Trajectory planning method and device for multiple unmanned devices, and unmanned device

Abstract

The invention relates to the technical field of trajectory planning of multiple unmanned devices, and provides a trajectory planning method and device for multiple unmanned devices, and an unmanned device, and the method comprises the steps: obtaining the environment information of a target environment and the task information of multiple unmanned devices; determining an initial track of each unmanned device based on the environment information and the task information; performing global optimization on the initial trajectory of each unmanned device to obtain an optimized trajectory of each unmanned device; and determining a trajectory planning result of the plurality of unmanned devices based on the optimized trajectory of each unmanned device. According to the invention, the problems of poor global optimization and low convergence speed of trajectory planning of multiple unmanned devices can be solved, the global trajectory planning can be improved on the basis of considering the trajectory of each unmanned device, the global optimal solution can be converged more quickly, and the trajectory planning efficiency is improved. And a more reliable technical support is provided for the application of the unmanned equipment in a complex environment.

Description

Technical Field

[0001] The present disclosure relates to the technical field of trajectory planning for multiple unmanned devices, and in particular to a trajectory planning method and device for multiple unmanned devices, and unmanned devices. Background Art

[0002] With the rapid development of unmanned equipment technology and the continuous expansion of its application scenarios, unmanned equipment such as drones are increasingly used in logistics, disaster relief, agricultural monitoring, urban inspection and other fields. However, in practical applications, unmanned equipment usually needs to perform tasks in complex environments, which may contain a large number of dense obstacles, such as urban buildings, forests, mountains, etc. In this case, how to plan safe, efficient and globally optimal flight trajectories for multiple unmanned equipment systems has become one of the key issues currently concerned by academia and industry.

[0003] In some cases, trajectory planning methods based on intelligent optimization algorithms can be used to achieve global trajectory planning for multi-unmanned equipment systems. However, these methods still face problems such as local optimal traps, poor global optimization, and slow convergence. Summary of the invention

[0004] The present disclosure provides a trajectory planning method and device for multiple unmanned devices, and unmanned devices, to at least solve the problem of poor global optimization and slow convergence speed in trajectory planning of multiple unmanned devices. The technical solution of the present disclosure is as follows: According to a first aspect of the present disclosure, a trajectory planning method for multiple unmanned devices is provided, the trajectory planning method comprising: obtaining environmental information of a target environment and task information of multiple unmanned devices, wherein the target environment includes multiple obstacles, and the task information includes a starting position of each unmanned device and a target position to be reached; determining an initial trajectory of each unmanned device based on the environmental information and the task information; globally optimizing the initial trajectory of each unmanned device to obtain an optimized trajectory of each unmanned device; and determining trajectory planning results of the multiple unmanned devices based on the optimized trajectory of each unmanned device.

[0005] Optionally, the position in the target environment is represented by a node, and the environmental information includes the position information of multiple nodes in the target environment, wherein, for each unmanned device, the initial trajectory is determined in the following manner: by performing at least one search iteration operation, a target node corresponding to each search iteration operation is obtained; in response to the target node corresponding to the current search iteration operation being the target position, the initial trajectory is determined based on all determined target nodes, wherein the search iteration operation includes: based on the environmental information and the task information of the current unmanned device, a trajectory evaluation index of each neighbor node of the current node is determined, wherein the trajectory evaluation index represents the first cost of the unmanned device to reach the neighbor node from the starting position and the second cost from the neighbor node to the target position, and the neighbor node refers to a node adjacent to the current node in the target environment; based on the trajectory evaluation index, a target neighbor node with the smallest trajectory evaluation index among the neighbor nodes of the current node is determined; based on the target neighbor node, a target node corresponding to this search iteration operation is determined, and the target node is used as the current node of the next search iteration operation, wherein the current node of the first search iteration operation is the starting position.

[0006] Optionally, determining the target node corresponding to this search iteration operation based on the target neighbor node includes: in response to the target neighbor node being unoccupied, determining the target neighbor node as the target node; in response to the target neighbor node being occupied, determining the target node through random search based on candidate neighbor nodes among the neighbor nodes of the current node except the current target neighbor node, wherein the target neighbor node being occupied means that: the target neighbor node has been used as the target node of other unmanned equipment; or, there is an obstacle at the target neighbor node.

[0007] Optionally, the optimized trajectory is obtained by performing at least one of the following optimization iterative operations: determining an expansion coefficient corresponding to an initial optimized trajectory of each unmanned device, wherein the expansion coefficient is related to the length of the trajectory; globally optimizing the initial trajectory of each unmanned device by reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices to obtain an intermediate optimized trajectory for each unmanned device; determining an initial optimized trajectory for the next optimization iterative operation based on the intermediate optimized trajectory of each unmanned device until a preset iteration condition is met, wherein the initial optimized trajectory of the first optimization iterative operation is the initial trajectory.

[0008] Optionally, determining the initial optimization trajectory of the next optimization iterative operation based on the intermediate optimization trajectory includes: determining the solution migration probability based on the number of iterations of the current optimization iterative operation and a preset probability relationship, wherein the probability relationship represents the relationship between the number of times the optimization iterative operation is performed and the solution migration probability, and the solution migration probability represents the probability of obtaining the optimal solution in this optimization iterative operation; in response to the solution migration probability being less than a preset value, determining the intermediate optimization trajectory as the initial optimization trajectory of the next optimization iterative operation; in response to the solution migration probability being greater than or equal to the preset value, determining the optimization amount based on the number of iterations of the current optimization iterative operation; based on the optimization amount, adjusting the intermediate optimization trajectory of at least one unmanned device among the multiple unmanned devices, and determining the adjusted intermediate optimization trajectory as the initial optimization trajectory of the next optimization iterative operation.

[0009] Optionally, the position in the target environment is represented by a node, and the optimized trajectory of each unmanned device includes multiple nodes, wherein the trajectory planning results of the multiple unmanned devices are determined based on the optimized trajectories of each unmanned device, including: smoothing the optimized trajectory of each unmanned device to obtain the smooth trajectory of each unmanned device; in response to the smooth trajectory of each unmanned device being a collision-free trajectory, using the smooth trajectory of each unmanned device as the trajectory planning result of the multiple unmanned devices, wherein the collision-free trajectory means: there are no overlapping nodes between the smooth trajectory of the current unmanned device and the smooth trajectories of other unmanned devices; and there are no overlapping nodes between the smooth trajectory of the current unmanned device and obstacles, wherein the smoothing process includes: for each optimized trajectory, deleting the node with the same forward direction as the previous node; and / or reducing the number of turns of each optimized trajectory.

