Moving track control method of tidal robot

Abstract

The invention discloses a moving track control method of a tidal robot, and relates to the field of track control of tidal robots. According to the moving track control method of the tidal robot, road data, initial traffic image data and the initial position of the tidal robot are comprehensively analyzed, a target shunting position and a plurality of moving tracks are generated, the reliability index of each road section is calculated in combination with real-time track image data, optimization analysis is carried out, and a path with the highest reliability is selected; by analyzing the road data of the road where the tidal robot is located and the initial traffic image data and combining a route planning algorithm, the optimal route of the robot from the initial position coordinates to the target diversion position coordinates is dynamically planned, and the target diversion position is accurately obtained. According to different road conditions, planning of moving points and track sections can be automatically adjusted, the defect that traditional path planning depends on static paths is overcome, and scientificity and real-time performance of track planning are ensured.

Description

Technical Field

[0001] The present invention relates to the field of tidal robot trajectory control, and in particular to a moving trajectory control method of a tidal robot. Background Art

[0002] A tidal robot is a dynamic device used in intelligent transportation systems. It aims to optimize traffic flow by adjusting road resource allocation. Its name comes from the regular changes in tidal phenomena. This type of robot adapts to changes in traffic demand by dynamically adjusting the direction, number or use of lanes at specific times, similar to the rise and fall of the tide. Tidal robots are usually equipped with high-precision positioning equipment, sensors and communication modules. With the development of intelligent transportation systems, tidal robots, as a dynamic traffic optimization device, are widely used in scenarios to alleviate traffic congestion. Tidal robots dynamically adjust road resource allocation to achieve vehicle diversion during peak hours and optimization of road section traffic efficiency.

[0003] Prior art, such as a patent application with announcement number: CN109857108B, discloses a mobile robot trajectory tracking method and system based on an internal model control algorithm, the steps of which are: respectively obtaining the state variables of the mobile robot at a specific moment, the state variables including the state variables of the motion trajectory, the state variables of the odometer and the state variables of the mobile robot, obtaining the deviation between the actual motion trajectory and the preset motion trajectory through the motion trajectory, the odometer and the state variables; using the obtained deviation as the input of the internal model controller to obtain the output at the corresponding moment; transmitting the output to the motor of the mobile robot, and obtaining the state variables of the odometer at the next moment, and obtaining the predicted value of the state variables of the mobile robot at the next moment according to the kinematic equation of the mobile robot. The method and system of the present invention realize an improved internal model controlled robot trajectory with small computational complexity and good real-time performance.

[0004] Based on the above scheme, it is found that the limitations of the existing technology include at least the following problems. First, the existing technology lacks a comprehensive analysis of actual road data and robot behavior in complex dynamic traffic environments, which easily leads to error accumulation in traffic environment applications, and ignores factors such as road dynamic characteristics, real-time traffic conditions, and path planning reliability, which causes the robot's movement trajectory to deviate from the preset trajectory, affecting the accuracy and stability of trajectory tracking. Secondly, it is difficult for the existing technology to perform reliability evaluation and optimization selection on multiple candidate paths of the movement trajectory, and thus it is difficult to adapt to real-time changing traffic conditions and complex road environments, which easily leads to inflexible trajectory planning and reduces the robot's adaptability in dynamic environments. At the same time, the existing technology only plans the robot's target diversion position coordinates based on static settings, and fails to fully combine the road environment and traffic conditions to dynamically optimize and comprehensively analyze the movement path, resulting in inefficiency of the robot in path diversion, and thus it is difficult to improve the overall traffic efficiency of the traffic road. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for controlling the movement trajectory of a tidal robot, which solves the problem that the prior art lacks a comprehensive analysis of actual road data and robot behavior in a complex dynamic traffic environment, which easily leads to error accumulation in traffic environment applications, and ignores factors such as road dynamic characteristics, real-time traffic conditions, and path planning reliability, thereby causing the robot's movement trajectory to deviate from the preset trajectory, affecting the accuracy and stability of trajectory tracking. Secondly, it is difficult for the prior art to perform reliability evaluation and optimization selection on multiple candidate paths of the movement trajectory, and thus it is difficult to adapt to real-time changing traffic conditions and complex road environments, which easily leads to inflexible trajectory planning and reduces the robot's adaptability in a dynamic environment. At the same time, the prior art only plans the robot's target diversion position coordinates based on static settings, and fails to fully combine the road environment and traffic conditions to dynamically optimize and comprehensively analyze the movement path, thereby causing the robot to be inefficient in path diversion, and thus it is difficult to improve the overall traffic efficiency of the traffic road.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for controlling the movement trajectory of a tidal robot, comprising the following steps: obtaining road data, initial traffic image data and initial position coordinates of the tidal robot in a set traffic road, and performing preprocessing, wherein the road data are specifically the road width value, road length value, starting point position coordinates and end point position coordinates of each traffic section, the initial traffic image data are specifically the pixel value and two-dimensional position coordinates of each pixel point in the traffic image, and the initial position coordinates are specifically the initial three-dimensional vertical position coordinate value, the initial three-dimensional horizontal position coordinate value and the initial three-dimensional height position coordinate value; performing data analysis on the preprocessed road data and initial traffic image data in the set traffic road to obtain the target diversion position coordinates of the tidal robot arriving at the set traffic road, and combining the route planning algorithm to control the tidal robot The robot's initial position coordinates and the target diversion position coordinates arriving at the set traffic road are comprehensively analyzed to obtain several moving trajectories of the tidal robot in the set traffic road; each moving trajectory of the tidal robot in the set traffic road is divided into sections to obtain several moving sections of each moving trajectory of the tidal robot in the set traffic road; the trajectory image data of each moving section of each moving trajectory of the tidal robot in the set traffic road is acquired in real time, and a comprehensive analysis is performed to obtain the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road, wherein the trajectory image data specifically includes the trajectory pixel value and the trajectory two-dimensional position coordinates of each trajectory pixel point; the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road is comprehensively analyzed in real time, and trajectory adjustment measures are taken based on the analysis results.

[0007] The present invention has the following beneficial effects:

[0008] (1) The mobile trajectory control method of the tidal robot analyzes the road data and initial traffic image data of the road where the tidal robot is located, and combines the route planning algorithm to dynamically plan the optimal path of the robot from the initial position coordinates to the target diversion position coordinates, so that the planning of the moving point and the trajectory section can be automatically adjusted according to different road conditions, overcoming the defect of traditional path planning that relies on static paths, ensuring the scientificity and real-time nature of trajectory planning, and improving the robot's adaptability to complex traffic scenes.

[0009] (2) The mobile trajectory control method of the tidal robot obtains trajectory image data in real time and calculates the reliability index of the trajectory, thereby evaluating the safety and effectiveness of each mobile trajectory from multiple dimensions, thereby effectively identifying potential risks and obstacles, and eliminating inappropriate paths based on reliability evaluation, ensuring that the tidal robot always chooses the optimal path to operate, thereby improving the accuracy of trajectory evaluation, and significantly reducing the risk of path failure, ensuring the operational stability of the tidal robot.

[0010] (3) The mobile trajectory control method of the tidal robot formulates a control strategy based on the reliability index analysis results of each mobile trajectory, thereby achieving refined management of the tidal robot's operating status, and dynamically adjusting the control strategy to effectively respond to sudden traffic conditions or trajectory obstacles, ensuring that the robot is always in the optimal operating state. In addition, through real-time feedback adjustment strategies, the tidal robot can efficiently complete diversion tasks in complex traffic roads, comprehensively improving task execution effects and resource utilization.

[0011] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of a moving trajectory control method of a tidal robot according to the present invention.

[0013] Figure 2 The present invention is a flow chart of the steps of obtaining the target diversion position coordinates of the tidal robot within a set traffic road in a moving trajectory control method of the tidal robot. DETAILED DESCRIPTION

[0014] The embodiment of the present application solves the problem that the prior art lacks comprehensive analysis of actual road data and robot behavior in complex dynamic traffic environments through a mobile trajectory control method for a tidal robot, which easily leads to error accumulation in traffic environment applications, and ignores factors such as road dynamic characteristics, real-time traffic conditions, and path planning reliability, causing the robot's movement trajectory to deviate from the preset trajectory, affecting the accuracy and stability of trajectory tracking. Secondly, it is difficult for the prior art to perform reliability evaluation and optimization selection on multiple candidate paths of the movement trajectory, and thus it is difficult to adapt to real-time changing traffic conditions and complex road environments, which easily leads to inflexible trajectory planning and reduces the robot's adaptability in dynamic environments. At the same time, the prior art only plans the robot's target diversion position coordinates based on static settings, and fails to fully combine the road environment and traffic conditions to dynamically optimize and comprehensively analyze the movement path, resulting in the robot being inefficient when diverting paths, and thus it is difficult to improve the overall traffic efficiency of traffic roads.

[0015] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0016] First, the road data, initial traffic image data and initial position coordinates of the tidal robot in the set traffic road are obtained, the collected data are preprocessed, and the preprocessed data are comprehensively analyzed to generate the target diversion position coordinates of the tidal robot in the set traffic road. Combined with the route planning algorithm, the initial position coordinates and the target diversion position coordinates of the tidal robot are comprehensively analyzed to obtain several moving trajectories of the tidal robot in the set traffic road, and each trajectory is divided into sections to obtain multiple moving sections. The trajectory image data of each section of each trajectory is obtained in real time to obtain the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road. Finally, the reliability index of the first moving section of each moving trajectory is analyzed first, and the best path is selected as the optimal trajectory. Then, the reliability index of each subsequent section is continuously comprehensively analyzed and optimized until the tidal robot reaches the target diversion position coordinates in the set traffic road.

