Trajectory Planning Method and Device for Multiple Unmanned Devices, and Unmanned Device

Abstract

The present disclosure relates to the technical field of multi-unmanned device trajectory planning, and provides a trajectory planning method and apparatus for multi-unmanned devices, and an unmanned device. The method includes: obtaining environmental information of a target environment and task information of multiple unmanned devices; determining an initial trajectory of each unmanned device based on the environmental information and the task information; globally optimizing the initial trajectories of the unmanned devices to obtain an optimized trajectory of each unmanned device; and determining a trajectory planning result of the multiple unmanned devices based on the optimized trajectories of the unmanned devices. The present disclosure can solve the problems of poor global optimization and slow convergence speed in the trajectory planning of multi-unmanned devices, can improve the overall global trajectory planning on the basis of considering the trajectories of each unmanned device, and can converge to the global optimal solution faster, providing more reliable technical support for the application of unmanned devices in complex environments.

Description

Technical Field

[0001] The present disclosure relates to the technical field of multi-unmanned device trajectory planning, and particularly to a trajectory planning method and apparatus for multi-unmanned devices, and an unmanned device. Background Art

[0002] With the rapid development of unmanned device technology and the continuous expansion of its application scenarios, unmanned devices such as unmanned aerial vehicles are increasingly widely used in fields such as logistics transportation, disaster relief, agricultural monitoring, and urban inspection. However, in actual applications, unmanned devices usually need to perform tasks in complex environments, which may contain a large number of dense obstacles, such as urban building complexes, forests, mountains, etc. In this case, how to plan safe, efficient, and globally optimal flight trajectories for multi-unmanned device systems has become one of the key issues of concern in the current academic and industrial circles.

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

[0004] The present disclosure provides a trajectory planning method and apparatus for multi-unmanned devices, and an unmanned device, so as to at least solve the problems of poor global optimization and slow convergence speed in the trajectory planning of multi-unmanned devices. The technical solutions of the present disclosure are as follows:

[0005] According to a first aspect of the present disclosure, there is provided a trajectory planning method for multi-unmanned devices, the trajectory planning method including: obtaining environmental information of a target environment and task information of a plurality of unmanned devices, wherein the target environment includes a plurality of obstacles, and the task information includes a starting position and a target position to be reached of each unmanned device; determining an initial trajectory of each unmanned device based on the environmental information and the task information; globally optimizing the initial trajectories of the unmanned devices 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 trajectories of the unmanned devices.

[0006] Optionally, a position in the target environment is represented by a node, and the environment information includes position information of multiple nodes in the target environment. For each unmanned device, the initial trajectory is determined as follows: 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, based on all the determined target nodes, the initial trajectory is determined. The search iteration operation includes: Based on the environment information and the task information of the current unmanned device, determine the trajectory evaluation index of each neighbor node of the current unmanned device at the current node, where the trajectory evaluation index is determined based on the first cost for the unmanned device to reach the neighbor node from the starting position and the second cost from the neighbor node to the target position. The neighbor node refers to a node adjacent to the current node in the target environment; Based on the trajectory evaluation index, determine the target neighbor node with the minimum trajectory evaluation index among the neighbor nodes of the current node; Based on the target neighbor node, determine the target node corresponding to the current search iteration operation, and use the target node as the current node for the next search iteration operation, where the current node of the first search iteration operation is the starting position.

[0007] Optionally, the determining the target node corresponding to the current search iteration operation based on the target neighbor node includes: in response to the target neighbor node not being occupied, determining the target neighbor node as the target node; in response to the target neighbor node being occupied, based on candidate neighbor nodes other than the current target neighbor node among the neighbor nodes of the current node, determine the target node through random search, where the target neighbor node being occupied means: the target neighbor node has been used as the target node of another unmanned device; or, there is an obstacle at the target neighbor node.

[0008] Optionally, the optimized trajectory is obtained by performing at least one of the following optimization iteration operations: Determine the expansion coefficient corresponding to the initial optimized trajectory of each unmanned device, where the expansion coefficient is related to the length of the trajectory; By reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices, globally optimize the initial trajectories of each unmanned device to obtain the intermediate optimized trajectory of each unmanned device; Based on the intermediate optimized trajectories of each unmanned device, determine the initial optimized trajectory for the next optimization iteration operation until a preset iteration condition is met, where the initial optimized trajectory of the first optimization iteration operation is the initial trajectory.

[0009] Optionally, determining an initial optimization trajectory for the next optimization iteration operation based on the intermediate optimization trajectory includes: determining a solution migration probability based on the number of iterations of the current optimization iteration operation and a preset probability relationship, where the probability relationship represents the relationship between the number of times of performing the optimization iteration operation and the solution migration probability, and the solution migration probability represents the probability of obtaining an optimal solution in the current optimization iteration operation; in response to the solution migration probability being less than a preset value, determining the intermediate optimization trajectory as the initial optimization trajectory for the 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; adjusting the intermediate optimization trajectory of at least one unmanned device among the multiple unmanned devices based on the optimization amount, and determining the adjusted intermediate optimization trajectory as the initial optimization trajectory for the next optimization iteration operation.

[0010] Optionally, positions in the target environment are represented by nodes, and the optimization trajectory of each unmanned device includes multiple nodes. Wherein, determining the trajectory planning result of the multiple unmanned devices based on the optimization trajectories of the unmanned devices includes: smoothing the optimization trajectories of the unmanned devices to obtain the smoothed trajectories of the unmanned devices; in response to the smoothed trajectories of all the unmanned devices being non-collision trajectories, taking the smoothed trajectories of the unmanned devices as the trajectory planning result of the multiple unmanned devices, where the non-collision trajectory means that there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the smoothed trajectories of other unmanned devices; and there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the obstacles, where the smoothing process includes: for each optimization trajectory, deleting the nodes with the same forward direction as the previous node; and / or reducing the number of turning points of each optimization trajectory.

[0011] According to a second aspect of the present disclosure, there is provided a trajectory planning device for multiple unmanned devices, the trajectory planning device including: an acquisition unit configured to acquire environmental information of a target environment and task information of multiple unmanned devices, where the target environment includes multiple obstacles, and the task information includes the starting position and the target position to be reached of each unmanned device; 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 trajectories of the unmanned devices to obtain an optimized trajectory of each unmanned device; and a planning result determination unit configured to determine a trajectory planning result of the multiple unmanned devices based on the optimized trajectories of the unmanned devices.

