Robot navigation method and system based on communication control two-way assistance

Abstract

The invention discloses a robot navigation method and system based on communication control two-way assistance, which are applied to the technical field of information, and the method comprises the following steps: obtaining a channel data set, the channel data set comprising transmission channel data between a robot and a sensor; constructing a hierarchical model, wherein the hierarchical model comprises a high-level planning algorithm and a low-level planning algorithm; processing the channel data set through the high-level planning algorithm to obtain a global rough path; the global rough path is optimized through the low-level planning algorithm, and a motion track and an obstacle avoidance motion instruction are generated; and the robot navigates based on the motion trail and the obstacle avoidance motion instruction.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a robot navigation method and system based on bidirectional assistance of communication control. Background Art

[0002] With the rapid development of IoT technology, edge intelligence faces limitations in communication range and energy supply when processing large amounts of data. Robotic communication (RoboCom) technology aims to use mobile robots to collect sensor device data and optimize the channels between wireless transceivers by virtue of mobility, thereby solving the data collection problem. This technology is currently widely used in scenarios such as warehousing logistics, exploration and inspection. In addition, robotic communication technology can also be applied to multi-machine collaborative perception to overcome the problems of limited perception and blocked vision.

[0003] In the robot communication system, the robot not only undertakes the task of autonomous movement, but also needs to coordinate the communication system to ensure the efficiency of data transmission. However, the existing methods ignore the interaction between communication and control, lack the ability to jointly optimize resources and tasks across layers, and the real-time performance of path planning is insufficient. When dealing with environments with severely obstructed vision, collisions with dynamic obstacles cannot be effectively avoided. This makes the communication and control efficiency of robots in complex environments insufficient, and there is a certain risk of collision in autonomous navigation.

[0004] In order to overcome these defects, the present application proposes a robot navigation method and system based on two-way assistance of communication control. Summary of the invention

[0005] The purpose of this application is to provide a robot navigation method and system based on bidirectional assistance of communication control, aiming to solve the above problems.

[0006] To achieve the above objectives, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides a robot navigation method based on bidirectional assistance of communication control, comprising:

[0008] Acquire a channel data set, wherein the channel data set includes transmission channel data between the robot and the sensor;

[0009] Constructing a hierarchical model, the hierarchical model including a high-level planning algorithm and a low-level planning algorithm; processing the channel data set by the high-level planning algorithm to obtain a global rough path;

[0010] Optimizing the global rough path through the low-level planning algorithm to generate motion trajectories and obstacle avoidance motion instructions;

[0011] The robot navigates based on the motion trajectory and the obstacle avoidance motion instruction.

[0012] In a second aspect, the present application provides a robot navigation system based on bidirectional assistance of communication control, comprising:

[0013] Acquisition module: acquires a channel data set, wherein the channel data set includes transmission channel data between the robot and the sensor;

[0014] Model construction module: constructing a hierarchical model, the hierarchical model including a high-level planning algorithm and a low-level planning algorithm; processing the channel data set by the high-level planning algorithm to obtain a global rough path;

[0015] Optimization module: optimizes the global rough path through the low-level planning algorithm to generate motion trajectory and obstacle avoidance motion instructions;

[0016] Navigation module: The robot navigates based on the motion trajectory and the obstacle avoidance motion instructions.

[0017] In a third aspect, the present application provides a device, comprising a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing a robot navigation method based on communication control and two-way assistance; and the processor is used to execute the program instructions stored in the memory to implement a robot navigation method based on communication control and two-way assistance.

[0018] In a fourth aspect, the present application provides a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute a robot navigation method based on bidirectional assistance of communication control.

[0019] The present application provides a robot navigation method and system based on bidirectional assistance of communication control, which has the following beneficial effects:

[0020] (1) Through a hierarchical model, the integration of motion-assisted communication and communication-assisted obstacle avoidance is achieved. On the one hand, according to the current channel conditions of the robot, a path that can improve the quality of future communication is planned; on the other hand, according to the current blind spot of the vehicle's field of view, the sensor nodes that complement the vehicle's field of view are autonomously selected for communication, and a forward-looking obstacle avoidance path is planned.

