Crane path planning and obstacle avoidance early warning system

Abstract

The invention discloses a crane path planning and obstacle avoidance early warning system, relates to the technical field of engineering machinery, and solves the problem that the spatial position of an obstacle in a vehicle body coordinate system is determined by acquiring a coordinate conversion relation between a radar coordinate system where an obstacle point cloud point is located and the vehicle body coordinate system in the prior art. The technical problem that the risk of collision between the vehicle body and the obstacle is increased due to the fact that the position of the obstacle is changed is not considered; according to the invention, the real-time environment map is constructed through the real-time environment information; screening out a plurality of alternative paths from the plurality of paths according to the static obstacle; calculating roadblock evaluation coefficients of the plurality of alternative paths according to the dynamic obstacles; selecting a target path from the plurality of alternative paths according to the roadblock evaluation coefficient; the method comprises the following steps: acquiring a real-time image of a crane within a preset range through image acquisition equipment, judging whether avoidance is needed or not according to the real-time image, and continuously detecting the real-time image; and the collision risk between the vehicle body and the obstacle is reduced to a great extent.

Description

Technical Field

[0001] The invention belongs to the technical field of engineering machinery, relates to sensors and detection technology, and specifically is a crane path planning and obstacle avoidance warning system. Background Art

[0002] Crane path planning aims to find an optimal path from the starting position to the target position to reduce unnecessary movement and waiting time, thereby improving work efficiency. Through intelligent algorithm path planning, the shortest path or optimal path can be calculated to reduce the idle time of the crane during the lifting process. In addition, the geometric shape, dynamic model and other factors of the crane will be considered in the path planning process to ensure that the crane maintains stability and safety during movement. At the same time, potential safety risks are reduced by avoiding collisions with the surrounding environment and objects. In complex scenarios, such as narrow spaces and environments with multiple obstacles, traditional manual operations often find it difficult to ensure the accuracy and safety of the path. Through path planning technology, a path that adapts to complex scenarios can be automatically calculated to reduce misoperation and path redundancy. The obstacle avoidance warning system can monitor the status of the environment and objects around the crane in real time. Once a potential collision risk is found, a warning signal will be immediately issued to remind the operator to take corresponding measures, which will help prevent collision accidents.

[0003] The prior art (the invention patent application with the publication number CN116022657A) discloses a path planning method, device and crane; in the prior art, the path planning method includes: obtaining an environmental image during the vehicle's driving process; using a trained target detection model to process the environmental image to identify obstacles in the environment where the vehicle is located; when an environmental point cloud corresponding to the environmental image is obtained and the system performance index meets the preset conditions, the spatial position of the obstacle is determined using the environmental point cloud corresponding to the environmental image; according to the current position of the vehicle, the spatial position of the obstacle and the target position of the vehicle, the initial driving path of the vehicle is adjusted. Through the above method, the driving path of the crane can be automatically planned, and the flexibility and planning effect of the crane's driving path planning can be improved; the prior art determines the spatial position of the obstacle in the vehicle coordinate system by obtaining the coordinate conversion relationship between the radar coordinate system where the obstacle point cloud point is located and the vehicle coordinate system, but does not take into account that the position of the obstacle is variable, which may increase the risk of collision between the vehicle body and the obstacle.

[0004] The present invention provides a crane path planning and obstacle avoidance warning system to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a crane path planning and obstacle avoidance warning system, which is used to solve the technical problem that the prior art determines the spatial position of the obstacle in the vehicle body coordinate system by obtaining the coordinate conversion relationship between the radar coordinate system where the obstacle point cloud points are located and the vehicle body coordinate system, but does not take into account that the position of the obstacle is variable, which may increase the risk of collision between the vehicle body and the obstacle.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides a crane path planning and obstacle avoidance warning system, comprising: an environment perception module, a path planning module and a real-time obstacle avoidance module;

[0007] Environmental perception module: obtains real-time environmental information of crane operation; the real-time environmental information includes the location, shape, size and environmental dimension of static obstacles; and builds a real-time environmental map based on the real-time environmental information;