[0012] According to a third aspect of the present disclosure, there is provided an electronic device, which includes: a processor; and a memory for storing instructions executable by the processor, wherein when the instructions executable by the processor are run by the processor, the processor is caused to execute the method for trajectory planning of multiple unmanned devices according to the present disclosure.

[0013] According to a fourth aspect of the present disclosure, there is provided an unmanned device, which includes the electronic device according to the present disclosure, or the unmanned device is communicatively connected to the electronic device according to the present disclosure.

[0014] According to a fifth aspect of the present disclosure, there is provided a computer-readable storage medium, 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 method for trajectory planning of multiple unmanned devices according to the present disclosure.

[0015] According to a sixth aspect of the present disclosure, there is provided a computer program product, including computer-executable instructions, which when executed by at least one processor, implement the method for trajectory planning of multiple unmanned devices according to the present disclosure.

[0016] The technical solution provided by the present disclosure at least brings the following beneficial effects:

[0017] According to the present disclosure, based on environmental information and task information, an initial trajectory of multiple unmanned devices can be determined, and the initial trajectory can be globally optimized, so as to obtain an optimized trajectory for each unmanned device, and based on the optimized trajectory, a final trajectory planning result can be obtained. In this way, on the basis of considering the trajectories of each unmanned device, the global trajectory planning can be improved as a whole, so that it can converge to the global optimal solution faster, providing more reliable technical support for the application of unmanned devices in complex environments.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure and do not constitute an improper limitation to the present disclosure.

[0020] Figure 1 is a schematic flowchart of a method for trajectory planning of multiple unmanned devices according to an exemplary embodiment of the present disclosure.

[0021] Figure 2 is a schematic diagram of a scenario of global trajectory planning of multiple unmanned aerial vehicles in a large-range dense obstacle environment according to an exemplary embodiment of the present disclosure.

[0022] Figure 3 It is a schematic diagram of the simulation of the multi-UAV global trajectory planning method in a large-scale dense obstacle environment according to an exemplary embodiment of the present disclosure.

[0023] Figure 4 It is a schematic flowchart of the search iteration operation in the trajectory planning method of multi-unmanned devices according to an exemplary embodiment of the present disclosure.

[0024] Figure 5 It is a schematic flowchart of the optimization iteration operation in the trajectory planning method of multi-unmanned devices according to an exemplary embodiment of the present disclosure.

[0025] Figure 6 It is a schematic block diagram of the trajectory planning device of multi-unmanned devices according to an exemplary embodiment of the present disclosure.

[0026] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. Detailed implementation manners

[0027] In order to enable those of ordinary skill 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 with reference to the accompanying drawings.

[0028] 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 do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances 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. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0029] It should be noted here that "at least one of several items" in the present disclosure all represents the inclusion of three parallel situations: "any one of the several items", "any combination of several items", and "all of the several items". For example, "including at least one of A and B" includes the following three parallel situations: (1) including A; (2) including B; (3) including A and B. Another example is "performing at least one of step one and step two", which means the following three parallel situations: (1) performing step one; (2) performing step two; (3) performing step one and step two.

[0030] As mentioned above, in the related art, there are problems such as local optimal traps and slow convergence speed in the trajectory planning of multi-unmanned devices.

[0031] Taking drones as an example, traditional trajectory planning methods mainly focus on the path planning of single drones, such as those based on the Dijkstra algorithm, A algorithm or RRT (Rapidly-exploring Random Tree) algorithm, etc. These methods perform well in obstacle avoidance and path optimization of single drones. However, in the scenario of multi-drone cooperation, a single path planning method often fails to meet the requirements.

[0032] Specifically, the trajectory planning of multi-drone systems needs to consider the following key issues simultaneously: (1) Global optimization requirements: When multi-drones execute tasks, they not only need to plan their respective flight paths but also optimize the overall task efficiency from a global perspective to avoid path conflicts and resource waste. (2) Computational efficiency: In a large-scale dense obstacle environment, the search space is huge, and traditional algorithms may face the problem of excessively high computational complexity and thus fail to meet the 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, and the planning method needs to have the ability to quickly adapt to environmental changes.

[0033] In response to this, trajectory planning methods based on intelligent optimization algorithms have gradually attracted attention, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (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 speeds in a large-scale dense obstacle environment. In addition, multi-drone trajectory planning also needs to combine specific task requirements to design a dedicated planning strategy to balance various performance indicators such as path length, energy consumption, and task completion time, and these factors are not considered in these methods.

[0034] In view of the above, the exemplary embodiments of the present disclosure propose a trajectory planning method for multi-unmanned devices, a trajectory planning device for multi-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.

[0035] In the first aspect of the exemplary embodiments of the present disclosure, a trajectory planning method for multi-unmanned devices is provided.

[0036] The trajectory planning method for multi-unmanned devices according to the exemplary embodiments of the present disclosure can be applied to the scenario where a user interacts with software. For example, software can be loaded on a 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 multi-unmanned devices according to the exemplary embodiments of the present disclosure.

[0037] Specifically, the user terminal can obtain the environmental information of the target environment and the task information of multiple unmanned devices. Among them, the target environment includes multiple obstacles, and the task information includes the starting position and the target position to be reached for each unmanned device. 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 further globally optimize the initial trajectories of the unmanned devices to obtain the optimized trajectory of each unmanned device. The user terminal can also determine the trajectory planning result of multiple unmanned devices based on the optimized trajectories of the unmanned devices.

[0038] The above user terminal can be, for example, a tablet computer, a notebook 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 embodiments of the present disclosure can also be applied to other application scenarios. For example, it can also be that the user requests the server to perform trajectory planning for multiple unmanned devices through the network on the user terminal (such as 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 embodiments of the present disclosure. Here, the server can be an independent server, a server cluster, a cloud computing platform or a virtualization center.

[0039] The trajectory planning method for multiple unmanned devices according to the exemplary embodiments of the present disclosure can improve the overall global trajectory planning on the basis of considering the trajectories of each unmanned device, providing more reliable technical support for the application of unmanned devices in complex environments.