[0021] (2) Jointly optimize robot motion control and sensor communication resources, using an alternating optimization algorithm to increase the transmission power and bandwidth of sensor nodes that complement the ego vehicle’s field of view, thereby improving collaborative perception and obstacle avoidance capabilities;

[0022] (3) The ego vehicle and obstacles are modeled as polygons, and the strong duality property is used to solve the obstacle avoidance problem under the condition of non-particle model. By integrating the communication-assisted collision avoidance mechanism into the optimization step, the real-time data provided by external sensors is used to improve the robot's control strategy and achieve more efficient obstacle avoidance.

[0023] (4) Different from the traditional offline path planning method, this application adopts a real-time rolling optimization algorithm with an operating frequency of more than 20 Hz, which can adjust the robot motion and sensor communication power and its bandwidth in real time in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of a robot navigation method based on bidirectional assistance of communication control according to Embodiment 1 of the present application;

[0025] Figure 2 This is a schematic diagram of a bidirectional auxiliary architecture for communication control according to Embodiment 1 of the present application;

[0026] Figure 3 This is a data processing flow chart of the five-stage field of view matching algorithm of Example 1 of the present application;

[0027] Figure 4 This is a flowchart of the alternating iterative optimization solution of Example 1 of the present application;

[0028] Figure 5 This is a schematic diagram of the bidirectional auxiliary effect of communication control in Example 1 of the present application;

[0029] Figure 6 This is a schematic diagram of the structure of a robot navigation system based on bidirectional assistance of communication control according to Example 2 of the present application;

[0030] Figure 7 This is a schematic diagram of the device structure of Example 3 of the present application;

[0031] Figure 8 This is a schematic diagram of the storage medium structure of Example 4 of the present application. DETAILED DESCRIPTION

[0032] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] The following is an analysis of the solutions in the prior art in combination with related technologies.

[0034] The concept of communication and control co-design has been widely studied in recent years. This solution often includes machine motion control and communication resource scheduling. However, existing methods are loosely coupled, that is, the influence between communication and control is one-way. Specifically, references [1]-[3] focus on control-assisted communication, that is, adjusting the robot position to improve communication quality. However, in order to accurately perceive dynamic obstacles in the environment, the robot also needs to rely on sensor measurements from other nodes to overcome its own limited perception capabilities. That is, references [1]-[3] ignore the assistance of communication to control. On the other hand, references [4]-[6] study communication-assisted control. However, these studies do not consider how robot motion affects communication performance, resulting in the robot's communication with sensors being easily interrupted during movement, and are not applicable to robot communication systems. Therefore, the interaction between communication and control should be bidirectional, with robot motion affecting communication quality and communication improvement affecting robot motion. There is currently no work that considers bidirectional assistance between communication and control. In addition, the execution frequency of existing communication and control co-design solutions [1]-[6] in robot navigation is relatively low. This limitation makes them unsuitable for real-time obstacle avoidance and can only be used to calculate global data acquisition trajectories offline.

[0035] Existing robot communication methods have the following major shortcomings: (1) Ignoring the bidirectional dependence between communication and control: Traditional control-assisted communication methods calculate a communication-friendly trajectory and ignore how to use the communication data provided by external sensors to improve the control strategy. Traditional communication-assisted control methods do not consider the impact of robot motion on communication performance. Therefore, existing methods do not fully consider the interdependence between communication and control, resulting in low efficiency in complex dynamic environments. (2) Failure to fully utilize the potential of sensor communication: In existing technologies, sensor resources are not fully optimized, which limits the ability of data collection and communication-assisted control, resulting in the robot's communication with the sensor being easily interrupted during movement, affecting the performance of the overall system. (3) Inefficient collision avoidance schemes: Most current methods treat robots and obstacles as point models or circles, and do not consider multi-dimensional shapes. This limits the application of obstacle avoidance methods in narrow spaces. In addition, traditional collision avoidance methods usually rely solely on the robot's own perception and fail to combine information from external sensors, which can easily lead to unnecessary collisions or path deviations due to blind spots. (4) Unfeasibility of offline optimization: Most existing robot communication methods rely on offline path planning, which means that the pre-calculated path may not be able to cope with real-time changing environmental conditions, such as the appearance of dynamic obstacles. This offline optimization method leads to deviations between path planning and real-time control, reducing the adaptability of the system.

