Intelligent robot navigation method and system

Abstract

The invention discloses an intelligent robot navigation method and system, and the method comprises the steps: obtaining a current advancing path and an advancing path environment of a robot, and inputting the current advancing path and the advancing path environment of the robot into a robot navigation system; initializing a robot navigation system, and obtaining a quantum state space and an action space of the robot according to the advancing path and the advancing path environment; an optimal quantum action is obtained by selecting a quantum state space, a reward mechanism is adopted to process the optimal quantum action, the quantum state is continuously updated, a robot navigation system issues and adjusts the advancing path and the advancing path environment of the current robot, and the intelligent optimal navigation method of the robot is obtained. The method provided by the invention not only can cope with complex and changeable environments and provide efficient path planning, but also can avoid the problems of navigation precision and robustness caused by machine learning, improves the calculation speed by amplifying the advantages of quantum calculation, and improves the performance and reliability of the robot in practical application.

Description

Technical Field

[0001] The present invention relates to the field of intelligent robots and quantum technology processing, and in particular to an intelligent robot navigation method and system. Background Art

[0002] With the rapid development of artificial intelligence and robotics, robot navigation systems play an increasingly important role in various application scenarios. Traditional robot navigation methods mainly rely on deterministic algorithms, such as the A* algorithm and the Dijkstra algorithm, which perform relatively well in static and simple environments. With the complexity and diversification of application requirements, robots need to navigate autonomously in dynamic and complex environments, and the limitations of these methods are gradually revealed. In recent years, navigation algorithms based on machine learning and deep learning have gradually attracted attention. These methods achieve path planning and obstacle avoidance by training models and have strong environmental adaptability. However, these algorithms are very inefficient when faced with tasks with large computational loads, such as drastic environmental changes and high path precision requirements. The application of quantum computing solves the problem of large computational load faced by traditional robots. However, my country's application in the field of quantum computing is still in the first stage and faces many challenges and problems.

[0003] Some experts have proposed that the integration of machine learning and quantum computing can realize intelligent robot navigation. However, later studies have shown that although the robot navigation method based on machine learning has a certain degree of adaptive ability, its model training needs to rely on a large amount of sample data. At this time, the domestic sample training set has not reached the level of foreign countries, and there may be overfitting or underfitting problems during the training process. Under this premise, combined with quantum computing, it will lead to insufficient navigation accuracy and robustness of robots in practical applications. In addition, existing methods generally lack environmental modeling of the path in the robot navigation planning process, and the computational complexity is very high. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an intelligent robot navigation method to solve the problems raised in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an intelligent robot navigation method, comprising:

[0008] Acquire the current travel path and travel path environment of the robot, and input the current travel path and travel path environment of the robot into the robot navigation system;

[0009] Initializing the robot navigation system, and obtaining the quantum state space and action space of the robot according to the travel path and the travel path environment;

[0010] The optimal quantum action is obtained by selecting the quantum state space, and the reward mechanism is used to process the optimal quantum action. The quantum state is continuously updated, and the robot navigation system is used to adjust the current robot's travel path and travel path environment to obtain the robot's intelligent optimal navigation method.

[0011] As a preferred solution of the intelligent robot navigation method of the present invention, the method includes: obtaining the current travel path and travel path environment of the robot, and inputting the current travel path and travel path environment of the robot into the robot navigation system, including:

[0012] In the robot navigation system, the robot's current travel path environment is converted into super quantum bits, and a super quantum sensor network composed of super quantum bits is deployed, and the super quantum sensor network senses the environmental changes of the robot on the current travel path.

[0013] As a preferred solution of the intelligent robot navigation method of the present invention, wherein: the ultra-quantum sensor network senses the environmental changes of the robot on the current travel path, including:

[0014] The number of obstacles, the number of path turning points, the number of parameter changes in environmental conditions, and the number of communication delays.

