Intelligent ship autonomous collision avoidance whole process path planning method

Abstract

The invention discloses an intelligent ship autonomous collision avoidance whole process path planning method, which comprises the following steps: establishing an error distribution model of a fusion target according to dynamic information and static information of a target ship, and analyzing to obtain a classification model of the target ship; according to the classification model of the target ship, combining a quaternary ship domain model to establish a ship domain model of dynamic and static obstacles; establishing local path planning based on an artificial potential field method, establishing a repulsive force field around an obstacle in the ship domain model, establishing a gravitational field at a target point, introducing a global path return mechanism, and establishing a local collision avoidance path planning algorithm considering the return mechanism; gravitational force self-adaptive adjustment is achieved based on the current ship path deviation degree and the current ship navigation deviation degree, a dynamically-optimized local collision avoidance path planning algorithm considering a self-adaptive homeward voyage mechanism is formed, and path planning of the ship is conducted. After collision avoidance of the ship, the ship is controlled to return to the global route reasonably and efficiently, and the navigation safety and efficiency of the ship are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of waterway transportation, and in particular relates to a full-process path planning method for autonomous collision avoidance of intelligent ships. Background Art

[0002] In recent years, with the increasing domestic and international trade, the shipping industry has ushered in unprecedented development. The number of ships has increased dramatically, the size of the fleet has continued to expand, and the density of ship navigation has also increased. Especially in offshore waterways and ports, ships are highly concentrated and maritime collisions continue to occur, which has brought serious economic losses and safety threats to the shipping industry. Studies have found that the root cause of most collision accidents is human factors. Human factors such as crew misjudgment, fatigue, inattention and operational errors are the main problems leading to accidents. Research on autonomous collision avoidance of ships has become a key means to solve this type of problem and has important practical significance. Intelligent ships have strong autonomous decision-making and control capabilities and can quickly generate collision avoidance strategies in complex navigation environments. They are an inevitable trend for the future safety and green development of ships. However, traditional intelligent ships pay more attention to the effectiveness of collision avoidance decisions and path planning, and their return process after autonomous collision avoidance is less valued. In fact, after avoiding collisions and letting other ships go, there is often a large deviation from the path or a large angle turn, which makes it difficult for the ship to return to the global route safely and reasonably, increasing the energy consumption of the ship and the risk of accidents. Summary of the invention

[0003] The purpose of the present invention is to provide a full-process path planning method for autonomous collision avoidance of intelligent ships, which controls the ship to return to the global route reasonably and efficiently after the ship avoids collision, thereby ensuring the safety and efficiency of the ship's navigation.

[0004] In order to solve the above technical problems, the technical solution of the present invention is: a method for planning the whole process of autonomous collision avoidance of an intelligent ship, comprising the following steps:

[0005] S1. Obtain the dynamic information and static information of the target ship through the navigation radar and the automatic identification system, establish the error distribution model of the fused target according to the dynamic information and static information of the target ship, and obtain the classification model of the target ship according to the error distribution model of the fused target;

[0006] S2. Based on the classification model of the target ship and the four-element ship field model, a ship field model of dynamic and static obstacles is established;

[0007] S3. Establish local path planning based on the artificial potential field method, establish a repulsive field around obstacles in the ship domain model, establish a gravitational field at the target point, and introduce a global path return mechanism. Establish a gravitational field at the nodes of the global path, and establish a local collision avoidance path planning algorithm that takes into account the return mechanism;

[0008] S4. Adaptive gravity adjustment is implemented based on the degree of deviation of the current ship path and navigation deviation. The dynamic ship field model and repulsive field are adjusted based on the relative motion state of the obstacle. Given a time step, the local path planning algorithm is corrected according to the error of each planning and execution to form a dynamically optimized local collision avoidance path planning algorithm considering the adaptive return mechanism and the path planning of the ship is performed according to the local collision avoidance path planning algorithm.

[0009] The dynamic information of the target ship in S1 at least includes the real-time distance, speed, heading, position, longitude and latitude and time of the target ship, and the static information at least includes the ship name, water mobile service identification code and navigation status of the target ship.

[0010] S1 is specifically:

[0011] The original data of each ship is collected through the navigation radar and automatic identification system. The original data includes at least speed, heading, longitude and latitude, and time. The historical trajectory of the ship is generated through the time sequence and longitude and latitude changes in the original data. The prominent features of the historical trajectory data are collected. The prominent features include at least the speed change rate, the heading change rate, and the position change;

[0012] Clean up the data of prominent features, check missing values ​​and abnormal data, interpolate and fill the missing values ​​according to the historical trajectory, remove abnormal data, and ensure the integrity of the historical trajectory data of the ship;

[0013] Set the parameters of the OPTICS clustering model, including the minimum number of neighbors and the maximum neighborhood distance, and develop a metric method;

[0014] The normalized historical trajectory data is used as the input of the OPTICS clustering model, and the OPTICS clustering model is executed to automatically identify and sort the density reachability of data points, and output a clustering result with variable density. The clustering result at least includes the reachable distance and its sorting graph, as well as the cluster label. The cluster division is determined according to the reachable distance sorting graph, and the clusters are classified according to the output cluster label.