[0036] In response to the above-mentioned shortcomings, the present application proposes a robot navigation method and system based on two-way assistance of communication control. Through the model predictive control communication (MPCC) scheme, it realizes the integration of motion-assisted communication and communication-assisted obstacle avoidance, thereby improving the communication efficiency and safety of the robot in complex dynamic environments.

[0037] [1]Y.Guo, CY ou, C.Yin, and R.Zhang, "UA V trajectory and communication co-design: Flexible path discretization and path compression," IEEE J.Sel.AreasCommun., vol.39, no.11, pp.3506–3523, 2021.

[0038] [2] E.Chen, P.Wu, Y.-C.Wu, and M.Xia, "UGV-assisted wireless backscatter communications for large-scale IoT networks," vol.21, no.5, IEEETrans.Wireless Commun., 2022.

[0039] [3]J.Yan, L.Zhang,X.Yang,C.Chen,and

[0040] [4] PY Lajoie, B. Ramtoula, F. Wu, and G. Beltrame, "Towards collaborativesimultaneous localization and mapping: a survey of the current research landscape," ArXiv Preprint: 2108.08325, 2021.

[0041] [5] Z.Li, S.Wang, S.Zhang, M.Wen, K.Ye, YCWu, and DWKNg, "Edge-assistedV2X motion planning and power control under channel uncertainty," IEEE Trans.Veh.Technol., vol.72, no.7, pp.9641–9646, Jul.2023.

[0042] [6] S. Zhang, S. Wang, SY u, JJQY u, and M. Wen, "Collision avoidance predictive motion planning based on integrated perception and V2V communication," IEEE Trans.Intell.Transp.Syst., vol.23, no.7, pp.9640–9653, Jul.2022.

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0044] Example 1

[0045] See also Figure 1 , is a flow chart of a robot navigation method based on bidirectional assistance of communication control according to Example 1 of the present application; the steps include:

[0046] S1: Acquire a channel data set, where the channel data set includes transmission channel data between the robot and the sensor.

[0047] S2: constructing a hierarchical model, wherein the hierarchical model includes a high-level planning algorithm and a low-level planning algorithm; processing the channel data set by the high-level planning algorithm to obtain a global rough path.

[0048] In this embodiment, a communication model is constructed based on the channel data set: the data in the channel data set is stored as a graph structure, and the channel data set is modeled using a parameterized mathematical model, and the parameters in the communication model are obtained through curve fitting.

[0049] According to the established communication model and the physical location of the sensors, the sensors are divided into several clusters, and an anchor point is assigned to each cluster. The anchor point is the location where the robot arrives and collects data. The anchor point connection relationship is generated by occupying the grid map and the path planning algorithm, and a topology map is generated based on the anchor points and the anchor point connection relationship.

[0050] The robot passes through different paths, that is, the order of visiting anchor points, which will result in different movement times. Determine the length of time to stay at each anchor point to ensure that the data of all sensors are collected as needed. Generate a global rough path through the topological graph, and the global rough path consists of waypoints.

[0051] It can be understood that in order to prevent the robot from deviating from the path, the global rough path will be used as a reference for the low-level planning algorithm. During the actual driving process, the robot will make fine adjustments based on the global rough path to ensure that it can avoid collisions and collect the maximum amount of data during the movement.

[0052] S3: Optimizing the global rough path through the low-level planning algorithm to generate a motion trajectory and obstacle avoidance motion instructions.

[0053] In this embodiment, the step of optimizing the global rough path by the low-level planning algorithm to generate a motion trajectory and an obstacle avoidance motion instruction includes steps S31 to S34, and the specific steps are as follows:

[0054] S31: Optimizing robot motion trajectory and sensor resources through motion-assisted communication technology.