[0040] Next, reference will be made to Figures 1 to 5 Describe an example of the trajectory planning method for multiple unmanned devices according to the embodiments of the present disclosure.

[0041] As Figure 1 shown, the trajectory planning method for multiple unmanned devices may include the following steps:

[0042] In step S110, the environmental information of the target environment and the task information of multiple unmanned devices can be obtained.

[0043] 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 more than 3 km × 3 km; the dense obstacle group may be, for example, an obstacle group with a distribution spacing between obstacles in the range of 50 m to 100 m. The task information may include the starting position and the target position to be reached for each unmanned device, and the task information of different unmanned devices may be different. For example, the starting positions and target positions of different unmanned devices may be different.

[0044] As an example, the unmanned device can be, for example, a drone, an unmanned vehicle, an unmanned ship, etc. Multiple unmanned devices can form an unmanned device system, and these unmanned devices can, for example, execute their respective tasks in parallel. Multiple unmanned devices can complete complex tasks through autonomous and intelligent collaborative strategies, and can be applied to multiple fields such as industry, agriculture, and transportation.

[0045] In this step, the environmental information can include, for example, a map of the target environment (such as a three-dimensional map) and the position information of obstacles. For example, a three-dimensional map file of the target environment can be read, and the longitude, latitude, and altitude information data of the obstacles in the target environment can be obtained. In this step, the starting positions, target positions, and map resolution of multiple unmanned devices can also be determined in the target environment.

[0046] Figure 2 Fig. shows a scenario example of global trajectory planning for multiple drones in a target environment. Figure 3 Fig. shows a simulation example of global trajectory planning for multiple drones. Here, as an example, x and y respectively represent the length and width, and the units are both km; N represents the height, and the unit is m. Here, taking the area size of the map area of the target environment as 2 km 2 km 200 m (length width height) as an example, as Figure 2 and Figure 3 shown, the cube represents an obstacle, and the triangle represents the starting position and target position of the unmanned device. In this example, the number of unmanned devices is 20, and the initial positions and target positions of each unmanned device are randomly generated. The map resolution includes: the horizontal resolution is 50 m, and the vertical resolution is 10 m.

[0047] 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 passed by the unmanned device 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 rasters can be 40 40 × 20 = 32000. Each raster can be used as a node.

[0048] Although the above describes 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 can also be characterized in other ways, as long as the characteristics of each position in the target environment (such as whether there is an obstacle) can be represented.

[0049] In step S120, based on the environmental information and the task information, the initial trajectory of each unmanned device can be determined.

[0050] In this step, based on the environmental information and task information, preliminary trajectory planning can be performed for multiple unmanned devices. As an example, the initial trajectories of each of the multiple unmanned devices can be searched in sequence. 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 in other words, there is no overlapping trajectory.

[0051] As an example, positions in the target environment can be represented by nodes, and the environmental information can include the position information of multiple nodes in the target environment. In this step S120, for each unmanned device, the initial trajectory can be determined in the following manner: by performing at least one search iteration operation, obtaining a target node corresponding to each search iteration operation; 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.

[0052] Specifically, for each unmanned device, starting from the starting position, iteratively search for the next target node until reaching the target position or a deadlock occurs at the target node, then stop the search. In the case where the search reaches the target position, all the target nodes searched in the current iteration can be output to form an initial trajectory based on these target nodes. For example, the connection of these target nodes can be used as the initial trajectory.

[0053] For example, as Figure 4 shown, the search iteration operation can include the following steps:

[0054] In step S410, based on the environmental information and the task information of the current unmanned device, the trajectory evaluation index of each neighbor node of the current unmanned device at the current node can be determined.

[0055] Here, the current node of the first search iteration operation can be the starting position. The trajectory evaluation index can be determined based on the first cost for the unmanned device to reach the neighbor node from the starting position and the second cost from the neighbor node to the target position. A neighbor node can refer to a node adjacent to the current node in the target environment. In a three-dimensional map, neighbor nodes can be nodes in the front, back, up, down, left, right, front - left - up, front - left - down, front - right - up, front - right - down, back - left - up, back - left - down, back - right - up, and back - right - down directions of the current node.

[0056] The first cost can represent the actual cost for the unmanned device to reach the neighbor node from the starting position, and the second cost can represent the estimated cost for the unmanned device to reach the target position from the neighbor node. Here, the cost can include the distance between two positions, the energy consumption of the unmanned device from one position to another position, etc. For example, the Euclidean distance can be used to calculate the cost.

[0057] As an example, the trajectory evaluation index can characterize the sum of the first cost for the unmanned device to reach a neighbor node from the starting position and the second cost from the neighbor node to the target position. For example, the trajectory evaluation index can be expressed by the following formula (1):

[0058] (1)

[0059] where represents the trajectory evaluation function, which can be used as the above-mentioned trajectory evaluation index, represents the unmanned device the actual cost from the node where its starting position is located to the current node and represents the unmanned device from node to the estimated cost of the node where the target position is located.

[0060] As an example, in this step, parameters such as the number of map grid cells, the starting nodes and target nodes of the multiple unmanned aerial vehicles can be initialized based on the A search method, and the neighbors of each node passed by the unmanned aerial vehicle are iteratively checked to update their values. For example, the trajectory evaluation indexes of all neighbor nodes of the current node can be calculated.

[0061] As another example, the trajectory evaluation index can be determined based on the first cost, the second cost, a preset dynamic weight coefficient for the second cost, and a preset noise. Specifically, the dynamic weight coefficient can be used to weight the second cost, and it 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. For example, it can be Gaussian noise. By adding this noise, a balance can be found between the determinism and randomness of the algorithm, thereby improving the comprehensive performance of the algorithm in complex, dynamic, or imperfect information environments.

[0062] For example, the trajectory evaluation index can be expressed by the following formula (2):

[0063] (2)

[0064] where represents the trajectory evaluation function, which can be used as the above-mentioned trajectory evaluation index, represents the unmanned device the actual cost from the node where its starting position is located to the current node and represents the unmanned device from the current node to the estimated cost of the node where the target position is located,​ is Gaussian noise with a mean of 0 and a variance of , and can be set according to actual needs. For example, .