[0015] According to the clustering results, combined with the static information provided by the automatic identification system, the ship classification decision is made to complete the construction of the classification model of moving ships and moored ships;

[0016] If the ship trajectory of a cluster in the clustering result is similar to that of a moored ship, and the static information of the automatic identification system indicates that the ship is stationary or moored, then the cluster is marked as a moored ship; if the ship trajectory of a cluster is similar to that of a moving ship, and its static information indicates that the ship is moving, then the cluster is marked as a moving ship. The four-element ship domain model QSD in S2 is expressed as:

[0017] QSD={(x,y)∣f(x,y;Q)≤1,Q={R fore ,R aft ,R starb ,R port}}

[0018]

[0019] Among them, f(x, y; Q) is the function that determines the boundary of the four-dimensional ship area, x and y are the spatial coordinates of the ship, Q is an intermediate variable, representing the four radii of the ship area, sgn(x) and sgn(y) represent a symbolic function; R fort , R aft , R starb , R port Respectively represent the radius of the quaternary ship field in the front, rear, right and left directions;

[0020] The quaternary ship domain is elliptical in shape, and its domain size is determined by R fort , R aft , R starb , R port Four radius parameters are determined, by which the boundary shape of the quaternion ship field is adjusted to cope with different heading scenarios.

[0021] The method of establishing the ship field model of dynamic and static obstacles is as follows: according to the classification model of the target ship, the four-element ship model of different types of target ships is adjusted to establish the static and dynamic ship field models respectively;

[0022] If the target ship is a moving ship, it can be regarded as a dynamic obstacle, that is, a dynamic ship field model is established for it, and the values ​​of its four radius parameters are:

[0023]

[0024] Where L is the length of the ship, k AD k is the gain coefficient of the AD in the ship's loop, DT is the gain coefficient of the initial diameter of the gyre DT;

[0025] If the target ship is a moored ship, it can be regarded as a static obstacle, that is, a static ship field model is established for it, and the values ​​of its four radius parameters are:

[0026]

[0027] S3 is specifically:

[0028] According to the artificial potential field method, a repulsive field is established around the obstacle, the field is scaled and adjusted according to the radius parameters of dynamic and static obstacles, a gravitational field is constructed around the target point, and a return gravitational field is established according to the path nodes;

[0029] The potential field of a ship is changing at every moment. Suppose at a certain moment, there are N s static obstacles, N d dynamic obstacles, the current sum of the artificial potential field is the sum of the gravitational potential field and the repulsive potential field, expressed as:

[0030]

[0031] Among them, P f is the sum of the current artificial potential field, P att is the gravitational field, P ref is the repulsive field, P g is the target point potential field, P n is the gravitational field of the global path node, P s,i is the potential field of point obstacle i, P d,j is the potential field of domain-type obstacle j;

[0032] In the process of local collision avoidance of ships, the collision risk index CRI is used to determine the time points for the ship to start collision avoidance and return. When the CRI is higher than the first collision threshold, the ship starts to avoid collision. The minimum value of the potential field near the ship is found by combining the current sum of the artificial potential field with map modeling. The ship will tend to move towards the area with a smaller potential field. As the collision avoidance process of the ship is completed, the collision risk decreases. When the CRI is lower than the second collision threshold, the time to return is confirmed, thereby realizing autonomous decision-making for local path planning and obstacle avoidance.

[0033] S4 is specifically:

[0034] After confirming the timing of returning, deviation detection is performed to calculate the distance from the ship to the global path, as well as the deviation between the ship's heading and the global path heading;

[0035] The formula for the distance that a ship deviates from the global path is expressed as:

[0036] Δd=||(x,y)-(x p ,y p )||

[0037] Among them, Δd is the distance from the ship to the global path, (x, y) is the current position of the ship, (x p ,y p ) is the global path node closest to the ship;

[0038] The angle formula of the ship's deviation from the global heading is expressed as:

[0039] Δψ=|ψ-ψ0|

[0040] Among them, Δψ is the deviation between the ship heading and the global path heading, ψ is the current heading angle of the ship, and ψ0 is the global path heading angle;

[0041] To unify the dimensions, normalization is performed, assuming that the maximum path deviation does not exceed d m , the maximum heading deviation does not exceed ψ m ;

[0042] The degree of deviation from the path d d It is expressed as:

[0043]

[0044] The deviation degree of heading ψ It is expressed as:

[0045]

[0046] Dynamically adjust the gravitational potential field based on the deviation error, and if there is an obstacle, adjust the dynamic ship field and repulsive potential field based on the relative motion state of the obstacle;

[0047] Establish an adaptive return mechanism, including: parameter and target initialization, adaptive gravitational field adjustment, dynamic ship field and adaptive repulsive field adjustment, adaptive total potential field adjustment and update.

