a mathematical method to provide a "safe" flight space for drones during flight. This potential field is a potential force field in which obstacles create a repulsive force, guiding the drone away from these dangerous areas. Simultaneously, the navigation path itself creates an attractive force, guiding the drone toward its target. This force field combines the repulsive force of obstacles and the attractive force of the navigation path. It is also constrained by the drone's minimum turning radius to ensure that the drone does not lose balance during turns due to confined spaces or excessively sharp path changes.

[0054] It's easy to understand that by combining closed differential forms with the drone's navigation path, a safe potential field is constructed that allows for both obstacle avoidance and stable flight. This potential field not only takes obstacle avoidance into account but also ensures the smoothness and safety of the flight path within the constraints of a minimum turning radius, avoiding sharp turns or excessively steep paths, effectively improving flight stability, and reducing the risk of collisions during flight.

[0055] In a possible implementation, S3 specifically includes:

[0056] S301: Sum each closed differential form to obtain an obstacle repulsion term.

[0057] S302: Determine a correction factor for the obstacle rejection item based on the minimum turning radius of the UAV.

[0058] The calculation formula of the correction factor is:

[0059]

[0060] in, represents the correction factor, Indicates the maximum centripetal acceleration of the drone, Indicates the real-time speed of the drone. Represents the empirical calibration coefficient.

[0061] S303: Establishing a UAV navigation path constraint item in the form of a Gaussian function.

[0062] S304: Establishing a UAV safety potential field by combining the obstacle rejection term, the correction factor, and the UAV navigation path constraint term.

[0063] The formula of the drone safety potential field is as follows:

[0064]

[0065] in, Indicates the take-off point of the drone To current location The closed differential form integral of represents the natural exponential function, represents the weight parameter describing the attraction strength of the drone’s navigation path to the drone, Indicates the position of the drone at time t Navigating paths with drones The Euclidean distance of Indicates the position of the drone at time t UAV safety potential field under the state.

[0066] It should be noted that by constructing a safety potential field for the drone, combined with obstacle rejection terms, correction factors, and navigation path constraints, the drone is ensured to avoid obstacles and maintain a smooth path during flight. First, the obstacle rejection term is obtained by summing the closed differential form. Then, based on the drone's minimum turning radius and real-time speed, a correction factor is calculated to ensure that the flight path complies with physical constraints. Next, a navigation path constraint term is established using a Gaussian function to ensure that the path follows the intended route. Finally, these elements are combined to form a safety potential field, which optimizes the drone's flight trajectory, avoiding dangerous areas and guiding it towards its target. The advantage of this method lies in its ability to comprehensively consider the effects of obstacles, flight constraints, and path smoothness, thereby improving flight safety, efficiency, and stability. It is particularly effective in obstacle avoidance and path planning in complex environments.

[0067] S4: Combined with the drone safety potential field, a Riemannian metric tensor describing the drone's flight trajectory is defined in three-dimensional space.

[0068] The Riemannian metric tensor of a drone's flight trajectory describes the drone's flight path in three-dimensional space using the tools of Riemannian geometry. The Riemannian metric tensor is a mathematical object used to measure the distance and angle changes between different points on a path. Specifically, it accurately models the spatial distortions and curvatures of a drone's path, enabling the drone to better adapt to flight in complex environments while maintaining smoothness. By defining the Riemannian metric tensor, the trajectory can be optimized based on the flight environment and obstacle configuration, guiding the drone to select the optimal flight path while considering path length, flight smoothness, and dynamic constraints.

[0069] It's important to note that the introduction of the Riemann metric tensor allows for a precise description of the geometric structure of a drone's flight path, specifically its curvature and spatial distortion. This allows the drone to avoid obstacles while maintaining smoothness and continuity in complex three-dimensional space, avoiding unnecessary sharp turns or unnatural trajectories, and improving flight efficiency and stability.

[0070] In a possible implementation, S4 specifically includes:

[0071] S401: Based on the Kronecker function, determine the Euclidean reference term that describes the spatial dimension.

