Path planning method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a path planning method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of path planning. The method comprises the following steps: acquiring environment information required by path planning; determining a Hamiltonian model according to the environment information; determining evolution time of quantum random walk based on a Hamiltonian model; and based on the evolution time and a Hamiltonian model, obtaining an evolution result of the photons after quantum random walking in the waveguide array, and determining an optimal path according to the evolution result. Due to the adoption of a quantum random walking mechanism, path exploration can be carried out in parallel in a quantum superposition state, so that a plurality of paths are searched and evaluated at the same time, and the calculation complexity of path planning is remarkably reduced; moreover, the quantum random walk can more effectively explore the global path space through the quantum interference effect, and the problem that a traditional algorithm is easy to fall into local optimum is avoided, so that the global optimal path is more accurately found, and efficient and global optimal path planning is realized.

Description

Technical Field

[0001] The present invention relates to the field of path planning technology, and in particular to a path planning method, device, electronic device and computer-readable storage medium. Background Art

[0002] Path planning is particularly important in application scenarios such as robot navigation, autonomous vehicles, logistics and distribution, and intelligent transportation systems. The goal of path planning is to find the optimal path from the starting point to the end point under given environmental and constraint conditions.

[0003] Among existing path planning methods, traditional path planning algorithms such as the Dijkstra algorithm, the A* algorithm, and the RRT (Rapidly-exploring Random Tree) algorithm are usually used for path planning. These path planning algorithms rely on the processing power of classical computers and use graph search or probability-based methods to explore possible paths in the environment. When dealing with complex environments, they have problems with high computational complexity and insufficient global optimization capabilities. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a path planning method, device, electronic device and computer-readable storage medium to solve the problems of high computational complexity and insufficient global optimization capability of existing path planning methods when dealing with complex environments.

[0005] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0006] In a first aspect, the present invention provides a path planning method, the method comprising:

[0007] Obtain environmental information required for path planning;

[0008] Determining a Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between the positions in the environmental information and the waveguides, and the waveguides corresponding to the positions constitute a waveguide array;

[0009] Determining the evolution time of the quantum random walk based on the Hamiltonian model;

[0010] Based on the evolution time and the Hamiltonian model, an evolution result of the photon after performing quantum random walk in the waveguide array is obtained, and an optimal path is determined according to the evolution result.

[0011] In an optional embodiment, the evolution result includes a probability of detecting a photon in a target waveguide after removing different edges of the waveguide array, where the edges represent edges formed between two waveguides in a connected relationship;

[0012] The step of obtaining an evolution result of a photon after performing a quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determining an optimal path according to the evolution result, includes:

[0013] A probability of detecting a photon in a target waveguide after removing different sides of the waveguide array is determined according to the evolution time and the Hamiltonian model, and an optimal path is determined according to the probability of detecting a photon in the target waveguide after removing different sides of the waveguide array.

[0014] In an optional embodiment, determining, based on the evolution time and the Hamiltonian model, a probability of detecting a photon in a target waveguide after removing different edges of the waveguide array, and determining an optimal path based on the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array includes:

[0015] Starting from the starting waveguide, the probability of detecting a photon in the target waveguide after removing each edge connected to the starting waveguide is calculated according to the evolution time and the Hamiltonian model;

[0016] Determining a target edge from the edges where the starting waveguide is located according to probabilities corresponding to the edges where the starting waveguide is located;

[0017] Taking the waveguide on the target edge connected to the starting waveguide as a new starting waveguide, and determining a new target edge based on the new starting waveguide until the target waveguide is located on the target edge;

[0018] Generates the optimal path based on all target edges.

[0019] In an optional embodiment, determining the target edge from the edges where the starting waveguide is located according to the probabilities corresponding to the edges where the starting waveguide is located includes:

[0020] According to the probabilities corresponding to the edges where the starting waveguide is located, an edge corresponding to the minimum probability value among the edges where the starting waveguide is located is determined as the target edge.

