Mobile robot navigation method, device, mobile robot and medium

Abstract

The present invention discloses a mobile robot navigation method, device, mobile robot, and medium. The method includes: after the mobile robot performs a current movement operation, inputting the mobile robot's current sensor data into a pre-trained target network model to obtain output first controller parameters; determining second controller parameters based on the mobile robot's current actual trajectory parameters and current preset trajectory parameters; and controlling the mobile robot to perform the next movement operation based on the first controller parameters and the second controller parameters. Embodiments of the present invention address the problem of existing mobile robot navigation methods relying on a single error correction algorithm, thereby improving the mobile robot's navigation accuracy.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a navigation method and device for a mobile robot, a mobile robot, and a medium. Background Art

[0002] With the rapid development of science and technology, a robot system with mobile functions composed of sensors, automatic controllers and other mechanisms has become one of the hot research directions. Mobile robots are widely used in manufacturing, construction, monitoring and other industries.

[0003] In actual scenario applications, mobile robots usually need to navigate and move along a preset trajectory. However, due to the influence of the mobile robot's body performance parameters and the external environment, there is usually a trajectory error between the actual trajectory of the mobile robot and the preset trajectory, resulting in the mobile robot being unable to accurately reach the target position to perform task operations.

[0004] Existing mobile robot navigation methods mainly rely on a single correction algorithm, resulting in low navigation accuracy of mobile robots. Summary of the Invention

[0005] Embodiments of the present invention provide a mobile robot navigation method, device, mobile robot, and medium to solve the problem that the existing mobile robot navigation method relies on a single correction algorithm, thereby improving the navigation accuracy of the mobile robot.

[0006] According to one embodiment of the present invention, a navigation method for a mobile robot is provided, the method comprising:

[0007] After the mobile robot performs a current movement operation, current sensor data of the mobile robot is input into a pre-trained target network model to obtain output first controller parameters;

[0008] determining a second controller parameter based on a current actual trajectory parameter and a current preset trajectory parameter of the mobile robot;

[0009] Based on the first controller parameter and the second controller parameter, the mobile robot is controlled to perform a next moving operation.

[0010] According to another embodiment of the present invention, a navigation device for a mobile robot is provided, the device comprising:

[0011] A first controller parameter determination module is configured to input current sensor data of the mobile robot into a pre-trained target network model after the mobile robot performs a current movement operation, to obtain output first controller parameters;

[0012] a second controller parameter determination module, configured to determine second controller parameters based on current actual trajectory parameters and current preset trajectory parameters of the mobile robot;

[0013] The mobile robot control module is used to control the mobile robot to perform a next movement operation based on the first controller parameter and the second controller parameter.

[0014] According to another embodiment of the present invention, there is provided a mobile robot, the mobile robot comprising: a drive controller and at least one sensor;

[0015] Wherein, the sensor is used to collect sensor data;

[0016] The drive controller includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the navigation method of the mobile robot described in any embodiment of the present invention.

[0017] According to another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the navigation method of a mobile robot according to any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention is to input the current sensor data of the mobile robot into a pre-trained target network model after the mobile robot performs the current moving operation, obtain the output first controller parameters, determine the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot, and control the mobile robot to perform the next moving operation based on the first controller parameters and the second controller parameters. The controller parameters are obtained based on two dimensions respectively, which solves the problem that the existing mobile robot navigation method relies on a single correction algorithm, and improves the navigation accuracy of the mobile robot.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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.

[0021] Figure 1 A flowchart of a mobile robot navigation method provided by one embodiment of the present invention;

[0022] Figure 2 A flowchart of another mobile robot navigation method provided by one embodiment of the present invention;

[0023] Figure 3 An architectural diagram of a target network model provided by one embodiment of the present invention;

[0024] Figure 4 A flowchart of a specific example of a navigation method for a mobile robot provided by one embodiment of the present invention;

[0025] Figure 5 A schematic structural diagram of a navigation device for a mobile robot provided by one embodiment of the present invention;

[0026] Figure 6 A schematic structural diagram of a mobile robot provided by one embodiment of the present invention;

[0027] Figure 7 A schematic structural diagram of a drive controller provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Figure 1 This is a flow chart of a mobile robot navigation method provided by one embodiment of the present invention. This embodiment is applicable to situations where navigation and tracking of a mobile robot is performed. The method can be executed by a navigation device of the mobile robot. The navigation device of the mobile robot can be implemented in the form of hardware and / or software. The navigation device of the mobile robot can be configured in the mobile robot. Figure 1 As shown, the method includes:

[0031] S110 . After the mobile robot performs the current moving operation, the current sensor data of the mobile robot is input into a pre-trained target network model to obtain output first controller parameters.