[0017] See also Figure 1, an embodiment of the present invention provides a technical solution: a method for controlling the movement trajectory of a tidal robot, comprising the following steps: obtaining road data, initial traffic image data and initial position coordinates of the tidal robot within a set traffic road (in this implementation example, all three-dimensional position coordinates are based on the device that takes the initial traffic image as the origin, the X-axis points to the right side of the device, from the perspective of the device, the Y-axis points to the front of the device, that is, from the position of the device, it points to the target object or the farther direction in the scene, and the Z-axis represents the vertical height of the vehicle or obstacle relative to the ground), and pre-processing, wherein the road data is specifically for each The road width value, road length value, starting point position coordinates of the traffic section (i.e., the starting three-dimensional vertical position coordinate value of the section, the starting three-dimensional horizontal position coordinate value of the section, and the starting three-dimensional height position coordinate value of the section), and the end point position coordinates of the section (i.e., the end point three-dimensional vertical position coordinate value of the section, the end point three-dimensional horizontal position coordinate value of the section, and the end point three-dimensional height position coordinate value of the section), the initial traffic image data specifically includes the pixel value and two-dimensional position coordinates of each pixel point in the traffic image (with the upper left corner of the image as the origin of the two-dimensional position coordinate system, and the X-axis: pointing to the horizontal direction of the image (from left to right), and the Y-axis: pointing to the vertical direction of the image ( from top to bottom), the initial position coordinates are specifically the initial three-dimensional vertical position coordinate value, the initial three-dimensional horizontal position coordinate value, and the initial three-dimensional height position coordinate value; the pre-processed road data and the initial traffic image data in the set traffic road are analyzed to obtain the target diversion position coordinates of the tidal robot arriving at the set traffic road, and the initial position coordinates of the tidal robot and the target diversion position coordinates of the tidal robot arriving at the set traffic road are comprehensively analyzed in combination with the route planning algorithm to obtain several movement trajectories of the tidal robot in the set traffic road; each movement trajectory of the tidal robot in the set traffic road is separately analyzed. The road segmentation is performed to obtain a number of moving sections of each moving trajectory of the tidal robot in the set traffic road; the trajectory image data of each moving section of each moving trajectory of the tidal robot in the set traffic road is acquired in real time, and a comprehensive analysis is performed to obtain a reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road, wherein the trajectory image data specifically includes a trajectory pixel value and a trajectory two-dimensional position coordinate of each trajectory pixel point; a comprehensive analysis is performed on the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road in real time, and trajectory adjustment measures are taken based on the analysis results.

[0018] Specifically, Figure 2As shown, the specific steps for obtaining the target diversion position coordinates of the tidal robot in the set traffic road are as follows: the pixel value and the two-dimensional position coordinates of each pixel point in the image in the set traffic road are input into the pre-established target detection model for comprehensive analysis, and the vehicle two-dimensional position coordinates and vehicle confidence of several vehicle prediction boundary boxes in the set traffic road are obtained, as well as the obstacle two-dimensional position coordinates and obstacle confidence of several obstacle prediction boundary boxes; the vehicle prediction two-dimensional position coordinates and the vehicle prediction confidence of each vehicle prediction boundary box in the set traffic road are comprehensively analyzed, and the obstacle prediction two-dimensional position coordinates and obstacle confidence of several vehicle boundary boxes in the set traffic road are obtained; the vehicle two-dimensional position coordinates of each vehicle boundary box in the set traffic road and the obstacle two-dimensional position coordinates of several obstacle boundary boxes are obtained; the vehicle two-dimensional position coordinates of each vehicle boundary box in the set traffic road and the obstacle two-dimensional position coordinates of each obstacle boundary box are comprehensively analyzed. The three-dimensional position coordinates of each vehicle boundary box in the set traffic road and the three-dimensional position coordinates of each obstacle boundary box are obtained, wherein the three-dimensional position coordinates of the vehicle are the three-dimensional vertical position coordinate values ​​of the vehicle, the three-dimensional horizontal position coordinate values ​​of the vehicle, and the three-dimensional height position coordinate values ​​of the vehicle, and the three-dimensional position coordinates of the obstacle are the three-dimensional vertical position coordinate values ​​of the obstacle, the three-dimensional horizontal position coordinate values ​​of the obstacle, and the three-dimensional height position coordinate values ​​of the obstacle; the three-dimensional position coordinates of each vehicle boundary box in the set traffic road and the three-dimensional position coordinates of each obstacle boundary box are comprehensively analyzed with the road width and road length values ​​of each traffic section to obtain the congestion index of each traffic section in the set traffic road; the congestion index of each traffic section in the set traffic road is arranged in descending order to generate a congestion arrangement section table, and the section starting point position coordinates of the first sequence of sections are marked as target diversion position coordinates.

[0019] The specific process of obtaining the vehicle two-dimensional position coordinates of several vehicle boundary boxes and the obstacle two-dimensional position coordinates of several obstacle boundary boxes within the set traffic road is as follows: the vehicle prediction confidence of each vehicle prediction boundary box and the obstacle prediction confidence of each obstacle prediction boundary box are sorted in descending order, and the prediction boundary box and the obstacle prediction boundary box of the vehicle prediction confidence, obstacle prediction confidence in the first sequence are selected as the "reference frame", and the overlap with other boundary boxes is calculated. If the overlap between the current boundary box and the reference frame is greater than a preset threshold (such as 0.5), it is considered that the two frames overlap too much, and the frame with high overlap is deleted. Otherwise, the frame is retained, and the bounding box that has not been deleted is used as a new candidate frame, and then the frame with the highest confidence is selected for the next round of suppression until all frames are processed, and the vehicle prediction two-dimensional position coordinates, vehicle prediction confidence of several vehicle prediction boundary boxes, and the obstacle prediction two-dimensional position coordinates and obstacle prediction confidence of several obstacle prediction boundary boxes that are saved are marked as the vehicle two-dimensional position coordinates of each vehicle boundary box and the obstacle two-dimensional position coordinates of each obstacle boundary box.

[0020] The specific process of obtaining the vehicle three-dimensional position coordinates of each vehicle boundary box within the set traffic road is: obtaining the horizontal focal length value, vertical focal length value, horizontal resolution value, vertical resolution value, physical width value of the sensor (in the shooting device), physical height value of the sensor (in the shooting device) and vehicle depth information value corresponding to each vehicle pixel point in each vehicle boundary box of the corresponding shooting device within the set traffic road, and performing comprehensive analysis to obtain the vehicle three-dimensional vertical position coordinate value and vehicle three-dimensional horizontal position coordinate value of each vehicle pixel point in each vehicle boundary box within the set traffic road, using the vehicle depth information value corresponding to the vehicle pixel point as the vehicle three-dimensional height value, and marking it as the vehicle three-dimensional position coordinates of each vehicle pixel point in each vehicle boundary box within the set traffic road.

[0021] The specific process of obtaining the obstacle three-dimensional position coordinates of each obstacle boundary box within the set traffic road is consistent with the vehicle three-dimensional position coordinate process of each vehicle boundary box, and the calculation logic and calculation formula are consistent.

[0022] In this implementation scheme, the target detection model is introduced and the bounding box is screened and optimized in combination with the non-maximum suppression method, thereby improving the accuracy of vehicle and obstacle detection. The non-maximum suppression method effectively removes redundant and overlapping bounding boxes, retains the optimal detection results, and ensures that the final output two-dimensional position coordinates have high confidence, thereby avoiding detection errors caused by overlap and noise, thereby improving the tidal robot's ability to recognize the traffic environment and the accuracy of planning paths. Secondly, by comprehensively utilizing the physical parameters of the shooting equipment (such as horizontal focal length, resolution and sensor size) and depth information, a mapping relationship from two-dimensional to three-dimensional for vehicles and obstacles is established, thereby ensuring the accuracy of three-dimensional spatial data, and providing strong spatial information support for subsequent path planning and congestion analysis. Finally, based on the three-dimensional position coordinate data of vehicles and obstacles and the width and length information of the road, the congestion index of each section is quantified, and a congested section table is generated by descending order, so that the tidal robot can prioritize the identification of sections with greater traffic pressure and determine the target diversion points, and fully consider the dynamic characteristics of traffic, and can adjust the diversion strategy in real time, thereby improving the overall efficiency of traffic flow and alleviating congestion problems.

[0023] Specifically, the target detection model is a convolutional neural network model, and the pre-establishment steps of the target detection model are as follows: obtaining several groups of initial traffic image data within a set traffic road, and establishing a traffic data set, and dividing the traffic data set into a traffic training data set and a traffic verification data set; initializing the convolutional neural network model, specifically initializing the connection weights of each layer in the convolutional neural network model (the "connection strength" between the input signal of each layer of neurons and the output signal of the previous layer); training the pre-established target detection model based on the traffic training data set, and calculating the traffic regression loss function; and evaluating the performance of the target detection model based on the traffic verification data set, and adjusting the model parameters according to the verification results until the model prediction results meet the expected standards.

[0024] It should be explained that the convolutional neural network model includes an input layer, a feature extraction layer, a candidate box layer, a bounding box regression and classification layer, and an output layer.

[0025] The input layer is used to receive the input image and normalize it to suit the network requirements.

[0026] The feature extraction layer is used to extract features from the input image through convolution and pooling operations.

[0027] The candidate box layer is used to generate candidate boxes based on features and locate areas where objects may exist.

[0028] The bounding box regression and classification layer is used to accurately locate and classify the candidate box and output the bounding box and category of the target.

[0029] The output layer is used to output the category bounding box and the corresponding confidence.

[0030] Among them, the specific process of obtaining the vehicle two-dimensional position coordinates and vehicle confidence of each vehicle prediction boundary box in the set traffic road and the obstacle two-dimensional position coordinates and obstacle confidence of each obstacle prediction boundary box is: inputting the image data in the set traffic road into the input layer of the target detection model.

[0031] The input image is normalized, resized, and data augmented (such as rotation, flipping, cropping, etc.) to adapt to the input requirements of the model.