[0015] As a preferred solution of the intelligent robot navigation method of the present invention, the robot navigation system is initialized, and the quantum state space and action space of the robot are obtained according to the travel path and the travel path environment, including:

[0016] Initialize the previously stored travel path and travel path environment in the navigation system, use topological quantum computing to instantly analyze the environmental changes perceived by the super quantum sensor network, and extract the characteristics of environmental changes;

[0017] Using the environmental change characteristics, create a complex environment $CE$ and a simple environment $SE$;

[0018] Define the robot's quantum state space $S$ and quantum action space $A$, and initialize the quantum action value function $Q(s,a)$.

[0019] As a preferred solution of the intelligent robot navigation method described in the present invention, the optimal quantum action is obtained by selecting the quantum state space, the optimal quantum action is processed by using a reward mechanism, the quantum state is continuously updated, and the robot navigation system adjusts the current robot's travel path and travel path environment to obtain the intelligent optimal navigation method of the robot, including:

[0020] If the current environment is complex $CE$, use the quantum gate to put the quantum state $s$ into a quantum superposition state to obtain multiple quantum actions;

[0021] For each time step $t$ in the quantum state space, a quantum action $a$ is selected from the quantum superposition state according to the quantum action value function $Q(s,a)$;

[0022] The selected quantum action $a$ is returned to the robot navigation system, and the reward $r$ and the new quantum state $s′$ are recorded and calculated through the reward mechanism.

[0023] As a preferred solution of the intelligent robot navigation method of the present invention, it also includes:

[0024] If the current environment is a simple environment $SE$, a topological quantum weaving operation is used to create a topological quantum superposition state for the quantum state $s$;

[0025] Select a quantum action $a$ from the topological quantum superposition state, and reconstruct the current quantum state and the selected quantum action into a new superposition state;

[0026] According to the new superposition state, it is returned to the robot navigation system, and the reward $r$ and the updated quantum action value function $Q(s,a)$ are recorded and calculated through the reward mechanism.

[0027] As a preferred solution of the intelligent robot navigation method of the present invention, the reward mechanism includes:

[0028] Path efficiency reward E(t), environmental adaptability reward A(t), resource utilization reward R(t), operation stability reward S(t), and measurement accuracy reward M(t).

[0029] In a second aspect, the present invention provides an intelligent robot navigation system, which comprises:

[0030] A robot path input module is configured to obtain the current travel path and path environment of the robot, and input the current travel path and travel path environment of the robot into the robot navigation system;

[0031] A robot navigation initialization module is configured to initialize the robot navigation system and obtain the robot's quantum state space and action space according to the travel path and the travel path environment;

[0032] The robot navigation optimization strategy module is configured to obtain the optimal quantum action by selecting the quantum state space, process the optimal quantum action using a reward mechanism, continuously update the quantum state, and adjust the current robot's travel path and travel path environment issued by the robot navigation system to obtain the robot's intelligent optimal navigation method.

[0033] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above method when executed by a processor.

[0035] Compared with the prior art, the invention has the following beneficial effects: the present invention obtains the current travel path and travel path environment of the robot, and inputs the current travel path and travel path environment of the robot into the robot navigation system; initializes the robot navigation system, and obtains the quantum state space and action space of the robot according to the travel path and travel path environment; obtains the optimal quantum action by selecting the quantum state space, processes the optimal quantum action by adopting a reward mechanism, continuously updates the quantum state, and the robot navigation system adjusts the current travel path and travel path environment of the robot to obtain the intelligent optimal navigation method of the robot; this method can not only cope with complex and changeable environments and provide efficient path planning, but also avoid the navigation accuracy and robustness problems brought by machine learning, and improves the computing speed by amplifying the advantages of quantum computing, thereby improving the performance and reliability of robots in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0037] Figure 1 This is an overall flow chart of an intelligent robot navigation method according to an embodiment of the present invention;

[0038] Figure 2A comparison diagram of the robustness of different navigation systems of the intelligent robot navigation method according to an embodiment of the present invention;