[0048] The parameters and target initialization are as follows: Assume that the speed at the moment of returning after the collision is v1, the heading angle is ψ1, the path deviation is Δd1, the heading deviation is Δψ1, and the initial ship area radius is R fore , R aft , R starb , R port , the minimum encounter distance is DCPA;

[0049] To achieve the return goal, set the path deviation to be less than d min When the heading deviation is less than ψ min When , it is regarded as returning to the global heading;

[0050] The specific steps of adaptive gravitational field adjustment are:

[0051] Set the time step Δt, and update the path deviation d every Δt d , heading deviation d ψ , speed deviation Δv and recalculate the weights of the return gravitational field and the target gravitational field. The calculation formula is expressed as:

[0052]

[0053] Among them, k1 and k2 are attenuation degrees. The larger the value, the faster the attenuation.

[0054] The adaptive gravitational field is expressed as:

[0055] P att =(1+ω1)P n +(1+ω 2) P g

[0056] Among them, ω1 and ω2 are the weights of the return gravitational field and the target gravitational field.

[0057] The specific steps of dynamic ship field and adaptive repulsion field adjustment are:

[0058] After the ship avoids collision, the collision risk of the ship is judged according to the relative position, relative speed and relative heading of the ship. In order to better detect the collision risk, the ship area is expanded k times and an external safety distance d is added. s , expressed as:

[0059] d s = k·R

[0060]

[0061] in, It represents the boundary detection range of the expanded ship area, R is the length from the center of the area to the boundary, and k is the multiple of the expanded range;

[0062] According to different encounter scenarios, it is judged whether the ship and the target ship are in danger of collision. A radius of d is constructed with the ship as the center. s The detection circle is adjusted according to different scenes;

[0063] A detection line is constructed based on the relative position and relative heading of the ships. Whether the detection line passes through the range of the detection circle is judged to determine whether there is a collision risk between the two ships. The closest encounter distance DCPA is one of the main factors for judging the risk of ship collision. It indicates the closest distance between the two trajectories when the two ships continue to sail at the established heading and speed in the current state of motion. The smaller the DCPA value, the greater the collision risk. The size of the dynamic ship area is adjusted based on the speed, heading and position to enable the ship to start collision avoidance in advance. The specific steps are as follows:

[0064] Assuming that R1 is the distance from the intersection of the DCPA line and the ship area to the ship, and R2 is the distance from the intersection of the DCPA extension line and the detection circle to the ship, then,

[0065]

[0066] Among them, ω DCPAIndicates the adjustment weight of the ship's field radius; is the attenuation coefficient. The larger its value, the faster the attenuation. DCPA indicates the closest encounter distance between the own ship and the target ship.

[0067] Adjust the ship domain based on the direction of the ship encounter; when there is a risk of collision, adjust the ship domain model QSD based on the direction of the target ship e , the adjustment formula is expressed as:

[0068] QSD e ={(x,y)|f e (x,y;Q)≤1,

[0069] Q e = {R fore +ΔR fore ,R aft +ΔR aft ,R starb +ΔR starb ,R port +ΔR port}}

[0070] ΔR r =R r ·ω DCPA

[0071] Among them, f e (x, y; Q) represents the boundary of the ship area, Q e Represents the adjustment value of the radius of the four areas, ΔR r Indicates the dynamic adjustment value of the field radius, r indicates the direction of adjustment, ΔR fore Indicates the dynamic adjustment of the forward radius in the ship area, ΔR aft Indicates the dynamic adjustment of the backward radius in the ship area, ΔR starb Indicates the dynamic adjustment of the starboard radius in the ship area, ΔR port Indicates the dynamic adjustment of the port radius of the ship, R r ∈{R fore ,R aft ,R starb ,R port};

[0072] After avoiding collision, return to the destination. For obstacles behind, the return process needs to avoid collision with obstacles behind. Define Δv OT = The speed of the ship divided by the speed of the obstacle behind it, expressed as:

[0073]

[0074] Among them, v OS Indicates the ship's speed, v TSIndicates the speed of the target ship;

[0075] Δθ OT is the angle between the speed directions of the own ship and the target ship, and the velocity repulsion field P of the dynamic obstacle is introduced. v , which is expressed as:

[0076]

[0077] in, is the relative velocity Δv OT The gain factor, Δv OT The smaller it is, the greater the possibility that the obstacle behind will catch up. A larger value should be taken;

[0078] Adaptive repulsive field P of the ship ref for:

[0079]

[0080] The specific steps of adaptive total potential field adjustment and update are:

[0081] After each time step Δt, the current DCPA is recalculated, and the relative speed Δv OT and the angle Δθ OT , update the current total adaptive total potential field; during the ship's return process, its adaptive total potential field is:

[0082]

[0083] Among them, p att is the current total gravitational field, P ref is the current total repulsive force field, P g is the target point potential field, P n is the gravitational field of the global path node, P s,i is the potential field of point obstacle i, P d,j is the potential field of domain-type obstacle j, P v is the velocity repulsion field of the dynamic obstacle, ω1 and ω2 are the update weights of the return gravitational field and the target gravitational field.