[0032] The mobile robot in this embodiment can be a soft robot or a mechanical robot, and is particularly suitable for a soft robot. Among them, a soft robot is made of soft intelligent materials. Unlike mechanical robots, the driving method of the soft robot mainly depends on the intelligent material used. For example, the intelligent material is generally a dielectric elastomer (DE), an ionic polymer metal composite (IPMC), a shape memory alloy (SMA), a shape memory polymer (SMP), etc. The intelligent material used in the soft robot is not limited here.

[0033] Since mechanical robots can rely on traditional mechanical algorithms to construct mechanical or kinematic models, most existing technologies directly calculate the controller parameters of mechanical robots based on sensor data. However, the driving characteristics of soft robots are not suitable for traditional mechanical algorithms, and there is no curve pattern that can be fitted between sensor data and controller parameters. Based on this, an embodiment of the present invention proposes a method of using a target network model to simulate the association pattern between sensor data and controller parameters.

[0034] Exemplarily, current sensor data includes, but is not limited to, current velocity of three axes, current acceleration of three axes, current angular velocity of three axes, current angle of three axes, current temperature, current wind speed, and the like, where the three axes include the x-axis, y-axis, and z-axis. The current sensor data collected by the mobile robot is not limited here; users can configure the required sensors on the mobile robot based on actual needs.

[0035] Among them, exemplary, the model architecture of the target network model includes but is not limited to CNN network (Convolutional Neural Networks), FCN network (Fully Convolutional Networks), residual network (ResNet), DNN network (Deep Neural Networks), RNN network (Recurrent Neural Network), multi-channel LSTM network (Long Short-Term Memory) or Transformer network, etc., and the model architecture of the target network model is not limited here.

[0036] Among them, illustratively, the first controller parameters include but are not limited to the current amplitude, current frequency, current duty cycle, current duration, voltage amplitude, voltage frequency, voltage duty cycle and voltage duration, etc. The first controller parameters are not limited here, and users can customize them according to actual needs.

[0037] S120 : Determine second controller parameters based on current actual trajectory parameters and current preset trajectory parameters of the mobile robot.

[0038] Specifically, the current actual trajectory parameters are used to represent the actual trajectory parameters of the mobile robot after executing the current movement operation. For example, the actual trajectory parameters include but are not limited to actual position coordinates, actual heading angle, etc. The actual trajectory parameters are not limited here.

[0039] Specifically, the current preset trajectory parameters are used to represent the preset trajectory parameters of the mobile robot after performing the current movement operation. Exemplarily, the preset trajectory parameters include but are not limited to preset position coordinates, preset heading angles, etc. The preset trajectory parameters are not limited here.

[0040] In an optional embodiment, determining the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot includes: using a preset closed-loop control algorithm to determine the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot.

[0041] Exemplarily, the preset closed-loop control algorithm includes but is not limited to a position control algorithm, a PID control algorithm, a robust control algorithm, a Dalin algorithm, and an LQR (Linear Quadratic Regulator) algorithm, etc. The preset closed-loop control algorithm is not limited here.

[0042] Based on the above embodiment, optionally, the method further includes: acquiring current actual trajectory parameters collected by a visual device, and / or determining current actual trajectory parameters based on current sensor data.

[0043] In an optional embodiment, a visual device is installed on the mobile robot or in the scene environment, for determining the current actual trajectory parameters based on the collected trajectory image.

[0044] In another optional embodiment, the current actual trajectory parameters are determined based on the previous actual trajectory parameters and the current sensor data.

[0045] In another optional embodiment, the average of the current actual trajectory parameters collected by the visual device and the current actual trajectory parameters determined based on the current sensor data is used as the current actual trajectory parameters of the mobile robot.

[0046] S130 : Based on the first controller parameter and the second controller parameter, control the mobile robot to perform the next movement operation.

[0047] In an optional embodiment, the average of the first controller parameter and the second controller parameter is used as the target controller parameter, and the mobile robot is controlled to perform the next movement operation based on the target controller parameter. Specifically, the target controller parameter is used to represent parameter data input into a drive controller of the mobile robot. Exemplarily, the drive controller includes but is not limited to a controlled power supply and a PWM module.