[0010] According to a second aspect of the present disclosure, a trajectory planning device for multiple unmanned devices is provided, and the trajectory planning device includes: an acquisition unit, configured to acquire environmental information of a target environment and task information of multiple unmanned devices, wherein the target environment includes multiple obstacles, and the task information includes a starting position of each unmanned device and a target position to be reached; an initial trajectory unit, configured to determine an initial trajectory of each unmanned device based on the environmental information and the task information; an optimized trajectory determination unit, configured to globally optimize the initial trajectory of each unmanned device to obtain an optimized trajectory of each unmanned device; and a planning result determination unit, configured to determine trajectory planning results of the multiple unmanned devices based on the optimized trajectory of each unmanned device.

[0011] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor, wherein the instructions executable by the processor, when executed by the processor, prompt the processor to execute the trajectory planning method for multiple unmanned devices according to the present disclosure.

[0012] According to a fourth aspect of the present disclosure, an unmanned device is provided, the unmanned device comprising the electronic device according to the present disclosure, or the unmanned device is communicatively connected to the electronic device according to the present disclosure.

[0013] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the trajectory planning method for multiple unmanned devices according to the present disclosure.

[0014] According to a sixth aspect of the present disclosure, a computer program product is provided, comprising computer executable instructions, which, when executed by at least one processor, can implement the trajectory planning method for multiple unmanned devices according to the present disclosure.

[0015] The technical solution provided by the present disclosure brings at least the following beneficial effects: According to the present disclosure, the initial trajectories of multiple unmanned devices can be determined based on environmental information and task information, and the initial trajectories can be globally optimized to obtain the optimized trajectory of each unmanned device, so as to obtain the final trajectory planning result based on the optimized trajectory. In this way, the global trajectory planning can be improved as a whole on the basis of considering the trajectory of each unmanned device, so that it can converge to the global optimal solution more quickly, providing more reliable technical support for the application of unmanned devices in complex environments.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0018] Figure 1 is a schematic flowchart of a trajectory planning method for multiple unmanned devices according to an exemplary embodiment of the present disclosure.

[0019] Figure 2 It is a schematic diagram of a scenario of global trajectory planning of multiple UAVs in a large-scale dense obstacle environment according to an exemplary embodiment of the present disclosure.

[0020] Figure 3 It is a simulation schematic diagram of a multi-UAV global trajectory planning method in a large-scale dense obstacle environment according to an exemplary embodiment of the present disclosure.

[0021] Figure 4 It is a schematic flowchart of a search iteration operation in a trajectory planning method for multiple unmanned devices according to an exemplary embodiment of the present disclosure.

[0022] Figure 5 It is a schematic flowchart of optimizing iterative operations in a trajectory planning method for multiple unmanned devices according to an exemplary embodiment of the present disclosure.

[0023] Figure 6 It is a schematic block diagram of a trajectory planning apparatus for multiple unmanned devices according to an exemplary embodiment of the present disclosure.

[0024] Figure 7 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0027] It should be noted that the phrase "at least one of the items" in the present disclosure includes three types of parallel situations: "any one of the items", "a combination of any number of the items", and "all of the items". For example, "including at least one of A and B" includes the following three types of parallel situations: (1) including A; (2) including B; (3) including A and B. Another example is "executing at least one of step 1 and step 2" which means the following three types of parallel situations: (1) executing step 1; (2) executing step 2; (3) executing step 1 and step 2.

[0028] As mentioned above, in the relevant technologies, the trajectory planning of multiple unmanned devices has problems such as local optimal traps and slow convergence speed.

[0029] Taking UAV as an example, traditional trajectory planning methods mainly focus on the path planning of a single UAV, such as those based on Dijkstra algorithm, A Algorithms or RRT (fast random tree) algorithms, etc. These methods perform well in obstacle avoidance and path optimization of a single UAV, but in multi-UAV collaborative scenarios, a single path planning method often fails to meet the needs.

[0030] Specifically, the trajectory planning of a multi-UAV system needs to consider the following key issues at the same time: (1) Global optimization requirements: When performing tasks, multiple UAVs not only need to plan their own flight paths, but also need to optimize the overall mission efficiency from a global perspective to avoid path conflicts and resource waste. (2) Computational efficiency: In a large-scale environment with dense obstacles, the search space is huge. Traditional algorithms may face the problem of high computational complexity and are difficult to meet real-time requirements. (3) Dynamic obstacle avoidance and environmental adaptability: In a complex environment, the distribution of obstacles may be extremely dense and dynamically changing. The planning method needs to have the ability to quickly adapt to environmental changes.

[0031] In this regard, trajectory planning methods based on intelligent optimization algorithms have gradually attracted attention, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony algorithm (ACO), etc. These algorithms can show good performance in the global optimization of complex problems. However, these methods still face problems such as local optimal traps and slow convergence speed in large-scale dense obstacle environments. In addition, multi-UAV trajectory planning also needs to be combined with specific mission requirements and design a special planning strategy to balance multiple performance indicators such as path length, energy consumption and mission completion time. These factors are not taken into account in these methods.

[0032] In view of the above, exemplary embodiments of the present disclosure propose a trajectory planning method for multiple unmanned devices, a trajectory planning device for multiple unmanned devices, an electronic device, a computer-readable storage medium, and a computer program product, which can solve or at least alleviate the above problems.

[0033] In a first aspect of an exemplary embodiment of the present disclosure, a trajectory planning method for multiple unmanned devices is provided.

[0034] The trajectory planning method for multiple unmanned devices according to the exemplary embodiment of the present disclosure can be applied to scenarios where users interact with software. For example, the software can be loaded on the user terminal, and the user can input trajectory planning instructions for multiple unmanned devices on the user terminal. The user terminal can obtain the final trajectory planning result by executing the trajectory planning method for multiple unmanned devices according to the exemplary embodiment of the present disclosure.

[0035] Specifically, the user terminal can obtain environmental information of the target environment and task information of multiple unmanned devices, wherein the target environment includes multiple obstacles, and the task information includes the starting position of each unmanned device and the target position to be reached. The user terminal can also determine the initial trajectory of each unmanned device based on the environmental information and the task information. The user terminal can also globally optimize the initial trajectory of each unmanned device to obtain the optimized trajectory of each unmanned device. The user terminal can also determine the trajectory planning results of multiple unmanned devices based on the optimized trajectory of each unmanned device.

[0036] The above-mentioned user terminal can be, for example, a tablet computer, a laptop computer, a digital assistant, a wearable device, etc. However, the implementation scenario of the above method is only an example scenario. The trajectory planning method for multiple unmanned devices according to the exemplary embodiment of the present disclosure can also be applied to other application scenarios. For example, the user can request the server to perform trajectory planning for multiple unmanned devices through the network on the user terminal (for example, a mobile phone, a desktop computer, a tablet computer, etc.). The server can complete the request by executing the trajectory planning method for multiple unmanned devices according to the exemplary embodiment of the present disclosure. Here, the server can be an independent server, a server cluster, a cloud computing platform, or a virtualization center.