[0008] Path planning module: obtain the operation information of the crane, divide several paths according to the operation information and the real-time environment map; obtain the static obstacles in several paths, and select several alternative paths from several paths according to the static obstacles; and obtain the dynamic obstacles on several alternative paths; calculate the roadblock evaluation coefficients of several alternative paths according to the dynamic obstacles; select the target path from several alternative paths according to the roadblock evaluation coefficient;

[0009] Real-time obstacle avoidance module: The real-time image within the preset range of the crane is collected through the image acquisition device, and it is determined whether avoidance is required based on the real-time image; if yes, an avoidance warning signal is generated; if not, the real-time image is continuously detected.

[0010] Preferably, constructing a real-time environment map according to the real-time environment information includes:

[0011] Obtain a sensor distribution map, install visual sensors and lidars according to the sensor distribution map, and obtain distance information of the environment through the lidar; capture environmental images through visual sensors, use image processing technology to identify feature points to obtain feature information; and build a real-time environmental map based on distance information and feature information.

[0012] The present invention uses lidar to accurately measure the distance and shape of objects in the environment, which is not affected by lighting conditions and is particularly suitable for obtaining accurate depth information in complex or dynamic environments. It uses visual sensors to capture rich color and texture visual information, and uses image processing technology to identify feature points, obstacles, and traffic signs in the environment, thereby enhancing the diversity of environmental understanding.

[0013] Preferably, the step of selecting a plurality of candidate paths from a plurality of paths according to static obstacles comprises:

[0014] Retrieve static obstacles and mark them in the real-time environment map to obtain marked points;

[0015] Determine whether the marked point is on several paths; if yes, remove the corresponding path; if not, keep the corresponding path and mark the retained path as an alternative path.

[0016] By marking static obstacles and identifying them in a real-time environmental map, the present invention can avoid collisions between cranes and obstacles to a large extent, thereby significantly improving the safety of the driving process; by eliminating paths containing static obstacles, the system can focus more on selecting paths that are both effective and safe, which helps to reduce invalid movements during the navigation process and improve navigation efficiency.

[0017] Preferably, the calculating of the roadblock assessment coefficients of the plurality of candidate paths according to the dynamic obstacles comprises:

[0018] Retrieve the number of dynamic obstacles ZG of several alternative paths; obtain the travel time XS and energy consumption data NH of several alternative paths;

[0019] The roadblock assessment coefficient is calculated by the formula LPX=ln(ZG×θ1+XS×θ2+NH×θ3+1); wherein θ1, θ2, and θ3 are all proportional coefficients greater than 0.

[0020] By considering the number of dynamic obstacles, the system of the present invention can more accurately evaluate the obstacle conditions on the path, thereby selecting the best path to avoid obstacles and reducing driving obstacles and potential risks caused by obstacles; by incorporating driving time into the evaluation factors, the system can select a path that takes less time and improve travel efficiency; by considering energy consumption factors, the system can select a more energy-saving path and reduce energy consumption.

[0021] Preferably, the step of selecting a target path from a plurality of candidate paths according to a roadblock evaluation coefficient includes:

[0022] Retrieving the roadblock evaluation coefficients of several candidate paths; sorting the several candidate paths from small to large according to the roadblock evaluation coefficients to obtain an obstacle sorting table;

[0023] The alternative path corresponding to the first obstacle evaluation coefficient is selected from the obstacle sorting table as the target path.

[0024] The present invention takes the roadblock assessment coefficient into comprehensive consideration and provides a quantitative assessment standard for path selection according to the number of obstacles, driving time and energy consumption, so that path selection no longer relies solely on subjective judgment or a single factor, but is based on comprehensive and objective data analysis; by sorting and selecting the path with the smallest roadblock assessment coefficient, the system can automatically find the optimal driving path under current conditions; this selection method helps to reduce uncertainty and risk in driving and improve the scientificity and accuracy of path selection.