[0039] Figure 3 This is a performance comparison chart of different navigation systems of the intelligent robot navigation method described in one embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0043] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0044] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0045] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Example 1

[0047] Reference Figure 1 , which is the first embodiment of the present invention, and provides an intelligent robot navigation method, comprising:

[0048] S1. Obtain the current path and path environment of the robot, and input the current path and path environment of the robot into the robot navigation system;

[0049] Furthermore, in the robot navigation system, the robot's current path environment is converted into super quantum bits, and a super quantum sensor network composed of super quantum bits is deployed, and the super quantum sensor network senses the environmental changes of the robot on the current path;

[0050] Specifically, the robot's current path environment needs to be preprocessed before being input into the robot's navigation system, where it is converted into a super quantum bit through quantum state encoding;

[0051] It should be noted that placing data preprocessing outside the robot navigation system can improve the processing efficiency of the navigation system;

[0052] Specifically, the super quantum sensor network includes super quantum sensor nodes, which can read super quantum bit information, and each super quantum sensor node is linked by quantum entanglement to form a super quantum sensor network;

[0053] Specifically, quantum entanglement refers to a fast synchronized data state between sensor nodes;

[0054] Furthermore, the environmental changes of the robot on the current travel path include the number of obstacle appearances, the number of path turning points, the number of parameter changes of environmental conditions, and the number of communication delays;

[0055] Specifically, the parameters of the environmental conditions include changes in ambient light, temperature, humidity, sound, vibration, electromagnetic interference, airflow, and wind direction; wherein sound changes simulate noise, and electromagnetic interference simulates communication interference;

[0056] Specifically, the path turning point refers to the change in direction of the robot during its movement. The more the number of path turning points, the more the number of direction changes, which also means that the current path environment is more complex. Of course, these also need to be judged in combination with the number of other environmental change parameters. For example, assuming that the number of path turning points is large, but the other environmental change parameters are small, it means that the current path environment is simple, but the robot makes too many path decisions, resulting in unnecessary calculations. Therefore, it is necessary to evaluate the robot's quantum state space and action space to provide efficient path planning and improve calculation speed.

[0057] S2, initializing the robot navigation system, and obtaining the robot's quantum state space and action space according to the travel path and the travel path environment;

[0058] Furthermore, the previously stored travel paths and travel path environments in the navigation system are initialized, and topological quantum computing is used to instantly analyze the environmental changes perceived by the super quantum sensor network to extract the characteristics of environmental changes;

[0059] It should be noted that the initialization of the navigation system takes into account that the system will retain the last travel path and travel path environment, which will affect the accuracy of the current navigation system;

[0060] Furthermore, by utilizing the characteristics of environmental changes, we can create complex environments $CE$ and simple environments $SE$;

[0061] Specifically, the judgment of complex environment and simple environment includes the keywords involving environmental change parameters in the extraction of environmental change features, which need to be marked according to the actual application environment; for example, if it is currently in a closed environment, then there is no need to involve airflow and wind direction changes; and the judgment criteria of complex environment and simple environment are also determined according to the number of obstacles appearing, the number of path turning points, and the number of parameter changes of environmental conditions; among them, the number of communication delays is affected by the environmental marking keywords;

[0062] Furthermore, define the robot’s quantum state space $S$ and quantum action space $A$, and initialize the quantum action value function $Q(s,a)$;

[0063] Specifically, the quantum state space of a robot refers to the set of all possible states that the robot may exist in its path and environment, including the robot's current coordinates and direction, the robot's movement speed and acceleration, and the robot's internal state (such as battery power and processor load);

[0064] Specifically, the robot's quantum action space refers to the set of all possible actions that the robot can take in a given state, including the robot's forward, backward, turning, acceleration and deceleration; the robot's grasping, placing, scanning and measuring; the robot's interaction with the environment by changing its path;

[0065] S3. The optimal quantum action is obtained by selecting the quantum state space, and the reward mechanism is used to process the optimal quantum action. The quantum state is continuously updated, and the robot navigation system is sent to adjust the current robot's travel path and travel path environment to obtain the robot's intelligent optimal navigation method;