[0048] Exemplarily, the voltage amplitude in the first controller parameter is 5V, the voltage amplitude in the second controller parameter is 7V, and the voltage amplitude in the target controller parameter is 6V.

[0049] The technical solution of this embodiment is to input the current sensor data of the mobile robot into a pre-trained target network model after the mobile robot performs the current moving operation, obtain the output first controller parameters, determine the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot, and control the mobile robot to perform the next moving operation based on the first controller parameters and the second controller parameters. The controller parameters are obtained based on two dimensions respectively, which solves the problem that the existing mobile robot navigation method relies on a single correction algorithm, and improves the navigation accuracy of the mobile robot.

[0050] Figure 2 This is a flow chart of another mobile robot navigation method provided by one embodiment of the present invention. This embodiment further refines the target network model in the above embodiment. Figure 2 As shown, the method includes:

[0051] S210 , inputting each training sensor data in the training sensor data set into the initial network model to obtain at least one output prediction controller parameter.

[0052] Exemplarily, the training sensor data set may be sensor data collected by sensors during the movement of the mobile robot.

[0053] S220 : Determine a loss function based on each prediction controller parameter and the standard controller parameters corresponding to each prediction controller parameter.

[0054] For example, the standard controller parameters may be parameter data input into the drive controller during the movement of the mobile robot.

[0055] In an optional embodiment, the standard controller parameters corresponding to the current training sensor data collected at the current moment are the standard controller parameters collected at the next moment.

[0056] Exemplarily, loss functions include, but are not limited to, square loss function, logarithmic loss function, exponential loss function, logistic regression loss function, Huber loss function, cross entropy loss function, and Kullback-Leibler divergence loss function, etc. The loss function is not limited here.

[0057] S230. Based on the loss function, adjust the model parameters of the initial network model until the loss function converges to obtain a trained target network model.

[0058] Based on the above embodiment, optionally, the method further includes: inputting each verification sensor in the verification sensor data set into the target network model to obtain at least one prediction controller parameter as output, and determining the performance parameters of the target network model based on each prediction controller parameter and the standard controller parameters corresponding to each prediction controller parameter; if the performance parameters do not meet the preset parameter standards, adjusting the hyperparameters of the target network model, and continuing to train the adjusted target network model.

[0059] Exemplarily, hyperparameters include but are not limited to the number of network layers, the number of network nodes, the number of iterations, the learning rate, and the like.

[0060] The advantage of this setting is that it can improve the generalization ability of the trained target network model.

[0061] In this embodiment, the target network model is a multi-channel long short-term memory model. In an optional embodiment, the target network model includes an output module and at least one long short-term memory module, wherein the first long short-term memory module is used to output a first long short-term feature vector based on the current sensor data input, the i-th long short-term memory module is used to output the i-th long short-term feature vector based on the current sensor data input and the i-1-th long short-term feature vector output by the i-1-th long short-term memory module, and the output module is used to output a first controller parameter based on the n-th long short-term feature vector output by the n-th long short-term memory module; wherein i is an integer greater than 1 and less than n, and n represents the number of long short-term memory modules in the target network model.

[0062] In an optional embodiment, the i-th long short-term memory module includes an input unit and a long short-term memory unit, the input unit includes a first linear layer, a self-attention layer, and a second linear layer, the first linear layer is used to linearly encode the input current sensor data and output a first sensor feature, the self-attention layer is used to output fused sensor data based on the first sensor feature output by the first linear layer, the second linear layer is used to linearly encode the fused sensor data output by the self-attention layer and output a second sensor feature, and the long short-term memory unit is used to output an i-th long short-term feature vector based on the second sensor feature output by the second linear layer and the i-1th long short-term feature vector output by the i-1th long short-term memory module.

[0063] Among them, the self-attention layer is used to realize multi-channel data fusion. For example, the self-attention layer satisfies the formula:

[0064]

[0065] Among them, Q represents the query vector matrix, K represents the key vector matrix, V represents the value vector matrix, d kRepresents the vector used for query.

[0066] Specifically, the long short-term memory unit includes a memory unit c, an input gate i, a forget gate f, and an output gate o. The update process of the long short-term memory unit at time t satisfies the following formula:

[0067] f t =σ(W f ·[h t-1 ,x t ]+b f );i t =σ(W i ·[h t-1 ,x t ]+b i );

[0068]

[0069] o t =σ(W o ·[h t-1 ,x t ]+b o );h t =o t ⊙tanh(c t )

[0070] Among them, x t represents the input data of the long short-term memory unit at time t, f t 、i t 、c t and o t Represent the values ​​of the forget gate, input gate, memory unit and output gate at time t respectively, Represents the candidate memory state value of the memory unit, h t represents the long and short-term feature vector output by the long and short-term memory unit, σ represents the logistic sigmoid function, W f 、W i 、W c 、W o 、b f 、b i 、b c and b o represents the weight matrix.

