CN114088094A - Intelligent route planning method and system for unmanned ship - Google Patents
Intelligent route planning method and system for unmanned ship Download PDFInfo
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Abstract
The invention relates to an intelligent route planning method and system for an unmanned ship, which comprises the following steps: loading external environment data, an electronic chart, navigation data, photoelectric data and task information; determining a task area and a boundary thereof according to task information, and determining the current position of the unmanned ship according to external environment data, an electronic chart, navigation data and photoelectric data; if the current position of the unmanned ship is outside the task area, performing global route planning; and if the current position of the unmanned ship is located in the task area, local route planning is carried out. The invention adopts the idea of layered planning, designs a path planning unit, a track planning unit and a task planning unit based on the self characteristics, environmental constraints and the like of the unmanned ship, and adopts an algorithm of multi-information intelligent fusion processing to form an integrated system of intelligent, autonomous and comprehensive air route planning.
Description
Technical Field
The invention relates to the technical field of ship navigation planning, in particular to a comprehensive intelligent navigation planning method and system for unmanned ship path planning, track planning, task planning and the like.
Background
In recent years, with the gradual exploitation of ocean resources, the world nations are turning national defense and foreign exchange to oceans and military equipment is developing towards the direction of unmanned, and under the trend, unmanned surface boats are receiving more and more attention from expert scholars at home and abroad. As an important link of normal navigation of the unmanned ship, path planning means that the unmanned ship can plan an optimal path or a suboptimal path without collision from a starting position state to a target position state in an environment with obstacles, and meets all constraint conditions, and is one of key technologies for realizing the intellectualization of the unmanned ship. At present, most of the applicable scenes of the path planning algorithm are mainly free navigation of the unmanned ship, the task is single, reasonable planning under different task requirements cannot be met, the collision prevention capability is simple and thin, the underactuated characteristic and the mobility limitation of the unmanned ship are not fully considered, and the unmanned ship planning directly only stays on the algorithm research and has a large gap with the actual work.
At present, in the technical research field, the contents of the path planning and collision avoidance of unmanned surface vehicles are studied in depth abroad for a long time, and the path planning and collision avoidance technology has many innovations. In contrast, the unmanned surface vehicle path planning, task planning, collision avoidance methods and the like in China are weak, the unmanned surface vehicle has a single task, and many new technical functions are yet to be further researched and developed and broken through.
Aiming at the problems, the invention provides a design scheme of an unmanned ship intelligent navigation integrated system, which is responsible for task planning, path trajectory and navigation planning and can complete different tasks. The method mainly comprises a water surface search task, a constant speed reconnaissance task, a constant speed cruise task, autonomous task optimization and the like.
Disclosure of Invention
The invention provides an intelligent route planning method and system of an unmanned ship aiming at the technical problems in the prior art, and the intelligent route planning integrated system with intelligence, autonomy and comprehensiveness is formed by adopting a layered planning idea, designing a path planning unit, a track planning unit and a task planning unit based on the self characteristics, environmental constraints and the like of the unmanned ship and adopting an algorithm of multi-information intelligent fusion processing.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the present invention provides an intelligent route planning method for an unmanned ship, including:
loading external environment data, an electronic chart, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
determining a task area and a boundary thereof according to task information, and determining the current position of the unmanned ship according to external environment data, an electronic chart, navigation data and photoelectric data;
if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area;
if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
Further, the method further comprises: and updating the navigation point on the planned route according to the barrier information in the process that the unmanned ship advances according to the planned route.
Further, the updating the navigation point on the planned route according to the obstacle information includes:
judging whether the obstacle is a static obstacle or not,
if the obstacle is a static obstacle, calculating a navigation safety angle of the unmanned ship, planning an obstacle avoidance point, and inserting the obstacle avoidance point serving as a next navigation point into a navigation point sequence of an original route;
and if the obstacle is a dynamic obstacle, constructing an obstacle avoidance model according to the unmanned ship motion state information and the obstacle motion state information, dynamically planning obstacle avoidance points, and inserting the obstacle avoidance points into the navigation point sequence of the original route as next navigation points.
Further, the collision model is shown as follows:
the model is based on a rectangular coordinate system established by taking the current position of the unmanned ship A as an origin, wherein V isAIs the speed of the unmanned ship A, alpha is the initial angle of the unmanned ship, VBIs the navigational speed of the obstacle B, beta is the initial angle of the obstacle, theta is the included angle between the AB connecting line and the X axis of the rectangular coordinate system, and VsIs a VAAnd VBThe resultant velocity Δ V of (A) is resolved into a velocity in the AB direction, VθIs a VAAnd VBThe resultant velocity Δ V is resolved to a velocity perpendicular to the AB direction;
and adjusting the navigation attitude of the unmanned ship in real time according to the collision model and the navigation safety angle mu of the unmanned ship relative to the AB connection line.