[0084] A computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] The present invention uses an adaptive return mechanism to enable the ship to return to the global route more efficiently and safely during the return process after collision avoidance, and can adapt to complex offshore navigation environments, thereby improving the intelligence level of the ship's autonomous collision avoidance and return. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a schematic diagram of a flow chart of an embodiment of the present invention;

[0088] Figure 2 This is a flowchart of navigation data processing in an embodiment of the present invention;

[0089] Figure 3 Schematic diagram of the adaptive return artificial potential field in an embodiment of the present invention;

[0090] Figure 4 This is a flow chart of the adaptive return mechanism in an embodiment of the present invention;

[0091] Figure 5 This is a flowchart of the full process of autonomous collision avoidance path planning in an embodiment of the present invention. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0093] The technical solution of the present invention is:

[0094] A full-process path planning method for autonomous collision avoidance of an intelligent ship, comprising:

[0095] Step 1: Fuse the target ship data of the Automatic Identification System (AIS) and the marine radar to establish the error distribution model of the fused target. Obtain the target ship information through the marine radar and AIS, including the ship's speed, heading, position, navigation status and other information. AIS provides the ship's name, Maritime Mobile Service Identity (MMSI), navigation status and other information. The marine radar can obtain the target ship's real-time distance, speed, heading, position and other information; the close-range dynamic information of the marine radar is more accurate, and the long-range information of the AIS performs better. By combining the characteristics of AIS providing static information and marine radar providing dynamic information, the dynamic information of the marine radar and the speed and heading information of the AIS are fused to establish the error distribution model of the fused target to improve the positioning accuracy of the target ship.

[0096] According to the error distribution model of the fusion target, the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm is used to establish a classification model for moored ships and moving ships. First, the original data of each ship, including speed, heading, longitude and latitude, and time, are collected through AIS and navigation radar. The historical trajectory of the ship is generated using the time sequence and longitude and latitude changes. Some prominent features of the data, such as speed change rate, heading change rate, and position change, are collected. The data is cleaned, missing values ​​and abnormal data are checked, and missing values ​​are interpolated and filled according to the historical trajectory to remove abnormal data to ensure that the ship trajectory data is complete. Then, the parameters of the OPTICS model are set, including the minimum number of neighbors and the maximum domain distance, and a measurement method is formulated. Then, the OPTICS clustering model is executed, and the normalized data set is used as input. Using the set parameters, the clustering model is executed. The algorithm will automatically identify and sort the density accessibility of the data points, and output a clustering result with variable density, mainly including accessibility distance, sorting, and clustering labels. The cluster division is determined by observing the sorting graph of the accessibility distance, and the clusters are classified according to the output clustering labels. Finally, based on the clustering results of OPTICS and the ship information provided by AIS, the ship classification decision is made to complete the construction of the classification model of moving ships and moored ships. If the ship trajectory of a cluster in the clustering results is similar to that of a moored ship, and the AIS data shows that the ship is stationary or moored, then the cluster is marked as a moored ship. If the ship trajectory of a cluster is similar to that of a moving ship, and the AIS data shows that the ship is moving, then the cluster is marked as a moving ship. The navigation data processing process is as follows Figure 2 shown.

[0097] Step 2: Based on the classification model of the target ship and combined with the four-element ship model, a ship field model of dynamic and static obstacles is established.

[0098] First, a four-dimensional ship domain model is established; the four-dimensional ship domain is elliptical in shape, and the domain size is mainly determined by R fore , R aft , R starb , R port Four parameters are determined, and the boundary shape of the quaternion ship domain is adjusted by these four parameters to cope with different heading scenarios. The quaternion ship domain (QSD) can be described as:

[0099] QSD={(x,y)∣f(x,y;Q)≤1,Q={R fore ,R aft ,R starb ,R port}}

[0100]

[0101] Among them, f(x, y; Q) is the function that determines the boundary of the four-dimensional ship area, x and y are the spatial coordinates of the ship, Q is an intermediate variable, representing the four radii of the ship area, sgn(x) and sgn(y) represent a symbolic function; R fore , R aft , R starb , R port Respectively represent the radius of the quaternary ship field in the front, rear, right and left directions;

[0102] According to the established target ship classification model, the quaternary ship models of different types of target ships are adjusted, and static and dynamic quaternary ship field models are established respectively.

[0103] If the target ship is a moving ship, it can be regarded as a dynamic ship, and the values ​​of the four radius parameters are:

[0104]

[0105] Where, L is the length of the ship; k AD k is the gain coefficient of the AD in the ship's loop, DT is the gain coefficient of the initial diameter of the gyre DT;

[0106] If the target ship is a moored ship, it can be regarded as a static obstacle, and the four radius parameters can be adjusted as follows:

[0107]

[0108] Step 3: Establish local path planning based on the artificial potential field (APF) method, establish a repulsive field around the obstacle, establish a gravitational field at the target point, and introduce a global path return mechanism. Establish a gravitational field at the nodes of the global path, and establish a local collision avoidance path planning algorithm that takes the return mechanism into consideration.

[0109] According to the artificial potential field method, a repulsive field is established around the obstacle, the field is scaled and adjusted according to the dynamic and static obstacles, a gravitational field is constructed around the target point, and a return gravitational field is established according to the path nodes.

[0110] The potential field of a ship is changing at every moment. Suppose at a certain moment, there are N s static obstacles, N d dynamic obstacles, the current sum of artificial potential fields can be expressed as the sum of gravitational potential fields and repulsive potential fields, and the formula is as follows:

[0111]

[0112] Among them, Patt is the gravitational field, P ref is the repulsive field, P g is the target point potential field, P n is the gravitational field of the global path node, P s,i is the potential field of point obstacle i, P d,j is the potential field of domain-type obstacle j.