[0021] In an optional embodiment, each edge of the waveguide array has a corresponding preset direction;

[0022] The determining the target edge from the edges where the starting waveguide is located according to the probabilities corresponding to the edges where the starting waveguide is located includes:

[0023] Selecting a candidate edge whose current direction is consistent with the corresponding preset direction from each edge where the starting waveguide is located;

[0024] According to the probabilities corresponding to the candidate edges, the candidate edge corresponding to the minimum probability is determined as the target edge.

[0025] In an optional embodiment, determining the evolution time of the quantum random walk based on the Hamiltonian model includes:

[0026] Calculating the probability of detecting a photon in a target waveguide after the photon performs a quantum random walk with different evolution times based on the Hamiltonian model;

[0027] The evolution time corresponding to the maximum probability is determined as the evolution time of quantum random walk.

[0028] In an optional embodiment, determining a Hamiltonian model according to the environmental information includes:

[0029] Converting the environmental information into a graph model;

[0030] The environmental information is quantized based on the graphical model to obtain a corresponding Hamiltonian model.

[0031] In a second aspect, the present invention provides a path planning device, comprising:

[0032] Information acquisition module, used to obtain environmental information required for path planning;

[0033] a quantization processing module, configured to determine a Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between positions in the environmental information and waveguides, wherein the waveguides corresponding to the positions constitute a waveguide array;

[0034] An evolution time determination module, configured to determine the evolution time of the quantum random walk based on the Hamiltonian model;

[0035] The optimal path determination module is used to obtain the evolution result of the photon after performing quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determine the optimal path according to the evolution result.

[0036] In a third aspect, the present invention provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the path planning method as described in any one of the aforementioned embodiments.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the path planning method as described in any one of the aforementioned embodiments.

[0038] Embodiments of the present invention provide a path planning method, device, electronic device, and computer-readable storage medium. The method comprises: obtaining environmental information required for path planning; determining a Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between positions in the environmental information and waveguides, with the waveguides corresponding to each position forming a waveguide array; determining the evolution time of a quantum random walk based on the Hamiltonian model; obtaining the evolution results of photons after performing a quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determining the optimal path based on the evolution results. Due to the use of a quantum random walk mechanism, path exploration can be performed in parallel in a quantum superposition state, allowing multiple paths to be searched and evaluated simultaneously, significantly reducing the computational complexity of path planning. Furthermore, the quantum random walk can more effectively explore the global path space through quantum interference effects, avoiding the local optimality problem that traditional algorithms are prone to, thereby more accurately finding the globally optimal path and achieving efficient and globally optimal path planning.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A schematic diagram of a process flow of a path planning method provided by an embodiment of the present invention is shown;

[0042] Figure 2 An example diagram showing a graph model structure;

[0043] Figure 3 A functional module diagram of a path planning device provided by an embodiment of the present invention is shown;

[0044] Figure 4 A block diagram of an electronic device provided by an embodiment of the present invention is shown.

[0045] Icon: 100 - electronic device; 110 - memory; 120 - processor; 130 - communication module; 600 - path planning device; 610 - information acquisition module; 620 - quantization processing module; 630 - evolution time determination module; 640 - optimal path determination module. DETAILED DESCRIPTION

[0046] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0047] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0048] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0049] Quantum walks are a generalization of classical random walks to quantum computing. In a quantum random walk, particles (often described as quantum bits or quantum states) can move along multiple paths simultaneously, making them a fundamental tool for implementing quantum algorithms. With the development of quantum computing, quantum random walks are considered a promising tool in quantum computing, capable of solving certain types of problems, such as search and optimization. In a quantum random walk, particles (such as quantum bits) can explore multiple paths simultaneously through superposition and interference effects, which provides new solutions to path planning problems.

[0050] Based on this, embodiments of the present invention provide a path planning method, device, electronic device, and computer-readable storage medium. By introducing a quantum random walk mechanism into path planning, the problems of high computational complexity and insufficient global optimization capabilities of existing path planning algorithms when processing complex environments are overcome, thereby achieving more efficient and globally optimal path planning.

[0051] Hereinafter, various embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0052] Please refer to Figure 1 , is a flow chart of a path planning method provided by an embodiment of the present invention. It should be noted that the path planning method of the present invention is not based on Figure 1 The specific order below is limited. It should be understood that in other embodiments, the order of some steps in the path planning method of the present invention can be interchanged according to actual needs, or some steps can be omitted or deleted. The path planning method can be applied to computer devices such as quantum computers that use quantum bits for calculations. Figure 1 The specific process shown is explained in detail.