Further, the method further comprises the steps of after the electronic chart is loaded, rasterizing the electronic chart, and carrying out binarization on the rasterized electronic chart according to the electronic chart information, the navigation information and the length of the unmanned ship, wherein the navigable area is set to be 1, and the non-navigable area is set to be 0.
Further, the method further comprises: and in the process that the unmanned ship advances according to the planned route, the unmanned ship is subjected to track planning by combining the ship speed, the ship direction, the steering angular speed and the minimum turning radius of the unmanned ship between adjacent navigation points on the planned route.
Further, the trajectory planning includes:
constructing a dynamic model of the unmanned ship according to the dynamic characteristics of the unmanned ship and determining the dynamic constraint of the model;
and according to the dynamic constraint of the unmanned ship, performing track smoothing treatment on the original route by using a B-spline curve.
In a second aspect, the present invention provides an intelligent route planning system for unmanned ships, comprising
The data loading module is used for loading external environment data, electronic charts, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
the task planning module is used for determining a task area and a boundary thereof according to the task information and determining the current position of the unmanned ship according to external environment data, the electronic chart, navigation data and photoelectric data;
the route planning module is used for judging the relative position of the unmanned ship and the task area and planning the route; if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area; if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
In a third aspect, the present invention provides an electronic device comprising:
a memory for storing a computer software program;
and the processor is used for reading and executing the computer software program stored in the memory, so as to realize the intelligent route planning method of the unmanned ship in the first aspect of the invention.
In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium, wherein the storage medium stores a computer software program for implementing the intelligent route planning method for an unmanned surface vehicle according to the first aspect of the present invention.
The invention has the beneficial effects that:
(1) autonomous navigation of unmanned ship
The unmanned ship navigation method has the advantages that path planning and track planning are combined, on the basis of a path planning algorithm with multiple influence factors, an optimal track meeting safe navigation of the unmanned ship is designed based on self characteristics of the unmanned ship, including unmanned ship dynamics constraints such as speed, angular velocity, acceleration and angular acceleration, the unmanned ship has high autonomous path planning and intelligent obstacle avoidance capacity, the autonomy and adaptability of the system can be effectively improved, and more complex conditions can be met.
(2) Intelligent navigation for unmanned ship
Unmanned ship navigation is guided by tasks, and under different tasks, the planning strategy of the flight path is also greatly different. The task planning is the top-level planning of unmanned ship navigation, and the task planning is incorporated into an unmanned ship navigation system, so that the task scheduling of single navigation of the unmanned ship can be realized. Due to the design of the task planning unit, the task attributes of the unmanned ship are greatly enriched, and meanwhile, the planning efficiency and the operational capacity of the unmanned ship are effectively improved.
Drawings
FIG. 1 is a schematic diagram of an intelligent route planning method and system;
fig. 2 is a schematic diagram of a Bohai Bay rasterization simulation result;
FIG. 3 is a diagram of a collision model;
FIG. 4 is a schematic diagram of a mission planning area search plan result;
FIG. 5 is a schematic flow chart of an intelligent route planning method;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a non-transitory computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The invention provides an intelligent route planning method and system for an unmanned ship. The task planning is real-time planning and can comprise obstacle avoidance, burst task processing, offline task issuing and the like; the path planning is to plan the route of the issued task reasonably and quickly, and comprises offline global planning, dynamic local planning and the like; and the trajectory planning is to carry out dynamic constraint on the planned line to form a non-collision trajectory which can be navigated by the unmanned ship.
The unmanned surface vehicle firstly performs global planning according to the electronic chart and the received superior task, and the task planning module generates an execution action sequence of the unmanned vehicle execution mechanism under the comprehensive influence of task information, chart information, detection target information, environment information, hydrological meteorological information and received external information. In the process of task execution, the task planning module can adjust a task planning sequence according to real-time task return information, current environment target information and emergency, and can coordinate tasks according to a new task instruction.
The task planning of the unmanned ship single ship aims to specify a task execution action for carrying a task load on the basis of an optimal navigation route of the unmanned ship and finish a specified task issued by a superior level. Meanwhile, a task execution sequence generated by the task planning needs to meet the requirements of security constraint, platform mobility constraint, load equipment performance constraint, communication capability constraint, sensor detection capability constraint, environmental condition constraint and the like.