[0066] Furthermore, according to the characteristics of environmental changes, choose a complex environment or a simple environment:

[0067] S301.1. If the current environment is complex $CE$, use the quantum gate to put the quantum state $s$ into a quantum superposition state to obtain multiple quantum actions;

[0068] S301.2. For each time step $t$ in the quantum state space, select a quantum action $a$ from the quantum superposition state according to the quantum action value function $Q(s,a)$;

[0069] S301.3. Return the selected quantum action $a$ to the robot navigation system, record it and calculate the reward $r$ and the new quantum state $s′$ through the reward mechanism;

[0070] Furthermore, iterate S301.1 to S301.3, and obtain the optimal quantum action by continuously selecting quantum actions;

[0071] Specifically, quantum gates can achieve the purpose of controlling quantum state space and action space;

[0072] S302.1. If the current environment is a simple environment $SE$, use the topological quantum weaving operation to create a topological quantum superposition state for the quantum state $s$;

[0073] S302.2. Select a quantum action $a$ from the topological quantum superposition state, and reconstruct the current quantum state and the selected quantum action into a new superposition state;

[0074] S302.3. Return to the robot navigation system according to the new superposition state, record and calculate the reward $r$ and update the quantum action value function $Q(s,a)$ through the reward mechanism;

[0075] Furthermore, by iterating S302.1 to S302.3, the optimal quantum action can be obtained by continuously updating the quantum action value function;

[0076] It should be noted that the robot navigation system can obtain the optimal quantum action in both simple and complex environments.

[0077] Furthermore, the reward mechanism includes path efficiency reward E(t), environmental adaptability reward A(t), resource utilization reward R(t), operation stability reward S(t), and measurement accuracy reward M(t);

[0078] Specifically, the path efficiency reward E(t) is expressed as:

[0079]

[0080] Specifically, the environmental adaptability reward A(t) is expressed as:

[0081]

[0082] Specifically, the resource utilization reward R(t) is expressed as:

[0083]

[0084] Specifically, the operational stability reward S(t) is expressed as:

[0085]

[0086] Specifically, the measurement accuracy reward M(t) is expressed as:

[0087]

[0088] Where T is the total travel time of the robot from the starting point to the end point, Δd i is the distance traveled by the robot in the kth time step, Δt i is the time the robot travels in k time steps, δ j is the number of times the jth obstacle appears, θ k represents the direction angle θ, φ of the robot in the kth time step k represents the rotation angle of the robot in the kth time step, γ k is the curve shape adjustment parameter of the environment adaptability reward function, η k To balance the impact of environmental change characteristics on environmental adaptability rewards, ξ k represents the environmental changes perceived by the robot in the kth time step, ∈ l represents the amount of the lth resource consumed by the robot when performing complex environment tasks or simple environment tasks, κ l It is expressed as the residual amount of the lth resource consumed by the robot, ρ lis the utilization rate of the lth resource during task execution, σ is the fluctuation of resource consumption, usually 1 is oscillation and 0 is smooth; cosh is the hyperbolic cosine function, sinh is the hyperbolic sine function, ψ represents the stability of the robot during walking, ω represents the path weight generated by the robot during walking, τ represents a certain moment, α p represents the accuracy p perceived by the robot during the measurement process, δ q represents the number of errors q that occurred during the measurement process of the robot;

[0089] Furthermore, the optimal quantum action is evaluated by a reward mechanism to obtain a reward score, and the quantum state is continuously updated to obtain the ideal state of the quantum state space, that is, the reward score value reaches the maximum, and the robot navigation system sends out adjustments to the current robot's travel path and travel path environment;

[0090] It should be noted that continuously updating the quantum state to obtain the ideal state of the quantum state space can further improve navigation accuracy based on the computing speed advantage of quantum computing itself; and adjusting the current robot's travel path and travel path environment through the latest quantum state, which enables the navigation system to maintain stable and efficient performance in uncertain and dynamic environments, thereby improving the robustness of the system.