[0025] Preferably, the determining whether avoidance is required based on the real-time image includes:

[0026] Retrieve the real-time image and determine whether there is a dynamic obstacle in the real-time image; if yes, predict the running trajectory of the dynamic obstacle; if no, determine that no avoidance is required;

[0027] According to the running trajectory of the dynamic obstacle, it is determined whether the dynamic obstacle will collide with the crane; if yes, it is determined that avoidance is required; if not, it is determined that avoidance is not required.

[0028] Through real-time image monitoring, the system of the present invention can promptly detect dynamic obstacles in the working area, ensuring timely discovery of potential sources of danger; combined with the prediction of the running trajectory of dynamic obstacles, the system can determine in advance whether there is a risk of collision between the obstacle and the crane, thereby issuing a warning signal in time to avoid the occurrence of collision accidents.

[0029] Preferably, the predicting the running trajectory of the dynamic obstacle includes:

[0030] Obtain historical data of active obstacles from a database, wherein the historical data includes: type, speed, and direction of the obstacle; and use a deep learning model to predict the movement trajectory of the active obstacle based on the historical data.

[0031] The present invention can process a large amount of historical data through a deep learning model, including multi-dimensional information on the type, position, speed and acceleration of obstacles. These rich data provide comprehensive input for the model, enabling it to better capture the movement patterns and trends of obstacles, thereby more accurately predicting their future trajectories.

[0032] Preferably, the determining whether the dynamic obstacle will collide with the crane includes:

[0033] Obtain the distance difference and speed difference between the crane and the obstacle on the alternative target path from the database; mark the distance difference JC and speed difference between the crane and the obstacle on the alternative target path as SC, and mark the obstacle risk factor as ZL; obtain the collision risk factor threshold from the database and mark it as T1, obtain the collision risk factor danger threshold and mark it as T2; wherein T2>T1;

[0034] The collision risk factor is calculated by the formula PFX = ZL / (JC + SC) × 100%;

[0035] When PFX ≥ T2, the emergency brake system of the crane is triggered;

[0036] When T2>PFX≥T1, a warning signal is issued and the vehicle decelerates to avoid the collision;

[0037] When T1>PFX, the crane will slow down and avoid.

[0038] It should be noted that errors in the prediction algorithm and inaccuracy in sensor data may lead to uncertainty in collision risk assessment; real-time monitoring and dynamic adjustment of the assessment results are required.

[0039] The present invention obtains real-time physical parameters between cranes and obstacles from a database, enabling the system to perform accurate risk assessment based on these data. This assessment method reduces the uncertainty and error of subjective judgment; by real-time monitoring of the state changes of cranes and obstacles, and dynamically updating the obstacle hazard factor accordingly, the timeliness and accuracy of risk assessment are ensured.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention can monitor obstacles in the surrounding environment of the crane in real time through the real-time obstacle avoidance module, and immediately trigger the obstacle avoidance action when a potential collision risk is found, thereby effectively avoiding the occurrence of collision accidents; this instant response capability can significantly reduce the safety risks during the operation of the crane; the data obtained by the environmental perception module can predict and evaluate the potential collision risks in advance. When the collision risk coefficient reaches or exceeds the preset threshold, the system will issue a warning signal to remind the operator or the automation system to take corresponding safety measures.

[0042] 2. By comprehensively considering the roadblock assessment coefficient, a quantitative assessment standard is provided for path selection based on the number of obstacles, driving time and energy consumption, so that path selection no longer relies solely on subjective judgment or a single factor, but is based on comprehensive and objective data analysis; by sorting and selecting the path with the smallest roadblock assessment coefficient, the system can automatically find the optimal driving path under current conditions; this selection method helps to reduce uncertainty and risk in driving and improve the scientificity and accuracy of path selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of the relationship between modules included in the present invention;

[0045] Figure 2 It is a schematic diagram of the specific steps of the present invention;

[0046] Figure 3 A schematic diagram of a specific process of path planning of the present invention;

[0047] Figure 4 It is a schematic diagram of the specific process of real-time obstacle avoidance of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] See also Figure 1-Figure 2 , the first aspect of the present invention provides a crane path planning and obstacle avoidance warning system, including: an environment perception module, a path planning module and a real-time obstacle avoidance module;