The general scheme of unmanned ship path planning is shown in fig. 1, and the design idea is based on the development of a layering strategy of unmanned ships. The layering strategy of the unmanned boat comprises two parts: deliberate AI model: the method is also called as a model-based planning method, carries out global collision-free path planning on the basis of an environment model, can conveniently find a global target, is difficult to adapt to a dynamically changing environment, and is generally used for the global and the local. Reactive AI model: the platform directly responds to the environmental change without planning in advance, and avoids the obstacle in real time according to the environmental data detected by the sensor, has the characteristics of rapidness, real time and high efficiency, and is generally used for local and intelligent obstacle avoidance. Wherein the layering strategy is designed as: the method comprises three parts of global planning, local planning and intelligent obstacle avoidance, and the three parts respectively correspond to planning schemes in different stages. The man-machine interaction part mainly has the functions of task issuing, database loading, strategy determination and interactive interface display. In addition, because the task planning and intelligent obstacle avoidance need to sense the data information, an intelligent sensing unit is introduced, mainly comprising photoelectric data, radar detection data and navigation information, and new information is provided for local planning and intelligent obstacle avoidance. And finally, optimizing and smoothing the track under the conditions of the dynamics constraint of the unmanned ship and the like to form a track planning unit, and sending the course and the speed information to the control unit.
And (3) task issuing: and issuing a task instruction to the unmanned ship, wherein the main tasks comprise patrol, search and rescue and tracking tasks. The method comprises the steps of loading task data, such as task boundary information, patrol point sequences, search and rescue areas, information of tracking targets and the like.
Determination of the hierarchical strategy: strategies are mainly classified as global planning + local planning or local planning. The selection strategies of the unmanned ship and the task boundary are realized according to the position of the unmanned ship or the task requirement of the unmanned ship.
A database: the database stores all data information of the unmanned ship for whole-course track planning, which comprises the following steps: global obstacle information extracted from the electronic chart; external data information of the unmanned ship navigation environment, such as: ocean currents, monsoon, sea waves and the like (calculation of safe distance in obstacle avoidance and re-planning processes). Constraint information (for trajectory planning) inherent to the unmanned vehicle itself.
Specifically, the embodiment of the invention provides an intelligent route planning method for an unmanned ship, which comprises the following steps:
s1, data loading: the method comprises loading of external environment data, electronic charts, navigation data, photoelectric data, task information and other data.
Specifically, the external environment data mainly comprises sea state data and meteorological data, the electronic chart adopts a standard S-57 format, the navigation data is the attitude and the azimuth of the carrier, the photoelectric data is the information output of target detection, and the task information comprises task category, task point coordinates and task working range information.
S2, rasterizing the electronic chart, wherein the electronic chart rasterizing module is a multiplexing module, and can be used in both global planning and local planning. Rasterization mainly realizes conversion of an electronic chart (vector diagram) into an image, and can output the electronic chart under different spatial resolutions.
Specifically, the map layers of attributes such as sea/land, obstacles, channels, area boundaries, landforms, and the like are analyzed. And obtaining the obstacle distribution condition of the global environment by reading three data files of points, lines and surfaces representing the obstacles, and constructing an environment model for the flight path planning according to the distribution of the obstacles. The binarization of the rasterize depends on several factors:
electronic chart information: information of no-navigation area, water depth, static obstacle (including island, boundary, etc.) in electronic chart
Navigation information: roll, pitch and sea state information for a vessel
Length of unmanned ship
By utilizing the information of the three, the navigable area can be determined, and in the rasterization process, the navigable area is set to be 1, and the non-navigable area is set to be 0. One key part of the rasterization method is to judge whether the point is in a navigation area, and since the electronic chart is composed of vector data, how to judge whether the point is in the vector chart of the navigation area is an important step of rasterization. For irregular images, it is generally possible to judge by ray method. The basic principle is as follows: and calculating the intersection points of the rays and each side of the polygon, wherein if the intersection points are even numbers, the points are outside the polygon, and otherwise, the points are inside the polygon. Calculating the intersection of ray and line segment, and judging the point t and line segment s1s2The conditions for the intersection were:
in the formula, x and y represent horizontal and vertical coordinates of a point, and slope represents a line segment s1s2The slope of (a).
The results of the rasterization simulation are shown in fig. 2.
And S3, adjusting the planning strategy according to different tasks to achieve the goal of reasonable planning under different tasks. The whole process of task planning is as follows:
s3.1, entering task data, determining boundary information of a task, judging the distance between the current position of the unmanned ship and a task area, and if the distance is long, constructing a coarse-grained electronic chart; and constructing a fine-grained electronic chart at a relatively close time, taking the starting point of the current position and the task boundary as the starting point and the ending point of the global planning, generating a navigation point sequence, and navigating according to the navigation point sequence.