[0113] In the process of local collision avoidance of ships, the Collision Risk Index (CRI) is used to determine the time point when the ship starts to avoid collision and starts to return. When the CRI is higher than the first collision threshold T1, the ship starts to avoid collision. Through the sum of the current artificial potential field combined with map modeling, the minimum value of the potential field near the ship can be found. The ship will tend to move towards the area with a smaller potential field, thereby realizing local path planning and autonomous decision-making for obstacle avoidance. As the ship's collision avoidance process is completed, the collision risk decreases. When the CRI of the collision risk is lower than the second collision threshold T2, the time to return is confirmed; at this time, the ship often has a large path deviation and heading deviation. It is difficult to achieve the gradual return of the ship to the global path by relying solely on the gravitational field of the target point. Therefore, an adaptive return mechanism is established to guide the ship back to the global route.

[0114] Step 4: Establish a local collision avoidance path planning algorithm that takes into account the adaptive return mechanism, and correct the algorithm's decision based on the errors in each algorithm execution and planning. Adaptive gravity adjustment is implemented based on the current ship path deviation and the degree of navigation deviation. The dynamic ship field model and repulsion field are adjusted based on the relative motion state of the obstacle. Given a time step, the local path planning algorithm is corrected based on the errors in each planning and execution.

[0115] After confirming the timing of returning home, deviation detection is first performed to calculate the distance from the ship to the global path and the deviation between the ship's heading and the global path heading.

[0116] The formula for the distance a ship deviates from the global path is:

[0117] Δd=||(x,y)-(x p ,y p )||

[0118] Among them, (x, y) is the current position of the ship, (x p ,y p ) is the node (nearest point) of the global path;

[0119] The formula for the angle at which a ship deviates from its global heading is:

[0120] Δψ=|ψ-ψ0|

[0121] Among them, ψ is the current heading angle of the ship, and ψ0 is the global path heading angle;

[0122] To unify the dimensions, normalization is performed, assuming that the maximum path deviation does not exceed d m , the maximum heading deviation does not exceed ψ m ;

[0123] The degree of deviation from the path is:

[0124]

[0125] The deviation from the heading is:

[0126]

[0127] With the deviation parameters, the gravitational potential field can be dynamically adjusted based on the deviation error. If there is an obstacle, the dynamic ship field and repulsive potential field are adjusted based on the relative motion state of the obstacle. An adaptive return mechanism is established. The flow chart of the adaptive return mechanism can be seen in Figure 4 , the basic steps are as follows:

[0128] Step 4.1: Initialize parameters and set targets

[0129] Assume that the speed at the moment of returning after the collision is v1, the heading angle is ψ1, the path deviation is Δd1, the heading deviation is Δψ1, and the initial ship area radius is R fore , R aft , R starb , R port , the minimum encounter distance is DCPA;

[0130] To achieve the return goal, set the path deviation to be less than d min When , it can be regarded as returning to the global route, and the heading deviation is less than ψ min , it can be regarded as returning to the global heading.

[0131] Step 4.2: Adaptive gravitational field adjustment

[0132] Set the time step Δt, and update the path deviation d every Δt d , heading deviation d ψ , speed deviation Δv and recalculate the weights of the return gravitational field and the target gravitational field. The calculation formula is as follows:

[0133]

[0134]

[0135] Among them, k1 and k2 are the attenuation degrees. The larger the value, the faster the attenuation.

[0136] The adaptive gravitational field is:

[0137] Patt =(1+ω1)P n +(1+ω2)P g

[0138] Among them, ω1 and ω2 are the weights of the return gravitational field and the target gravitational field;

[0139] During the return process, in addition to the gravity of the return and target points, there may also be repulsion from obstacles on the ship. Therefore, in order to better complete the return planning, it is also necessary to adjust the ship field and repulsive potential field based on the relative motion state of the obstacle and the ship.

[0140] After the ship avoids collision, the collision risk of the ship is judged according to the relative position, relative speed and relative heading of the ship. In order to better detect the collision risk, the ship area is expanded k times and an external safety distance is added;

[0141] d s = k·R

[0142]

[0143] Among them, R is the length from the center of the area to the boundary, and k is the multiple of the expanded range. Considering collision safety, k=2 can be taken;

[0144] According to different encounter scenarios, it is judged whether the ship and the target ship are in danger of collision. A radius of d is constructed with the ship as the center. s The detection circle is adjusted according to different scenarios, such as 3 to 4 nautical miles for overtaking and 5 to 6 nautical miles for encounter or cross-encounter. A detection line is constructed based on the relative position and relative heading of the ships. It is determined whether the detection line passes through the range of the detection circle to determine whether there is a risk of collision between the two ships. The degree of danger of collision risk is determined based on the minimum encounter distance (Distance to the Closest Point of Approach, DCPA), and then the size of the dynamic ship field is adjusted based on speed, heading and position to enable ships to start collision avoidance in advance. The specific process is as follows:

[0145] Step 4.3: Dynamic ship field and adaptive repulsion field adjustment

[0146] Assuming that R1 is the distance from the intersection of the DCPA line and the ship area to the ship, and R2 is the distance from the intersection of the DCPA extension line and the detection circle to the ship, then,

[0147]

[0148] Among them, ω DCPA Indicates the adjustment weight of the ship's area radius, is the attenuation coefficient. The larger the value, the faster the attenuation. DCPA represents the closest encounter distance between the own ship and the target ship.