[0037] According to the trajectory planning method for multiple unmanned devices of the exemplary embodiment of the present disclosure, the global trajectory planning can be improved as a whole on the basis of considering the trajectory of each unmanned device, thereby providing more reliable technical support for the application of unmanned devices in complex environments.

[0038] The following will refer to Figures 1 to 5 An example of a trajectory planning method for multiple unmanned devices according to an embodiment of the present disclosure is described.

[0039] like Figure 1 As shown, the trajectory planning method for multiple unmanned devices may include the following steps: In step S110, environmental information of the target environment and task information of multiple unmanned devices may be acquired.

[0040] Here, the target environment may include multiple obstacles. For example, the target environment may be a large-scale environment including a dense obstacle group. Here, the large-scale environment may be, for example, an environment with an area of ​​3km×3km or more; a dense obstacle group may be, for example, an obstacle group with a distribution spacing between obstacles ranging from 50m to 100m. The mission information may include the starting position of each unmanned device and the target position to be reached. The mission information of different unmanned devices may be different, for example, the starting position and target position of different unmanned devices may be different.

[0041] As an example, unmanned equipment can be drones, unmanned vehicles, unmanned ships, etc. Multiple unmanned equipment can form an unmanned equipment system, and these unmanned equipment can perform their respective tasks in parallel. Multiple unmanned equipment can complete complex tasks through autonomous and intelligent collaborative strategies, and can be applied to multiple fields such as industry, agriculture, and transportation.

[0042] In this step, the environmental information may include, for example, a map of the target environment (e.g., a three-dimensional map) and location information of obstacles. For example, the three-dimensional map file of the target environment may be read, and the longitude, latitude, and altitude information data of the obstacles in the target environment may be obtained. In this step, the starting position, target position, and map resolution of multiple unmanned devices may also be determined in the target environment.

[0043] Figure 2 An example scenario of global trajectory planning for multiple UAVs in a target environment is shown. Figure 3 The simulation example of global trajectory planning of multiple UAVs is shown, where x and y represent length and width respectively, both in km; N represents height in m. Here, the area of ​​the map area of ​​the target environment is 2 km. 2km 200m (length Width High) as an example, Figure 2 and Figure 3 As shown, the cube represents an obstacle, and the triangle represents the starting position and target position of the unmanned equipment. In this example, the number of unmanned equipment is 20, and the initial position and target position of each unmanned equipment are randomly generated. The map resolution includes: horizontal resolution of 50m and vertical resolution of 10m.

[0044] In addition, each position in the target environment can be represented by a node, and the trajectory of the unmanned device can be planned by determining the nodes that the unmanned device passes through from the starting position to the target position. For example, in the example where the environmental information includes a map of the target environment, the map can be rasterized. For a three-dimensional map, three-dimensional rasterization can be performed. Taking the above example as an example, the number of map grids can be 40 40 20 = 32000. Each grid can be used as a node.

[0045] Although it is described above that the environmental information includes a map of the target environment, the embodiments of the present disclosure are not limited thereto, and the environmental information of the target environment may be represented by other means as long as the characteristics of each location in the target environment (such as whether there are obstacles) can be represented.

[0046] In step S120, the initial trajectory of each unmanned device may be determined based on the environmental information and the task information.

[0047] In this step, preliminary trajectory planning can be performed for multiple unmanned devices based on environmental information and task information. As an example, the initial trajectory of each of the multiple unmanned devices can be searched in sequence, and the search order can be random or predetermined. The initial trajectory of the unmanned device searched later does not conflict with the initial trajectory of the unmanned device searched earlier, or there is no overlapping trajectory.

[0048] As an example, the location in the target environment can be represented by a node, and the environmental information can include the location information of multiple nodes in the target environment. In step S120, the initial trajectory can be determined for each unmanned device in the following manner: by performing at least one search iteration operation, a target node corresponding to each search iteration operation is obtained; in response to the target node corresponding to the current search iteration operation being the target location, the initial trajectory is determined based on all determined target nodes.

[0049] Specifically, for each unmanned device, you can start from the starting position and iteratively search for the next target node until you reach the target position or the target node is deadlocked, then stop searching. When the search reaches the target position, you can output all the target nodes searched by the current iteration to form an initial trajectory based on these target nodes. For example, the lines connecting these target nodes can be used as the initial trajectory.

[0050] For example, Figure 4 As shown, the search iteration operation may include the following steps: In step S410, based on the environmental information and the task information of the current unmanned device, the trajectory evaluation index of each neighboring node of the current node can be determined.

[0051] Here, the current node of the first search iteration operation may be the starting position. The trajectory evaluation index may be determined based on the first cost of the unmanned device reaching the neighbor node from the starting position and the second cost from the neighbor node to the target position. The neighbor node may refer to a node adjacent to the current node in the target environment. In a three-dimensional map, the neighbor node may be a node located in front of, behind, above, below, left, right, left front upper, left front lower, right front upper, right front lower, left rear upper, left rear lower, right rear upper, and right rear lower of the current node.

[0052] The first cost may represent the actual cost of the unmanned device from the starting position to the neighboring node, and the second cost may represent the estimated cost of the unmanned device from the neighboring node to the target position. Here, the cost may include the distance between the two positions, the energy consumption of the unmanned device from one position to another, etc. For example, the cost may be calculated using the Euclidean distance.

[0053] As an example, the trajectory evaluation index can represent the sum of the first cost of the unmanned device from the starting position to the neighboring node and the second cost from the neighboring node to the target position. For example, the trajectory evaluation index can be expressed by the following formula (1): (1) in, represents the trajectory evaluation function, which can be used as the above trajectory evaluation index, Indicates unmanned equipment From the node where its starting position is to the current node The actual cost of Indicates unmanned equipment Slave Node The estimated cost to reach the node where the target location is located.

[0054] As an example, in this step, based on A Search method, initializing the number of map grids, the starting nodes and target nodes of the multiple drones, and iteratively checking the drones Neighbors of each node passed by, update their For example, the trajectory evaluation index of all neighboring nodes of the current node can be calculated.

[0055] As another example, the trajectory evaluation index can be determined based on the first cost, the second cost, a dynamic weight coefficient preset for the second cost, and a preset noise. Specifically, the dynamic weight coefficient can be used to weight the second cost, which can decay as the number of iterations of the search iteration operation increases, or is negatively correlated with the number of iterations. The preset noise can be in any form, such as Gaussian noise. By adding this noise, a balance can be found between the determinism and randomness of the algorithm, thereby improving the overall performance of the algorithm in complex, dynamic or imperfect information environments.