Specifically, the a-algorithm is a heuristic search algorithm in global path planning, and the core of the a-algorithm is to comprehensively consider the actual cost from the starting point to the current node and the estimated cost from the current node to the end point, and the global path cost f (n) is calculated in the following manner:
f(n)＝g(n)+h(n) (2)
wherein g (n) represents the cost from the starting point to the current node n; the heuristic function h (n) represents the estimated cost from the current node n to the end point.
S3.2, after the global planning is finished, at the moment, the unmanned ship enters a task boundary area, the task type and the task point information are determined, whether a task navigation point is given or not is judged, if yes, the local planning step is skipped, and the manual setting of a navigation point sequence is directly carried out; if not, the method enters task type judgment, wherein the task type mainly comprises three typical tasks: a pursuit task, a search and rescue task, and a patrol (circle) task; and respectively adopting corresponding planning algorithms with different strategies, generating an electronic chart with fine granularity, and forming a navigation point sequence through planning.
Specifically, the task planning and the obstacle avoidance updating strategies are different, the obstacle avoidance needs to be continuously refreshed in real time, but the task planning can be updated at a certain time interval, and only the deviation amount of the task in the actual process is required to be ensured to meet the task requirement. In addition, in order to distinguish from the information of the common navigation points, points generated by task planning or required by the task are uniformly defined as task points, the attributes of the task points are more than those of the common navigation points, the priority is higher, and points generated by obstacle avoidance are defined as obstacle avoidance points, and the priority is highest. When the three are generated simultaneously, the obstacle avoidance is carried out preferentially, then the task point is executed, and finally the navigation point is obtained. The navigation points are generated by an off-line algorithm, the task points can be set off-line or generated in real time, and the obstacle avoidance points are generated in real time. Taking the area patrol task as an example, the result is shown in fig. 4.
S3.3, during navigation of global planning and local planning, information provided by the intelligent sensing module and the navigation module is sent to the unmanned ship in an interrupted mode, after target detection and identification, whether an object is an obstacle or a tracking target is determined, and if the object is the tracking target, target information is sent to the tracking module of the local planning to plan a path again; if the obstacle is the obstacle, entering a collision avoidance part, detecting the speed and the course of the obstacle through radar interframe information, if the obstacle is the static obstacle, calculating a safety angle, re-planning a new waypoint, and inserting the new waypoint into the current sequence to be used as the next waypoint. If the obstacle is a dynamic obstacle, a local collision avoidance method is adopted for emergency obstacle avoidance.
Specifically, during autonomous navigation of the unmanned surface vehicle, collision judgment possibly occurring needs to be performed, and corresponding avoidance measures need to be taken, so that accurate judgment of collision conditions is an important component in intelligent avoidance tasks of the unmanned surface vehicle. The collision model is defined as follows:
as shown in fig. 3, a rectangular coordinate system XOY (XY axes may be defined by longitude and latitude and may also be defined based on practical situations) is constructed with the current position of the unmanned boat a as the origin, in this embodiment, the XY axes are defined by longitude and latitude, that is, the X axis direction is the positive east direction, and the Y axis direction is the positive north direction, and the corresponding speed of the unmanned boat is VAAnd the ship fore angle is alpha (the unmanned ship is simplified into a point). The barrier B is a radius R (which can be understood as the sum of a circumscribed circle of the barrier and a safe radius), and the navigational speed is VBThe heading angle is beta. Wherein the angle between the line of sight AB and the X-axis is theta and DeltaV is VAAnd VBIn the direction of
The angle between the delta V and the line of sight AB is gamma. Decomposing Δ V to a velocity V along the AB directionsAnd a component velocity V perpendicular to ABθ：
From the above collision model it can be derived: the angle γ between the relative velocity Δ V and the line of sight AB can be calculated from the velocity components of the relative velocity in the AB and perpendicular AB directions:
i.e. tan gamma is with respect to VA,α,VBβ is a function of:
in actual operation, the unmanned ship can only adjust the speed and the course of the unmanned ship and cannot adjust the movement behavior of the obstacle, wherein mu represents the safety angle of the unmanned ship relative to the sight line AB, gamma represents the included angle between the current resultant speed and the sight line AB, and the adjustment range of delta gamma is as follows: [ μ - γ, μ + γ ];
combining the collision model formula with the above equation yields:
using Δ α, Δ VAAnd adjusting the navigation attitude of the unmanned ship in real time to avoid the obstacle.