[0032] The input layer passes the pixel values ​​and two-dimensional position coordinates of the image to the feature extraction layer.

[0033] Features are extracted through convolutional layers and pooling layers, including edge, texture, region, shape, and size features of the image.

[0034] Use deep networks to extract global and local features and retain important information in the image.

[0035] The feature map is passed to the candidate box layer, and the region proposal network (RPN) generates candidate regions based on the feature map sliding window. Each sliding window predicts multiple candidate boxes (of different scales and aspect ratios) and assigns a confidence value of "whether it may contain an object" to each candidate box.

[0036] The candidate boxes are passed to the bounding box regression and classification layers.

[0037] In the bounding box regression layer, the position coordinates of each candidate box are corrected to fit the target object more accurately. The position coordinate values ​​of the upper left corner and the lower right corner are output.

[0038] In the classification layer, a category label (such as vehicle, obstacle) is assigned to each candidate box, and the confidence value of the candidate box is predicted, indicating the possibility of containing an object in the box.

[0039] The output layer of the model integrates the location coordinates, categories, and confidence values ​​of all candidate boxes.

[0040] In this implementation scheme, through the multi-layer design of the convolutional neural network model (such as feature extraction, candidate box generation, bounding box regression and classification layer), it is possible to quickly extract key features in complex traffic scenes, including boundary information, categories and confidence values ​​of vehicles and obstacles, and then effectively cope with diversified scenes in dynamic traffic environments (such as multiple means of transportation and sudden obstacles), thereby improving the accuracy and real-time performance of the tidal robot's decision-making. Secondly, in the step of pre-establishing the target detection model, the training effect of the model is ensured by dividing the traffic training data set and the verification data set, combining the regression loss function and model parameter tuning, and the verification data set evaluation further ensures the high reliability and high precision of the model in practical applications, thereby significantly reducing the false detection rate and missed detection rate, laying a solid foundation for the subsequent tidal robot positioning and path planning. In addition, the input image is standardized , data enhancement (such as rotation, flipping, etc.) and convolution operations not only expand the diversity of traffic data sets, but also enhance the model's adaptability to different scenarios. Through multi-scale candidate box generation and deep feature extraction of the region proposal network (RPN), the model can accurately capture the local and global features of various targets in the image, reduce the detection bias caused by lighting changes, vehicle size differences or occlusions, and then enable the tidal robot to cope with different traffic conditions more stably, further improving the intelligent level of traffic management. Finally, the candidate box layer is combined with the region proposal network to generate possible target areas, and then the bounding box regression is used to correct the position coordinates and the classification layer to judge the category, which can effectively eliminate background noise and reduce the waste of computing resources, thereby significantly improving the target detection efficiency. The tidal robot can quickly complete target recognition and path planning, and significantly optimize the response speed in high-traffic sections.

[0041] Specifically, the specific steps for obtaining the congestion index of each section in the set traffic road are as follows: comprehensively analyzing the vehicle three-dimensional position coordinates of each vehicle boundary box in the set traffic road, the obstacle three-dimensional position coordinates of each obstacle boundary box, and the length value and width value of each traffic section by combining statistical methods to obtain the vehicle three-dimensional position coordinates of several vehicle turning points of several vehicle boundary boxes of each traffic section in the set traffic road and the obstacle three-dimensional position coordinates of several obstacle turning points of several obstacle boundary boxes; reading the vehicle three-dimensional position coordinates of each vehicle turning point of each vehicle boundary box of each traffic section in the set traffic road and the obstacle three-dimensional position coordinates of each obstacle turning point of each obstacle boundary box, and performing comprehensive analysis to obtain the vehicle road occupation area and obstacle road occupation area of ​​each traffic section in the set traffic road; comprehensively analyzing the vehicle road occupation area, obstacle road occupation area, width value, and length value of each traffic section in the set traffic road to obtain. A road utilization index for each traffic section in the traffic road is set; and the three-dimensional vehicle position coordinates of each vehicle turning point of each vehicle boundary box of each traffic section in the set traffic road and the three-dimensional obstacle position coordinates of each obstacle turning point of each obstacle boundary box are comprehensively analyzed by combining statistical methods to obtain the three-dimensional vehicle position coordinates of adjacent vehicle turning points of several groups of adjacent vehicle boundary boxes of each traffic section in the set traffic road (i.e., the two closest turning points in the adjacent vehicle boundary boxes; if there are turning points with the same distance, one group of turning points is selected as the adjacent vehicle turning points) and the three-dimensional obstacle position coordinates of adjacent obstacle turning points of several groups of adjacent obstacle boundary boxes, and a comprehensive analysis is performed to obtain the vehicle spacing and obstacle spacing of each section in the set traffic road; the road utilization index, vehicle spacing, and obstacle spacing of each traffic section in the set traffic road are standardized (i.e., de-unitized), and a comprehensive analysis is performed to obtain the congestion index of each traffic section in the set traffic road.

[0042] The specific process of obtaining the vehicle three-dimensional position coordinates of several vehicle turning points of several vehicle boundary boxes of each traffic section in the set traffic road and the obstacle three-dimensional position coordinates of several obstacle turning points of several obstacle boundary boxes is as follows: obtaining the starting point position coordinates of each traffic section in the set traffic road, and calculating the sum of the length value and the width value of each traffic section with the starting point position coordinates of each traffic section in the set traffic road to obtain the position coordinate interval of each traffic section, and respectively judging and analyzing the vehicle three-dimensional position coordinates of each vehicle boundary box and the obstacle three-dimensional position coordinates of each obstacle boundary box in the set traffic road with the position coordinate interval (i.e., vertical position coordinate value interval, horizontal position coordinate value interval) of each traffic section; if the vehicle three-dimensional vertical position coordinate value and the vehicle horizontal position coordinate value of the vehicle boundary box and the obstacle three-dimensional vertical position coordinate value and the obstacle horizontal position coordinate value of the obstacle boundary box all belong to the position coordinate interval of the traffic section, then the vehicle boundary box and the obstacle boundary box are marked as this traffic section, and the number of vehicle boundary boxes and obstacle boundary box marks is counted.

[0043] The specific process of obtaining the vehicle three-dimensional position coordinates of adjacent vehicle inflection points of several groups of adjacent vehicle boundary boxes and the obstacle three-dimensional position coordinates of adjacent obstacle inflection points of several groups of adjacent obstacle boundary boxes for each traffic section in the set traffic road is: performing distance calculation on the vehicle three-dimensional position coordinates of each vehicle inflection point between each vehicle boundary box and the obstacle three-dimensional position coordinates of each obstacle inflection point between each obstacle boundary box to obtain the vehicle distance between each vehicle inflection point of every two vehicles and the obstacle distance between each obstacle inflection point of every two obstacles, and performing comparative analysis to obtain the two vehicle inflection points of every two vehicles with the closest vehicle distance, which are marked as adjacent vehicle inflection points. Similarly, the adjacent obstacle inflection points are obtained.

[0044] The calculation formulas for calculating the vehicle road occupancy area, road utilization index, vehicle spacing, and congestion index for each traffic section within the set traffic road are as follows: Among them, DzL i is the vehicle road area of ​​the i-th traffic section in the set traffic road, X inj Y is the three-dimensional vertical position coordinate value of the jth vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. in(j+1) Y is the three-dimensional horizontal position coordinate value of the j+1th vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. inj X is the three-dimensional horizontal position coordinate value of the jth vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. in(j+1)X is the three-dimensional vertical position coordinate value of the j+1th vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. inj0 Y is the three-dimensional vertical position coordinate value of the j0th vehicle turning point of the nth vehicle boundary box in the i-th road section. in1 Y is the three-dimensional horizontal position coordinate value of the first vehicle turning point of the nth vehicle boundary box in the i-th road section of the traffic road. inj0 X is the three-dimensional horizontal position coordinate value of the j0th vehicle turning point of the nth vehicle boundary box in the i-th road section. in1 DyL is the three-dimensional vertical position coordinate value of the first vehicle turning point of the nth vehicle boundary box in the i-th road section within the traffic road. i To set the road utilization index of the i-th traffic section in the traffic road, DwL i CdZ is the obstacle road area of ​​the i-th traffic section in the traffic road. i To set the length of the i-th traffic section in the traffic road, KdZ i To set the width of the i-th traffic section in the traffic road, JcL i is the vehicle spacing of the ith traffic section in the traffic road, X im1 is the three-dimensional vertical position coordinate value of the first turning point of the adjacent vehicle turning points of the mth group of adjacent vehicle boundary boxes in the i-th traffic section within the traffic road, X im2 Y is the three-dimensional vertical position coordinate value of the second turning point in the mth group of adjacent vehicle boundary boxes of the i-th traffic section in the traffic road, im1 Y is the three-dimensional horizontal position coordinate value of the first turning point of the adjacent vehicle turning points of the mth group of adjacent vehicle boundary boxes in the i-th traffic section within the traffic road, im2 YjD is the three-dimensional horizontal position coordinate value of the second turning point of the adjacent vehicle turning points of the mth group of adjacent vehicle boundary boxes in the i-th traffic section within the traffic road, i To set the congestion index of the ith traffic section in the traffic road, DyL i ′ is the road utilization index of the ith traffic section in the set traffic road after standardization, β1 is the road coefficient in the set traffic road stored in the database, JcL i ′ is the vehicle spacing of the ith traffic section in the set traffic road after standardization, β2 is the vehicle coefficient in the set traffic road stored in the database, ZcL i′ is the obstacle spacing of the i-th traffic section in the set traffic road after normalization, β3 is the obstacle coefficient in the set traffic road stored in the database, and β1+β2+β3=1, n0=m0+1, i=1,2,3,…,i0,i0 is the number of traffic sections, n=1,2,3,…,n0,n0 is the number of vehicle bounding boxes, j=1,2,3,…,j0,j0 is the number of vehicle turning points, and m=1,2,3,…,m0,m0 is the number of adjacent vehicle bounding box groups.