[0053] Step S101: Acquire environmental information required for path planning.

[0054] In this embodiment, before path planning begins, it is necessary to collect the necessary environmental information, such as the starting and ending points, the route area, floor information (parking lot), and lane one-way and restricted traffic information. This information can be obtained through sensors, map data, or a preset environmental model.

[0055] Step S102 , determining a Hamiltonian model according to the environmental information; the Hamiltonian model reflects the correspondence between the positions in the environmental information and the waveguides, and the waveguides corresponding to the respective positions constitute a waveguide array.

[0056] In this embodiment, in order to facilitate subsequent quantum computing, the collected environmental information needs to be converted into a Hamiltonian model suitable for quantum computing. Since the Hamiltonian model reflects the correspondence between the locations in the environmental information and the waveguides, the waveguide array composed of the waveguides corresponding to each location is consistent with the structure of the graph model formed by each location in the environmental information (such as the graph model of a parking lot in a certain area). It should be noted that the locations in the environmental information can be key locations in the real environment, such as intersections, turns, elevator entrances, etc. in the parking lot.

[0057] Step S103: determining the evolution time of the quantum random walk based on the Hamiltonian model.

[0058] In this embodiment, in order to detect photons as much as possible on the target waveguide (the waveguide corresponding to the end position in the environmental information) during the subsequent quantum random walk evolution, it is necessary to determine the appropriate quantum random walk evolution time based on the Hamiltonian model, so that the detection probability is relatively large, while avoiding the evolution time being too long, which may cause the photon random walk to form a loop and affect the judgment of the shortest path.

[0059] Step S104 , based on the evolution time and Hamiltonian model, obtain the evolution result of the photon after performing quantum random walk in the waveguide array, and determine the optimal path according to the evolution result.

[0060] In this embodiment, the Feynman PAQS optical quantum platform can be used to calculate the evolution results of photons after quantum random walk in the waveguide array. The evolution results reflect the importance of different paths (edges) to the photons from the starting waveguide (starting position) to the target waveguide (end position), and the optimal path is determined based on the evolution results.

[0061] In other embodiments, a real waveguide array can be set up based on the Hamiltonian model, and the number of quantum random walk steps of the photon can be determined based on the evolution time. After the photon performs a certain number of quantum random walk steps from the starting waveguide in the waveguide array, the quantum state is measured to obtain the corresponding evolution result.

[0062] After determining the optimal path, the optimal path is output and provided to the user or automation system for actual path execution. The output optimal path includes the starting location, the end location, and all locations between the starting and end locations.

[0063] It can be seen that the path planning method provided by the embodiment of the present invention obtains the environmental information required for path planning; determines the Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between the position in the environmental information and the waveguide, and the waveguide corresponding to each position constitutes a waveguide array; determines the evolution time of the quantum random walk based on the Hamiltonian model; obtains the evolution result of the photon after performing a quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determines the optimal path based on the evolution result. Due to the use of the quantum random walk mechanism, path exploration can be carried out in parallel in a quantum superposition state, allowing multiple paths to be searched and evaluated simultaneously, significantly reducing the computational complexity of path planning; and, through the quantum interference effect, the quantum random walk can more effectively explore the global path space, avoiding the local optimal problem that traditional algorithms are prone to, thereby more accurately finding the global optimal path, achieving efficient and globally optimal path planning.

[0064] In one embodiment, the above step S102 may specifically include: converting the environmental information into a graphical model; and performing quantization processing on the environmental information based on the graphical model to obtain a corresponding Hamiltonian model.