[0055] Motion-assisted communication technology maximizes the amount of data obtained from sensors by optimizing the robot's trajectory planning and sensor resources, where the robot's motion trajectory and sensor resources are the key variables to be optimized.

[0056] Robot control is based on a dynamic environment and requires high-frequency path replanning, such as 20Hz. Robot control is performed in a rolling time domain. In this embodiment, a dynamic model, namely the Ackerman model, is used to describe the state evolution of the robot in a dynamic environment. The state of the robot at time step t is represented as (x t ,y t ,θ t ); where (x t ,y t ) is the position of the robot, θ t is the robot's orientation angle. The actions performed by the robot Including linear velocity and angular velocity, the control of the robot is constrained by the dynamic model:

[0057] s t+1 =E(st ,u t )=A t s t +B t u t +c t ,

[0058] Among them, A t , B t , c t The matrix defined for the kinetic model; s t+1 is the state of the robot at time t+1; E(s t ,u t ) is the state evolution function, according to the current state s t and control input u t To predict the state s at the next moment t+1 ;s t is the state of the robot at time t; u t is the control input of the robot at time t, including linear velocity and angular velocity.

[0059] The range of robot control actions is limited as follows:

[0060] u min ≤u t ≤u max ,

[0061] a min ≤u t+1 -u t ≤a max ,

[0062] Among them, u min and u max are the minimum and maximum values ​​of the control vector respectively; a min and a max These constraints ensure that the robot's motion is within the physical range and can cope with various changes in the dynamic environment.

[0063] In order to improve the efficiency of data collection, the resources of sensors are optimized through motion-assisted communication technology. t When , the communication rate is:

[0064]

[0065] Among them, R k,t (s t ,p k ) is the communication rate of sensor k at time t, B k is the bandwidth allocated to sensor k, p k is the power allocated to sensor k; (ak ,b k ) is the position of sensor k; ρ = 0.001, α = 3 are channel parameters.

[0066] The communication rate between the robot and sensor k is expressed as:

[0067]

[0068] Among them, C k ({s t ,p k ,B k}) is the total communication rate between the robot and sensor k in the entire time domain; H is the number of time steps in the time domain; τ is the step size of each time step.

[0069] S32: Using communication-assisted obstacle avoidance technology to dynamically adjust the allocation of communication resources and generate obstacle avoidance motion instructions.

[0070] The goal of communication-assisted collision avoidance technology is to dynamically adjust the allocation of communication resources, including communication power {p k} and communication bandwidth {B k According to the current blind spot of the robot's vision, match the sensor node that complements the vehicle's vision, and increase the transmission power and communication bandwidth of the sensor node, so as to receive its perception data with ultra-low latency, obtain global perception information, and plan a forward-looking obstacle avoidance path.

[0071] See also Figure 3 , is a data processing flow chart of the five-stage field of view matching algorithm of Example 1 of the present application. Specifically divided into: sensor candidate, data association, data fusion, data adjustment, and matching degree analysis.

[0072] Sensor candidate stage: The goal of this stage is to select some nearby sensors with the robot as the axis, and exclude the situation where the communication resources are excessively wasted due to the sensor being too far away from the robot. Specifically, the minimum communication power and bandwidth for the robot to exchange perception data with the sensor are calculated; if the current power and bandwidth exceed the preset resource budget limit, the corresponding sensor is excluded from the candidate list.

[0073] Data association stage: The goal of this stage is to complete the target association between the robot and different sensors in the candidate list. In a dynamic environment, data association belongs to the "many-to-one" problem. The commonly used distance-based data association algorithm, such as the Hungarian algorithm, is only applicable to the "one-to-one" data association problem. If it is used to solve the "many-to-one" data association problem, the computational complexity is relatively high. This embodiment converts the "many-to-one" data association problem into a data clustering problem, and adopts a density space-based clustering algorithm (DBSCAN) to effectively complete multi-perspective data association without knowing the number of targets. The complexity of this clustering algorithm is O(nlog(n)).