[0045] It needs to be explained that the specific acquisition process of β1, β2, and β3 is: read the road utilization index, vehicle spacing, and obstacle spacing of each section in the set traffic road after standardization, perform mean analysis, obtain the mean of the road utilization index, the mean of the vehicle spacing, and the mean of the obstacle spacing in the set traffic road, and perform sum analysis to obtain the congestion sum value in the set traffic road, perform proportion analysis on the mean of the road utilization index, the mean of the vehicle spacing, and the mean of the obstacle spacing in the set traffic road and the congestion sum value, respectively, and use the proportion analysis results as the corresponding coefficients.

[0046] The obstacle road occupation area of ​​each road section in the traffic road is set to be consistent with the calculation logic of the vehicle road occupation area, and the calculation steps are also consistent.

[0047] The calculation logic of the obstacle spacing and vehicle spacing for each road section in the traffic road is consistent, and the calculation formula is also consistent.

[0048] A specific implementation example of calculating the congestion index of a certain road section within a set traffic road, with the following data:

[0049] The length of this road section is 30 meters and the width is 9 meters.

[0050] This road section includes 5 vehicle boundary boxes and 2 obstacle boundary boxes, and the 5 vehicle boundary boxes each include 4 vehicle inflection points. The obstacle inflection points of the 2 obstacle boundary boxes are 3 and 5 respectively, and the vehicle three-dimensional height value of each vehicle inflection point and the obstacle three-dimensional height value of each vehicle inflection point and the obstacle inflection point of each vehicle inflection point are set to 0.

[0051] The coordinates of the four inflection points of the first vehicle bounding box are: (0, 4, 0), (5, 4, 0), (5, 6, 0), (0, 6, 0).

[0052] The coordinates of the four inflection points of the second vehicle bounding box are: (6, 3, 0), (10, 3, 0), (10, 5, 0), and (6, 5, 0).

[0053] The coordinates of the four inflection points of the third vehicle bounding box are: (12, 4, 0), (17, 4, 0), (17, 6, 0), and (12, 6, 0).

[0054] The coordinates of the four inflection points of the fourth vehicle bounding box are: (18, 2, 0), (23, 2, 0), (23, 4, 0), and (17, 4, 0).

[0055] The coordinates of the four inflection points of the fifth vehicle bounding box are: (24, 4, 0), (28, 4, 0), (28, 6, 0), and (24, 6, 0).

[0056] The coordinates of the three inflection points of the first obstacle bounding box are: (3, 7, 0), (3.5, 7.5, 0), and (4, 7.2, 0).

[0057] The coordinates of the five inflection points of the second obstacle bounding box are: (7, 7.2, 0), (7, 7.5, 0), (7.3, 7.6, 0), (7.2, 6, 0), (7, 6.9, 0).

[0058] Then the adjacent inflection points of the first group of adjacent vehicle bounding boxes are (5, 4, 0) and (10, 3, 0).

[0059] The coordinates of the adjacent inflection points of the second group of adjacent vehicle bounding boxes are (6, 3, 0) and (12, 4, 0).

[0060] The coordinates of the adjacent inflection points of the third group of adjacent vehicle bounding boxes are (12, 4, 0) and (18, 2, 0).

[0061] The coordinates of the adjacent inflection points of the fourth group of adjacent vehicle bounding boxes are (12, 4, 0) and (24, 4, 0).

[0062] The adjacent inflection points of the obstacle bounding box are (4,7.2,0), (7,7.2,0).

[0063] Substituting the above data into the calculation formula of the vehicle road occupied area, road utilization index, vehicle spacing, congestion index of each traffic section in the set traffic road, the obstacle road occupied area, obstacle spacing, and congestion index of each road section in the set traffic road, the following is obtained: the vehicle road occupied area of ​​a certain road section in the set traffic road (square meters) = (1 / 2)*[|(0*4-4*5)+(5*6-4*6)+(5*0-6*6)+(0*4-6*0)|+|(6*3-10*3)+(10 +|(24*4-4*28)+(28*6-4*28)+(28*6-6*24)+(24*4-6*24)|]=29.

[0064] Set the obstacle road occupancy area (square meters) of a certain section of the traffic road = (1 / 2)*[|(0*4-4*5)+(3*7.5-7.5*4)+(4*7-7.2*3)|+|(7*7.5-7.2*7)+(7*7.3-7.5*7.6)+(7.3*6-7.6*7.0)+(7.2*6.9-6*7)+(7*7.2-6.9*7)|]≈0.4.

[0065] Set the road utilization index of a certain section of the traffic road = (29 + 0.4) / (30 * 9) ≈ 0.1 Set the vehicle spacing (meters) of a certain section of the traffic road = ( 1 / 2 ((5-10) 2 +(4-3) 2 ))+( 1 / 2 ((6-12) 2 +(3-4) 2 ))+( 1 / 2 ((12-18) 2 +(4-2) 2 ))+( 1 / 2 ((12-24) 2 +(4-4) 2 ))≈29.5.

[0066] Set the obstacle distance of a certain section of the traffic road (meters) = 1 / 2 ((4-7) 2 +(7.2-7.2) 2 )=3.

[0067] The road utilization index of a certain section of the set traffic road after standardization is ≈0.25.

[0068] The vehicle spacing of a certain section of a set traffic road after standardization is ≈0.71.

[0069] The obstacle spacing of a certain section of the set traffic road after standardized processing is ≈0.21.

[0070] The road coefficient within the traffic road is set to ≈0.31.

[0071] The vehicle coefficient within the traffic road is set to ≈0.45.

[0072] The obstacle coefficient within the traffic road is set to ≈0.34.

[0073] The congestion index of a certain section of the traffic road is set to ln(1+0.31*0.25+0.45*(1 / (0.71+1))+0.34*(1 / (0.21+1)))≈0.49.

[0074] In this implementation, the road occupancy area is calculated by the three-dimensional position coordinates of vehicles and obstacles, and then combined with parameters such as road length and width, the road utilization rate of each road section is accurately evaluated. At the same time, through the analysis of the distance between adjacent vehicles or obstacles, the congestion hotspots in the local area can be identified, thereby greatly improving the accuracy of the congestion index and being able to more realistically reflect the actual congestion situation of the road. Secondly, the road utilization index, vehicle spacing and obstacle spacing are standardized to eliminate the differences in data scales of different road sections, making the congestion index of different road sections more comparable, so that the road conditions (such as width, Obstacle distribution, etc.) is inconsistent, objective congestion evaluation results can be obtained, which is convenient for the tidal robot to make dynamic decisions between multiple road sections. In addition, through the mean analysis and proportion analysis in the congestion index calculation process, the weight coefficients of roads, vehicles and obstacles are dynamically generated, so that it can flexibly adapt to the actual traffic characteristics of different sections. Finally, the congestion index of each section integrates information such as road utilization, vehicle spacing and obstacle distribution, providing the tidal robot with a refined section capacity assessment, which in turn helps to optimize the current mobile path and provide reliable data support for long-term traffic forecasts.

[0075] Specifically, the specific steps for obtaining several moving trajectories of the tidal robot in the set traffic road are as follows: comprehensively analyze the initial position coordinates of the tidal robot and the target diversion position coordinates arriving at the set traffic road in combination with the interpolation method to obtain the potential position coordinates of several potential moving points of the tidal robot in the set traffic road; and comprehensively analyze the vehicle three-dimensional position coordinates of each vehicle boundary box between the initial position coordinates of the tidal robot and the target diversion position coordinates arriving at the set traffic road and the obstacle three-dimensional position coordinates of each obstacle boundary box with the potential position coordinates of each adjacent potential moving point to obtain the position coordinates of several moving points of the tidal robot in the set traffic road, that is, the moving three-dimensional vertical position coordinates. Values, moving three-dimensional horizontal position coordinate values, moving three-dimensional height position coordinate values ​​(and in this implementation example, all three-dimensional height position coordinate values ​​are set to 0); mark every two adjacent moving points of the tidal robot in the set traffic road as the starting point and end point of a group of trajectory sections (that is, the moving point close to the tidal robot is the starting point, and the moving point far away is the end point), and read the starting point position coordinates and the end point position coordinates of each group of trajectory sections in the set traffic road; and conduct a comprehensive analysis of the starting point position coordinates and the end point position coordinates of each group of trajectory sections of the tidal robot in the set traffic road, and obtain the path position coordinates of several path moving points of the tidal robot in each group of trajectory sections in the set traffic road (compared with the tidal robot in The steps for setting the position coordinates of several moving points in the traffic road are consistent), namely, the three-dimensional vertical position coordinate value of the path, the three-dimensional horizontal position coordinate value of the path, and the three-dimensional height position coordinate value of the path; the three-dimensional position coordinates of the vehicle boundary box of each vehicle in the set traffic road, the three-dimensional position coordinates of the obstacle of each obstacle boundary box, and the trajectory position coordinates of each path moving point are comprehensively analyzed by combining the statistical method to obtain the vehicle three-dimensional position coordinates of the trajectory vehicle boundary box (i.e., the vehicle boundary box closest to each path moving point) of each path moving point of each group of trajectory sections in the set traffic road and the three-dimensional position coordinates of the obstacle of each trajectory obstacle boundary box (i.e., the obstacle boundary box closest to each path moving point); for each group of trajectory sections in the set traffic road, the three-dimensional position coordinates of the obstacle of each trajectory obstacle boundary box (i.e., the obstacle boundary box closest to each path moving point) are obtained. Comprehensively analyze the trajectory position coordinates of each path moving point of the group trajectory section, the vehicle three-dimensional position coordinates of the trajectory vehicle boundary box, the obstacle three-dimensional position coordinates of the trajectory obstacle boundary box and the preset safety distance of the tidal robot to obtain the offset distance of each path moving point of each group of trajectory sections of the tidal robot in the set traffic road; comprehensively analyze the trajectory position coordinates and offset distance of each path moving point of each group of trajectory sections of the tidal robot in the set traffic road to obtain the offset position coordinates of several deviated moving points (corresponding to each path moving point) of each group of trajectory sections of the tidal robot in the set traffic road, that is, the offset three-dimensional vertical position coordinate value, the offset three-dimensional horizontal position coordinate value, and the offset three-dimensional height position coordinate value;Connect each deviation moving point and each path moving point of each group of track sections of the tidal robot in the set traffic road, generate several moving tracks of each group of track sections of the tidal robot in the set traffic road, and mark each deviation moving point and each path moving point in each moving track of each group of track sections of the tidal robot in the set traffic road as a track moving point, and mark the offset position coordinates of each deviation moving point and the path position coordinates of each path moving point as track position coordinates, i.e., track three-dimensional vertical position coordinate value, track three-dimensional horizontal position coordinate value, and track three-dimensional height position coordinate value. ;

[0076] Among them, the interpolation method is to estimate the value at the unknown position through known data points (i.e., the initial position coordinates and the target reserved position coordinates), and the spline interpolation is adopted in this embodiment. Spline interpolation is an interpolation method that uses piecewise polynomials to smooth over data points. It uses cubic polynomials for interpolation, and the derivative (rate of change) between every two adjacent data points is continuous.