[0071] Figure 3This is an architectural diagram of a target network model provided by one embodiment of the present invention. Specifically, the target network model includes an output module and long short-term memory modules corresponding to n moments, wherein the output module includes a linear layer and an activation layer, and each long short-term memory module includes an input unit and a long short-term memory unit, wherein the input unit includes a first linear layer, a self-attention layer, and a second linear layer. The first controller parameters output by the target network model include voltage parameters for voltage modulation, current parameters for current modulation, and duty cycle for PWM modulation.

[0072] S240: After the mobile robot performs the current moving operation, the current sensor data of the mobile robot is input into a pre-trained target network model to obtain output first controller parameters.

[0073] S250 : Determine second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot.

[0074] S260 : Based on the first controller parameter and the second controller parameter, control the mobile robot to perform the next movement operation.

[0075] In an optional embodiment, based on the first controller parameters and the second controller parameters, the mobile robot is controlled to perform the next movement operation, including: determining the controller parameter error based on the first controller parameters and the second controller parameters; determining the target controller parameters based on the first controller parameters and the controller parameter error, and controlling the mobile robot to perform the next movement operation based on the target controller parameters.

[0076] Specifically, the controller parameter error is used to represent the proportional difference data between the first controller parameter and the second controller parameter.

[0077] Among them, exemplary, the target controller parameter α * Satisfies the formula:

[0078] α * =α1±λ|α1-α2|

[0079] Wherein, α1 represents the first controller parameter, λ represents the proportional coefficient, and α2 represents the second controller parameter.

[0080] Specifically, when the first controller parameter is less than the second controller parameter, the target controller parameter is equal to the sum of the first controller parameter and the controller parameter error; when the first controller parameter is greater than the second controller parameter, the target controller parameter is equal to the difference between the first controller parameter and the controller parameter error.

[0081] In another optional embodiment, target controller parameters are determined based on the second controller parameters and the controller parameter error, and the mobile robot is controlled to perform the next movement operation based on the target controller parameters.

[0082] The advantage of this setting is that it can further improve the accuracy of the target controller parameters, thereby further improving the navigation accuracy of the mobile robot.

[0083] Figure 4 A flowchart of a specific example of a navigation method for a mobile robot provided by an embodiment of the present invention is provided, which is illustrated by taking the mobile robot as a soft robot as an example. Specifically, the soft robot is provided with a drive controller, a sensor and a visual device.

[0084] Among them, after the soft robot performs the current movement operation and before reaching the end point, the current actual trajectory data collected by the visual device is sent to the PID algorithm module, so that the PID algorithm module determines the second controller parameter α2 based on the current preset trajectory parameters and the current actual trajectory parameters. The current sensor data collected by the sensor is input into the target network model to obtain the output first controller parameter α1. The target controller parameter α determined based on the first controller parameter α1 and the second controller parameter α2 is * The data is input into the drive controller on the soft robot so that the drive controller controls the soft robot to perform the next movement operation.

[0085] The technical solution of this embodiment is to input each training sensor data in the training sensor data set into the initial network model to obtain at least one prediction controller parameter as output, determine the loss function based on each prediction controller parameter and the standard controller parameters corresponding to each prediction controller parameter, and adjust the model parameters of the initial network model based on the loss function until the loss function converges to obtain a trained target network model, thereby solving the training problem of the target network model. By selecting a multi-channel LSTM model as the target network model, the adaptability of the target network model to the navigation scenario of the mobile robot is guaranteed, thereby further improving the navigation accuracy of the mobile robot.

[0086] Figure 5 This is a schematic diagram of the structure of a navigation device for a mobile robot provided by one embodiment of the present invention. Figure 5 As shown, the device includes: a first controller parameter determination module 310, a second controller parameter determination module 320 and a mobile robot control module 330.

[0087] The first controller parameter determination module 310 is configured to input the current sensor data of the mobile robot into the pre-trained target network model after the mobile robot performs the current movement operation, and obtain the output first controller parameters;

[0088] A second controller parameter determination module 320 is configured to determine second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot;

[0089] The mobile robot control module 330 is configured to control the mobile robot to perform a next movement operation based on the first controller parameter and the second controller parameter.