[0149] The ship domain is adjusted based on the direction in which the ships meet; when there is a risk of collision, the ship domain model is adjusted based on the direction of the target ship. The adjustment formula is as follows:

[0150] QSD e ={(x,y)|f e (x,y;Q)≤1,

[0151] Q e = {R fore +ΔR fore ,R aft +ΔR aft ,R stard +ΔR stard ,R port +ΔR port}}

[0152] ΔR r =R r ·ω DCPA

[0153] Among them, f e (x, y; Q) represents the boundary of the ship area, Q e Represents the adjustment value of the radius of the four areas, ΔR r Indicates the dynamic adjustment value of the field radius, r indicates the direction of adjustment, ΔR fore Indicates the dynamic adjustment of the forward radius in the ship area, ΔR aft Indicates the dynamic adjustment of the backward radius in the ship area, ΔR starb Indicates the dynamic adjustment of the starboard radius in the ship area, ΔR port Indicates the dynamic adjustment of the port radius of the ship, R r ∈{R fore ,R aft ,R starb ,R port};

[0154] After avoiding collision, return to the destination. For obstacles behind, the return process needs to avoid collision with obstacles behind. Define Δv OT = The speed of the ship divided by the speed of the obstacle behind it, expressed as:

[0155]

[0156] Among them, v OS Indicates the ship's speed, v TS Indicates the speed of the target ship;

[0157] Δθ OTis the angle between the speed directions of the own ship and the target ship, and the velocity repulsion field P of the dynamic obstacle is introduced. v , which is expressed as:

[0158]

[0159] in, is the relative velocity Δv OT The gain factor, Δv OT The smaller it is, the greater the possibility that the following ship will catch up. A larger value should be taken;

[0160] The adaptive repulsive field of the ship is:

[0161]

[0162] Step 4.4: Adaptive total potential field adjustment and update

[0163] After each time step Δt, the current DCPA is recalculated, and the relative speed Δv OT and the angle Δθ OT , update the current total adaptive total potential field; during the ship's return process, its adaptive total potential field is:

[0164]

[0165] Among them, P att is the current total gravitational field, P ref is the current total repulsive force field, P g is the target point potential field, P n is the gravitational field of the global path node, P s,i is the potential field of point obstacle i, P d,j is the potential field of domain-type obstacle j, P v is the velocity repulsion field of the dynamic obstacle, ω1 and ω2 are the update weights of the return gravitational field and the target gravitational field. Figure 3 .

[0166] Determine whether the path deviation value and heading deviation value reach the return target setting value. If not, repeat the above steps until the ship returns to the global path.

[0167] The autonomous collision avoidance path planning flow chart of the whole process of navigating from the global path, autonomously avoiding collisions when encountering obstacles, and returning to the global path is as follows: Figure 5 shown.

[0168] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A full-process path planning method for autonomous collision avoidance of intelligent ships, characterized in that: The following steps are involved: S1. Obtain the dynamic information and static information of the target ship through the navigation radar and the automatic identification system, establish the error distribution model of the fused target according to the dynamic information and static information of the target ship, and obtain the classification model of the target ship according to the error distribution model of the fused target; S2. Based on the classification model of the target ship and the four-element ship field model, a ship field model of dynamic and static obstacles is established; S3. Establish local path planning based on the artificial potential field method, establish a repulsive field around obstacles in the ship domain model, establish a gravitational field at the target point, and introduce a global path return mechanism. Establish a gravitational field at the nodes of the global path, and establish a local collision avoidance path planning algorithm that takes into account the return mechanism; S4. Adaptive gravity adjustment is implemented based on the degree of deviation of the current ship path and navigation deviation. The dynamic ship field model and repulsive field are adjusted based on the relative motion state of the obstacle. Given a time step, the local path planning algorithm is corrected according to the error of each planning and execution to form a dynamically optimized local collision avoidance path planning algorithm considering the adaptive return mechanism and the path planning of the ship is performed according to the local collision avoidance path planning algorithm. The dynamic information of the target ship in S1 at least includes the real-time distance, speed, heading, position, longitude and latitude and time of the target ship, and the static information at least includes the name of the target ship, the water mobile service identification code and the navigation status.