[0077] The statistical method of nearest neighbor matching selected in this implementation example is specifically: for each path moving point, by calculating its distance from all vehicles and obstacles, the nearest one (or the one with the smallest distance) is selected as the closest bounding box.

[0078] The specific process of obtaining the position coordinates of several moving points of the tidal robot in the set traffic road is as follows: the three-dimensional position coordinates of the vehicle of each vehicle boundary box within the initial position coordinates of the tidal robot in the set traffic road and the target diversion position coordinates and the three-dimensional position coordinates of the obstacles of each obstacle boundary box are respectively analyzed with the potential position coordinates of each adjacent potential moving point to obtain the vehicle moving distance (i.e., the distance between the vehicle and the adjacent potential moving point) of each vehicle boundary box within the initial position coordinates of the tidal robot in the set traffic road and the target diversion position coordinates, and the three-dimensional position coordinates of each obstacle boundary box. The obstacle moving distance of the box (i.e., the distance between the obstacle and the adjacent potential moving point) is calculated, and compared with the preset safety distances respectively. If the moving distance of the vehicle boundary box and the moving distance of the obstacle boundary box within the initial position coordinates and the target diversion position coordinates of the tidal robot in the set traffic road are higher than the preset safety distance, the potential moving point is marked as a path moving point; if the moving distance of the vehicle boundary box and the moving distance of the obstacle boundary box within the initial position coordinates and the target diversion position coordinates of the tidal robot in the set traffic road are lower than the preset safety distance, the potential moving point is eliminated.

[0079] The specific process of obtaining the offset distance of each path moving point of the tidal robot in each group of trajectory sections within the set traffic road is: comprehensively analyze the vehicle three-dimensional position coordinates of the trajectory vehicle boundary box of each path moving point in each group of trajectory sections within the set traffic road, the obstacle three-dimensional position coordinates of the trajectory obstacle boundary box and the preset safety distance of the tidal robot, and obtain the vehicle trajectory distance (i.e., the distance between the trajectory vehicle boundary box and the adjacent path moving point) and the obstacle trajectory distance (i.e., the distance between the trajectory obstacle boundary box and the adjacent path moving point) of each path moving point in each group of trajectory sections within the set traffic road, and compare the vehicle trajectory distance and the obstacle trajectory distance of each path moving point in each group of trajectory sections within the set traffic road. If the vehicle trajectory distance is higher than the obstacle trajectory distance, the vehicle trajectory distance is marked as the offset distance; if the obstacle trajectory distance is higher than the vehicle trajectory distance, the obstacle trajectory distance is marked as the offset distance.

[0080] The formula for calculating the vehicle trajectory distance of each path moving point of each group of trajectory sections within the set traffic road is as follows: Among them, PyJ gd Xg is the vehicle trajectory distance of the dth path moving point of the gth group of trajectory sections in the set traffic road, gd C′ is the three-dimensional vertical position coordinate value of the vehicle boundary box of the d-th path moving point of the g-th group of trajectory sections in the traffic road. gd Yg is the three-dimensional vertical position coordinate value of the path of the dth path moving point of the gth group of trajectory sections in the traffic road. gd The three-dimensional horizontal position coordinate value of the vehicle of the trajectory vehicle boundary box of the dth path moving point of the gth group of trajectory sections in the traffic road, C″ gd is the three-dimensional horizontal position coordinate value of the path of the dth path moving point of the gth group of trajectory segments in the set traffic road, AqJ is the preset safety distance of the tidal robot in the set traffic road, g = 1, 2, 3, ..., g0, g0 is the number of trajectory segment groups, d = 1, 2, 3, ..., d0, d0 is the number of path moving points.

[0081] The calculation logic of the obstacle trajectory distance and the vehicle trajectory distance of each trajectory moving point of each group of trajectory sections in the traffic road are consistent, and the calculation formula is also consistent.

[0082] The specific formula for calculating the offset position coordinates of each deviation moving point of the tidal robot in each set of trajectory sections within the set traffic road is as follows: Among them, PyX gs To set the offset three-dimensional vertical position coordinate value of the sth deviation moving point of the gth group of trajectory sections in the traffic road, C′ gdC′ is the three-dimensional vertical position coordinate value of the dth path moving point of the gth group of trajectory sections in the traffic road. g(d+1) C″ is the path three-dimensional vertical position coordinate value of the d+1th path moving point of the gth group of trajectory sections in the traffic road. g(d+1) C″ is the path three-dimensional horizontal position coordinate value of the d+1th path moving point of the gth group of trajectory sections in the traffic road. gd To set the path three-dimensional horizontal position coordinate value of the d-th path moving point of the g-th group of trajectory sections in the traffic road, PyJ gd PzY is the vehicle path distance of the dth path moving point of the gth group of trajectory sections in the set traffic road, gs To set the offset three-dimensional horizontal position coordinate value of the sth deviation moving point of the gth group of trajectory sections in the traffic road, g = 1, 2, 3, ..., g0, g0 is the number of trajectory section groups, d = 1, 2, 3, ..., d0, d0 is the number of path moving points.

[0083] In this implementation scheme, by analyzing the position coordinates of the potential moving points between the initial position coordinates of the tidal robot and the target diversion position coordinates, the path points that the robot may pass through can be accurately calculated, thereby ensuring that the robot can travel smoothly and accurately between each moving point in the set traffic road, and ensuring the feasibility and safety of the path. Secondly, the distance between the three-dimensional boundary box of each vehicle and obstacle and the potential moving point will be calculated and compared. If the distance is lower than the preset safety distance, the potential point will be eliminated to prevent the robot from colliding with the obstacle, so that the robot can adaptively adjust its path in a complex traffic environment and reduce accidental collisions. The robot can avoid external collisions and dangers, especially in the presence of multiple obstacles or busy traffic, to ensure that the robot's driving is safer. By calculating the offset position coordinates of each path point, the robot can adjust its trajectory in time when encountering obstacles or traffic conditions change, thereby generating multiple paths to ensure safe and efficient arrival at the target location. Finally, by combining statistical analysis methods, the robot can evaluate the distance between vehicles and obstacles at each path point in real time and respond to environmental changes in a timely manner, so that the robot can adapt to dynamically changing traffic environments, such as dealing with uncertain factors such as vehicle lane changes and pedestrians crossing, greatly improving the robot's environmental adaptability.

[0084] Specifically, the specific steps for obtaining the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road are as follows: divide each moving trajectory of the tidal robot in the set traffic road based on a preset moving distance, and obtain the trajectory position coordinates of several trajectory moving points of each moving section of each moving trajectory of the tidal robot in the set traffic road; comprehensively analyze the trajectory image data of each moving section of each moving trajectory of the tidal robot in the set traffic road, and obtain the real-time vehicle position coordinates of several real-time trajectory vehicle boundary boxes of each moving section of each moving trajectory of the tidal robot in the set traffic road, the real-time vehicle position coordinates are the real-time trajectory three-dimensional vertical position coordinate value, the real-time trajectory three-dimensional horizontal position coordinate value, and the real-time trajectory three-dimensional height position coordinate value; and obtain the speed value of the tidal robot in the set traffic road; predict and analyze the initial position coordinates, speed value, and trajectory position coordinates of each trajectory moving point of each moving section of each moving trajectory of the tidal robot in the set traffic road, and obtain the reliability index of the tidal robot in the set traffic road. The predicted time value of each moving trajectory of each group of trajectory sections in the road (that is, the predicted travel time required for the tidal robot to pass through the moving trajectory); the predicted time value of each moving trajectory of the tidal robot in each group of trajectory sections in the set traffic road is divided to obtain several predicted time points of each moving section of each moving trajectory of the tidal robot in the set traffic road (the interval between time points is the same as the interval between each real-time trajectory vehicle boundary box); and the predicted three-dimensional vertical position coordinate value, predicted three-dimensional horizontal position coordinate value, predicted three-dimensional height position coordinate value of each moving section of each moving trajectory of the tidal robot in the set traffic road (within the predicted time) at each predicted time point, that is, the predicted real-time position coordinates; and the preset safety distance of the tidal robot in the set traffic road, the position coordinates of each real-time trajectory vehicle boundary box of each moving section of each moving trajectory, and the predicted real-time position coordinates at each predicted time point are comprehensively analyzed to obtain the predicted collision distance of each moving trajectory of the tidal robot in each group of trajectory sections in the set traffic road, which is marked as the reliability index.

[0085] Among them, the steps of obtaining the real-time vehicle position coordinates of each real-time trajectory vehicle boundary box of each moving section of each moving trajectory of the tidal robot in the set traffic road are consistent with the vehicle three-dimensional position coordinates of each vehicle boundary box.