[0090] The technical solution of this embodiment is to input the current sensor data of the mobile robot into a pre-trained target network model after the mobile robot performs the current moving operation, obtain the output first controller parameters, determine the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot, and control the mobile robot to perform the next moving operation based on the first controller parameters and the second controller parameters. The controller parameters are obtained based on two dimensions respectively, which solves the problem that the existing mobile robot navigation method relies on a single correction algorithm, and improves the navigation accuracy of the mobile robot.

[0091] Based on the above embodiment, optionally, the device further includes:

[0092] a target network model training module, configured to input each training sensor data in the training sensor data set into the initial network model to obtain at least one predictive controller parameter as output;

[0093] determining a loss function based on each predictive controller parameter and a standard controller parameter corresponding to each predictive controller parameter;

[0094] Based on the loss function, the model parameters of the initial network model are adjusted until the loss function converges to obtain the trained target network model.

[0095] Based on the above embodiment, optionally, the target network model includes an output module and at least one long short-term memory module, wherein the first long short-term memory module is used to output a first long short-term feature vector based on the input current sensor data, the i-th long short-term memory module is used to output the i-th long short-term feature vector based on the input current sensor data and the i-1-th long short-term feature vector output by the i-1-th long short-term memory module, and the output module is used to output a first controller parameter based on the n-th long short-term feature vector output by the n-th long short-term memory module; wherein i is an integer greater than 1 and less than n, and n represents the number of long short-term memory modules in the target network model.

[0096] Based on the above embodiment, optionally, the i-th long short-term memory module includes an input unit and a long short-term memory unit, the input unit includes a first linear layer, a self-attention layer, and a second linear layer, the first linear layer is used to linearly encode the input current sensor data and output a first sensor feature, the self-attention layer is used to output fused sensor data based on the first sensor feature output by the first linear layer, the second linear layer is used to linearly encode the fused sensor data output by the self-attention layer and output a second sensor feature, and the long short-term memory unit is used to output an i-th long short-term feature vector based on the second sensor feature output by the second linear layer and the i-1th long short-term feature vector output by the i-1th long short-term memory module.

[0097] Based on the above embodiment, optionally, the mobile robot control module 330 is specifically configured to:

[0098] determining a controller parameter error based on the first controller parameter and the second controller parameter;

[0099] Based on the first controller parameter and the controller parameter error, a target controller parameter is determined, and based on the target controller parameter, the mobile robot is controlled to perform a next movement operation.

[0100] Based on the above embodiment, optionally, the second controller parameter determination module 320 is specifically configured to:

[0101] A preset closed-loop control algorithm is adopted to determine the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot.

[0102] Based on the above embodiment, optionally, the device further includes:

[0103] The current actual trajectory parameter determination module is used to obtain the current actual trajectory parameters collected by the visual device and / or determine the current actual trajectory parameters based on the current sensor data.

[0104] The mobile robot navigation device provided in the embodiment of the present invention can execute the mobile robot navigation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0105] Figure 6 This is a schematic structural diagram of a mobile robot provided by one embodiment of the present invention. The mobile robot includes a drive controller 410 and at least one sensor 420, where the sensor 420 is used to collect sensor data. Figure 6An example of a mobile robot equipped with two sensors 420 is provided for illustrative purposes. Exemplarily, the at least one sensor 420 includes, but is not limited to, an odometer, a gyroscope, an inertial sensor, a pressure sensor, a temperature sensor, and the like. The components, their connections and relationships, and their functions shown in the embodiments of the present invention are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0106] Based on the above embodiment, optionally, the mobile robot further includes a visual device 430 for collecting current actual trajectory parameters. Exemplarily, the visual device 430 may be a visual sensor.

[0107] Figure 7 This is a schematic diagram of the structure of a drive controller provided by one embodiment of the present invention. Figure 7 As shown, the drive controller 410 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor 11. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the drive controller 410 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] Several components in the drive controller 410 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the drive controller 410 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the navigation method of the mobile robot.

[0110] In some embodiments, the navigation method of the mobile robot can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the drive controller 410 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the navigation method of the mobile robot described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the navigation method of the mobile robot by any other appropriate means (for example, by means of firmware).

[0111] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute a navigation method for a mobile robot, the method comprising:

[0113] After the mobile robot performs a current movement operation, current sensor data of the mobile robot is input into a pre-trained target network model to obtain an output first controller parameter;

[0114] determining a second controller parameter based on a current actual trajectory parameter and a current preset trajectory parameter of the mobile robot;

[0115] Based on the first controller parameter and the second controller parameter, the mobile robot is controlled to perform a next movement operation.