[0056] For example, the trajectory evaluation index can be expressed by the following formula (2): (2) in, represents the trajectory evaluation function, which can be used as the above trajectory evaluation index, Indicates unmanned equipment From the node where its starting position is to the current node The actual cost of Indicates unmanned equipment From the current node The estimated cost to reach the node where the target location is located, The mean is 0 and the variance is Gaussian noise, It can be set according to actual needs, for example .

[0057] In the above formula (2), Represents the dynamic weight coefficient, which changes with the number of iterations Attenuation, as an example, the dynamic weight coefficient can be expressed by the following formula (3): (3) in, Represents the attenuation rate, which can be set according to actual needs. It can be in the range (0,1), for example, =0.1; Indicates the number of iterations; and Respectively represent the upper and lower bounds of the weight, which can be set according to actual needs, for example .

[0058] In step S420, based on the trajectory evaluation index, a target neighbor node having the smallest trajectory evaluation index among neighbor nodes of the current node may be determined.

[0059] In this step, a neighbor node with the smallest trajectory evaluation index can be determined from the trajectory evaluation indexes of all neighbor nodes of the current node as the target neighbor node.

[0060] In step S430, a target node corresponding to the current search iteration operation may be determined based on the target neighbor node, and the target node may be used as the current node of the next search iteration operation.

[0061] Here, the target neighbor node with the smallest trajectory evaluation index can be used as a target node, and the next search can be performed based on the target node searched this time, thereby realizing an iterative search from the starting position to the target position. Since the neighbor node with the smallest trajectory evaluation index is used as the target node during the search process, the performance of the final initial trajectory can be better.

[0062] In this example, we can also consider the situation where nodes deadlock during the search process. Here, deadlock means that when the unmanned equipment plans the trajectory, it falls into an infinite loop at a certain node and cannot move forward because the planned trajectory occupies the neighbor space that can be explored by the current node.

[0063] Specifically, step S430 includes: in response to the target neighbor node being unoccupied, determining the target neighbor node as the target node; in response to the target neighbor node being occupied, determining the target node through random search based on candidate neighbor nodes among the neighbor nodes of the current node except the current target neighbor node.

[0064] Here, the target neighbor node being occupied may mean that: the target neighbor node has been used as a target node of other unmanned equipment; or, there is an obstacle at the target neighbor node.

[0065] In the case where the target neighbor node is occupied, for example, the target node can be randomly determined from the candidate neighbor nodes. Specifically, in any search iteration operation, when the target neighbor node is searched, it can be determined whether the target neighbor node is occupied. In the case where the target neighbor node is occupied, it can be started from the current stop node, and the remaining nodes can be traversed to randomly explore the unvisited and occupied neighbor nodes, such as randomly selecting a neighbor node as the target node. If the target node is successfully found or it is impossible to continue exploring, the currently found trajectory is returned through node backtracking. As an example, a pseudo-random search method can be used to implement this process.

[0066] In this way, in each search iteration operation, on the one hand, the preferred node among the neighboring nodes can be efficiently searched based on the trajectory evaluation index, and on the other hand, the diversity of nodes can be taken into account through random search when the preferred node is occupied, so that the planning result is more balanced. Here, in the case of deadlock in the search for nodes based on the trajectory evaluation index, this random search instead of a fixed rule search (for example, selecting the neighboring node with the second best trajectory evaluation index) can improve the algorithm's exploration ability in the solution space and avoid fixed rules restricting the algorithm's learning ability. Random search can provide an underlying guarantee for the evaluation effect of the trajectory evaluation index. For example, in the case of deadlock, other neighboring nodes are likely to be searched, avoiding the excessive differentiation of these neighboring nodes caused by the use of fixed rule search methods.

[0067] In particular, when using A In the example of combining the search method with the pseudo-random search method, A The efficiency of the algorithm and the diversity of pseudo-random search methods, specifically, by The algorithm is combined with the pseudo-random search method to solve multi-UAV planning, which can overcome the A The algorithm itself can only solve the calculation problem of a single object in a two-dimensional plane. It cannot solve the related problems involving multiple objects in three-dimensional space, nor can it be directly applied to the solution process of three-dimensional space or multiple objects, and it cannot be applied to the trajectory planning problem of multiple unmanned equipment to be solved in the embodiments of the present disclosure. In this regard, in the embodiments of the present disclosure, by The algorithm is combined with a pseudo-random search method to retain A While taking advantage of search efficiency, the algorithm combines the advantages of pseudo-random search methods in search diversity to achieve efficient path planning for multiple unmanned devices in three-dimensional space.

[0068] The above-mentioned iterative operation can be performed multiple times to search for the target position from the starting position. If the node where the target position is located is successfully found or the iteration limit is reached (for example, the preset number of iterations or iteration time is reached), the iteration is stopped, and the initial trajectory of the unmanned device of the current iteration can be constructed through node backtracking, and the nodes it passes through (for example, grids on a map) are marked as occupied, so that the initial trajectory of the current unmanned device is taken into account in subsequent unmanned device iterations.

[0069] After the initial trajectory of the current unmanned device is planned, the initial trajectory of the next unmanned device can be planned by performing the above search iteration operation again until the initial trajectories of all unmanned devices are output.

[0070] Return to reference Figure 1 In step S130, the initial trajectory of each unmanned device can be globally optimized to obtain the optimized trajectory of each unmanned device.

[0071] In this step, the trajectories of multiple unmanned devices can be comprehensively considered and globally optimized.

[0072] As an example, Figure 5 As shown, the optimization trajectory can be obtained by performing at least one of the following optimization iterations: In step S510, an expansion coefficient corresponding to the initial optimized trajectory of each unmanned device may be determined.

[0073] Here, the initial optimization trajectory of the first optimization iteration operation is the initial trajectory of the unmanned device. The expansion coefficient may be related to the length of the trajectory, for example, the expansion coefficient is positively correlated with the length of the trajectory. As an example, the expansion coefficient may be determined according to a preset conversion relationship, for example, in the case of a known trajectory, the length of the trajectory may be determined, and the distance may be brought into the preset conversion relationship to determine the corresponding expansion coefficient.

[0074] As an example, the expansion coefficient may be determined based on environmental information, for example, based on path cost and threat cost (or security cost). For example, the expansion coefficient may be determined by the following formula (4): Expansion coefficient = (4) in, and is the adjustment coefficient, which can be set according to actual needs, for example, =0.7, ; , are the path cost and path cost threshold at the current moment respectively, , Here, the path cost threshold and the security cost threshold can be set according to actual needs.