[0072] It's important to note that i and j, as the "index labels" of the three-dimensional coordinate axes, allow the Riemannian metric to precisely control the "space distortion rules for each direction and each pair of directions": Topological risk zones: stretching space in specific directions or pairs of directions, forcing paths to take detours. Sharp turn zones: hardening space in specific directions, forcing paths to smooth. Smoothing space in specific directions, forcing paths to follow the drone's navigation path.

[0073] S402: Determine the curvature constraint term of the UAV in combination with the Euclidean benchmark term.

[0074] S403: Determine a path stretching term related to the UAV safety potential field.

[0075] The function of this item is to "stretch the space" by adjusting the "size" of the second-order derivative in the topological risk area (such as a cavity) or the area outside the preset path, so that the path around the risk area or deviating from the path will "appear longer" in the distorted space and will naturally be eliminated during optimization.

[0076] S404: Determine the drone direction constraint item under the drone navigation path.

[0077] S405: Combining the Euclidean benchmark term, the UAV curvature constraint term, the path stretching term, and the UAV direction constraint term, the Riemannian metric tensor is obtained.

[0078] The formula of the Riemann metric tensor is:

[0079]

[0080] Among them, i and j represent the spatial dimension index labels. When the spatial dimension index label value is 1, it corresponds to the x direction, when the value is 2, it corresponds to the y direction, and when the value is 3, it corresponds to the z direction. represents the Kronecker function related to i and j, that is, the Euclidean basis term, 、 and They represent the path stretching term, the UAV curvature constraint term, and the UAV direction constraint term, respectively. 、 and They represent the weight of the path stretching term, the weight of the UAV curvature constraint term, and the weight of the UAV direction constraint term, respectively. T represents transposition. represents the partial derivative, Represents the coordinate component of the current position x in the direction of the i-th coordinate axis, Represents the coordinate component of the current position x in the direction of the j-th coordinate axis, Indicates the maximum turning curvature of the drone, It represents the effective length in three-dimensional space corresponding to the movement of the drone along the i-th direction and the j-th direction at the current position x, namely the Riemann metric tensor.

[0081] Specifically, in an ideal scenario with no obstacles, no dynamic constraints, and no pre-set path guidance, the three-dimensional space in which a drone flies is Euclidean space (the "flat space" we commonly know). In this case, the Riemannian metric is completely dominated by the Euclidean reference term, and the calculation of path length degenerates to ordinary Euclidean distance (for example, if you move 1 meter in the x-direction, the length is exactly 1 meter, without any "spatial distortion").

[0082] Among them, the minimum turning radius is the derivative of the maximum turning curvature, so, Specifically, it represents the square of the minimum turning radius. The stretch term forces the path to avoid traps, the curvature term forces the path to be smooth, and the direction term forces the path to follow the global coordinates. Ultimately, the drone finds the shortest path in "warped space," naturally satisfying all constraints.

[0083] in, is the velocity vector of the UAV navigation path, and its outer product This is a 3×3 matrix where the component in row i and column j describes the product of the path velocity's directional component in the i-th direction and the path velocity's directional component in the j-th direction. This term smooths the space along the drone's navigation path. For example, if the path moves in the x-direction, the x-metric will be smoothed, and during optimization, the shortest path will naturally extend in the x-direction, ensuring that the drone does not deviate from the global plan.

[0084] It should be noted that by defining the Riemannian metric tensor, the drone's flight path is optimized to avoid obstacles while meeting smooth and efficient flight requirements. First, based on the Kronecker function, the Euclidean basis terms of the spatial dimension are defined to precisely control spatial distortion in each direction. Then, combining the drone's minimum turning radius and flight curvature, the path is appropriately stretched, smoothed, and guided through path stretching terms, curvature constraints, and directional constraints, respectively, to ensure the continuity and smoothness of the drone's flight path. Ultimately, the drone finds the shortest path in this "distorted space" that satisfies all obstacle avoidance and dynamic constraints. The advantage of this method lies in its ability to comprehensively consider the flight path's curvature, smoothness, obstacle avoidance, and directional constraints, ensuring that the drone completes its mission efficiently and safely in complex environments, avoiding unnecessary path deviations or sharp turns.