[0116] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0120] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0121] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A navigation method for a mobile robot, characterized in that: include: After the mobile robot performs a current movement operation, current sensor data of the mobile robot is input into a pre-trained target network model to obtain output first controller parameters; wherein the target network model is used to simulate the association between the sensor data and the controller parameters; determining a second controller parameter based on a current actual trajectory parameter and a current preset trajectory parameter of the mobile robot; determining a controller parameter error based on the first controller parameter and the second controller parameter; Based on the first controller parameter and the controller parameter error, a target controller parameter is determined, and based on the target controller parameter, the mobile robot is controlled to perform the next movement operation; wherein, when the first controller parameter is less than the second controller parameter, the target controller parameter is equal to the sum of the first controller parameter and the controller parameter error, and when the first controller parameter is greater than the second controller parameter, the target controller parameter is equal to the difference between the first controller parameter and the controller parameter error.

2. The method according to claim 1, characterized in that The training method of the target network model includes: Inputting each training sensor data in the training sensor data set into the initial network model to obtain at least one predictive controller parameter as output; Determining a loss function based on each of the predictive controller parameters and standard controller parameters corresponding to each of the predictive controller parameters; Based on the loss function, the model parameters of the initial network model are adjusted until the loss function converges, thereby obtaining a trained target network model.

3. The method according to claim 1 or 2, characterized in that The target network model includes an output module and at least one long short-term memory module, wherein the first long short-term memory module is used to output a first long short-term feature vector based on the input current sensor data, the i-th long short-term memory module is used to output the i-th long short-term feature vector based on the input current sensor data and the i-1-th long short-term feature vector output by the i-1-th long short-term memory module, and the output module is used to output a first controller parameter based on the n-th long short-term feature vector output by the n-th long short-term memory module; wherein i is an integer greater than 1 and less than n, and n represents the number of long short-term memory modules in the target network model.

4. The method according to claim 3, characterized in that The i-th long short-term memory module includes an input unit and a long short-term memory unit, the input unit includes a first linear layer, a self-attention layer, and a second linear layer, the first linear layer is used to linearly encode the input current sensor data and output a first sensor feature, the self-attention layer is used to output fused sensor data based on the first sensor feature output by the first linear layer, the second linear layer is used to linearly encode the fused sensor data output by the self-attention layer and output a second sensor feature, and the long short-term memory unit is used to output an i-th long short-term feature vector based on the second sensor feature output by the second linear layer and the i-1th long short-term feature vector output by the i-1th long short-term memory module.

5. The method according to claim 1, wherein The determining of the second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot includes: A preset closed-loop control algorithm is adopted to determine second controller parameters based on the current actual trajectory parameters and the current preset trajectory parameters of the mobile robot.

6. The method according to claim 1, characterized in that The method further comprises: Acquire current actual trajectory parameters collected by the visual device, and / or determine the current actual trajectory parameters based on the current sensor data.

7. A navigation device for a mobile robot, characterized in that: include: a first controller parameter determination module configured to input current sensor data of the mobile robot into a pre-trained target network model after the mobile robot performs a current movement operation, thereby outputting first controller parameters; wherein the target network model is configured to simulate a correlation between sensor data and controller parameters; a second controller parameter determination module, configured to determine second controller parameters based on current actual trajectory parameters and current preset trajectory parameters of the mobile robot; a mobile robot control module, configured to control the mobile robot to perform a next movement operation based on the first controller parameter and the second controller parameter; Wherein, the mobile robot control module is specifically used to: determining a controller parameter error based on the first controller parameter and the second controller parameter; Based on the first controller parameter and the controller parameter error, a target controller parameter is determined, and based on the target controller parameter, the mobile robot is controlled to perform the next movement operation; wherein, when the first controller parameter is less than the second controller parameter, the target controller parameter is equal to the sum of the first controller parameter and the controller parameter error, and when the first controller parameter is greater than the second controller parameter, the target controller parameter is equal to the difference between the first controller parameter and the controller parameter error.

8. A mobile robot, characterized in that: The mobile robot includes: a drive controller and at least one sensor; Wherein, the sensor is used to collect sensor data; The drive controller includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the navigation method of the mobile robot described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the navigation method of a mobile robot according to any one of claims 1 to 6 when executed.

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