[0075] The path cost may represent the actual cost of the unmanned device from the starting position to the target position according to the trajectory planned at the current moment. The path cost may be positively correlated with the length of the trajectory. In addition, the concept of cost has been described above, so it will not be repeated here.

[0076] The safety cost can represent the threat posed by obstacles within the preset safety cost calculation range centered on each node on the trajectory planned at the current moment to the movement of the current unmanned equipment. Here, the threat posed by obstacles to the movement of the current unmanned equipment can be determined based on the distance between the obstacle and the unmanned equipment. For example, at least one distance interval can be preset, and each distance interval corresponds to a safety cost value. When the distance between the obstacle and the unmanned equipment falls into any distance interval, the threat posed by the obstacle to the movement of the unmanned equipment is the safety cost value corresponding to the distance interval, wherein the safety cost value is negatively correlated with the distance between the obstacle and the unmanned equipment. The closer the obstacle is, the greater the obstruction or danger it causes to the movement of the unmanned equipment. Therefore, a higher safety cost value can be assigned. The sum of the safety cost values ​​corresponding to all obstacles within the preset safety cost calculation range of each node on the trajectory planned at the current moment can be used as the above safety cost. As an example, the above-mentioned preset safety cost calculation range can be, for example, a circle with each node on the currently planned trajectory as the center and a preset radius. The preset radius can be set according to actual needs, for example, it can be 5 meters. In addition, if there are no obstacles within the preset safety cost calculation range, the safety cost can be 0.

[0077] In step S520, the initial trajectory of each unmanned device may be globally optimized by reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices, thereby obtaining an intermediate optimized trajectory of each unmanned device.

[0078] In this step, the initial trajectories of all unmanned devices can be optimized by optimizing the sum of the expansion coefficients, and the intermediate optimized trajectory of each unmanned device can be determined.

[0079] For example, the initial trajectory of at least a portion of the unmanned equipment (e.g., a portion of the unmanned equipment with a larger initial expansion coefficient) can be adjusted according to a preset first optimization amount, so that the expansion coefficient of the intermediate optimized trajectory obtained after adjustment is smaller than the expansion coefficient of the original initial trajectory. Here, the intermediate optimized trajectory can be a trajectory obtained by adjusting the initial trajectory, or it can be the initial trajectory. Specifically, for unmanned equipment whose initial trajectory has been adjusted, the intermediate optimized trajectory is the adjusted trajectory; for unmanned equipment whose initial trajectory has not been adjusted, the intermediate optimized trajectory is the initial trajectory.

[0080] As an example, adjusting the initial trajectory based on the first optimization amount may include adjusting the position of at least one node on the initial trajectory based on the optimization amount to adjust the trajectory, for example, the optimization amount may be added to or subtracted from the position coordinates of the node.

[0081] In step S530, an initial optimization trajectory for the next optimization iteration operation may be determined based on the intermediate optimization trajectory of each unmanned device until a preset iteration condition is met.

[0082] In one example, the intermediate optimization trajectory may be directly used as the initial optimization trajectory of the next optimization iteration operation.

[0083] In another example, the intermediate optimization trajectory of each unmanned device can be further optimized, and the optimized trajectory can be used as the initial optimization trajectory for the next optimization iteration operation.

[0084] As an example, step S530 may include: determining a solution migration probability based on the number of iterations of the current optimization iteration operation and a preset probability relationship; and determining an initial optimization trajectory of the next optimization iteration operation according to the solution migration probability.

[0085] Here, the probability relationship may represent the relationship between the number of times the optimization iteration operation is performed and the solution migration probability, and the solution migration probability may represent the probability of obtaining the optimal solution in this optimization iteration operation.

[0086] Specifically, in response to the solution migration probability being less than a preset value, the intermediate optimization trajectory is determined as the initial optimization trajectory for the next optimization iteration operation.

[0087] In response to the solution migration probability being greater than or equal to a preset value, an optimization amount is determined based on the number of iterations of the current optimization iterative operation; based on the optimization amount, an intermediate optimization trajectory of at least one of the multiple unmanned devices is adjusted, and the adjusted intermediate optimization trajectory is determined as the initial optimization trajectory of the next optimization iterative operation.

[0088] Here, the probability relationship may be preset, which may characterize the correlation between the number of optimization iterations and the solution migration probability, for example, the number of optimization iterations may be positively correlated with the solution migration probability. In addition, the preset value of the solution migration probability may be set according to actual needs.

[0089] When the solution migration probability is high, for example, when it is greater than or equal to a preset value, it means that it is more likely to further optimize the trajectory in this optimization iteration operation to obtain the optimal solution. Therefore, the intermediate optimization trajectory of at least one device can be adjusted or optimized based on the optimization amount to obtain a more optimized trajectory, and the trajectory is used as the initial optimization trajectory for the next optimization iteration operation. As an example, adjusting the intermediate optimization trajectory based on the optimization amount here may include adjusting the position of at least one intermediate node in the intermediate optimization trajectory based on the optimization amount to adjust the trajectory, for example, the optimization amount can be added to or subtracted from the position coordinates of the node.

[0090] As an example, the optimization amount (also referred to as the second optimization amount) described herein may be determined based on the number of iterations of the current optimization iteration operation, for example, it may be positively correlated with the number of iterations of the current optimization iteration operation.

[0091] Through the above method, in each optimization iteration operation, the initial optimization trajectory can be adjusted based on the first optimization amount to obtain the intermediate optimization trajectory, and then the intermediate optimization trajectory can be further optimized based on the second optimization amount according to the probability of the optimal solution appearing in this iterative operation. This can help the solution converge faster.

[0092] As an example, a multiverse optimization method can be used to implement a single iteration operation.

[0093] Specifically, the initial trajectories of multiple unmanned devices can be initialized as multiple universes in the universe group, and the expansion coefficient of each universe is calculated. The current universe is transmitted from the white hole to the black hole using the roulette mechanism, thereby optimizing the initial trajectory. For example, at least one initial trajectory can be optimized based on a preset first optimization amount to obtain an intermediate optimization trajectory of this iterative operation.

[0094] For this intermediate optimization trajectory, the wormhole theory in the multiverse optimization method can be used to calculate the wormhole existence probability WEP and the travel distance rate TDR, which are respectively expressed by the following equations (5) and (6): (5) (6) in, , Represent the maximum and minimum values, respectively. Indicates the current iteration number, represents the maximum number of iterations (the maximum number of iterations may be preset, for example), represents the exploration accuracy during the iteration process. Here, , and can be preset, for example, , , .