[0085] S5: Combine the closed differential form and the UAV navigation path to determine the UAV flight path constraints.

[0086] UAV flight path constraints refer to the various restrictions that must be considered when planning a flight path to ensure the drone can complete its mission safely, smoothly, and efficiently. By combining closed differential forms with the drone's navigation path, various flight path constraints can be clearly and precisely defined. This not only helps prevent unsafe or unintended trajectories during flight, but also ensures that the path conforms to the constraints of the actual flight environment. This allows the drone to more intelligently adapt to flight missions in complex environments, improving mission success rates and flight stability.

[0087] In one possible implementation, the drone flight path constraints specifically include a first constraint and a second constraint. The first constraint specifically requires that the sum of the products of each non-contractible closed chain topological trap generator and the closed line integral of the drone flight path be zero, ensuring that the path can be continuously contracted to a point. The second constraint specifically requires that the path deviation between the drone flight path and the drone navigation path be less than a preset path deviation value.

[0088] It should be noted that those skilled in the art can set the size of the preset deviation value according to actual needs, and the present invention is not limited thereto.

[0089] The formula form of the first constraint is:

[0090]

[0091] in, Represents the flight path of the drone The closed line integral of .

[0092] The formula form of the second constraint is:

[0093]

[0094] in, Indicates the preset path deviation value, represents the flight path of the drone at time t.

[0095] It should be noted that the two path constraints in this process ensure the continuity of the flight path and its proximity to the target path, respectively. The first constraint requires that the sum of the closed line integral of the drone's flight path multiplied by the topological trap generator of the obstacle is zero, which ensures the continuity of the path and avoids unnecessary path interruptions. The second constraint ensures that the deviation of the drone's flight path from the predetermined navigation path is less than the set predetermined value, which ensures the accuracy of the path and its proximity to the target. The introduction of these constraints helps ensure the stable flight of the drone in complex environments, avoids the risks caused by excessive path deviation or discontinuity, and improves the accuracy and safety of the flight path.

[0096] S6: Under the constraints of the UAV flight path, the shortest path optimization is performed in combination with the Riemann metric tensor to output the optimal flight path of the UAV.

[0097] It's important to note that by combining drone flight path constraints with the Riemann metric tensor, it's possible to precisely optimize the flight path while taking into account flight restrictions, obstacles, and path smoothness. This shortest path optimization ensures the drone completes its mission in the shortest possible time, while avoiding obstacles and maintaining flight stability. This step not only improves flight efficiency, but also reduces energy consumption and avoids unnecessary path deviations, enabling the drone to fly efficiently and safely in complex environments.

[0098] In a possible implementation, S6 specifically includes:

[0099] S601: Calculate the curvature difference between the UAV flight path curvature and the UAV optimal cruising curvature.

[0100] S602: Calculate the effective flight path of the UAV based on the Riemann metric tensor.

[0101] S603: During the unmanned global flight process, the optimal flight path of the UAV is obtained by optimizing the effective flight path, the curvature difference, and the sum of the path deviation values ​​of the UAV.

[0102] The optimization formula for the optimal flight path of the UAV is as follows:

[0103]

[0104] =

[0105] in, represents the optimal flight path of the UAV, Indicates the effective flight path of the drone. represents the normalized interval of time, represents three-dimensional space, Indicates that it is first-order continuous and differentiable in the normalized interval and three-dimensional space, Indicates that L is continuously differentiable in the normalized interval and three-dimensional space. Indicates L with the minimum function value, represents the derivative of path L, i.e. the velocity vector of path L, and Respectively The components under the i-th spatial dimension index label and the j-th spatial dimension index label, Represents the path point at time t About The inner product of represents the optimal cruising curvature of the UAV, represents the curvature of the UAV flight path at time t, Represent the curvature difference weights, Indicates the path deviation value weight.