[0074] Data fusion stage: The goal of this stage is to fuse different detection results associated with the same target. These different detection results of the same target may come from different observation angles, and the model's grasp of different output results is also different. Therefore, this application proposes a method based on weighted average of detection result confidence, which can fully consider the detection results of different observation angles and effectively utilize the uncertainty and confidence of the detection results.

[0075] Data adjustment stage: After the data fusion stage, due to the problem of noise data, there may still be overlaps between all fused candidate bounding boxes. Therefore, the goal of this stage is to correct the data. There are two possibilities. One possibility is that the overlapping data is the same object. Due to the problem of detection uncertainty, the association cannot be completed in the data association stage, resulting in deviation and being mistaken for two objects. At this time, one of them should be deleted. Another possibility is that the overlapping data is two objects. Due to the uncertainty of recognition, the data deviation causes the overlap of the candidate bounding boxes. At this time, both data should be retained. The non-maximum suppression algorithm is used to calculate the result of IoU. If the IoU is greater than the preset threshold, the candidate bounding box with low confidence is deleted; otherwise, both candidate bounding boxes are retained.

[0076] Matching analysis stage: By analyzing the difference between the robot sensor collaborative perception and the robot's own perception, the output set J includes the sensor IDs of communication-assisted obstacle avoidance. When the difference is greater than the preset threshold, the sensor is selected as the node of communication-assisted obstacle avoidance; when the difference is less than the preset threshold, the sensor is defined as a normal sensor. The perception difference can be obtained by comparing the IoU of the bounding boxes before and after the sensor is introduced.

[0077] Furthermore, after the field of view matching is completed, the robot and the sensor officially enter the real-time perception data interaction process. At this time, the exchanged data is no longer the bounding box, but the perception data or perception features.

[0078] By increasing the communication power and bandwidth, a higher communication rate can be achieved:

[0079]

[0080] Among them, D0 is the lower limit of the rate of real-time perception data interaction.

[0081] On the other hand, when detecting obstacles, the robot not only relies on its own sensors, but also can assist in identification through communication with external sensors j∈J. The robot's obstacle avoidance conditions can be listed as:

[0082]

[0083] Among them, dist m,t (s t ) is the distance between the robot and the obstacle, d safe is the safe distance, E t is the set of obstacles detected by the vehicle, S j,t Represents the set of obstacles detected by the sensor with ID j.

[0084] S33: Jointly optimize sensor communication power and bandwidth resource allocation and robot motion control via an alternating optimization approach.

[0085] Based on steps S31 and S32, a cross-layer optimization problem MPCC is proposed, which is expressed as:

[0086]

[0087] Among them, p k is the transmission power of sensor k, in watts; B k is the bandwidth allocated to sensor k, in Hz; s t is the state of the robot at time t; A t , B t 、c t is the coefficient matrix of Ackerman dynamics; D0 is the minimum data rate for real-time sensor data exchange.

[0088] See also Figure 4 , which is a flowchart of the alternating iterative optimization solution of Example 1 of the present application. First, data from the wireless channel and the sensor are received, and these data are used to solve two sub-problems. Sub-problem P1 focuses on optimizing the power bandwidth during the communication process, and sub-problem P2 focuses on optimizing the control signal according to the model prediction, and the two are optimized alternately.

[0089] In order to solve the problem of {s t ,u t} and {p k , B k}, an alternating optimization method is used to solve {s t ,u t} sub-problem and {p k} subproblems and iterate. Given {p k}, and in each iteration, fix {p k}Optimize {s t ,u t}; By fixing {s t ,u t} and optimize {p using the new values ​​obtained from the problem k}, thereby optimizing P1 and P2 alternately.