[0095] Here, the above formula (5) can represent the probability relationship, the probability WEP can be used as the solution migration probability, and the travel distance rate TDR can be used as the second optimization quantity. As an example, a preset value can be set for the probability WEP. In response to the probability WEP being greater than or equal to the preset value, it can be considered that a wormhole exists, and the current universe is equal to the global optimal universe of this iterative operation, that is, the current optimization trajectory is the optimal solution of this iterative operation.

[0096] When using the multiverse algorithm, the strong global search capability of the multiverse algorithm can be utilized to achieve the rapid generation of collision-free trajectories for multiple unmanned devices in complex environments, thereby achieving efficient collaboration and global optimization of multiple unmanned devices in complex environments.

[0097] In step S130, in response to the number of optimization iterations being greater than or equal to the preset maximum number of iterations, the iteration may be terminated and the optimized trajectory of each unmanned device may be output; otherwise, the next optimization iteration may be performed. Greater than or equal to the maximum number of iterations , the iteration can be ended.

[0098] In step S140, trajectory planning results of multiple unmanned devices may be determined based on the optimized trajectory of each unmanned device.

[0099] In one example, the optimized trajectory of each unmanned device may be used as the trajectory planning result of multiple unmanned devices.

[0100] In another example, when the position in the target environment is represented by nodes, the optimized trajectory of each unmanned device may include multiple nodes. In this step, the trajectory planning results of multiple unmanned devices can be determined in the following manner: smoothing the optimized trajectory of each unmanned device to obtain a smoothed trajectory of each unmanned device; and determining the trajectory planning result based on the smoothed trajectory.

[0101] The smoothing process may include, for example, but is not limited to: for each optimization trajectory, deleting a node with the same moving direction as a previous node; and / or reducing the number of turns of each optimization trajectory.

[0102] Specifically, since the position in the target environment is represented by nodes, the obtained optimized trajectory may also include multiple nodes. In the trajectory planning of the previous steps, since planning and optimization are performed in units of nodes, the trajectory of the unmanned equipment may be limited to the node position, so that the optimized trajectory of each unmanned equipment may have non-smooth curves or broken lines, etc. In this regard, in an embodiment of the present disclosure, after obtaining the optimized trajectory, the optimized trajectory can be smoothed without considering the node division in the target environment, so that the total distance of the optimized trajectory is as short as possible, thereby achieving further optimization.

[0103] As an example, the iterative approximation idea can be used to smooth the optimized trajectory of each unmanned device. For example, the same-direction waypoints on the optimized trajectory (such as the nodes mentioned above) can be removed; the waypoints in the optimized trajectory can also be removed or retained with the goal of reducing the number of trajectory turns.

[0104] For example, for the optimized trajectory of any unmanned device, the trajectory between each preset number (the preset number is greater than or equal to 3) of adjacent nodes on the optimized trajectory can be smoothed. Specifically, the trajectory between these adjacent nodes can be smoothed by adjusting the middle nodes among the multiple adjacent nodes. For example, taking the preset number as 3 as an example, the trajectory of multiple unmanned devices can be smoothed based on the following formula (7): (7) in, , Represents the positions of three adjacent points of the current optimization trajectory (e.g., coordinates in the target environment map), Represents the smoothed trajectory curve between three points. , Represents the dynamic weight coefficient for smoothing, used to adjust the midpoint The strength of the influence on the curve shape, The optimization trajectory can be adjusted according to the distance between the three points. The smaller the distance between the three points along the optimization trajectory, The larger the value of .

[0105] Although the preset number is 3 as an example, the embodiments of the present disclosure are not limited thereto, and the preset number may be greater than 3. When the preset number is greater than or equal to 3, the general expression for smoothing processing may be, for example:

[0106] in, Indicates that except for the two nodes at the ends The location of the intermediate nodes (e.g. coordinates in the target environment map),N The preset quantity.

[0107] In addition, according to an embodiment of the present disclosure, in this step, collision detection can also be performed on the optimized trajectories of multiple unmanned equipment to ensure that there is no collision between the trajectories of the unmanned equipment in the final planning result.

[0108] Specifically, in response to the smooth trajectories of each unmanned device being collision-free trajectories, the smooth trajectories of each unmanned device can be used as trajectory planning results of multiple unmanned devices. Here, collision-free trajectories mean that there are no overlapping nodes between the smooth trajectory of the current unmanned device and the smooth trajectories of other unmanned devices; and there are no overlapping nodes between the smooth trajectory of the current unmanned device and obstacles.

[0109] In response to a collision on the smooth trajectory of any unmanned device, a new waypoint can be randomly searched to avoid the collision, for example, avoiding a collision with the trajectory of other unmanned devices or an obstacle, until the smooth trajectory of each unmanned device is a collision-free trajectory, and the current trajectory is output as the trajectory planning result to achieve global trajectory planning for multiple unmanned devices.

[0110] The method according to the embodiment of the present disclosure combines exploration and optimization methods, can quickly plan trajectories, and will greatly improve the coordination capabilities of multiple drones. Compared with traditional methods, this method can significantly improve the efficiency and quality of trajectory planning for multiple unmanned devices, and is particularly suitable for global trajectory planning for multiple unmanned devices in a large-scale environment with dense obstacles, providing more reliable technical support for the application of unmanned devices in complex environments.

[0111] In addition, the method according to the embodiment of the present disclosure can be based on A The search and pseudo-random search methods generate the initial trajectories of multiple unmanned devices. In this process, neighbor nodes are screened, deduplicated, and randomly selected to ensure the validity and diversity of the trajectories. This method can also use the multiverse algorithm to globally optimize the initial trajectories of multiple unmanned devices, generate optimized trajectories of multiple unmanned devices through white hole and black hole exchange, wormhole jump, etc., and then use iterative approximation to remove, retain, detect collisions, and avoid adjustments to the waypoints of the optimized trajectories of multiple unmanned devices, so as to achieve the generation of collision-free trajectories of multiple drones in complex environments.

[0112] In a second aspect of the exemplary embodiments of the present disclosure, a trajectory planning device for multiple unmanned devices is provided, such as Figure 6 As shown, the trajectory planning device includes an acquisition unit 610, an initial trajectory unit 620, an optimized trajectory determination unit 630 and a planning result determination unit 640.

[0113] The acquisition unit 610 is configured to acquire environmental information of a target environment and mission information of multiple unmanned devices, wherein the target environment includes multiple obstacles, and the mission information includes a starting position of each unmanned device and a target position to be reached.

[0114] The initial trajectory unit 620 is configured to determine the initial trajectory of each unmanned device based on the environment information and the task information.

[0115] The optimized trajectory determination unit 630 is configured to perform global optimization on the initial trajectory of each unmanned device to obtain an optimized trajectory for each unmanned device.