[0106] The integral time interval is 0 to 1, t is the time parameter of the path, not the actual physical time. Here, 1 is the normalized end time, and 0 represents the moment when the drone takes off. Compressing the entire integral time to 0 to 1 can eliminate the influence of the actual flight time.

[0107] Specifically. First, the difference between the curvature of the drone's flight path and the optimal cruise curvature is calculated to ensure smooth flight. Then, the Riemann metric tensor is used to calculate the drone's effective flight path, ensuring that the path not only avoids obstacles but also complies with dynamic constraints. The target path is optimized by minimizing the sum of the curvature difference, path deviation, and effective flight path. This process comprehensively considers the smoothness of the path, its proximity to the target path, and the drone's flight efficiency, ensuring that the drone can complete its mission efficiently and safely in complex environments while reducing energy consumption and avoiding unnecessary deviations.

[0108] In a possible implementation manner, after S6, the method further includes:

[0109] The drone navigation path is corrected according to the drone's optimal flight path, and the drone is controlled according to the corrected drone navigation path.

[0110] In practical applications, the flight path of a drone is optimized by combining algebraic topology, differential geometry, and dynamic constraints. First, by accurately modeling the obstacle region and using closed differential forms and the Riemann metric tensor to describe the geometric structure of the flight space, the drone is ensured to avoid obstacles and maintain smooth flight. Then, by setting path constraints such as minimum turning radius, curvature difference, and path deviation, the continuity and accuracy of the drone's path are ensured. On this basis, a shortest path optimization algorithm is used to optimize the flight path, reduce energy consumption, and improve efficiency. The advantage of this method is that it comprehensively considers the smoothness of the flight path, dynamic constraints, proximity to the target path, and obstacle avoidance requirements. This ensures that the drone can complete its mission efficiently and safely in complex environments, avoiding unnecessary deviations or sharp turns, thereby improving flight stability and mission success rate.

[0111] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0112] In an embodiment of the present invention, an algebraic topology algorithm is used to accurately describe the complex shapes of obstacles, especially complex areas such as concave areas, thereby avoiding the defects of traditional methods that only focus on avoiding obstacles while ignoring the tendency to fall into complex areas and cause cyclic obstacle avoidance. A closed differential form is used to accurately describe the three-dimensional structure of the obstacle area, and the Riemann metric tensor is combined to define the metric method of the flight path. This method constructs a safe potential field while considering the minimum turning radius and flight constraints, ensuring the smoothness and continuity of the flight path. By optimizing the shortest path, it can adapt to complex flight environments in real time and effectively avoid the problem of cyclic obstacle avoidance. This not only reduces energy consumption, but also improves flight efficiency. At the same time, the method introduces drone navigation as a constraint in the obstacle avoidance process, avoiding the computational burden of reloading the navigation path, and can quickly avoid obstacles in the minimum yaw state, thereby improving flight stability.

[0113] Reference Manual Figure 2 , shows a structural schematic diagram of a UAV path planning system during an obstacle avoidance process provided by an embodiment of the present invention.

[0114] The embodiment of the present invention provides a UAV path planning system 20 during an obstacle avoidance process, comprising: a processor 201 and a memory 202;

[0115] The memory 202 stores programs or instructions that can be run on the processor 201. When the programs or instructions are executed by the processor 201, the steps of the drone path planning method in the above-mentioned obstacle avoidance process are implemented, and the same technical effect can be achieved. To avoid repetition, the present invention will not be described in detail.

[0116] It should be understood that the processor 201 in the embodiment of the present invention may be a central processing unit (CPU), or may 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 any conventional processor, etc.

[0117] It should also be understood that the memory 202 in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (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).

[0118] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0119] 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.

[0120] 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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0121] Those skilled in the art will 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.

[0122] 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 merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] 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.

[0125] If the 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 the present invention, or the portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0126] An embodiment of the present invention provides a readable storage medium including: a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, the steps of the drone path planning method in the above-mentioned obstacle avoidance process are implemented, and the same technical effect can be achieved. To avoid repetition, the present invention will not be described in detail.