2. The method for autonomous collision avoidance of an intelligent ship during the whole process of path planning according to claim 1 is characterized in that: S1 is specifically: The original data of each ship is collected through the navigation radar and automatic identification system. The original data includes at least speed, heading, longitude and latitude, and time. The historical trajectory of the ship is generated through the time sequence and longitude and latitude changes in the original data. The prominent features of the historical trajectory data are collected. The prominent features include at least the speed change rate, the heading change rate, and the position change; Clean up the data of prominent features, check missing values ​​and abnormal data, interpolate and fill the missing values ​​according to the historical trajectory, remove abnormal data, and ensure the integrity of the historical trajectory data of the ship; Set the parameters of the OPTICS clustering model, including the minimum number of neighbors and the maximum neighborhood distance, and develop a metric method; The normalized historical trajectory data is used as the input of the OPTICS clustering model, and the OPTICS clustering model is executed to automatically identify and sort the density reachability of data points, and output a clustering result with variable density. The clustering result at least includes the reachable distance and its sorting graph, as well as the cluster label. The cluster division is determined according to the reachable distance sorting graph, and the clusters are classified according to the output cluster label. According to the clustering results, combined with the static information provided by the automatic identification system, the ship classification decision is made to complete the construction of the classification model of moving ships and moored ships; If the ship trajectory of a cluster in the clustering result is similar to that of a moored ship, and the static information of the automatic identification system shows that the ship is stationary or moored, then the cluster is marked as a moored ship; if the ship trajectory of a cluster is similar to that of a moving ship, and its static information shows that the ship is moving, then the cluster is marked as a moving ship.

3. The method for autonomous collision avoidance of an intelligent ship during the whole process of path planning according to claim 1 is characterized in that: The quaternary ship domain model QSD in S2 is expressed as: QSD={(x,y)|f(x,y;Q)≤1,Q={R fore ,R aft ,R starb ,R port }} Among them, f(x, y; Q) is the function that determines the boundary of the four-dimensional ship area, x and y are the spatial coordinates of the ship, Q is an intermediate variable, representing the four radii of the ship area; sgn(x) and sgn(y) both represent a symbolic function, which is defined as: R fore , R aft , R starb , R port Represents the radius of the four-dimensional ship area in the front, back, right and left directions respectively; the four-dimensional ship area is elliptical, and its area size is R fore , R aft , R starb , R port Four radius parameters are determined, by which the boundary shape of the quaternion ship field is adjusted to cope with different heading scenarios.

4. The method for full-process path planning of an intelligent ship autonomous collision avoidance according to claim 3 is characterized in that: The method of establishing the ship field model of dynamic and static obstacles is as follows: according to the classification model of the target ship, the four-element ship model of different types of target ships is adjusted to establish the static and dynamic ship field models respectively; If the target ship is a moving ship, it can be regarded as a dynamic obstacle, that is, a dynamic ship field model is established for it, and the values ​​of its four radius parameters are: Where L is the length of the ship, k AD k is the gain coefficient of the AD in the ship's loop, DT is the gain coefficient of the initial diameter of the gyre DT; If the target ship is a moored ship, it can be regarded as a static obstacle, that is, a static ship field model is established for it, and the values ​​of its four radius parameters are:

5. The method for full-process path planning of autonomous collision avoidance of an intelligent ship according to claim 4 is characterized in that: S3 is specifically: According to the artificial potential field method, a repulsive field is established around the obstacle, the field is scaled and adjusted according to the radius parameters of dynamic and static obstacles, a gravitational field is constructed around the target point, and a return gravitational field is established according to the path nodes; The potential field of a ship is changing at every moment. Suppose at a certain moment, there are N s static obstacles, N d dynamic obstacles, the current sum of the artificial potential field is the sum of the gravitational potential field and the repulsive potential field, expressed as: Among them, P f is the sum of the current artificial potential field, P att is the gravitational field, P ref is the repulsive field, P g is the target point potential field, P n is the gravitational field of the global path node, P s，i is the potential field of point obstacle i, P d，j is the potential field of domain-type obstacle j; In the process of local collision avoidance of ships, the collision risk index CRI is used to determine the time points for the ship to start collision avoidance and return. When the CRI is higher than the first collision threshold, the ship starts to avoid collision. The minimum value of the potential field near the ship is found by combining the current sum of the artificial potential field with map modeling. The ship will tend to move towards the area with a smaller potential field. As the collision avoidance process of the ship is completed, the collision risk decreases. When the CRI is lower than the second collision threshold, the time to return is confirmed, thereby realizing autonomous decision-making for local path planning and obstacle avoidance.

6. The method for autonomous collision avoidance of an intelligent ship during the whole process of path planning according to claim 5 is characterized in that: S4 is specifically: After confirming the timing of returning, deviation detection is performed to calculate the distance from the ship to the global path, as well as the deviation between the ship's heading and the global path heading; The formula for the distance that a ship deviates from the global path is expressed as: Δd=||(x,y)-(x p ,y p )|| Among them, Δd is the distance from the ship to the global path, (x, y) is the current position of the ship, (x p ,y p ) is the global path node closest to the ship; The angle formula of the ship's deviation from the global heading is expressed as: Δψ=|ψ-ψ0| Among them, Δψ is the deviation between the ship heading and the global path heading, ψ is the current heading angle of the ship, and ψ0 is the global path heading angle; To unify the dimensions, normalization is performed, assuming that the maximum path deviation does not exceed d m , the maximum heading deviation does not exceed ψ m ; The degree of deviation from the path d d It is expressed as: The deviation degree of heading ψ It is expressed as: Dynamically adjust the gravitational potential field based on the deviation error, and if there is an obstacle, adjust the dynamic ship field and repulsive potential field based on the relative motion state of the obstacle; Establish an adaptive return mechanism, including: parameter and target initialization, adaptive gravitational field adjustment, dynamic ship field and adaptive repulsive field adjustment, adaptive total potential field adjustment and update.