[0065] In this embodiment, the collected environmental information is first converted into a graph model, in which the nodes represent the passable locations in the environmental information and the edges represent possible paths; then, the environmental information is quantized and the classical graph model is converted into a Hamiltonian model suitable for quantum computing. Each edge is assigned a corresponding weight parameter (usually the actual distance), and the coupling strength between the waveguides on the optical quantum chip is adjusted accordingly for the calculation of the subsequent quantum random walk evolution results on the optical quantum chip. The constructed Hamiltonian model is as follows:

[0066]

[0067] Where H represents the Hamiltonian matrix containing the waveguide structure information, C i,j represents the coupling strength between waveguide i and waveguide j, β i represents the diffusion constant of waveguide i, a represents the annihilation operator, represents the generation operator, and N represents the number of waveguides.

[0068] Taking the path planning of a parking lot as an example, based on the parking lot environment information, the key locations and lanes in the parking lot are mapped to nodes and edges in the graph model to obtain a graph model. The nodes of the graph model can be key locations in the parking lot environment, such as intersections, turns, elevator entrances, stair entrances, etc. in the parking lot. A key location can also be set as a node of the graph model at every preset distance (for example, 1 meter) on the lanes of the parking lot. The edges of the graph model can be lanes in the parking lot. If there are lanes connecting two key locations in the parking lot (there is a reachable path), there is an edge between the nodes corresponding to the two key locations on the graph model. Therefore, by constructing a graph model, each key location in the parking lot can be converted into a node in the graph model, and the lanes connecting two key locations in the parking lot can be converted into edges in the graph model.

[0069] The Hamiltonian model H is a mathematical description of a graph model, used to represent environmental information in quantum computing. After converting the graph model into a Hamiltonian model, the waveguide array structure on the photonic quantum chip reflected by the Hamiltonian model is consistent with the graph model structure. Therefore, each key position in the parking lot (a node in the graph model) corresponds to a waveguide on the photonic quantum chip, and the weight of the edge in the graph model (such as the actual distance or travel cost) is converted into the coupling strength between the waveguides on the photonic quantum chip; for example, if the path between two nodes is shorter, the coupling strength between the corresponding waveguides is greater. In this way, the parking lot environment is simulated through the waveguide array structure, and photons evolve in the waveguide array according to the quantum random walk rules. The optimal path from the starting position to the end position in the parking lot is determined based on the obtained evolution results.

[0070] In one embodiment, the evolution result includes the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array. An edge represents an edge formed between two waveguides that are connected. It is understood that since the structure of the waveguide array is consistent with that of the graph model, the edges in the waveguide array can be equivalent to the edges in the graph model. The above step S104 can specifically include:

[0071] Based on the evolution time and Hamiltonian model, the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array is determined, and the optimal path is determined based on the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array.

[0072] In this embodiment, the Feynman PAQS platform solves the Schrödinger equation based on the evolution time and Hamiltonian model, thereby calculating the probability distribution of quantum detection after removing each edge. By analyzing this probability distribution, the optimal path is obtained. In addition, parallel computing methods are used to execute path calculation tasks simultaneously on different cores, accelerating the entire process.

[0073] Specifically, the above steps may include determining the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array based on the evolution time and the Hamiltonian model, and determining the optimal path based on the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array.

[0074] Starting from the starting waveguide, the probability of detecting a photon in the target waveguide after removing each edge connected to the starting waveguide is calculated based on the evolution time and Hamiltonian model. The target edge is determined from the edges where the starting waveguide is located based on the probabilities corresponding to each edge. The waveguide connected to the starting waveguide on the target edge is used as the new starting waveguide, and a new target edge is determined based on the new starting waveguide until the target waveguide is located on the target edge. The optimal path is generated based on all target edges.

[0075] In this embodiment, the first starting waveguide determined is the waveguide corresponding to the starting position in the environmental information, and the target waveguide is the waveguide corresponding to the end position in the environmental information. Starting from the starting waveguide, the probability of detecting a photon in the target waveguide after removing all edges corresponding to the starting waveguide is compared. If this edge is on the optimal path, removing this edge will significantly reduce the probability of detecting a photon in the target waveguide. Therefore, by determining which edge has the lowest probability of detecting a photon in the target waveguide after removal, it is possible to determine which edge removal has the greatest impact on the path. This edge is then used as the target edge, and the waveguide on the target edge connected to the starting waveguide is used as the new starting waveguide. This process is repeated and so on, forming an optimal path from the starting position to the end position.