[0086] The specific steps for obtaining the predicted real-time position coordinates of the tidal robot at each predicted time point (within the predicted time) of each moving section of each moving trajectory within the set traffic road are: dividing the predicted time value of each moving section of each moving trajectory of the tidal robot within the set traffic road into several time points, and comprehensively analyzing the speed value, initial position coordinates and interval length values ​​of adjacent times of the tidal robot within the set traffic road to obtain the predicted real-time position coordinates of the tidal robot at each predicted time point of each moving section of each moving trajectory within the set traffic road.

[0087] The specific formula for calculating the predicted time value and predicted collision distance of each moving section of each moving trajectory of the tidal robot within the set traffic road is as follows:

[0088]

[0089] Among them, YcS rp is the predicted time value of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, X′ is the initial three-dimensional vertical position coordinate value of the tidal robot in the set traffic road, C′ rp1 is the three-dimensional vertical position coordinate value of the first moving point of the p-th moving section of the r-th moving trajectory of the tidal robot in the set traffic road, Y′ is the initial three-dimensional horizontal position coordinate value of the tidal robot in the set traffic road, C″ rp1 is the three-dimensional horizontal position coordinate value of the first moving point of the p-th moving section of the r-th moving trajectory of the tidal robot in the set traffic road, C′ rp(q+2) is the three-dimensional vertical position coordinate value of the q+2th trajectory moving point of the pth moving section of the rth moving trajectory in the set traffic road, C′ rp(q+1) is the three-dimensional vertical position coordinate value of the q+1th moving point of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, C″ rp(q+2) is the three-dimensional horizontal position coordinate value of the q+2th moving point of the tidal robot in the pth moving section of the rth moving trajectory in the set traffic road, C″ rp(q+1) is the three-dimensional horizontal position coordinate value of the q+1th trajectory moving point of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, SdZ is the speed value of the tidal robot in the set traffic road, PzJ rp Sx is the reliability index of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, rpeis the predicted three-dimensional vertical position coordinate value of the tidal robot at the e-th predicted time point of the p-th moving section of the r-th moving trajectory in the set traffic road, Xc rpt is the real-time trajectory three-dimensional vertical position coordinate value of the t-th real-time trajectory vehicle boundary box of the p-th moving section of the r-th moving trajectory in the set traffic road, Sy rpe Yc is the predicted three-dimensional horizontal position coordinate value of the tidal robot at the e-th predicted time point of the p-th moving section of the r-th moving trajectory in the set traffic road, rpt is the real-time trajectory three-dimensional horizontal position coordinate value of the t-th real-time trajectory vehicle boundary box of the p-th moving section of the g-th moving trajectory of the tidal robot in the set traffic road, AqJ is the preset safety distance of the tidal robot in the set traffic road, r=1,2,3,…,r0,r0 is the number of moving trajectories, p=1,2,3,…,p0,p0 is the number of moving sections, q=1,2,3,…,q0,q0 is the number of trajectory sections, e=1,2,3,…,e0,e0 is the number of prediction time points, and t=1,2,3,…,t0,t0 is the number of real-time trajectory vehicle boundary boxes.

[0090] In this implementation scheme, by performing real-time analysis on each moving trajectory of the tidal robot, the predicted three-dimensional position coordinate value of the tidal robot at each predicted time point can be accurately obtained, and the collision risk with surrounding obstacles can be evaluated in real time. By predicting the position coordinates of each trajectory point at a certain moment in the future, as well as the distance from the obstacle, the collision can be effectively avoided, and the path can be adjusted in real time to ensure the safe driving of the robot within the set traffic road, thereby improving the safety of the robot in a dynamic environment and reducing the occurrence of accidental collisions. Secondly, by performing a detailed analysis of the three-dimensional position coordinates of each trajectory point and combining it with the position coordinates of the real-time vehicle boundary box, the system can judge the collision risk between the current path and the obstacle in real time. By calculating the "predicted collision distance" and comparing it with the preset safety distance, it can evaluate the current Whether the previous path is safe, if the collision risk is too great, adjustment measures will be taken immediately, thereby effectively reducing the accident risk of the robot in complex traffic environments. In addition, by accurately predicting the time and position coordinates of each trajectory point, the tidal robot can optimize its driving speed and route, thereby improving driving efficiency. By analyzing the robot's initial position, speed and moving trajectory, the system can more reasonably arrange the travel time of each moving point, thereby ensuring that the robot passes through the set section in the best way, saving time and reducing unnecessary energy consumption. Finally, by setting a "reliability index" to evaluate the safety of each moving trajectory, the tidal robot can respond in a timely manner when encountering complex traffic conditions, so that the system can still maintain stability in unpredictable complex environments and reduce the occurrence of system failures or accidents.

[0091] Specifically, the specific steps of taking trajectory adjustment measures based on the analysis results are as follows: comparatively analyzing the reliability indexes of the first moving section of each moving trajectory of the tidal robot in the set traffic road, and selecting the corresponding moving trajectory of the first moving section of the tidal robot in the set traffic road as the optimal moving trajectory of the first moving section according to the analysis results (that is, selecting the moving trajectory corresponding to the moving section with the largest reliability index); after the tidal robot in the set traffic road reaches the end point of the first moving section on the selected optimal moving trajectory of the first moving section, discriminant analysis is performed on the reliability index of the second moving section of the tidal robot on the optimal moving trajectory of the first moving section in the set traffic road and the reliability index of the second moving section of each remaining moving trajectory, and according to the discriminant analysis, the corresponding moving trajectory of the second moving section of the tidal robot in the set traffic road is selected as the optimal moving trajectory of the second moving section; after the tidal robot in the set traffic road reaches the end point of the second moving section on the selected optimal moving trajectory of the second moving section, the discriminant analysis and the steps of selecting the optimal moving trajectory are repeated until the tidal robot reaches the target diversion position coordinates in the set traffic road.

[0092] The specific process of selecting the corresponding moving trajectory of the tidal robot in the second moving section within the set traffic road as the optimal moving trajectory of the second moving section according to the discriminant analysis is as follows:

[0093] If the reliability index of the second section of the current moving trajectory is lower than the reliability index of the second moving section of each remaining moving trajectory, the reliability index of the second moving section of each remaining moving trajectory is analyzed in combination with statistical methods to obtain the second moving section of the optimal path (i.e., the section with the largest reliability index), and the position coordinates of the second section starting point of the second section of the current moving trajectory and the position coordinates of the second section end point of the second moving section of the optimal path are read, and a comprehensive analysis is performed to obtain several second section moving points of the corrected second moving section, and a corrected second section trajectory is generated.

[0094] The specific process of the comprehensive analysis is: acquiring the image data between the second section of the current moving trajectory and the second moving section of the optimal path in real time, obtaining the real-time position coordinates of several vehicles and the real-time position coordinates of several obstacles, and combining the spline interpolation method with the second section starting point position coordinates of the second section of the current moving trajectory and the second section end point position coordinates of the second moving section of the optimal path for analysis, obtaining the second moving position coordinates of several second section moving points (consistent with the steps of obtaining the position coordinates of several moving points of the tidal robot in the set traffic road), and generating a corrected second section trajectory.

[0095] In this implementation scheme, by selecting the optimal path according to the reliability index at each stage, the road sections that are safe and unobstructed under the current traffic conditions are given priority, thereby effectively avoiding traffic jams, obstacles or other potential risks, thereby improving the reliability of the path. Secondly, by performing discriminant analysis and selecting the optimal path after the end of each road section, the robot's driving trajectory is adjusted in real time to avoid the dilemma of being unable to respond in time when encountering emergencies, thereby enabling the robot to adaptively adjust the path in a complex and dynamic traffic environment. In addition, by performing statistical analysis on the moving trajectory of each road section and combining the reliability indexes of multiple candidate paths for selection, the optimal path is accurately identified. At the same time, during the path adjustment process, the path is corrected by methods such as spline interpolation, which can smoothly connect the difference between the current trajectory and the optimal path, thereby helping to reduce path errors and ensuring that the robot does not deviate drastically or change the path suddenly during driving, avoiding excessive turning radius and unnecessary adjustments, and improving the smoothness and naturalness of the path.

[0096] In summary, this application has at least the following effects:

[0097] By analyzing the road data and initial traffic image data of the road where the tidal robot is located, and combining it with the route planning algorithm, the optimal path of the robot from the initial position coordinates to the target diversion position coordinates is dynamically planned, so that the planning of moving points and trajectory sections can be automatically adjusted according to different road conditions, overcoming the defect of traditional path planning that relies on static paths, ensuring the scientificity and real-time nature of trajectory planning, and improving the robot's adaptability to complex traffic scenarios.

[0098] By acquiring trajectory image data in real time and calculating the reliability index of the trajectory, the safety and effectiveness of each moving trajectory can be evaluated from multiple dimensions, so that potential risks and obstacles can be effectively identified, and inappropriate paths can be eliminated based on reliability evaluation, ensuring that the tidal robot always chooses the optimal path to operate, thereby improving the accuracy of trajectory evaluation, significantly reducing the risk of path failure, and ensuring the operational stability of the tidal robot.

[0099] By formulating a control strategy based on the reliability index analysis results of each mobile trajectory, we can achieve refined management of the tidal robot's operating status, and by dynamically adjusting the control strategy, we can effectively respond to sudden traffic conditions or trajectory obstacles and ensure that the robot is always in the optimal operating state. In addition, through real-time feedback adjustment strategies, the tidal robot can efficiently complete diversion tasks in complex traffic roads, comprehensively improving task execution effects and resource utilization.