[0065] In the above formula (2), represents the dynamic weight coefficient, which decays with the number of iterations . As an example, the dynamic weight coefficient can be expressed by the following formula (3):

[0066] (3)

[0067] where represents the decay rate, which can be set according to actual needs, can be in the range of (0, 1). For example, = 0.1; represents the number of iterations; and represent the upper bound value and the lower bound value of the weight respectively, which can be set according to actual needs. For example, .

[0068] In step S420, based on the trajectory evaluation index, the target neighbor node with the minimum trajectory evaluation index among the neighbor nodes of the current node can be determined.

[0069] In this step, the neighbor node with the minimum trajectory evaluation index can be determined from the trajectory evaluation indexes of all neighbor nodes of the current node as the target neighbor node.

[0070] In step S430, based on the target neighbor node, the target node corresponding to the current search iteration operation can be determined, and the target node is used as the current node for the next search iteration operation.

[0071] Here, the target neighbor node with the minimum trajectory evaluation index can be used as a target node, and the next search is performed based on the target node found in this search, so as to realize the iterative search from the starting position to the target position. Since the neighbor node with the minimum trajectory evaluation index is used as the target node during the search process, the performance of the finally obtained initial trajectory can be relatively good.

[0072] In this example, the situation of deadlock of nodes during the search process can also be considered. Here, deadlock means that when the unmanned device plans a trajectory, due to the planned trajectory occupying the neighbor space that can be explored by the current node, it falls into an infinite loop at a certain node and cannot move forward.

[0073] Specifically, step S430 includes: determining the target neighbor node as the target node in response to the target neighbor node being unoccupied; and determining the target node by random search based on candidate neighbor nodes other than the current target neighbor node among the neighbor nodes of the current node in response to the target neighbor node being occupied.

[0074] Here, the target neighbor node being occupied may mean that: the target neighbor node has been used as the target node of other unmanned devices; or, there are obstacles at the target neighbor node.

[0075] 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 has been occupied. In the case where the target neighbor node has been occupied, starting from the current stop node, the remaining nodes can be traversed, and the unvisited and unoccupied neighbor nodes can be randomly explored. For example, a neighbor node can be randomly selected as the target node. If the target node is successfully found or the exploration cannot continue, the current found trajectory can be returned through node backtracking. As an example, a pseudo-random search method can be used to implement this process.

[0076] In this way, in each search iteration operation, on the one hand, the preferred nodes among the neighbor nodes can be efficiently searched based on the trajectory evaluation index, and on the other hand, the diversity of the nodes can be considered through random search in the case where the preferred nodes are occupied, making the planning result more balanced. Here, in the case of deadlock when searching for nodes based on the trajectory evaluation index, through this random search rather than a fixed-rule search (such as selecting the neighbor node with the second-best trajectory evaluation index), the exploration ability of the algorithm in the solution space can be improved, and the learning ability of the algorithm can be avoided from being restricted by fixed rules. Random search can provide an underlying guarantee for the evaluation effect of the trajectory evaluation index. For example, in the case of deadlock, there is a possibility that other neighbor nodes are all searched, avoiding over-distinguishing these neighbor nodes by using a fixed-rule search method.

[0077] Especially, in the example of combining the A search method and the pseudo-random search method, the efficiency of the A algorithm and the diversity of the pseudo-random search method can be fully utilized. Specifically, by combining the A algorithm and the pseudo-random search method to solve the multi-UAV planning, the A The algorithm itself can only handle computational problems of a single object in a two-dimensional plane. It cannot solve problems involving multiple objects in three-dimensional space, nor can it be directly applied to the solution process in three-dimensional space or for multiple objects. Moreover, it cannot be applied to the trajectory planning problem of multiple unmanned devices to be solved in the embodiments of the present disclosure. For this reason, in the embodiments of the present disclosure, by combining the algorithm with a pseudo-random search method, it is possible to retain the advantages of the algorithm in search efficiency while combining the advantages of the pseudo-random search method in search diversity, thereby achieving efficient path planning for multiple unmanned devices in three-dimensional space.

[0078] The above iterative operation can be executed 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 (such as reaching the preset number of iterations or iteration time), the iteration is stopped, and the initial trajectory of the unmanned device in the current iteration can be constructed by backtracking the nodes, and the nodes it passes through (such as the grids on the map) are marked as occupied, so that the initial trajectory of the current unmanned device is taken into account during the subsequent iteration of the unmanned device.

[0079] After planning the initial trajectory of the current unmanned device, the initial trajectory of the next unmanned device can be planned by executing the above search and iteration operation again until the initial trajectories of all unmanned devices are output.

[0080] Returning to reference Figure 1 , in step S130, the initial trajectories of each unmanned device can be globally optimized to obtain the optimized trajectory of each unmanned device.

[0081] In this step, the trajectories of multiple unmanned devices can be comprehensively considered and globally optimized.

[0082] As an example, as Figure 5 shown, the optimized trajectory can be obtained by performing at least one of the following optimization iteration operations:

[0083] In step S510, the expansion coefficient corresponding to the initial optimized trajectory of each unmanned device can be determined.

[0084] Here, the initial optimized trajectory of the first optimization iteration operation is the initial trajectory of the unmanned device. The expansion coefficient can 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 can be determined according to a preset conversion relationship. For example, in the case of a known trajectory, the length of the trajectory can be determined, and this distance can be substituted into the preset conversion relationship to determine the corresponding expansion coefficient.

[0085] As an example, the expansion coefficient can be determined based on environmental information. For example, it can be determined based on the path cost and threat cost (or safety cost). For example, the expansion coefficient can be determined by the following formula (4):

[0086] Expansion coefficient = (4)

[0087] Wherein, and are adjustment coefficients, which can be set according to actual needs. For example, = 0.7, ; 、 are respectively the path cost and the path cost threshold at the current moment, 、 respectively represent the safety cost and the safety cost threshold at the current moment. Here, the path cost threshold and the safety cost threshold can be set according to actual needs.