[0076] In this embodiment, path planning needs to determine whether to consider directionality based on the actual scenario. Therefore, the established graph model can be a directed graph or an undirected graph. For example, in a transportation network, roads are often directional, such as one-way streets or highway entrances and exits. Therefore, a directed graph is needed to represent the transportation network to ensure that vehicles are traveling in the correct direction.

[0077] In one embodiment, if directionality is not required, the target edge can be determined directly based on the probability of detecting a photon in the target waveguide. Based on this, the above steps determine the target edge from the edges where the starting waveguide is located based on the probabilities corresponding to the edges where the starting waveguide is located, specifically including:

[0078] According to the probabilities corresponding to the edges where the starting waveguide is located, the edge corresponding to the minimum probability among the edges where the starting waveguide is located is determined as the target edge.

[0079] It can be understood that the smaller the probability corresponding to each edge where the starting waveguide is located, the greater the impact of removing the edge on the path, so the edge corresponding to the minimum probability is determined as the target edge.

[0080] In another embodiment, if directionality is required, in addition to determining the target edge based on the probability of detecting a photon in the target waveguide, it is also necessary to ensure that the direction of the determined target edge is correct. For this purpose, each edge in the waveguide array has a corresponding preset direction. The above steps determine the target edge from the edges where the starting waveguide is located based on the corresponding probabilities of the edges where the starting waveguide is located, specifically including:

[0081] A candidate edge whose current direction is consistent with the corresponding preset direction is selected from each edge where the starting waveguide is located; and according to the probabilities corresponding to the candidate edges, the candidate edge corresponding to the minimum probability is determined as the target edge.

[0082] like Figure 2 As shown, it is assumed that in the graph model corresponding to the environmental information, there are nodes A, B, C, and D. Nodes A, B, and C are connected to each other in pairs, and node B and node C are also connected to node D respectively. Node A corresponds to the starting position, and node D corresponds to the end position. The preset direction between node A and node B is A→B, the preset direction between node A and node C is A→C, the preset direction between node B and node C is B→C, the preset direction between node B and node D is B→D, and the preset direction between node C and node D is C→D. The preset directions between each node in the graph model are consistent with the preset directions between the corresponding waveguides in the waveguide array, so the preset direction corresponding to each edge in the waveguide array (the edge formed between two waveguides with a connection relationship) can be obtained.

[0083] Assume that starting from the starting waveguide (waveguide A' corresponding to node A), the target edge is determined to be A'B', and then waveguide B' corresponding to node B is used as the starting waveguide. The edges where waveguide B' is located include B'A' (the edge between waveguide B' and waveguide A' corresponding to node A), B'C' (the edge between waveguide B' and waveguide C' corresponding to node C), and B'D' (the edge between waveguide B' and waveguide D' corresponding to node D). After removing B'A', B'C', and B'D' respectively, the probability of detecting a photon in the target waveguide (waveguide D') is P1 = 0.25, P2 = 0.3, and P3 = 0.6. Through comparison, it is found that edge B'A' has the lowest probability. However, the current direction of edge B'A' is B'→A', while the preset direction should be A'→B' (i.e., A→B). Therefore, edge B'A' cannot be used as the target edge. In this case, the target edge can be selected from the other edges B'C' and B'D' where waveguide B' is located. In specific implementation, the candidate edges whose current directions are consistent with the corresponding preset directions can be selected from the three edges B'A', B'C', and B'D' where waveguide B' is located, namely B'C' and B'D'. Then, the candidate edge with the lowest probability among the candidate edges B'C' and B'D' is selected as the target edge, namely B'C'.

[0084] Similarly, after determining the target edge B'C', since waveguide C' is not the target waveguide (waveguide D'), waveguide C' is used as the new starting waveguide. Among the edges where waveguide C' is located, only the current direction of C'D' is consistent with the corresponding preset direction, so the final target edge determined is C'D'. In this way, based on all the determined target edges, namely A'B', B'C', and C'D', an optimal path A→B→C can be generated and output. It can be seen that in the process of determining the optimal path, by considering the characteristics of the directed graph, the legitimacy of the path and the correctness of the direction are ensured.