[0090] At the nth iteration, the robot's posture state is s t [n]; The sub-problem P1 of sensor communication power and bandwidth resource allocation is defined as:

[0091]

[0092] The optimal solution of subproblem P1 is denoted as {p k [n+1],B k [n+1]};

[0093] The subproblem P2 of robot motion control is defined as:

[0094]

[0095] Among them, subproblem P1 is a convex optimization problem and is solved by CVXPY solver. Subproblem P2 is a non-convex optimization problem and is solved by dual optimization and continuous lower bound maximization method. First, the distance function between the robot and the obstacle is transformed into a dual transformation, and the optimization constraints are obtained by solving the dual problem. In the non-convex communication utility function, a series of concave lower bounds are constructed using the surrogate function maximization strategy, thereby converting the problem into a set of easily solvable subproblems.

[0096] S34: A fast cross-layer optimization strategy based on rolling optimization to adjust robot motion as well as sensor communication power and bandwidth in real time.

[0097] The process of solving the cross-layer optimization problem MPCC includes two layers of iteration. The outer loop iterates between solving subproblems P1 and P2. The inner loop, solving subproblem P2 itself includes continuous optimization of the proxy lower bound. Therefore, the algorithm complexity is relatively high during actual runtime. The present application adopts a fast cross-layer optimization strategy based on rolling optimization, which stores and memorizes the solution process and results of the previous planning as the initial value of the variable for the next planning solution. With such reliance, there is no need to iterate for each planning solution.

[0098] By utilizing the length of adjacent planning intervals, the iterative process originally belonging to the same moment is allocated to the communication control solution at different moments, ensuring rapid convergence and effective control and communication optimization while ensuring real-time performance, adjusting the robot motion and sensor communication power and bandwidth.

[0099] S4: The robot navigates based on the motion trajectory and the obstacle avoidance motion instruction.

[0100] In summary, in Example 1 of the present application, a hierarchical model predictive control communication (MPCC) scheme is used. The high-level planning algorithm uses the topology map to optimize and generate global waypoints, while in the low-level planning algorithm, the sensor resources and robot behaviors are finely controlled through alternating optimization and strong duality algorithms, which significantly improves the communication efficiency and obstacle avoidance ability of the robot in a complex dynamic environment. By optimizing the robot motion trajectory planning and sensor resource allocation, the data transmission volume is maximized and collisions are avoided. In addition, a high-frequency rolling optimization algorithm is used to realize real-time dynamic adjustment of robot motion and sensor communication power and bandwidth, thereby enhancing real-time and robustness.

[0101] Example 2

[0102] See also Figure 6 , which is a schematic diagram of the structure of a robot navigation system based on bidirectional assistance of communication control according to Example 2 of the present application; the specific contents include:

[0103] Acquisition module: acquires a channel data set, wherein the channel data set includes transmission channel data between the robot and the sensor;

[0104] Model construction module: constructing a hierarchical model, the hierarchical model including a high-level planning algorithm and a low-level planning algorithm; processing the channel data set by the high-level planning algorithm to obtain a global rough path;

[0105] Optimization module: optimizes the global rough path through the low-level planning algorithm to generate motion trajectory and obstacle avoidance motion instructions;

[0106] Navigation module: The robot navigates based on the motion trajectory and the obstacle avoidance motion instructions.

[0107] Example 3

[0108] See also Figure 7 , is a schematic diagram of the device structure of Embodiment 3 of the present application. The device 50 includes a processor 51 and a memory 52 coupled to the processor 51 .

[0109] The memory 52 stores program instructions for implementing the above-mentioned robot navigation method based on bidirectional assistance of communication control.

[0110] The processor 51 is used to execute program instructions stored in the memory 52 to implement robot navigation based on communication control bidirectional assistance.

[0111] The processor 51 may also be referred to as a CPU (Central Processing Unit).

[0112] The processor 51 may be an integrated circuit chip with signal processing capabilities. The processor 51 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0113] Example 4

[0114] See also Figure 8 , which is a schematic diagram of the structure of the storage medium of Example 4 of the present application. The storage medium of the embodiment of the present application stores a program file 61 that can implement all the above methods, wherein the program file 61 can be stored in the above storage medium in the form of a software product, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer, a server, a mobile phone, a tablet, and other devices.