7. The method for full-process path planning of an intelligent ship autonomous collision avoidance according to claim 6 is characterized in that: The parameters and target initialization are as follows: Assume that the speed at the moment of returning after the collision is v1, the heading angle is ψ1, the path deviation is Δd1, the heading deviation is Δψ1, and the initial ship area radius is R fore , R aft , R starb , R port , the minimum encounter distance is DCPA; To achieve the return goal, set the path deviation to be less than d min When , it is regarded as returning to the global route; The heading deviation is less than ψ min When , it is regarded as returning to the global heading; The specific steps of adaptive gravitational field adjustment are: Set the time step Δt, and update the path deviation d every Δt d , heading deviation d ψ , speed deviation Δv and recalculate the weights of the return gravitational field and the target gravitational field. The calculation formula is expressed as: Among them, k1 and k2 are attenuation degrees. The larger the value, the faster the attenuation. The adaptive gravitational field is expressed as: P att =(1+ω1)P n +(1+ω2)P g Among them, ω1 and ω2 are the weights of the return gravitational field and the target gravitational field.

8. The method for full-process path planning of an intelligent ship autonomous collision avoidance according to claim 7 is characterized in that: The specific steps of dynamic ship field and adaptive repulsion field adjustment are: After the ship avoids collision, the collision risk of the ship is judged according to the relative position, relative speed and relative heading of the ship. In order to better detect the collision risk, the ship area is expanded k times and an external safety distance d is added. s , expressed as: d s =k·R in, It represents the boundary detection range of the expanded ship area, R is the length from the center of the area to the boundary, and k is the multiple of the expanded range; According to different encounter scenarios, it is judged whether the ship and the target ship are in danger of collision. A radius of d is constructed with the ship as the center. s The detection circle is adjusted according to different scenes; A detection line is constructed based on the relative position and relative heading of the ships. Whether the detection line passes through the range of the detection circle is judged to determine whether there is a collision risk between the two ships. The closest encounter distance DCPA is one of the main factors for judging the risk of ship collision. It indicates the closest distance between the two trajectories when the two ships continue to sail at the established heading and speed in the current state of motion. The smaller the DCPA value, the greater the collision risk. The size of the dynamic ship area is adjusted based on the speed, heading and position to enable the ship to start collision avoidance in advance. The specific steps are as follows: Assuming that R1 is the distance from the intersection of the DCPA line and the ship area to the ship, and R2 is the distance from the intersection of the DCPA extension line and the detection circle to the ship, then, Among them, ω DCPA Indicates the adjustment weight of the ship's field radius; is the attenuation coefficient. The larger its value, the faster the attenuation. DCPA indicates the closest encounter distance between the own ship and the target ship. Adjust the ship domain based on the direction of the ship encounter; when there is a risk of collision, adjust the ship domain model QSD based on the direction of the target ship e , the adjustment formula is expressed as: QSD e ={(x,y)|f e (x,y;Q)≤1, Q e ={R fore +ΔR fore ,R aft +ΔR aft ,R starb +ΔR starb ,R port +ΔR port }} ΔR r =R r ·oh DCPA Among them, f e (x, y; Q) represents the boundary of the ship area, Q e Represents the adjustment value of the radius of the four areas, ΔR r Indicates the dynamic adjustment value of the field radius, r indicates the direction of adjustment, ΔR fore Indicates the dynamic adjustment of the forward radius in the ship area, ΔR aft Indicates the dynamic adjustment of the backward radius in the ship area, ΔR starb Indicates the dynamic adjustment of the starboard radius in the ship area, ΔR port Indicates the dynamic adjustment of the port radius of the ship, R r ∈{R fore , R aft , R starb , R port }; After avoiding collision, return to the destination. For obstacles behind, the return process needs to avoid collision with obstacles behind. Define Δv OT = ... Among them, v OS Indicates the ship's speed, v TS Indicates the speed of the target ship; Δθ OT is the angle between the speed directions of the own ship and the target ship, and the velocity repulsion field P of the dynamic obstacle is introduced. v , which is expressed as; in, is the relative velocity Δv OT The gain factor, Δv OT The smaller it is, the greater the possibility that the obstacle behind will catch up. A larger value should be taken; Adaptive repulsive field P of the ship ref for:

9. The method for full-process path planning of an intelligent ship autonomous collision avoidance according to claim 8, characterized in that: The specific steps of adaptive total potential field adjustment and update are: After each time step Δt, the current DCPA is recalculated, and the relative speed Δv OT and the angle Δθ OT , update the current total adaptive total potential field; during the ship's return process, its adaptive total potential field is: Among them, P att is the current total gravitational field, P ref is the current total repulsive field, P g is the target point potential field, P n is the gravitational field of the global path node, P s，i is the potential field of point obstacle i, P d，j is the potential field of domain-type obstacle j, P v is the velocity repulsion field of the dynamic obstacle, ω1 and ω2 are the update weights of the return gravitational field and the target gravitational field.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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