[0050] Environmental perception module: obtains real-time environmental information of crane operation; the real-time environmental information includes the location, shape, size and environmental dimension of static obstacles; and builds a real-time environmental map based on the real-time environmental information;

[0051] Path planning module: obtain the operation information of the crane, divide several paths according to the operation information and the real-time environment map; obtain the static obstacles in several paths, and select several alternative paths from several paths according to the static obstacles; and obtain the dynamic obstacles on several alternative paths; calculate the roadblock evaluation coefficients of several alternative paths according to the dynamic obstacles; select the target path from several alternative paths according to the roadblock evaluation coefficient;

[0052] Real-time obstacle avoidance module: The real-time image within the preset range of the crane is collected through the image acquisition device, and it is determined whether avoidance is required based on the real-time image; if yes, an avoidance warning signal is generated; if not, the real-time image is continuously detected.

[0053] See also Figure 3 , the specific process of path planning, obtain the real-time environmental information of the crane operation; the real-time environmental information includes the position, shape, size and environmental dimension of the static obstacles; obtain the sensor distribution map, install the visual sensor and the laser radar according to the sensor distribution map, and obtain the distance information of the environment through the laser radar; capture the environmental image through the visual sensor, and use the image processing technology to identify the feature points to obtain the feature information; build a real-time environmental map based on the distance information and feature information; call the static obstacles, mark the static obstacles in the real-time environmental map to obtain the marked points; determine whether the marked points are on several paths; if yes, eliminate the corresponding path; if not, retain the corresponding path and mark the retained path as an alternative path;

[0054] Retrieve the number ZG of dynamic obstacles of several alternative paths; obtain the travel time XS and energy consumption data NH of several alternative paths; calculate the roadblock assessment coefficient through the formula LPX=ln(ZG×θ1+XS×θ2+NH×θ3+1); wherein θ1, θ2, θ3 are all proportional coefficients greater than 0; retrieve the roadblock assessment coefficients of several alternative paths; sort the several alternative paths from small to large according to the roadblock assessment coefficients to obtain an obstacle sorting table; select the alternative path corresponding to the first roadblock assessment coefficient from the obstacle sorting table as the target path.

[0055] For example, there is a warehouse. The location, shape, size and environmental dimension of static obstacles in the warehouse are obtained; visual sensors and lidar are installed according to the sensor distribution map, and the distance information of the environment is obtained through the lidar; the environmental image is captured by the visual sensor, and the feature points are identified using image processing technology to obtain feature information; a real-time environmental map is constructed based on the distance information and feature information; static obstacles are retrieved, and the outline of the objects is marked with rectangles, and the location of fixed equipment is marked with polygons, and text descriptions are added next to the obstacles, including the name, size and height information of the obstacles;

[0056] There are five paths from the starting point α to the target point β for a crane, namely A, B, C, D, and E. Among them, there are two fixed devices on the D path, three containers on the E path, and no static obstacles on the other paths. Then, the paths A, B, and C are marked as alternative paths. There are two dynamic obstacles on the A path, and the time required to complete the A path is 3 minutes, and the energy consumption value is 6 kJ. There is one dynamic obstacle on the B path, and the time required to complete the B path is 4 minutes, and the energy consumption value is 7 kJ. There is one dynamic obstacle on the C path, and the time required to complete the B path is 4 minutes, and the energy consumption value is 7 kJ. There are three dynamic obstacles. The time required to complete path C is 1 minute, and the energy consumption value is 8 kJ. Let θ1 be 0.2, θ2 be 0.4, and θ3 be 0.4. The roadblock evaluation coefficient of path A, the roadblock evaluation coefficient of path B, and the roadblock evaluation coefficient of path C are calculated by the formula LPX=ln(ZG×θ1+XS×θ2+NH×θ3+1) to be 1.61, 1.72, and 1.64, respectively. Since 1.72>1.64>1.61, path A is taken as the target path.