[0088] The path cost can represent the actual cost for the unmanned device to reach the target position from the starting position according to the trajectory planned at the current moment. The path cost, for example, can be positively correlated with the length of the trajectory. In addition, the concept of cost has been described above, so it will not be elaborated here.

[0089] The safety cost can represent the threat posed by the obstacles within the preset safety cost calculation range centered on each node of the trajectory planned at the current moment to the progress of the current unmanned device. Here, the threat posed by the obstacles to the progress of the current unmanned device can be determined based on the distance between the obstacles and the unmanned device. 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 device falls into any distance interval, the threat posed by the obstacle to the progress of the unmanned device is the safety cost value corresponding to that distance interval. Among them, the safety cost value is negatively correlated with the distance between the obstacle and the unmanned device. The closer the obstacle is to the unmanned device, the greater the impact of hindering or endangering the progress of the unmanned device. Therefore, a higher safety cost value can be assigned. The sum of the safety cost values corresponding to all the obstacles within the preset safety cost calculation range of each node of the trajectory planned at the current moment can be used as the above-mentioned safety cost . As an example, the above-mentioned preset safety cost calculation range can be, for example, a circle centered on each node of the trajectory planned at the current moment with a preset radius. The preset radius can be set according to actual needs, for example, it can be 5 meters. In addition, when there are no obstacles within the preset safety cost calculation range, the safety cost can be 0.

[0090] In step S520, the initial trajectories of each unmanned device can be globally optimized by reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices, and the intermediate optimized trajectory of each unmanned device can be obtained.

[0091] 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.

[0092] For example, according to a preset first optimization amount, the initial trajectories of at least some unmanned devices (such as some unmanned devices with relatively large initial expansion coefficients) can be adjusted so that the expansion coefficient of the obtained intermediate optimized trajectory after adjustment is smaller than that of the original initial trajectory. Here, the intermediate optimized trajectory can be a trajectory obtained by adjusting the initial trajectory or the initial trajectory. Specifically, for an unmanned device whose initial trajectory has been adjusted, the intermediate optimized trajectory is the adjusted trajectory; for an unmanned device whose initial trajectory has not been adjusted, the intermediate optimized trajectory is the initial trajectory.

[0093] 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 can be added to or subtracted from the position coordinates of the node.

[0094] In step S530, based on the intermediate optimized trajectories of each unmanned device, the initial optimized trajectory for the next optimization iteration operation can be determined until a preset iteration condition is met.

[0095] In one example, the intermediate optimized trajectory can be directly used as the initial optimized trajectory for the next optimization iteration operation.

[0096] In another example, the intermediate optimized trajectories of each unmanned device can be further optimized, and the optimized trajectories can be used as the initial optimized trajectories for the next optimization iteration operation.

[0097] As an example, this 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; determining the initial optimized trajectory for the next optimization iteration operation according to the solution migration probability.

[0098] Here, the probability relationship can represent the relationship between the number of times of performing the optimization iteration operation and the solution migration probability, and the solution migration probability can represent the probability of obtaining the optimal solution in this optimization iteration operation.

[0099] Specifically, in response to the solution migration probability being less than a preset value, the intermediate optimized trajectory is determined as the initial optimized trajectory for the next optimization iteration operation.

[0100] In response to the solution migration probability being greater than or equal to a preset value, determine an optimization amount based on the number of iterations of the current optimization iteration operation; based on the optimization amount, adjust the intermediate optimization trajectory of at least one of the multiple unmanned devices, and determine the adjusted intermediate optimization trajectory as the initial optimization trajectory for the next optimization iteration operation.

[0101] Here, the probability relationship can be preset, which can characterize the correlation between the number of times of performing the optimization iteration operation and the solution migration probability. For example, the number of times of performing the optimization iteration operation can be positively correlated with the solution migration probability. In addition, the preset value of the solution migration probability can be set according to actual needs.

[0102] In the case where the solution migration probability is relatively high, for example, when it is greater than or equal to the preset value, it means that there is a greater possibility of further optimizing the trajectory to obtain the optimal solution in this optimization iteration operation. 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 this 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 can 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 or subtracted from the position coordinates of the node.

[0103] As an example, the optimization amount (which can also be referred to as the second optimization amount) described here can be determined based on the number of iterations of the current optimization iteration operation. For example, it can be positively correlated with the number of iterations of the current optimization iteration operation.

[0104] By the above method, in each optimization iteration operation, when the initial optimization trajectory is adjusted based on the first optimization amount to obtain the intermediate optimization trajectory, further optimize the intermediate optimization trajectory based on the second optimization amount according to the probability of obtaining the optimal solution in this iteration operation. In this way, it is beneficial for the solution to converge faster.

[0105] As an example, the multi-universe optimization method can be used to implement a single iteration operation.

[0106] 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. Using the roulette wheel mechanism, the current universe is transmitted from the white hole to the black hole, thereby realizing the optimization of the initial trajectory. For example, based on a preset first optimization amount, at least one initial trajectory can be optimized to obtain the intermediate optimization trajectory of this iteration operation.

[0107] For this intermediate optimization trajectory, the wormhole existence probability WEP and the travel distance rate TDR in the multi-universe optimization method can be calculated, which are represented by the following formulas (5) and (6) respectively:

[0108] (5)

[0109] (6)

[0110] Among them, and represent the maximum value and the minimum value respectively, represents the current iteration number, represents the maximum iteration number (this maximum iteration number can be preset for example), represents the exploration accuracy during the iteration process. Here, and and can all be preset. For example, , , .

[0111] Here, the above formula (5) can represent a probability relationship. The probability WEP can be used as the above-mentioned solution transfer probability, and the travel distance rate TDR can be used as the above-mentioned 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 there is a wormhole, and the current universe is equal to the global optimal universe of the current iteration operation, that is, the current optimization trajectory is the optimal solution of the current iteration operation.

[0112] In the case of adopting the multi-universe algorithm, the strong global search ability of the multi-universe algorithm can be utilized to realize the rapid generation of collision-free trajectories of multiple unmanned devices in a complex environment, and the efficient cooperation and global optimization of multiple unmanned devices in a complex environment are realized.