[0100] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0101] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling the movement trajectory of a tidal robot, characterized in that: The following steps are involved: Obtain road data, initial traffic image data and initial position coordinates of the tidal robot within a set traffic road, and perform preprocessing, wherein the road data specifically includes the road width value, road length value, position coordinates of the starting point of the road section, and position coordinates of the end point of the road section for each traffic section, and the initial traffic image data specifically includes the pixel value and two-dimensional position coordinates of each pixel point in the traffic image, and the initial position coordinates specifically include the initial three-dimensional vertical position coordinate value, the initial three-dimensional horizontal position coordinate value, and the initial three-dimensional height position coordinate value; The pre-processed road data and initial traffic image data in the set traffic road are analyzed to obtain the target diversion position coordinates of the tidal robot arriving at the set traffic road, and the initial position coordinates of the tidal robot and the target diversion position coordinates arriving at the set traffic road are comprehensively analyzed in combination with the route planning algorithm to obtain several moving trajectories of the tidal robot in the set traffic road; Each moving track of the tidal robot in the set traffic road is divided into sections to obtain a plurality of moving sections of each moving track of the tidal robot in the set traffic road; Acquire the trajectory image data of each moving section of each moving trajectory of the tidal robot in the set traffic road in real time, and perform comprehensive analysis to obtain the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road, wherein the trajectory image data specifically includes the trajectory pixel value and the trajectory two-dimensional position coordinates of each trajectory pixel point; The reliability index of each moving section of each moving trajectory of the tidal robot within the set traffic road is comprehensively analyzed in real time, and trajectory adjustment measures are taken based on the analysis results.

2. The moving trajectory control method of the tidal robot according to claim 1, characterized in that: The specific steps to obtain the target diversion position coordinates of the tidal robot within the set traffic road are as follows: The pixel value and two-dimensional position coordinates of each pixel point in the image within the set traffic road are input into the pre-established target detection model for comprehensive analysis, and the vehicle two-dimensional position coordinates and vehicle confidence of several vehicle prediction bounding boxes within the set traffic road are obtained, as well as the obstacle two-dimensional position coordinates and obstacle confidence of several obstacle prediction bounding boxes; Comprehensively analyzing the vehicle predicted two-dimensional position coordinates and the vehicle prediction confidence of each vehicle prediction boundary box within the set traffic road, and the obstacle predicted two-dimensional position coordinates and the obstacle prediction confidence of each obstacle prediction boundary box, to obtain the vehicle two-dimensional position coordinates of several vehicle boundary boxes within the set traffic road and the obstacle two-dimensional position coordinates of several obstacle boundary boxes; Performing position coordinate conversion processing on the two-dimensional position coordinates of each vehicle boundary box in the set traffic road and the two-dimensional position coordinates of each obstacle boundary box, so as to obtain the three-dimensional position coordinates of each vehicle boundary box in the set traffic road and the three-dimensional position coordinates of each obstacle boundary box, wherein the three-dimensional position coordinates of the vehicle are the three-dimensional vertical position coordinate values ​​of the vehicle, the three-dimensional horizontal position coordinate values ​​of the vehicle, and the three-dimensional height position coordinate values ​​of the vehicle, and the three-dimensional position coordinates of the obstacle are the three-dimensional vertical position coordinate values ​​of the obstacle, the three-dimensional horizontal position coordinate values ​​of the obstacle, and the three-dimensional height position coordinate values ​​of the obstacle; Comprehensively analyzing the three-dimensional position coordinates of each vehicle boundary box in the set traffic road, the three-dimensional position coordinates of each obstacle boundary box, and the road width and road length values ​​of each traffic section, to obtain the congestion index of each traffic section in the set traffic road; The congestion index of each traffic section in the set traffic road is arranged in descending order to generate a congestion arrangement section table, and the section starting point position coordinates of the first sequence section are marked as the target diversion position coordinates.

3. The moving trajectory control method of the tidal robot according to claim 2, characterized in that: The target detection model is specifically a convolutional neural network model, and the steps of pre-establishing the target detection model are as follows: Acquire a plurality of sets of initial traffic image data within a set traffic road, establish a traffic data set, and divide the traffic data set into a traffic training data set and a traffic verification data set; Initialize the convolutional neural network model; Train the initialized convolutional neural network model based on the traffic training dataset and calculate the traffic regression loss function; The target detection model performance is evaluated based on the traffic validation dataset, and the model parameters are adjusted according to the validation results until the model prediction results meet the expected standards.

4. The moving trajectory control method of the tidal robot according to claim 2, characterized in that: The specific steps to obtain the congestion index of each road section in the set traffic road are: Comprehensively analyzing the vehicle three-dimensional position coordinates of each vehicle boundary box and the obstacle three-dimensional position coordinates of each obstacle boundary box within the set traffic road, as well as the length value and width value of each traffic section, to obtain the vehicle three-dimensional position coordinates of several vehicle turning points of several vehicle boundary boxes and the obstacle three-dimensional position coordinates of several obstacle turning points of several obstacle boundary boxes within each traffic section within the set traffic road; Read the vehicle three-dimensional position coordinates of each vehicle turning point of each vehicle boundary box of each traffic section in the set traffic road and the obstacle three-dimensional position coordinates of each obstacle turning point of each obstacle boundary box, and perform comprehensive analysis to obtain the vehicle road occupation area and obstacle road occupation area of ​​each traffic section in the set traffic road; Comprehensively analyze the vehicle road occupation area, obstacle road occupation area, width value, and length value of each traffic section in the set traffic road to obtain the road utilization index of each traffic section in the set traffic road; Comprehensively analyzing the vehicle three-dimensional position coordinates of each vehicle inflection point of each vehicle boundary box of each traffic section in the set traffic road and the obstacle three-dimensional position coordinates of each obstacle inflection point of each obstacle boundary box, obtaining the vehicle three-dimensional position coordinates of adjacent vehicle inflection points of several groups of adjacent vehicle boundary boxes of each traffic section in the set traffic road and the obstacle three-dimensional position coordinates of adjacent obstacle inflection points of several groups of adjacent obstacle boundary boxes, and performing comprehensive analysis to obtain the vehicle spacing and obstacle spacing of each section in the set traffic road; The road utilization index, vehicle spacing, and obstacle spacing of each traffic section within the set traffic road are standardized, and a comprehensive analysis is performed to obtain the congestion index of each traffic section within the set traffic road.

5. The moving trajectory control method of the tidal robot according to claim 4, characterized in that: The calculation formulas for calculating the vehicle road occupancy area, road utilization index, vehicle spacing, and congestion index for each traffic section within the set traffic road are as follows: Among them, DzL i is the vehicle road area of ​​the i-th traffic section in the set traffic road, X inj Y is the three-dimensional vertical position coordinate value of the jth vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. in(j+1) Y is the three-dimensional horizontal position coordinate value of the j+1th vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. inj X is the three-dimensional horizontal position coordinate value of the jth vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road. in(j+1) To set the three-dimensional vertical position coordinate value of the j+1th vehicle turning point of the nth vehicle boundary box in the i-th traffic section within the traffic road, Y is the three-dimensional vertical position coordinate value of the j0th vehicle turning point of the nth vehicle boundary box in the i-th road section, in1 To set the three-dimensional horizontal position coordinate value of the first vehicle turning point of the nth vehicle boundary box in the i-th road section within the traffic road, X is the three-dimensional horizontal position coordinate value of the j0th vehicle turning point of the nth vehicle boundary box in the i-th road section. in1 DyL is the three-dimensional vertical position coordinate value of the first vehicle turning point of the nth vehicle boundary box in the i-th road section within the traffic road. i To set the road utilization index of the i-th traffic section in the traffic road, DwL i CdZ is the obstacle road area of ​​the i-th traffic section in the traffic road. i To set the length of the i-th traffic section in the traffic road, KdZ i To set the width of the i-th traffic section in the traffic road, JcL i is the vehicle spacing of the ith traffic section in the traffic road, X im1 is the three-dimensional vertical position coordinate value of the first turning point of the adjacent vehicle turning points of the mth group of adjacent vehicle boundary boxes in the i-th traffic section within the traffic road, X im2 Y is the three-dimensional vertical position coordinate value of the second turning point in the mth group of adjacent vehicle boundary boxes of the i-th traffic section in the traffic road, im1 Y is the three-dimensional horizontal position coordinate value of the first turning point of the adjacent vehicle turning points of the mth group of adjacent vehicle boundary boxes in the i-th traffic section within the traffic road, im2 YjD is the three-dimensional horizontal position coordinate value of the second turning point of the adjacent vehicle turning points of the mth group of adjacent vehicle boundary boxes in the i-th traffic section within the traffic road, i To set the congestion index of the ith traffic section in the traffic road, DyL′ i is the road utilization index of the i-th traffic section in the set traffic road after standardization, β1 is the road coefficient of the set traffic road stored in the database, JcL′ i is the vehicle spacing of the i-th traffic section in the set traffic road after standardization, β2 is the vehicle coefficient in the set traffic road stored in the database, ZcL′ i is the obstacle spacing of the i-th traffic section in the set traffic road after standardized processing, β3 is the obstacle coefficient in the set traffic road stored in the database, and β1+β2+β3=1, n0=m0+1, i=1,2,3,…,i0,i0 is the number of traffic sections, n=1,2,3,…,n0,n0 is the number of vehicle bounding boxes, j=1,2,3,…,j0,j0 is the number of vehicle turning points, and m=1,2,3,…,m0,m0 is the number of adjacent vehicle bounding box groups.