[0091] Furthermore, this embodiment also provides an intelligent robot navigation system, including:

[0092] A robot path input module is configured to obtain the current travel path and path environment of the robot, and input the current travel path and travel path environment of the robot into the robot navigation system;

[0093] A robot navigation initialization module is configured to initialize the robot navigation system and obtain the robot's quantum state space and action space according to the travel path and the travel path environment;

[0094] The robot navigation optimization strategy module is configured to obtain the optimal quantum action by selecting the quantum state space, process the optimal quantum action using a reward mechanism, continuously update the quantum state, and adjust the current robot's travel path and travel path environment issued by the robot navigation system to obtain the robot's intelligent optimal navigation method.

[0095] This embodiment also provides a computer device, which is applicable to the case of the intelligent robot navigation method, including:

[0096] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent robot navigation method proposed in the above embodiment.

[0097] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0098] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent robot navigation method proposed in the above embodiment is implemented.

[0099] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0100] Example 2

[0101] Reference Figure 2 and Figure 3 , which is the second embodiment of the present invention, and this embodiment provides an intelligent robot navigation method, including: in order to verify the effectiveness of the intelligent robot navigation method, this experiment selected four different navigation systems for comparative experiments: a traditional A* algorithm navigation system, a navigation system based on machine learning, a navigation system based on quantum computing, and the method of the present invention (intelligent robot navigation system); the experimental site is set in a dynamically changing environment, including multiple obstacles and complex path turning points, simulating the complex environment in actual applications, and the experimental robots are all of the same model, equipped with the same sensors and computing resources to ensure the fairness of the experiment;

[0102] The experimental preparation is as follows: different types of obstacles are arranged in the experimental site, including moving obstacles, fixed obstacles, randomly appearing obstacles, and complex path turning points; different environmental parameter change points are set, including light, temperature, humidity, noise, and electromagnetic interference;

[0103] Initialize the navigation system:

[0104] Traditional A* algorithm navigation system: preset static map, define obstacle locations and path nodes;

[0105] Initialization of the navigation system based on machine learning: loading the pre-trained navigation model, which is trained based on a large amount of path data;

[0106] Initialization of the navigation system based on quantum computing: setting quantum bit states and quantum gate operations, and loading environmental change data;

[0107] Initialization of the intelligent robot navigation system: obtain the robot's current path and environment, convert the path and environment data into super quantum bits, deploy the super quantum sensor network, and initialize the navigation system;

[0108] Experimental process:

[0109] The robot navigates from the starting point to the end point under the guidance of the above four navigation systems. The number of obstacles, path turning points, environmental parameter changes and communication delays encountered by the robot during each navigation process are recorded. The performance of the four navigation systems in a dynamic environment is compared, with a focus on path planning time, path efficiency, environmental adaptability, resource utilization, operational stability and measurement accuracy.

[0110] Among them, path efficiency bonus score = path efficiency × 0.4; resource utilization bonus score = resource utilization × 0.3; measurement accuracy bonus score = measurement accuracy × 0.3; comprehensive bonus score = path efficiency bonus score + resource utilization bonus score + measurement accuracy bonus score;

[0111] Experimental results: as shown in Table 1;

[0112] Table 1

[0113]

[0114]

[0115] From Table 1, we can know that the traditional A* algorithm navigation system performs worst when encountering obstacles, with obstacles appearing up to 15 times, while the intelligent robot navigation system only appears 5 times, indicating that the intelligent system can better identify and avoid obstacles, and the intelligent robot navigation system maintains the lowest number of path turning points, environmental parameter changes, and communication delays, indicating that the intelligent robot navigation system is more efficient in planning paths and can find the optimal path based on the adaptability and response speed of the environment;

[0116] And by measuring the accuracy, it can be found that the intelligent robot navigation system is 5% to 15% higher in navigation accuracy than other navigation systems; thus, it can be seen that the navigation accuracy of the present invention is better; and by Figure 2 and Figure 3 It can be seen that the system of the present invention has also been improved in terms of robustness and performance, which fully demonstrates the practicality and reliability of the method of the present invention.