[0127] Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for planning a UAV path during obstacle avoidance, characterized in that: include: S1: Obtain the obstacle area on the drone’s navigation path; S2: combining an algebraic topology algorithm to represent the obstacle area as a closed differential form describing the three-dimensional structure of the obstacle area; S3: combining the closed differential form and the UAV navigation path to establish a UAV safety potential field under the constraint of the UAV's minimum turning radius; S4: Based on the UAV safety potential field, a Riemannian metric tensor describing the flight trajectory of the UAV is defined in three-dimensional space; S5: Determine the UAV flight path constraints by combining the closed differential form and the UAV navigation path; S6: Under the constraints of the UAV flight path constraints, the shortest path optimization is performed in combination with the Riemann metric tensor to output the optimal flight path of the UAV.

2. The method for planning a drone path during obstacle avoidance according to claim 1, wherein: The obstacle area includes convex obstacles and concave obstacles.

3. The method for planning a drone path during obstacle avoidance according to claim 1, wherein: The S2 specifically includes: S201: Determine a compact subset of the obstacle area and an obstacle boundary under the compact subset; S202: determining a one-dimensional homology group of the obstacle boundary, and performing direct sum decomposition on the one-dimensional homology group to obtain a plurality of topological trap generators in the form of non-contractible closed chains on the obstacle boundary; S203: converting each of the topological trap generators into a harmonic form on the obstacle boundary as a mathematical marker of topological risk according to the Hodge decomposition theorem; S204: Based on the local topological properties of the harmonic form, the harmonic form is expanded to three-dimensional space to obtain a closed differential form describing the three-dimensional structure of the obstacle area.

4. The method for planning a drone path during obstacle avoidance according to claim 1, wherein: The S3 specifically includes: S301: Summing each of the closed differential forms to obtain an obstacle repelling term; S302: Determine a correction factor for the obstacle rejection item according to the minimum turning radius of the UAV; S303: Establishing a UAV navigation path constraint item in the form of a Gaussian function; S304: Establishing the UAV safety potential field by combining the obstacle rejection item, the correction factor, and the UAV navigation path constraint item.

5. The method for UAV path planning during obstacle avoidance according to claim 1, characterized in that: The S4 specifically includes: S401: Based on the Kronecker function, determine the Euclidean base term describing the spatial dimension; S402: Determine a curvature constraint term of the UAV in combination with the Euclidean reference term; S403: Determine a path stretching term for the UAV safety potential field; S404: Determine the drone direction constraint item under the drone navigation path; S405: The Riemannian metric tensor is obtained by combining the Euclidean reference term, the UAV curvature constraint term, the path stretching term, and the UAV direction constraint term.

6. The method for UAV path planning during obstacle avoidance according to claim 3, characterized in that: The drone flight path constraint specifically includes a first constraint and a second constraint, wherein the first constraint is specifically: the sum of the products of each topological trap generator in the form of a non-contractible closed chain and the closed line integral of the drone flight path is zero, so as to ensure that the path can be continuously contracted to a point; the second constraint is specifically that the path deviation value between the drone flight path and the drone navigation path is less than a preset path deviation value.

7. The method for UAV path planning during obstacle avoidance according to claim 6, characterized in that: The S6 specifically includes: S601: Calculating the curvature difference between the UAV flight path curvature and the UAV optimal cruising curvature; S602: Calculating an effective flight path of the UAV according to the Riemann metric tensor; S603: During the unmanned global flight process, the optimal flight path of the UAV is obtained by optimizing the effective flight path of the UAV, the curvature difference, and the sum of the path deviation values.

8. The method for UAV path planning during obstacle avoidance according to claim 1, characterized in that: After S6, the method further includes: The drone navigation path is corrected according to the optimal flight path of the drone, and the drone is controlled according to the corrected drone navigation path.

9. A UAV path planning system during obstacle avoidance, characterized in that: include: processor and memory; The memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the drone path planning method in the obstacle avoidance process according to any one of claims 1 to 8 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the drone path planning method in the obstacle avoidance process as described in any one of claims 1 to 8 are implemented.

Images