[0085] In one embodiment, in order to obtain a suitable quantum random walk evolution time, the photons can be made to perform multiple quantum random walks with different evolution times, and the optimal evolution time can be selected from them. The above step S103 can specifically include:

[0086] The probability of detecting a photon in the target waveguide after the photon performs quantum random walks with different evolution times is calculated based on the Hamiltonian model; the evolution time corresponding to the maximum probability is determined as the evolution time of the quantum random walk.

[0087] In this embodiment, the Feynman PAQS platform can be used to calculate the probability of detecting a photon in a target waveguide at different evolution times, where the photon undergoes a quantum random walk at different evolution times in a waveguide array without removing edges. By continuously adjusting the evolution time of the quantum random walk, the probability of detecting the photon in the target waveguide at different evolution times is compared, and the optimal evolution time is selected. The evolution time can be increased from a small value. As the evolution time increases, the probability of detecting the photon in the target waveguide also increases. When the evolution time reaches a certain peak, further increasing the evolution time will cause the photon random walk to loop. At this point, the probability of detecting the photon in the target waveguide will slightly decrease, and the probability of detecting the photon in each waveguide in the waveguide array will be similar. Therefore, when determining the optimal evolution time, the evolution time corresponding to the maximum probability can be selected as the evolution time of the quantum random walk.

[0088] In summary, the path planning method provided by the embodiment of the present invention has the following technical effects:

[0089] 1. Significantly improve the computational efficiency of path planning. By utilizing a quantum random walk mechanism, path exploration can be performed in parallel in a quantum superposition state, allowing multiple paths to be searched and evaluated simultaneously. This parallel processing significantly reduces the computational complexity of path planning, enabling faster path search than traditional algorithms, especially when dealing with large-scale and complex environments.

[0090] 2. Improve the ability to search for the global optimal path. Quantum random walks can more effectively explore the global path space through quantum interference effects, avoiding the problem of traditional algorithms easily falling into local optimality. As a result, the present invention can more accurately find the global optimal path, ensuring the reliability and effectiveness of path planning, especially in complex and multi-obstacle environments.

[0091] 3. Reduce the limitations of relying on heuristic functions. Unlike traditional algorithms such as A* that rely on heuristic function design, this method explores paths through the natural evolution mechanism of quantum random walks, without relying on artificially designed heuristic functions. This reduces path planning errors that may result from improper heuristic function selection, thereby improving the algorithm's versatility and robustness. Furthermore, this method can also introduce a noise parameter to simulate the effects of environmental noise during the evolution of the quantum wave function; by adjusting this parameter, path selection under different noise intensities can be simulated to improve the algorithm's robustness.

[0092] 4. Excellent scalability and portability. Due to the fundamental characteristics of quantum random walks, this method can be applied not only to planar path planning but also to more complex scenarios such as three-dimensional space and time-dynamic path planning. Furthermore, this method is adaptable to different hardware platforms, including quantum computers and simulated quantum computing environments, and exhibits excellent portability.

[0093] In order to execute the corresponding steps in the above embodiments and various possible methods, a method for implementing a path planning device is given below. Figure 3 , which is a functional block diagram of a path planning device 600 provided in an embodiment of the present invention. It should be noted that the basic principles and technical effects of the path planning device 600 provided in this embodiment are the same as those in the aforementioned embodiments. For the sake of brevity, any details not mentioned in this embodiment are referred to the corresponding contents in the aforementioned embodiments. The path planning device 600 includes: an information acquisition module 610, a quantization processing module 620, an evolution time determination module 630, and an optimal path determination module 640.

[0094] The information acquisition module 610 is used to obtain the environmental information required for path planning.

[0095] It can be understood that the information acquisition module 610 can execute the above step S101.

[0096] The quantization processing module 620 is used to determine a Hamiltonian model based on the environmental information. The Hamiltonian model reflects the correspondence between the positions in the environmental information and the waveguides. The waveguides corresponding to each position constitute a waveguide array.

[0097] It can be understood that the quantization processing module 620 can execute the above step S102.

[0098] The evolution time determination module 630 is used to determine the evolution time of the quantum random walk based on the Hamiltonian model.