[0116] The planning result determination unit 640 is configured to determine trajectory planning results of multiple unmanned devices based on the optimized trajectory of each unmanned device.

[0117] As an example, the position in the target environment is represented by a node, and the environmental information includes the position information of multiple nodes in the target environment, wherein the initial trajectory unit 620 is configured to determine the initial trajectory for each unmanned device in the following manner: by performing at least one search iteration operation, a target node corresponding to each search iteration operation is obtained; in response to the target node corresponding to the current search iteration operation being the target position, the initial trajectory is determined based on all determined target nodes, wherein the search iteration operation includes: based on the environmental information and the task information of the current unmanned device, a trajectory evaluation index of each neighbor node of the current node is determined, wherein the trajectory evaluation index represents the first cost of the unmanned device to reach the neighbor node from the starting position and the second cost from the neighbor node to the target position, and the neighbor node refers to the node adjacent to the current node in the target environment; based on the trajectory evaluation index, the target neighbor node with the smallest trajectory evaluation index among the neighbor nodes of the current node is determined; based on the target neighbor node, the target node corresponding to this search iteration operation is determined, and the target node is used as the current node of the next search iteration operation, wherein the current node of the first search iteration operation is the starting position.

[0118] As an example, the initial trajectory unit 620 is configured to: in response to the target neighbor node being unoccupied, determine the target neighbor node as the target node; in response to the target neighbor node being occupied, determine the target node through random search based on candidate neighbor nodes among the neighbor nodes of the current node except the current target neighbor node, wherein the target neighbor node being occupied means that: the target neighbor node has been used as the target node of other unmanned equipment; or, there is an obstacle at the target neighbor node.

[0119] As an example, the optimization trajectory determination unit 630 is configured to obtain an optimized trajectory by performing at least one of the following optimization iterative operations: determining an expansion coefficient corresponding to an initial optimized trajectory of each unmanned device, wherein the expansion coefficient is related to the length of the trajectory; globally optimizing the initial trajectory of each unmanned device by reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices to obtain an intermediate optimized trajectory for each unmanned device; determining an initial optimized trajectory for the next optimization iterative operation based on the intermediate optimized trajectory of each unmanned device until a preset iteration condition is met, wherein the initial optimized trajectory of the first optimization iterative operation is the initial trajectory.

[0120] As an example, the optimization trajectory determination unit 630 is configured to: determine the solution migration probability based on the number of iterations of the current optimization iterative operation and a preset probability relationship, wherein the probability relationship represents the relationship between the number of times the optimization iterative operation is performed and the solution migration probability, and the solution migration probability represents the probability of obtaining the optimal solution in this optimization iterative operation; in response to the solution migration probability being less than a preset value, determine the intermediate optimization trajectory as the initial optimization trajectory for the next optimization iterative operation; in response to the solution migration probability being greater than or equal to the preset value, determine the optimization amount based on the number of iterations of the current optimization iterative operation; based on the optimization amount, adjust the intermediate optimization trajectory of at least one unmanned device among the multiple unmanned devices, and determine the adjusted intermediate optimization trajectory as the initial optimization trajectory for the next optimization iterative operation.

[0121] As an example, the planning result determination unit 640: performs smoothing on the optimized trajectory of each unmanned device to obtain the smooth trajectory of each unmanned device; in response to the smooth trajectories of each unmanned device being collision-free trajectories, uses the smooth trajectories of each unmanned device as trajectory planning results for multiple unmanned devices, wherein the collision-free trajectory means: there are no overlapping nodes between the smooth trajectory of the current unmanned device and the smooth trajectories of other unmanned devices; and there are no overlapping nodes between the smooth trajectory of the current unmanned device and obstacles, wherein the smoothing process includes: for each optimized trajectory, deleting the node with the same forward direction as the previous node; and / or reducing the number of turns of each optimized trajectory.

[0122] Regarding the device in the above-mentioned embodiment, the specific manner in which each unit performs the operation has been described in detail in the embodiment of the method. Each unit in the trajectory planning device for multiple unmanned equipment can execute the corresponding steps in the method according to the trajectory planning method for multiple unmanned equipment in the method embodiment of the first aspect above, which will not be elaborated in detail here.

[0123] Figure 7 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. Figure 7As shown, the electronic device 10 includes a processor 101 and a memory 102 for storing processor executable instructions. Here, when the processor executable instructions are executed by the processor, the processor is prompted to execute the trajectory planning method for multiple unmanned devices as described in the above exemplary embodiment.

[0124] As an example, the electronic device 10 is not necessarily a single device, but may be any collection of devices or circuits that can execute the above instructions (or instruction sets) individually or in combination. The electronic device 10 may also be part of an integrated control system or system manager, or may be configured as a server that is interconnected with a local or remote (e.g., via wireless transmission) interface.

[0125] In the electronic device 10, the processor 101 may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller or a microprocessor. As an example and not a limitation, the processor 101 may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0126] The processor 101 may execute instructions or codes stored in the memory 102, wherein the memory 102 may also store data. Instructions and data may also be sent and received over a network via a network interface device, wherein the network interface device may employ any known transmission protocol.

[0127] The memory 102 may be integrated with the processor 101, for example, by placing RAM or flash memory within an integrated circuit microprocessor or the like. In addition, the memory 102 may include a separate device, such as an external disk drive, a storage array, or any other storage device that can be used by a database system. The memory 102 and the processor 101 may be operatively coupled, or may communicate with each other, such as through an I / O port, a network connection, etc., so that the processor 101 can read files stored in the memory 102.

[0128] In addition, the electronic device 10 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.) All components of the electronic device 10 may be connected to each other via a bus and / or a network.

[0129] In an exemplary embodiment, an unmanned device may also be provided. The unmanned device may include the electronic device according to the present disclosure, or the unmanned device may be communicatively connected to the electronic device according to the present disclosure.

[0130] For example, the unmanned device itself can execute the trajectory planning method described in the embodiment of the present disclosure through an electronic device. It can be any unmanned device among multiple unmanned devices. These unmanned devices can communicate with each other. When any unmanned device executes the above-mentioned trajectory planning method to obtain a trajectory planning result, other unmanned devices among the multiple unmanned devices can receive the trajectory planning result from the unmanned device that obtains the trajectory planning result; for another example, the unmanned device can be communicatively connected to an electronic device such as a server to receive the trajectory planning result from the electronic device and / or send real-time positioning to the electronic device.