[0113] In this step S130, in response to the number of optimization iteration operations being greater than or equal to the preset maximum iteration number, the iteration can be ended and the optimization trajectories of each unmanned device can be output; otherwise, the next optimization iteration operation can be continued. For example, taking the above formulas (2) and (3) as an example, in response to the iteration number being greater than or equal to the maximum iteration number , the iteration can be ended.

[0114] In step S140, based on the optimization trajectories of each unmanned device, the trajectory planning results of multiple unmanned devices can be determined.

[0115] In an example, the optimization trajectories of each unmanned device can be used as the trajectory planning results of multiple unmanned devices.

[0116] 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: smooth the optimized trajectory of each unmanned device to obtain the smooth trajectory of each unmanned device; and determine the trajectory planning result based on the smooth trajectory.

[0117] The smoothing process may include, for example, but is not limited to: for each optimized trajectory, deleting the nodes having the same forward direction as the previous node; and / or reducing the number of turns of each optimized trajectory.

[0118] 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 the planning and optimization are carried out in units of nodes, the trajectory of the unmanned device may be limited to the node positions, resulting in an uneven curve or broken line in the optimized trajectory of each unmanned device. In the embodiments 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.

[0119] As an example, the iterative approximation idea can be adopted to smooth the optimized trajectory of each unmanned device. For example, the same-direction waypoints (such as the nodes described above) on the optimized trajectory can be removed; or the waypoints on the optimized trajectory can be removed or retained with the goal of reducing the number of turns of the trajectory.

[0120] For example, for the optimized trajectory of any unmanned device, the trajectory between every 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 intermediate node among the multiple adjacent nodes. For example, taking the preset number as 3, the multi-unmanned device trajectory can be smoothed based on the following formula (7):

[0121] (7)

[0122] Where , represents the positions of three adjacent points of the current optimized trajectory (such as the coordinates in the target environment map), represents the smoothed trajectory curve between the three points, , represents the dynamic weight coefficient for the smoothing process, which is used to adjust the influence intensity of the intermediate point on the curve shape, It can be adjusted according to the distances from the optimized trajectory to three adjacent points. The smaller the distances of the three points along the optimized trajectory, the larger the value.

[0123] Although the preset number is taken as 3 for illustration here, the embodiments of the present disclosure are not limited thereto, and the preset number can also be greater than 3. When the preset number is greater than or equal to 3, the general expression for the smoothing process can be, for example:

[0124]

[0125] where, represents the position (such as the coordinates in the target environment map) of the th intermediate node except for the two nodes at the ends, N and

[0126] is the preset number.

[0127] In addition, according to the embodiments of the present disclosure, in this step, collision detection can also be performed on the optimized trajectories of multiple unmanned devices to ensure that there is no collision between the trajectories of the unmanned devices in the finally obtained planning result.

[0128] Specifically, in response to the fact that the smoothed trajectories of all unmanned devices are collision-free trajectories, the smoothed trajectories of all unmanned devices can be used as the trajectory planning results of the multiple unmanned devices. Here, a collision-free trajectory means that there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the smoothed trajectories of other unmanned devices; and there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the obstacles.

[0128] In response to the fact that there is a collision in the smoothed trajectory of any unmanned device, new waypoints can be randomly searched to avoid collisions, such as avoiding collisions with the trajectories of other unmanned devices or obstacles, until the smoothed trajectories of all unmanned devices are collision-free trajectories, and the current trajectories are output as the trajectory planning results, so as to achieve the global trajectory planning of multiple unmanned devices.

[0129] The method according to the embodiments of the present disclosure combines exploration and optimization methods, can quickly plan trajectories, and will greatly improve the multi-UAV cooperation ability. Compared with the traditional method, this method can significantly improve the efficiency and quality of the trajectory planning of multiple unmanned devices, and is particularly suitable for the global trajectory planning of multiple unmanned devices in a large-range dense obstacle environment, providing more reliable technical support for the application of unmanned devices in complex environments.

[0130] In addition, the method according to the embodiments of the present disclosure can be based on A The search and pseudo-random search methods generate the initial trajectories of multiple unmanned devices. During this process, neighbor nodes are screened, duplicates are removed, and random selection is performed to ensure the effectiveness and diversity of the trajectories. This method can also use the multiverse algorithm to globally optimize the initial trajectories of multiple unmanned devices. Through the exchange of white holes and black holes, wormhole jumps, etc., optimized trajectories of multiple unmanned devices are generated. Then, iterative approximation is used to perform operations such as removing, retaining, collision detection, and avoidance adjustment on the waypoints of the optimized trajectories of multiple unmanned devices to achieve the generation of collision-free trajectories of multiple unmanned aerial vehicles in a complex environment.

[0131] In a second aspect of the exemplary embodiments of the present disclosure, a trajectory planning device for multiple unmanned devices is provided, as Figure 6 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.

[0132] The acquisition unit 610 is configured to acquire the environmental information of the target environment and the task information of multiple unmanned devices. Among them, the target environment includes multiple obstacles, and the task information includes the starting position and the target position to be reached of each unmanned device.

[0133] The initial trajectory unit 620 is configured to determine the initial trajectory of each unmanned device based on the environmental information and the task information.

[0134] The optimized trajectory determination unit 630 is configured to globally optimize the initial trajectories of each unmanned device to obtain the optimized trajectory of each unmanned device.

[0135] The planning result determination unit 640 is configured to determine the trajectory planning result of multiple unmanned devices based on the optimized trajectories of each unmanned device.

[0136] As an example, positions in the target environment are represented by nodes, and the environmental information includes the position information of multiple nodes in the target environment. Among them, 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, obtaining target nodes corresponding to each search iteration operation; 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, where the search iteration operation includes: based on the environmental information and the task information of the current unmanned device, determining the trajectory evaluation index of each neighbor node of the current unmanned device at the current node, where the trajectory evaluation index is determined by characterizing the first cost for 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, determining the target neighbor node with the minimum trajectory evaluation index among the neighbor nodes of the current node; based on the target neighbor node, determining the target node corresponding to the current search iteration operation, and using the target node as the current node for the next search iteration operation, where the current node of the first search iteration operation is the starting position.