[0099] It can be understood that the evolution time determination module 630 can execute the above step S103.

[0100] The optimal path determination module 640 is used to obtain the evolution results of photons after quantum random walking in the waveguide array based on the evolution time and Hamiltonian model, and determine the optimal path according to the evolution results.

[0101] It can be understood that the optimal path determination module 640 can execute the above step S104.

[0102] Optionally, the quantization processing module 620 is specifically configured to convert the environmental information into a graphical model; and perform quantization processing on the environmental information based on the graphical model to obtain a corresponding Hamiltonian model.

[0103] Optionally, the evolution time determination module 630 is specifically used to calculate the probability of detecting a photon in the target waveguide after the photon performs a quantum random walk with different evolution times based on the Hamiltonian model; and determine the evolution time corresponding to the maximum probability as the evolution time of the quantum random walk.

[0104] Optionally, the evolution result includes a probability of detecting a photon in the target waveguide after removing different edges of the waveguide array, where an edge represents an edge formed between two connected waveguides. The optimal path determination module 640 is specifically configured to determine, based on the evolution time and the Hamiltonian model, the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array, and determine the optimal path based on the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array.

[0105] Optionally, the optimal path determination module 640 is further specifically configured to start from the starting waveguide, and calculate, according to the evolution time and the Hamiltonian model, the probability of detecting a photon in the target waveguide after removing each edge connected to the starting waveguide; determine the target edge from the edges where the starting waveguide is located according to the probabilities corresponding to each edge where the starting waveguide is located; use the waveguide on the target edge connected to the starting waveguide as a new starting waveguide, and determine a new target edge based on the new starting waveguide until the target waveguide is located on the target edge; and generate an optimal path based on all target edges.

[0106] Optionally, the optimal path determination module 640 is further specifically configured to determine, according to the probabilities corresponding to the edges where the starting waveguide is located, an edge corresponding to the minimum probability among the edges where the starting waveguide is located as the target edge.

[0107] Optionally, each edge in the waveguide array has a corresponding preset direction; the optimal path determination module 640 is further specifically configured to select a candidate edge whose current direction is consistent with the corresponding preset direction from the edges where the starting waveguide is located; and based on the probabilities corresponding to the candidate edges, determine the candidate edge corresponding to the minimum probability as the target edge.

[0108] It can be seen that the path planning device provided by the embodiment of the present invention includes an information acquisition module, a quantization processing module, an evolution time determination module, and an optimal path determination module. The information acquisition module is used to obtain the environmental information required for path planning; the quantization processing module is used to determine the Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between the position in the environmental information and the waveguide, and the waveguide corresponding to each position constitutes a waveguide array; the evolution time determination module is used to determine the evolution time of the quantum random walk based on the Hamiltonian model; the optimal path determination module is used to obtain the evolution results of the photon after the quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determine the optimal path based on the evolution results. Due to the use of the quantum random walk mechanism, path exploration can be carried out in parallel in the quantum superposition state, allowing multiple paths to be searched and evaluated simultaneously, significantly reducing the computational complexity of path planning; and the quantum random walk can more effectively explore the global path space through the quantum interference effect, avoiding the local optimal problem that traditional algorithms are prone to fall into, thereby more accurately finding the global optimal path, achieving efficient and globally optimal path planning.

[0109] Please refer to Figure 4 , is a block diagram of an electronic device 100 provided in an embodiment of the present invention. The electronic device 100 is a computer that uses quantum bits for computation, and may be, for example, a quantum computer. The electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, processor 120, and communication module 130 are electrically connected to each other, directly or indirectly, to enable data transmission or exchange. For example, these components may be electrically connected to each other via one or more communication buses or signal lines.

[0110] Memory 110 is used to store programs or data. Memory 110 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0111] The processor 120 is used to read / write data or programs stored in the memory 110 and execute corresponding functions. For example, when the computer program stored in the memory 110 is executed by the processor 120, the path planning method disclosed in the above embodiments can be implemented.

[0112] The communication module 130 is used to establish a communication connection between the electronic device 100 and other devices through a network, and to send and receive data through the network.