[0117] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent robot navigation method, characterized in that: include: Acquire the current travel path and travel path environment of the robot, and input the current travel path and travel path environment of the robot into the robot navigation system; Initializing the robot navigation system, and obtaining the quantum state space and action space of the robot according to the travel path and the travel path environment; The optimal quantum action is obtained by selecting the quantum state space, and the reward mechanism is used to process the optimal quantum action. The quantum state is continuously updated, and the robot navigation system is used to adjust the current robot's travel path and travel path environment to obtain the robot's intelligent optimal navigation method.

2. The intelligent robot navigation method according to claim 1, characterized in that: Acquiring the current travel path and travel path environment of the robot, and inputting the current travel path and travel path environment of the robot into the robot navigation system, including: In the robot navigation system, the robot's current travel path environment is converted into super quantum bits, and a super quantum sensor network composed of super quantum bits is deployed, and the super quantum sensor network senses the environmental changes of the robot on the current travel path.

3. The intelligent robot navigation method according to claim 2, characterized in that: The ultra-quantum sensor network senses the environmental changes of the robot on the current travel path, including: The number of obstacles, the number of path turning points, the number of parameter changes in environmental conditions, and the number of communication delays.

4. The intelligent robot navigation method according to claim 3, characterized in that: Initializing the robot navigation system and obtaining the robot's quantum state space and action space according to the travel path and the travel path environment, including: Initialize the previously stored travel path and travel path environment in the navigation system, use topological quantum computing to instantly analyze the environmental changes perceived by the super quantum sensor network, and extract the characteristics of environmental changes; Using the environmental change characteristics, create a complex environment and simple environment Defining the robot’s quantum state space and quantum action space Initialize the quantum action value function 5. The intelligent robot navigation method according to claim 4, characterized in that: The optimal quantum action is obtained by selecting the quantum state space, and the optimal quantum action is processed by using a reward mechanism. The quantum state is continuously updated, and the robot navigation system sends and adjusts the current robot's travel path and travel path environment to obtain the robot's intelligent optimal navigation method, including: If the current environment is complex Then use quantum gates to put the quantum state $s$ into a quantum superposition state, and obtain multiple quantum actions; For each time step in the quantum state space According to the quantum action value function Selecting a quantum action from a quantum superposition The quantum action that will be chosen Return to the robot navigation system, record and calculate the reward through the reward mechanism and new quantum states 6. The intelligent robot navigation method according to claim 5, characterized in that: Also includes: If the current environment is simple Then use the topological quantum braiding operation to create a topological quantum superposition state for the quantum state $s$; Select a quantum action from the topological quantum superposition state Reconstruct the current quantum state and the selected quantum action into a new superposition state; According to the new superposition state, the robot navigation system is returned, and the reward $r$ and the quantum action value function are updated and calculated through the reward mechanism.

7. The intelligent robot navigation method according to any one of claims 4 to 6, characterized in that: The reward mechanism includes: Path efficiency reward E(t), environmental adaptability reward A(t), resource utilization reward R(t), operation stability reward S(t), and measurement accuracy reward M(t).

8. An intelligent robot navigation system, based on the intelligent robot navigation method according to any one of claims 1 to 7, characterized in that: include: A robot path input module is configured to obtain the current travel path and path environment of the robot, and input the current travel path and travel path environment of the robot into the robot navigation system; A robot navigation initialization module is configured to initialize the robot navigation system and obtain the robot's quantum state space and action space according to the travel path and the travel path environment; The robot navigation optimization strategy module is configured to obtain the optimal quantum action by selecting the quantum state space, process the optimal quantum action using a reward mechanism, continuously update the quantum state, and adjust the current robot's travel path and travel path environment issued by the robot navigation system to obtain the robot's intelligent optimal navigation method.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images