6. The moving trajectory control method of the tidal robot according to claim 1, characterized in that: The specific steps to obtain several moving trajectories of the tidal robot within the set traffic road are: Comprehensively analyze the initial position coordinates of the tidal robot and the target diversion position coordinates within the set traffic road to obtain the potential position coordinates of several potential moving points of the tidal robot within the set traffic road; The three-dimensional position coordinates of each vehicle boundary box between the initial position coordinates of the tidal robot and the target diversion position coordinates in the set traffic road and the three-dimensional position coordinates of each obstacle boundary box are respectively analyzed with the potential position coordinates of each adjacent potential moving point to obtain the position coordinates of several moving points of the tidal robot in the set traffic road, namely, the moving three-dimensional vertical position coordinate value, the moving three-dimensional horizontal position coordinate value, and the moving three-dimensional height position coordinate value; Marking every two adjacent moving points of the tidal robot in the set traffic road as the starting point and the end point of a set of track sections, and reading the starting point position coordinates and the end point position coordinates of each set of track sections in the set traffic road; And the starting point position coordinates and the end point position coordinates of each set of trajectory sections of the tidal robot in the set traffic road are comprehensively analyzed to obtain the path position coordinates of several path moving points of each set of trajectory sections of the tidal robot in the set traffic road, that is, the three-dimensional vertical position coordinate value of the path, the three-dimensional horizontal position coordinate value of the path, and the three-dimensional height position coordinate value of the path; Comprehensively analyzing the vehicle three-dimensional position coordinates of each vehicle boundary box within the set traffic road, the obstacle three-dimensional position coordinates of each obstacle boundary box, and the trajectory position coordinates of each path moving point, to obtain the vehicle three-dimensional position coordinates of the trajectory vehicle boundary box of each path moving point of each group of trajectory sections within the set traffic road and the obstacle three-dimensional position coordinates of each trajectory obstacle boundary box; Comprehensively analyze the vehicle three-dimensional position coordinates of the trajectory vehicle boundary box of the trajectory position coordinates of each path moving point of each set of trajectory sections in the set traffic road, the obstacle three-dimensional position coordinates of the trajectory obstacle boundary box, and the preset safety distance of the tidal robot to obtain the offset distance of each path moving point of each set of trajectory sections of the tidal robot in the set traffic road; Comprehensively analyze the trajectory position coordinates and offset distance of each path moving point of the tidal robot in each set of trajectory sections within the set traffic road, and obtain the offset position coordinates of several deviated moving points of the tidal robot in each set of trajectory sections within the set traffic road, that is, the offset three-dimensional vertical position coordinate value, the offset three-dimensional horizontal position coordinate value, and the offset three-dimensional height position coordinate value; Each deviated moving point and each path moving point of each group of trajectory sections of the tidal robot in the set traffic road are connected and processed to generate several moving trajectories of each group of trajectory sections of the tidal robot in the set traffic road, and each deviated moving point and each path moving point in each moving trajectory of each group of trajectory sections of the tidal robot in the set traffic road are uniformly marked as trajectory moving points, and the offset position coordinates of each deviated moving point and the path position coordinates of each path moving point are uniformly marked as trajectory position coordinates, that is, the three-dimensional vertical position coordinate value of the trajectory, the three-dimensional horizontal position coordinate value of the trajectory, and the three-dimensional height position coordinate value of the trajectory.

7. The moving trajectory control method of the tidal robot according to claim 6, characterized in that: The specific formula for calculating the offset position coordinates of each deviation moving point of the tidal robot in each set of trajectory sections within the set traffic road is as follows: Among them, PyX gs To set the offset three-dimensional vertical position coordinate value of the sth deviation moving point of the gth group of trajectory sections in the traffic road, C′ gd C′ is the three-dimensional vertical position coordinate value of the dth path moving point of the gth group of trajectory sections in the traffic road. g(d+1) C″ is the path three-dimensional vertical position coordinate value of the d+1th path moving point of the gth group of trajectory sections in the traffic road. g(d+1) C″ is the path three-dimensional horizontal position coordinate value of the d+1th path moving point of the gth group of trajectory sections in the traffic road. gd To set the path three-dimensional horizontal position coordinate value of the d-th path moving point of the g-th group of trajectory sections in the traffic road, PyJ gd PzY is the vehicle path distance of the dth path moving point of the gth group of trajectory sections in the set traffic road, gs To set the offset three-dimensional horizontal position coordinate value of the sth deviation moving point of the gth group of trajectory sections in the traffic road, g = 1, 2, 3, ..., g0, g0 is the number of trajectory section groups, d = 1, 2, 3, ..., d0, d0 is the number of path moving points.

8. The moving trajectory control method of the tidal robot according to claim 1, characterized in that: The specific steps for obtaining the reliability index of each moving section of each moving trajectory of the tidal robot in the set traffic road are as follows: Divide each moving trajectory of the tidal robot in the set traffic road based on a preset moving distance, and obtain the trajectory position coordinates of a plurality of trajectory moving points in each moving section of each moving trajectory of the tidal robot in the set traffic road; Comprehensively analyzing the trajectory image data of each moving section of each moving trajectory of the tidal robot in the set traffic road, obtaining the real-time vehicle position coordinates of several real-time trajectory vehicle boundary boxes of each moving section of each moving trajectory of the tidal robot in the set traffic road, wherein the real-time vehicle position coordinates are the real-time trajectory three-dimensional vertical position coordinate value, the real-time trajectory three-dimensional horizontal position coordinate value, and the real-time trajectory three-dimensional height position coordinate value; And obtain the speed value of the tidal robot within the set traffic road; Predict and analyze the initial position coordinates, speed value, and trajectory position coordinates of each moving point of each moving track of the tidal robot in the set traffic road, and obtain the predicted time value of each moving track of each group of track sections of the tidal robot in the set traffic road; The predicted time value of each moving trajectory of each group of trajectory sections of the tidal robot in the set traffic road is divided to obtain a number of predicted time points of each moving section of each moving trajectory of the tidal robot in the set traffic road; And obtain the predicted three-dimensional vertical position coordinate value, the predicted three-dimensional horizontal position coordinate value, and the predicted three-dimensional height position coordinate value of the tidal robot at each predicted time point of each moving section of each moving trajectory within the set traffic road, that is, the predicted real-time position coordinates; A comprehensive analysis is performed on the preset safety distance of the tidal robot within the set traffic road, the position coordinates of each real-time trajectory vehicle boundary box of each moving section of each moving trajectory, and the predicted real-time position coordinates of each predicted time point, and the predicted collision distance of each moving trajectory of the tidal robot in each group of trajectory sections within the set traffic road is obtained, which is marked as the reliability index.

9. The moving trajectory control method of the tidal robot according to claim 8, characterized in that: The specific formula for calculating the predicted time value and predicted collision distance of each moving section of each moving trajectory of the tidal robot within the set traffic road is as follows: Among them, YcS rp is the predicted time value of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, X′ is the initial three-dimensional vertical position coordinate value of the tidal robot in the set traffic road, C′ rp1 is the three-dimensional vertical position coordinate value of the first moving point of the p-th moving section of the r-th moving trajectory of the tidal robot in the set traffic road, Y′ is the initial three-dimensional horizontal position coordinate value of the tidal robot in the set traffic road, C″ rp1 is the three-dimensional horizontal position coordinate value of the first moving point of the p-th moving section of the r-th moving trajectory of the tidal robot in the set traffic road, C′ rp(q+2) is the three-dimensional vertical position coordinate value of the q+2th trajectory moving point of the pth moving section of the rth moving trajectory in the set traffic road, C′ rp(q+1) is the three-dimensional vertical position coordinate value of the q+1th moving point of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, C″ rp(q+2) is the three-dimensional horizontal position coordinate value of the q+2th moving point of the tidal robot in the pth moving section of the rth moving trajectory in the set traffic road, C″ rp(q+1) is the three-dimensional horizontal position coordinate value of the q+1th trajectory moving point of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, SdZ is the speed value of the tidal robot in the set traffic road, PzJ rp Sx is the reliability index of the pth moving section of the rth moving trajectory of the tidal robot in the set traffic road, rpe is the predicted three-dimensional vertical position coordinate value of the tidal robot at the e-th predicted time point of the p-th moving section of the r-th moving trajectory in the set traffic road, Xc rpt is the real-time trajectory three-dimensional vertical position coordinate value of the t-th real-time trajectory vehicle boundary box of the p-th moving section of the r-th moving trajectory in the set traffic road, Sy rpe Yc is the predicted three-dimensional horizontal position coordinate value of the tidal robot at the e-th predicted time point of the p-th moving section of the r-th moving trajectory in the set traffic road, rpt is the real-time trajectory three-dimensional horizontal position coordinate value of the t-th real-time trajectory vehicle boundary box of the p-th moving section of the g-th moving trajectory of the tidal robot in the set traffic road, AqJ is the preset safety distance of the tidal robot in the set traffic road, r=1,2,3,…,r0,r0 is the number of moving trajectories, p=1,2,3,…,p0,p0 is the number of moving sections, q=1,2,3,…,q0,q0 is the number of trajectory sections, e=1,2,3,…,e0,e0 is the number of prediction time points, and t=1,2,3,…,t0,t0 is the number of real-time trajectory vehicle boundary boxes.

10. The method for controlling the movement trajectory of a tidal robot according to claim 1, characterized in that: The specific steps for taking trajectory adjustment measures based on the analysis results are as follows: Comparatively analyzing the reliability index of the first moving section of each moving trajectory of the tidal robot in the set traffic road, and selecting the corresponding moving trajectory of the first moving section of the tidal robot in the set traffic road as the optimal moving trajectory of the first moving section according to the analysis result; After the tidal robot in the set traffic road reaches the end point of the first moving section on the selected optimal moving trajectory of the first moving section, a reliability index of the second moving section of the tidal robot on the optimal moving trajectory of the first moving section in the set traffic road and a reliability index of the second moving section of each remaining moving trajectory are discriminated and analyzed, and the corresponding moving trajectory of the second moving section of the tidal robot in the set traffic road is selected as the optimal moving trajectory of the second moving section according to the discriminant analysis; After the tidal robot in the set traffic road reaches the end point of the second moving section on the selected optimal moving trajectory of the second moving section, the steps of discriminant analysis and selecting the optimal moving trajectory are repeated until the tidal robot reaches the target diversion position coordinates in the set traffic road.

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