[0137] As an example, the initial trajectory unit 620 is configured to: in response to the target neighbor node not being occupied, determine the target neighbor node as the target node; in response to the target neighbor node being occupied, based on the candidate neighbor nodes other than the current target neighbor node among the neighbor nodes of the current node, determine the target node through random search, where the target neighbor node being occupied means that the target neighbor node has been used as the target node of other unmanned devices; or there is an obstacle at the target neighbor node.

[0138] As an example, the optimized trajectory determination unit 630 is configured to obtain the optimized trajectory by performing at least one of the following optimization iteration operations: determining the expansion coefficient corresponding to the initial optimized trajectory of each unmanned device, where the expansion coefficient is related to the length of the trajectory; globally optimizing the initial trajectories of each unmanned device by reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices, to obtain the intermediate optimized trajectory of each unmanned device; based on the intermediate optimized trajectories of each unmanned device, determining the initial optimized trajectory for the next optimization iteration operation until the preset iteration condition is satisfied, where the initial optimized trajectory of the first optimization iteration operation is the initial trajectory.

[0139] As an example, the optimization trajectory determination unit 630 is configured to: determine a solution migration probability based on the number of iterations of the current optimization iteration operation and a preset probability relationship, where the probability relationship represents the relationship between the number of times of performing the optimization iteration operation and the solution migration probability, and the solution migration probability represents the probability of obtaining an optimal solution in the current optimization iteration 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 iteration operation; in response to the solution migration probability being greater than or equal to the preset value, determine an optimization amount based on the number of iterations of the current optimization iteration operation; based on the optimization amount, adjust the intermediate optimization trajectory of at least one of the multiple unmanned devices, and determine the adjusted intermediate optimization trajectory as the initial optimization trajectory for the next optimization iteration operation.

[0140] As an example, the planning result determination unit 640: performs smoothing processing on the optimization trajectories of each unmanned device to obtain the smoothed trajectories of each unmanned device; in response to the smoothed trajectories of each unmanned device being non-collision trajectories, use the smoothed trajectories of each unmanned device as the trajectory planning result of the multiple unmanned devices, where a non-collision trajectory means that there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the smoothed trajectories of other unmanned devices; and there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the obstacle, where the smoothing processing includes: for each optimization trajectory, deleting the nodes having the same forward direction as the previous node; and / or reducing the number of turning points of each optimization trajectory.

[0141] Regarding the device in the above embodiments, the specific manners in which each unit performs operations have been described in detail in the embodiments related to the method. Each unit in the multi-unmanned device trajectory planning device can execute the corresponding steps in the method according to the multi-unmanned device trajectory planning method in the method embodiment of the first aspect above, and will not be elaborated in detail here.

[0142] Figure 7 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. As Figure 7 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 run by the processor, the processor is prompted to execute the multi-unmanned device trajectory planning method as described in the above exemplary embodiment.

[0143] As an example, the electronic device 10 does not have to be a single device, and can also be any aggregate of devices or circuits that can execute the above instructions (or instruction sets) alone or jointly. The electronic device 10 can also be a part of an integrated control system or a system manager, or can be configured to be interfaced and interconnected with a local or remote (e.g., via wireless transmission) server.

[0144] 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. By way of example and not 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, and the like.

[0145] The processor 101 may execute instructions or code stored in the memory 102, where the memory 102 may also store data. The instructions and data may also be sent and received via the network interface device over the network, where the network interface device may employ any known transmission protocol.

[0146] The memory 102 may be integrated with the processor 101. For example, RAM or flash memory may be disposed 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 other storage devices that may be used by any database system. The memory 102 and the processor 101 may be operatively coupled or may communicate with each other, for example, via an I / O port, a network connection, etc., such that the processor 101 can read files stored in the memory 102.

[0147] In addition, the electronic device 10 may further 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.

[0148] In an exemplary embodiment, an unmanned device may also be provided, which 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.

[0149] For example, the unmanned device itself may execute the trajectory planning method according to the embodiments of the present disclosure through the electronic device. It may be any one of a plurality of unmanned devices, and these unmanned devices may communicate with each other. In the case where any one of the unmanned devices executes the above trajectory planning method to obtain a trajectory planning result, other unmanned devices among the plurality of unmanned devices may receive the trajectory planning result from the unmanned device that obtained the trajectory planning result. Also, for example, the unmanned device may be communicatively connected to an electronic device such as a server to receive the trajectory planning result and / or send real-time positioning, etc. to the electronic device.

[0150] In an exemplary embodiment, a computer-readable storage medium may also be 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 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 disc memory, hard disk drive (HDD), solid state drive (SSD), cartridge memory (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 configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and provide the computer program and any associated data, data files, and data structures to a processor or computer such that the processor or computer can execute the computer program. The computer program in the above computer-readable storage medium may run in an environment deployed in computer devices such as clients, hosts, proxy devices, servers, 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 such 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.

[0151] According to an exemplary embodiment of the present disclosure, a computer program product may also be provided. The computer program product includes computer-executable instructions that, when executed by at least one processor, implement the trajectory planning method for multiple unmanned devices according to the exemplary embodiment of the present disclosure.

[0152] Those skilled in the art will readily conceive of 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 known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and embodiments are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0153] 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. Steps appearing in different drawings can be combined, and the execution order of each step can be changed, which will not be exhaustively listed here.