[0115] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0116] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0117] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the appended claims and their equivalents.

[0118] Of course, the present invention may have many other implementations. Based on this implementation, other implementations obtained by ordinary technicians in this field without any creative work are all within the scope of protection of the present invention.

Claims

1. A robot navigation method based on bidirectional assistance of communication control, characterized in that: include: Acquire a channel data set, wherein the channel data set includes transmission channel data between the robot and the sensor; Constructing a hierarchical model, the hierarchical model including a high-level planning algorithm and a low-level planning algorithm; processing the channel data set by the high-level planning algorithm to obtain a global rough path; Optimizing the global rough path through the low-level planning algorithm to generate motion trajectories and obstacle avoidance motion instructions; The robot navigates based on the motion trajectory and the obstacle avoidance motion instruction.

2. A robot navigation method based on communication control bidirectional assistance according to claim 1, characterized in that: In constructing a hierarchical model, the hierarchical model includes a high-level planning algorithm and a low-level planning algorithm; the step of processing the channel data set by the high-level planning algorithm to obtain a global rough path specifically includes the following steps: Building a communication model based on the channel data set; According to the communication model and the physical location of the sensor, the sensor is divided into several clusters, and an anchor point is assigned to each cluster; the anchor point is the location where the robot arrives and collects data, and the anchor point connection relationship is generated by occupying the grid map and the path planning algorithm; a topology map is generated based on the anchor points and the anchor point connection relationship; A global rough path is generated through the topology graph, where the global rough path consists of waypoints.

3. The robot navigation method based on communication control bidirectional assistance according to claim 1 is characterized in that: The step of optimizing the global rough path by the low-level planning algorithm and generating a motion trajectory and an obstacle avoidance motion instruction specifically includes: Optimize the robot's motion trajectory and sensor resources through motion-assisted communication technology; Adopt communication-assisted obstacle avoidance technology to dynamically adjust the allocation of communication resources and generate obstacle avoidance movement instructions; Jointly optimize sensor communication power and bandwidth resource allocation and robot motion control through an alternating optimization method; A fast cross-layer optimization strategy based on rolling optimization adjusts robot motion as well as sensor communication power and bandwidth in real time.

4. A robot navigation method based on communication control bidirectional assistance according to claim 3, characterized in that: The step of optimizing the motion trajectory of the robot and the resources of the sensor by using the motion-assisted communication technology specifically includes the following steps: The dynamic model is used to describe the state evolution of the robot in a dynamic environment; the state of the robot at time step t is represented as (x t ,y t ,θ t ); where (x t ,y t ) is the position of the robot, θ t is the orientation angle of the robot; Actions performed by the robot Including linear velocity and angular velocity, the control of the robot is constrained by the dynamic model: s t+1 =E(s t ,u t )=A t s t +B t u t +c t , Among them, A t , B t , c t The matrix defined for the kinetic model; s t+1 is the state of the robot at time t+1; E(s t ,u t ) is the state evolution function, according to the current state s t and control input u t To predict the state s at the next moment t+1 ;s t is the state of the robot at time t; u t is the control input of the robot at time t, including linear velocity and angular velocity; The range of robot control actions is limited as follows: in min in t in max , a min ≤u t+1 -u t ≤a max , Among them, u min and u max are the minimum and maximum values ​​of the control vector respectively; a min and a max are the minimum and maximum values ​​of acceleration; By optimizing the sensor resources through the motion-assisted communication technology, when the robot is in state s t When , the communication rate is: Among them, R k,t (s t ,p k ) is the communication rate of sensor k at time t, B k is the bandwidth allocated to sensor k, p k is the power allocated to sensor k; (a k ,b k ) is the position of sensor k; ρ = 0.001, α = 3 are channel parameters; The communication rate between the robot and sensor k is expressed as: Among them, C k ({s t ,p k ,B k }) is the total communication rate between the robot and sensor k in the entire time domain; H is the number of time steps in the time domain; τ is the step size of each time step.