[0057] See also Figure 4 , the specific process of real-time obstacle avoidance is to retrieve the real-time image and determine whether there is a dynamic obstacle in the real-time image; if yes, predict the running trajectory of the dynamic obstacle; if not, determine that no avoidance is required; obtain the historical data of the active obstacle from the database, where the historical data includes: the type, speed and direction of the obstacle; use the deep learning model to predict the movement trajectory of the active obstacle based on the historical data;

[0058] It should be noted that the deep learning model is built through standard training data, including:

[0059] Obtain standard training data from the database, extract the spatial features of the data through the convolutional layer of the convolutional neural network, and use the pooling layer to downsample to reduce computational complexity and capture local invariance; use the standard training data to train the model to build a deep learning model; wherein, the standard training data includes: standard input data consistent with the content attributes of historical data, and standard output data consistent with the content attributes of motion trajectory.

[0060] According to the running trajectory of the dynamic obstacle, determine whether the dynamic obstacle will collide with the crane; if yes, determine that avoidance is required; if not, determine that avoidance is not required; obtain the distance difference and speed difference between the crane and the obstacle on the alternative target path from the database; mark the distance difference JC and speed difference between the crane and the obstacle on the alternative target path as SC, and mark the obstacle risk factor as ZL; obtain the collision risk factor threshold from the database and mark it as T1, obtain the collision risk factor danger threshold and mark it as T2; where T2>T1;

[0061] The collision risk factor is calculated by the formula PFX = ZL / (JC + SC) × 100%;

[0062] When PFX ≥ T2, the emergency brake system of the crane is triggered;

[0063] When T2>PFX≥T1, a warning signal is issued and the vehicle decelerates to avoid the collision;

[0064] When T1>PFX, the crane will slow down and avoid.

[0065] For example, there are two dynamic obstacles a1 and a2 on path A. The types of the two moving obstacles are obtained from the database, namely, a person and a cart, where the speed of the person is 0.8m / s and the speed of the cart is 2m / s. Both dynamic obstacles a1 and a2 are moving opposite to the crane. The deep learning model is used to predict the movement trajectory of the moving obstacles based on the type, speed and direction of the obstacles.

[0066] The current speed of the crane is 1.5m / s, the distance difference between the crane and obstacle a1 is 10m, and the speed difference is 0.7m / s; the distance difference between the crane and obstacle a2 is 12m, and the speed difference is 0.5m / s; the risk factor of obstacle a1 is 2; the risk factor of obstacle a2 is 3; the collision risk factor threshold T1 obtained from the database is 10%, and the collision risk factor threshold T2 is 20%;

[0067] The formula PFX=ZL / (JC+SC)×100% is used to calculate that the collision risk coefficient between the crane and obstacle a1 is 18.7%, and the collision risk coefficient between the crane and obstacle a2 is 24%. Since 20%>18.7%>10%, when the crane detects obstacle a1, the emergency braking system of the crane is triggered; since 24%>20%, when the crane detects obstacle a2, the crane slows down to avoid it.

[0068] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0069] The working principle of the present invention is as follows: the present invention obtains real-time environmental information of crane operation; constructs a real-time environmental map according to the real-time environmental information; obtains the operation information of the crane, and divides a number of paths according to the operation information and the real-time environmental map; obtains static obstacles in the several paths, and selects a number of alternative paths from the several paths according to the static obstacles; and obtains dynamic obstacles on the several alternative paths; calculates the roadblock evaluation coefficients of the several alternative paths according to the dynamic obstacles; selects a target path from the several alternative paths according to the roadblock evaluation coefficient; collects real-time images within a preset range of the crane through an image acquisition device, and determines whether avoidance is required according to the real-time images; if yes, generates an avoidance warning signal; if not, continuously detects the real-time images.