[0113] It should be understood that Figure 4 The structure shown is only a schematic diagram of the structure of the electronic device 100. The electronic device 100 may also include Figure 4 More or fewer components than shown, or with Figure 4 Different configurations shown. Figure 4 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0114] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 120, the path planning method disclosed in the above embodiments is implemented.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a portion of code, and the module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0116] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0117] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0118] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A path planning method, characterized in that: The method comprises: Obtain environmental information required for path planning; Determining a Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between the positions in the environmental information and the waveguides, and the waveguides corresponding to the positions constitute a waveguide array; Determining the evolution time of the quantum random walk based on the Hamiltonian model; Based on the evolution time and the Hamiltonian model, an evolution result of the photon after performing quantum random walk in the waveguide array is obtained, and an optimal path is determined according to the evolution result.

2. The path planning method according to claim 1, characterized in that: The evolution result includes a probability of detecting a photon in a target waveguide after removing different edges of the waveguide array, wherein the edges represent edges formed between two waveguides in a connected relationship; The step of obtaining an evolution result of a photon after performing a quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determining an optimal path according to the evolution result, includes: A probability of detecting a photon in a target waveguide after removing different sides of the waveguide array is determined according to the evolution time and the Hamiltonian model, and an optimal path is determined according to the probability of detecting a photon in the target waveguide after removing different sides of the waveguide array.

3. The path planning method according to claim 2, characterized in that: Determining, based on the evolution time and the Hamiltonian model, a probability of detecting a photon in a target waveguide after removing different edges of the waveguide array, and determining an optimal path based on the probability of detecting a photon in the target waveguide after removing different edges of the waveguide array, includes: Starting from the starting waveguide, the probability of detecting a photon in the target waveguide after removing each edge connected to the starting waveguide is calculated according to the evolution time and the Hamiltonian model; Determining a target edge from the edges where the starting waveguide is located according to probabilities corresponding to the edges where the starting waveguide is located; Taking the waveguide on the target edge connected to the starting waveguide as a new starting waveguide, and determining a new target edge based on the new starting waveguide until the target waveguide is located on the target edge; Generates the optimal path based on all target edges.

4. The path planning method according to claim 3, characterized in that: The determining the target edge from the edges where the starting waveguide is located according to the probabilities corresponding to the edges where the starting waveguide is located includes: According to the probabilities corresponding to the edges where the starting waveguide is located, an edge corresponding to the minimum probability value among the edges where the starting waveguide is located is determined as the target edge.

5. The path planning method according to claim 3, characterized in that: Each edge of the waveguide array has a corresponding preset direction; The determining the target edge from the edges where the starting waveguide is located according to the probabilities corresponding to the edges where the starting waveguide is located includes: Selecting a candidate edge whose current direction is consistent with the corresponding preset direction from each edge where the starting waveguide is located; According to the probabilities corresponding to the candidate edges, the candidate edge corresponding to the minimum probability is determined as the target edge.

6. The path planning method according to claim 1, characterized in that: Determining the evolution time of the quantum random walk based on the Hamiltonian model includes: Calculating the probability of detecting a photon in a target waveguide after the photon performs a quantum random walk with different evolution times based on the Hamiltonian model; The evolution time corresponding to the maximum probability is determined as the evolution time of quantum random walk.

7. The path planning method according to any one of claims 1 to 6, characterized in that: The determining of the Hamiltonian model according to the environmental information includes: Converting the environmental information into a graph model; The environmental information is quantized based on the graphical model to obtain a corresponding Hamiltonian model.

8. A path planning device, characterized in that: The device comprises: Information acquisition module, used to obtain environmental information required for path planning; a quantization processing module, configured to determine a Hamiltonian model based on the environmental information; the Hamiltonian model reflects the correspondence between positions in the environmental information and waveguides, wherein the waveguides corresponding to the positions constitute a waveguide array; An evolution time determination module, configured to determine the evolution time of the quantum random walk based on the Hamiltonian model; The optimal path determination module is used to obtain the evolution result of the photon after performing quantum random walk in the waveguide array based on the evolution time and the Hamiltonian model, and determine the optimal path according to the evolution result.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the path planning method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the path planning method according to any one of claims 1 to 7 are implemented.

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