[0131] In an exemplary embodiment, a computer-readable storage medium may also be provided. When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the trajectory planning method for multiple unmanned devices as described in the above exemplary embodiment. The computer-readable storage medium may be, for example, a memory including instructions. Optionally, the computer-readable storage medium may be: read-only memory (ROM), random access memory (RAM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer programs and any associated data, data files and data structures in a non-transitory manner and provide the computer programs and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above-mentioned computer-readable storage medium can be run in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. In addition, in one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system, so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0132] According to an exemplary embodiment of the present disclosure, a computer program product may also be provided, the computer program product comprising computer executable instructions, which, when executed by at least one processor, implements a trajectory planning method for multiple unmanned devices according to an exemplary embodiment of the present disclosure.

[0133] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0134] In addition, it should be noted that although several examples of each step are described above with reference to specific drawings, it should be understood that the embodiments of the present disclosure are not limited to the combinations given in the examples, and the steps appearing in different drawings may be combined, and the order of execution of the steps may be changed, which is not exhaustive here.

[0135] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A trajectory planning method for multiple unmanned devices, characterized in that: The trajectory planning method comprises: Acquire environmental information of a target environment and mission information of a plurality of unmanned devices, wherein the target environment includes a plurality of obstacles, and the mission information includes a starting position of each unmanned device and a target position to be reached; Determining an initial trajectory of each unmanned device based on the environmental information and the mission information; Perform global optimization on the initial trajectory of each unmanned device to obtain the optimized trajectory of each unmanned device; Based on the optimized trajectory of each unmanned device, trajectory planning results of the multiple unmanned devices are determined.

2. The trajectory planning method according to claim 1, characterized in that: The location in the target environment is represented by a node, and the environment information includes location information of multiple nodes in the target environment. Wherein, for each unmanned device, the initial trajectory is determined by: By performing at least one search iteration operation, a target node corresponding to each search iteration operation is obtained; In response to the target node corresponding to the current search iteration operation being the target position, determining the initial trajectory based on all the determined target nodes, The search iteration operation includes: Based on the environment information and the task information of the current unmanned device, determining a trajectory evaluation index of each neighbor node of the current node of the current unmanned device, wherein the trajectory evaluation index is determined based on a first cost of the unmanned device reaching the neighbor node from the starting position and a second cost from the neighbor node to the target position, and the neighbor node refers to a node adjacent to the current node in the target environment; Based on the trajectory evaluation index, determine a target neighbor node with the smallest trajectory evaluation index among neighbor nodes of the current node; Based on the target neighbor node, a target node corresponding to the current search iteration operation is determined, and the target node is used as the current node of the next search iteration operation, wherein the current node of the first search iteration operation is the starting position.

3. The trajectory planning method according to claim 2, characterized in that: The determining, based on the target neighbor node, a target node corresponding to the current search iteration operation includes: In response to the target neighbor node being unoccupied, determining the target neighbor node as the target node, In response to the target neighbor node being occupied, the target node is determined by random search based on candidate neighbor nodes among neighbor nodes of the current node except the current target neighbor node, The target neighbor node being occupied means that: the target neighbor node has been used as a target node of other unmanned equipment; or, there is an obstacle at the target neighbor node.

4. The trajectory planning method according to any one of claims 1 to 3, characterized in that: The optimization trajectory is obtained by performing at least one of the following optimization iterations: Determining an expansion coefficient corresponding to an initial optimized trajectory of each unmanned device, wherein the expansion coefficient is related to a length of the trajectory; By reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices, the initial trajectories of each unmanned device are globally optimized to obtain the intermediate optimized trajectory of each unmanned device; Based on the intermediate optimization trajectories of each unmanned device, an initial optimization trajectory for the next optimization iteration operation is determined until a preset iteration condition is met, wherein the initial optimization trajectory for the first optimization iteration operation is the initial trajectory.

5. The trajectory planning method according to claim 4, characterized in that: The step of determining an initial optimization trajectory for a next optimization iteration operation based on the intermediate optimization trajectory includes: Determine the solution migration probability based on the number of iterations of the current optimization iteration operation and a preset probability relationship, wherein the probability relationship represents the relationship between the number of times the optimization iteration operation is performed and the solution migration probability, and the solution migration probability represents the probability of obtaining the optimal solution in this optimization iteration operation; In response to the solution migration probability being less than a preset value, determining the intermediate optimization trajectory as an initial optimization trajectory for a next optimization iteration operation; In response to the solution migration probability being greater than or equal to the preset value, determining an optimization amount based on the number of iterations of the current optimization iteration operation; Based on the optimization amount, an intermediate optimization trajectory of at least one unmanned device among the multiple unmanned devices is adjusted, and the adjusted intermediate optimization trajectory is determined as an initial optimization trajectory for the next optimization iteration operation.

6. The trajectory planning method according to claim 1, characterized in that: The position in the target environment is represented by a node, and the optimized trajectory of each unmanned device includes a plurality of nodes, wherein determining the trajectory planning results of the plurality of unmanned devices based on the optimized trajectory of each unmanned device includes: Smoothing the optimized trajectory of each unmanned device to obtain a smooth trajectory of each unmanned device; In response to the smooth trajectories of the unmanned devices being collision-free trajectories, the smooth trajectories of the unmanned devices are used as trajectory planning results of the multiple unmanned devices, wherein the collision-free trajectories refer to: there are no overlapping nodes between the smooth trajectory of the current unmanned device and the smooth trajectories of other unmanned devices, and there are no overlapping nodes between the smooth trajectory of the current unmanned device and the obstacles, The smoothing process includes: for each optimized trajectory, deleting a node with the same forward direction as a previous node; and / or reducing the number of turns of each optimized trajectory.

7. A trajectory planning device for multiple unmanned devices, characterized in that: The trajectory planning device comprises: An acquisition unit is configured to acquire environmental information of a target environment and mission information of a plurality of unmanned devices, wherein the target environment includes a plurality of obstacles, and the mission information includes a starting position of each unmanned device and a target position to be reached; An initial trajectory unit, configured to determine an initial trajectory of each unmanned device based on the environment information and the task information; An optimized trajectory determination unit is configured to globally optimize the initial trajectory of each unmanned device to obtain an optimized trajectory of each unmanned device; The planning result determination unit is configured to determine the trajectory planning results of the multiple unmanned devices based on the optimized trajectory of each unmanned device.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor, Wherein, when the processor executable instructions are executed by the processor, the processor is prompted to execute the trajectory planning method for multiple unmanned equipment according to any one of claims 1 to 6.

9. An unmanned device, characterized in that: The unmanned device includes the electronic device according to claim 8, or the unmanned device is communicatively connected to the electronic device according to claim 8.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the trajectory planning method for multiple unmanned devices according to any one of claims 1 to 6.

11. A computer program product comprising computer executable instructions, characterized in that: When the computer executable instructions are executed by at least one processor, the trajectory planning method for multiple unmanned devices according to any one of claims 1 to 6 is implemented.

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