[0070] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A crane path planning and obstacle avoidance warning system, It is characterized in that include: Environmental perception module, path planning module and real-time obstacle avoidance module; Environmental perception module: obtains real-time environmental information of crane operation; the real-time environmental information includes the location, shape, size and environmental dimension of static obstacles; and builds a real-time environmental map based on the real-time environmental information; Path planning module: obtain the operation information of the crane, divide several paths according to the operation information and the real-time environment map; obtain the static obstacles in several paths, and select several alternative paths from several paths according to the static obstacles; and obtain the dynamic obstacles on several alternative paths; calculate the roadblock evaluation coefficients of several alternative paths according to the dynamic obstacles; select the target path from several alternative paths according to the roadblock evaluation coefficient; Real-time obstacle avoidance module: The real-time image within the preset range of the crane is collected through the image acquisition device, and it is determined whether avoidance is required based on the real-time image; if yes, an avoidance warning signal is generated; if not, the real-time image is continuously detected.

2. A crane path planning and obstacle avoidance warning system according to claim 1, It is characterized in that The real-time environment map is constructed according to the real-time environment information, including: Obtain a sensor distribution map, install visual sensors and lidars according to the sensor distribution map, and obtain distance information of the environment through the lidar; capture environmental images through visual sensors, use image processing technology to identify feature points to obtain feature information; and build a real-time environmental map based on distance information and feature information.

3. A crane path planning and obstacle avoidance warning system according to claim 1, It is characterized in that The step of selecting a plurality of candidate paths from a plurality of paths according to static obstacles includes: Retrieve static obstacles and mark them in the real-time environment map to obtain marked points; Determine whether the marked point is on several paths; if yes, remove the corresponding path; if not, keep the corresponding path and mark the retained path as an alternative path.

4. A crane path planning and obstacle avoidance warning system according to claim 1, It is characterized in that The calculating of the roadblock assessment coefficients of the plurality of candidate paths according to the dynamic obstacles comprises: Retrieve the number of dynamic obstacles ZG of several alternative paths; obtain the travel time XS and energy consumption data NH of several alternative paths; The roadblock assessment coefficient is calculated by the formula LPX=ln(ZG×θ1+XS×θ2+NH×θ3+1); wherein θ1, θ2, and θ3 are all proportional coefficients greater than 0.

5. A crane path planning and obstacle avoidance warning system according to claim 1, It is characterized in that The step of selecting a target path from a plurality of candidate paths according to the roadblock evaluation coefficient includes: Retrieving the roadblock evaluation coefficients of several alternative paths; sorting the several alternative paths from large to small according to the roadblock evaluation coefficients to obtain an obstacle sorting table; The alternative path corresponding to the first obstacle evaluation coefficient is selected from the obstacle sorting table as the target path.

6. A crane path planning and obstacle avoidance warning system according to claim 1, It is characterized in that The determining whether avoidance is required according to the real-time image includes: Retrieve the real-time image and determine whether there is a dynamic obstacle in the real-time image; if yes, predict the running trajectory of the dynamic obstacle; if no, determine that no avoidance is required; According to the running trajectory of the dynamic obstacle, it is determined whether the dynamic obstacle will collide with the crane; if yes, it is determined that avoidance is required; if not, it is determined that avoidance is not required.

7. A crane path planning and obstacle avoidance warning system according to claim 6, It is characterized in that The predicting of the running trajectory of the dynamic obstacle includes: Obtain historical data of active obstacles from a database, wherein the historical data includes: type, speed, and direction of the obstacle; and use a deep learning model to predict the movement trajectory of the active obstacle based on the historical data.

8. A crane path planning and obstacle avoidance warning system according to claim 6, It is characterized in that The determining whether the dynamic obstacle will collide with the crane includes: Obtain the distance difference and speed difference between the crane and the obstacle on the alternative target path from the database; mark the distance difference JC and speed difference between the crane and the obstacle on the alternative target path as SC, and mark the obstacle risk factor as ZL; obtain the collision risk factor threshold from the database and mark it as T1, obtain the collision risk factor danger threshold and mark it as T2; wherein T2>T1; The collision risk factor is calculated by the formula PFX = ZL / (JC + SC) × 100%; When PFX ≥ T2, the emergency brake system of the crane is triggered; When T2>PFX≥T1, a warning signal is issued and the vehicle decelerates to avoid the collision; When T1>PFX, the crane will slow down and avoid.

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