5. The robot navigation method based on bidirectional assistance of communication control according to claim 3 is characterized in that: In the step of dynamically adjusting the allocation of communication resources by using the communication-assisted obstacle avoidance technology and generating obstacle avoidance motion instructions, the following steps are specifically included: Calculate the minimum communication power and bandwidth for the robot to exchange perception data with the sensor; if the current power and bandwidth exceed the preset resource budget limit, the corresponding sensor is excluded from the candidate list; Associating targets between the robot and different sensors in the sensor candidate list; Fusion of different detection results associated with the same target; After data fusion, the fused data is corrected; By analyzing the difference between the robot's sensor collaborative perception and the robot's own perception, the output includes the sensor ID for communication-assisted obstacle avoidance; When the difference is greater than the preset threshold, the sensor is selected as a node for communication-assisted obstacle avoidance; when the difference is less than the preset threshold, the sensor is defined as a normal sensor.

6. The robot navigation method based on communication control bidirectional assistance according to claim 3 is characterized in that: In the step of jointly optimizing sensor communication power and bandwidth resource allocation and robot motion control by an alternating optimization method, the following steps are specifically included: The expression of the cross-layer optimization problem MPCC is: Among them, p k is the transmission power of sensor k, in watts; B k is the bandwidth allocated to sensor k, in Hz; s t is the state of the robot at time t; A t , B t 、c t is the coefficient matrix of Ackerman dynamics; D0 is the minimum data rate for real-time sensor data exchange; Using the alternating optimization method, we can solve {s t ,u t } sub-problem and {p k } sub-problem and iterate; Given {p k }, and in each iteration, fix {p k }Optimize {s t ,u t }; By fixing {s t ,u t } and optimize {p using the new values ​​obtained from the problem k }; At the nth iteration, the robot's posture state is s t [n]; The sub-problem P1 of sensor communication power and bandwidth resource allocation is defined as: (P1) The optimal solution of subproblem P1 is denoted as {p k [n+1],B k [n+1]}; The subproblem P2 of robot motion control is defined as: (P2) Subproblem P1 is a convex optimization problem and is solved by the CVXPY solver; subproblem P2 is a non-convex optimization problem and is solved by dual optimization and continuous lower bound maximization methods.

7. A robot navigation method based on communication control bidirectional assistance according to claim 6, characterized in that: In the step of adjusting the robot motion and the sensor communication power and bandwidth in real time based on the fast cross-layer optimization strategy of rolling optimization, the following steps are specifically included: The process of solving the cross-layer optimization problem MPCC includes two layers of iterations: the outer loop iterates between solving subproblems P1 and P2; the inner loop, solving subproblem P2 itself includes continuous optimization of the proxy lower bound; Adopt a fast cross-layer optimization strategy based on rolling optimization, store the solution process and results of the previous planning and form a memory as the initial value of the variable for the next planning solution; By utilizing the length of time between adjacent planning intervals, the iterative process originally belonging to the same moment is allocated to the communication control solution at different moments, and the robot motion as well as the sensor communication power and bandwidth are adjusted in real time.

8. A system for a robot navigation method based on communication control bidirectional assistance according to claim 1, characterized in that: include: Acquisition module: acquires a channel data set, wherein the channel data set includes transmission channel data between the robot and the sensor; Model construction module: constructing a hierarchical model, the hierarchical model including a high-level planning algorithm and a low-level planning algorithm; processing the channel data set by the high-level planning algorithm to obtain a global rough path; Optimization module: optimizes the global rough path through the low-level planning algorithm to generate motion trajectory and obstacle avoidance motion instructions; Navigation module: The robot navigates based on the motion trajectory and the obstacle avoidance motion instructions.

9. A device, characterized in that: The device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing a robot navigation method based on communication control and two-way assistance as described in any one of claims 1-7; and the processor is used to execute the program instructions stored in the memory to implement a robot navigation method based on communication control and two-way assistance.

10. A storage medium, characterized in that: Program instructions executable by a processor are stored, and the program instructions are used to execute a robot navigation method based on bidirectional assistance of communication control as described in any one of claims 1-7.

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