[0154] 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 various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A trajectory planning method for multiple unmanned devices, characterized in that, The described trajectory planning method includes: Obtaining the environmental information of the target environment and the task information of multiple unmanned devices. Among them, the target environment includes multiple obstacles, and the task information includes the starting position and the target position to be reached of each unmanned device; Based on the environmental information and the task information, determining the initial trajectory of each unmanned device; Globally optimizing the initial trajectories of each unmanned device to obtain the optimized trajectory of each unmanned device; Based on the optimized trajectories of each unmanned device, determining the trajectory planning result of the multiple unmanned devices, wherein the optimized trajectory is obtained by performing at least one of the following optimization iteration operations: Determining the expansion coefficient corresponding to the initial optimized trajectory of each unmanned device. Among them, the expansion coefficient is determined based on the path cost and the safety cost. The path cost is positively correlated with the length of the trajectory, and the safety cost is negatively correlated with the distance between the obstacle and the unmanned device; Globally optimizing the initial trajectories of each unmanned device by reducing the sum of the expansion coefficients corresponding to the trajectories of all unmanned devices to obtain the intermediate optimized trajectory of each unmanned device; Based on the intermediate optimized trajectories of each unmanned device, determining the initial optimized trajectory of the next optimization iteration operation until the preset iteration condition is met. The initial optimized trajectory of the first optimization iteration operation is the initial trajectory, wherein determining the initial optimized trajectory of the next optimization iteration operation based on the intermediate optimized trajectories of each unmanned device includes: Determining the solution transfer probability based on the iteration number of the current optimization iteration operation and the preset probability relationship. Among them, the probability relationship represents the relationship between the number of times of performing the optimization iteration operation and the solution transfer probability, and the solution transfer probability represents the probability of obtaining the optimal solution in the current optimization iteration operation; In response to the solution transfer probability being less than the preset value, determining the intermediate optimized trajectory as the initial optimized trajectory of the next optimization iteration operation; In response to the solution transfer probability being greater than or equal to the preset value, determining the optimization amount based on the iteration number of the current optimization iteration operation; based on the optimization amount, adjusting the intermediate optimized trajectory of at least one unmanned device among the multiple unmanned devices, and determining the adjusted intermediate optimized trajectory as the initial optimized trajectory of the next optimization iteration operation.

2. The trajectory planning method according to claim 1, characterized in that The positions in the target environment are represented by nodes, 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 by the following method: Performing at least one search iteration operation to obtain the target node corresponding to each search iteration operation; 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, wherein the search iteration operation includes: Based on the environmental information and the task information of the current unmanned device, determining the trajectory evaluation index of each neighbor node of the current unmanned device at the current node. Among them, the trajectory evaluation index is determined based on the first cost for the unmanned device to reach the neighbor node from the starting position and the second cost from the neighbor node to the target position. The neighbor node refers to the node adjacent to the current node in the target environment; Based on the trajectory evaluation index, determine a target neighbor node with the minimum trajectory evaluation index among the neighbor nodes of the current node; Based on the target neighbor node, determine a target node corresponding to the current search iteration operation, and use the target node as the current node for the next search iteration operation, where the current node of the first search iteration operation is the starting position.

3. The trajectory planning method according to claim 2, wherein The determining the target node corresponding to the current search iteration operation based on the target neighbor node includes: In response to the target neighbor node not being occupied, determine the target neighbor node as the target node, In response to the target neighbor node being occupied, based on candidate neighbor nodes other than the current target neighbor node among the neighbor nodes of the current node, determine the target node through random search, where the target neighbor node being occupied means that the target neighbor node has been used as the target node of other unmanned devices; or there are obstacles at the target neighbor node.

4. The trajectory planning method according to claim 1, characterized in that Positions in the target environment are represented by nodes, and the optimized trajectory of each unmanned device includes multiple nodes. Among them, the determining the trajectory planning results of the multiple unmanned devices based on the optimized trajectories of the unmanned devices includes: Perform smoothing processing on the optimized trajectories of the unmanned devices to obtain the smoothed trajectories of the unmanned devices; In response to the smoothed trajectories of all unmanned devices being collision-free trajectories, use the smoothed trajectories of the unmanned devices as the trajectory planning results of the multiple unmanned devices, where the collision-free trajectory means that there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the smoothed trajectories of other unmanned devices, and there are no overlapping nodes between the smoothed trajectory of the current unmanned device and the obstacles, where the smoothing processing includes: for each optimized trajectory, deleting nodes with the same forward direction as the previous node; and / or reducing the number of turning points of each optimized trajectory.

5. A trajectory planning device for multiple unmanned devices, characterized in that The trajectory planning device includes: An acquisition unit configured to acquire the environmental information of the target environment and the task information of multiple unmanned devices, where there are multiple obstacles in the target environment, and the task information includes the starting position and the target position to be reached of each unmanned device; An initial trajectory unit configured to determine the 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 trajectories of the unmanned devices to obtain the optimized trajectories of each unmanned device; A planning result determination unit configured to determine the trajectory planning results of the multiple unmanned devices based on the optimized trajectories of the unmanned devices, Among them, the optimized trajectory determination unit is configured to obtain the optimized trajectory by performing at least one of the following optimized iterative operations: determining a dilation coefficient corresponding to the initial optimized trajectory of each unmanned device, where the dilation coefficient is determined based on a path cost and a safety cost, the path cost is positively correlated with the length of the trajectory, and the safety cost is negatively correlated with the distance between the obstacle and the unmanned device; globally optimizing the initial trajectories of each unmanned device by reducing the sum of the dilation coefficients corresponding to the trajectories of all unmanned devices to obtain an intermediate optimized trajectory for each unmanned device; determining the initial optimized trajectory for the next optimized iterative operation based on the intermediate optimized trajectories of each unmanned device until a preset iterative condition is satisfied, and the initial optimized trajectory of the first optimized iterative operation is the initial trajectory. Among them, the optimized trajectory determination unit is configured to: determine a solution migration probability based on the number of iterations of the current optimized iterative operation and a preset probability relationship, where the probability relationship represents the relationship between the number of times of performing the optimized iterative operation and the solution migration probability, and the solution migration probability represents the probability of obtaining an optimal solution in this optimized iterative operation; in response to the solution migration probability being less than a preset value, determining the intermediate optimized trajectory as the initial optimized trajectory for the next optimized iterative 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 optimized iterative operation; adjusting the intermediate optimized trajectory of at least one of the multiple unmanned devices based on the optimization amount, and determining the adjusted intermediate optimized trajectory as the initial optimized trajectory for the next optimized iterative operation.

6. An electronic device, characterized in that, The electronic device includes: a processor; a memory for storing instructions executable by the processor, wherein the instructions executable by the processor, when run by the processor, cause the processor to execute the trajectory planning method for multiple unmanned devices according to any one of claims 1 to 4.

7. An unmanned device, characterized in that, The unmanned device includes the electronic device according to claim 6, or the unmanned device is communicatively connected to the electronic device according to claim 6.

8. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the 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 4.

9. A computer program product comprising computer-executable instructions, characterized in that, The computer-executable instructions, when executed by at least one processor, implement the trajectory planning method for multiple unmanned devices according to any one of claims 1 to 4.

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