And S3.4, after obstacle avoidance is finished, judging whether a terminal point is reached or a task is finished, if so, finishing planning, and otherwise, continuing navigation.
S4, planning a track means that a certain included angle exists between the connection of adjacent navigation points, turning is needed at the moment, and the safe steering can be realized only when a certain constraint condition is met. Therefore, for such situations, a path needs to be planned, and the constraint considered is: boat speed, boat heading, steering angular velocity, minimum turn radius, etc.
S4.1, kinematic constraint of the unmanned ship, specifically, firstly, a ship body model of the unmanned ship is combined to analyze the dynamic characteristics of the unmanned ship, and in the aspect of dynamic analysis, the unmanned ship is generally usedSay with non-complete constraints. We can define the dynamics model of the unmanned boat as
Wherein (x, y) describes the current position of the unmanned vehicle,
describing the current heading of the unmanned ship, and (v, omega) describing the current speed and angular velocity of the unmanned ship. The state update equation of the unmanned ship in the time interval of delta t can be derived:
wherein
Acceleration and angular acceleration, respectively. In delta t time, the turning parameters of the unmanned boat can be calculated
Then, the dynamic parameters of the unmanned ship can be determined according to the dynamic characteristics of the unmanned ship. Through the kinetic parameters, the speed of the unmanned ship can be obtained, and the sampling interval of the angular speed is as follows:
wherein
The maximum acceleration and the angular acceleration are respectively obtained by analyzing the dynamic characteristics of the ship body.
S4.2, the smooth track means that a smooth function is constructed aiming at the turning position of the original air route, so that the unmanned ship can turn stably, and large steering is avoided. The minimum rotation radius of the unmanned ship can be obtained through dynamic constraint, the B spline curve method is a currently practical track smoothing algorithm, the method has a global optimization effect, the fitted track is smooth in transition, and sudden repeated steering cannot occur. Considering the complexity of the calculation and the practical effect, a quadratic or cubic B-spline curve is usually selected for smoothing. Taking a quadratic uniform B-spline curve as an example, the curvature expression of the B-spline curve of the track is deduced as follows. The parameter equation of each section of the quadratic uniform B-spline curve is recorded as:
according to the curve curvature calculation formula:
radius of curvature of curve
The actual curvature of the smoothed track can be calculated by an actual formula, and when the curvature radius satisfies that r is more than or equal to rminI.e. greater than the minimum turning radius, the kinematic constraints can be considered to be satisfied. The curvature change of each part of the B spline function is stable, and the first derivative and the second derivative of the B spline function are continuous, so that the speed change and the acceleration change of the flight path smoothed by the B spline function are also continuous. Therefore, when the unmanned ship turns, the curvature is changed very little, and large-angle steering during turning can be avoided.
The overall flow chart of the method is shown in fig. 5, and by the method, the method can be as follows:
(1) autonomous navigation of unmanned ship
The unmanned ship navigation method has the advantages that path planning and track planning are combined, on the basis of a path planning algorithm with multiple influence factors, an optimal track meeting safe navigation of the unmanned ship is designed based on self characteristics of the unmanned ship, including unmanned ship dynamics constraints such as speed, angular velocity, acceleration and angular acceleration, the unmanned ship has high autonomous path planning and intelligent obstacle avoidance capacity, the autonomy and adaptability of the system can be effectively improved, and more complex conditions can be met.
(2) Intelligent navigation for unmanned ship
Unmanned ship navigation is guided by tasks, and under different tasks, the planning strategy of the flight path is also greatly different. The task planning is the top-level planning of unmanned ship navigation, and the task planning is incorporated into an unmanned ship navigation system, so that the task scheduling of single navigation of the unmanned ship can be realized. Due to the design of the task planning unit, the task attributes of the unmanned ship are greatly enriched, and meanwhile, the planning efficiency and the operational capacity of the unmanned ship are effectively improved.
On the basis of the above method, an embodiment of the present invention further provides an intelligent route planning system for an unmanned ship, including:
the data loading module is used for loading external environment data, electronic charts, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
the task planning module is used for determining a task area and a boundary thereof according to the task information and determining the current position of the unmanned ship according to external environment data, the electronic chart, navigation data and photoelectric data;
the route planning module is used for judging the relative position of the unmanned ship and the task area and planning the route; if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area; if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
Fig. 6 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the present invention. As shown in fig. 6, an embodiment of the present invention provides an electronic device, which includes a memory 510, a processor 520, and a computer program 511 stored in the memory 520 and executable on the processor 520, wherein the processor 520 executes the computer program 511 to implement the following steps:
loading external environment data, an electronic chart, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
determining a task area and a boundary thereof according to task information, and determining the current position of the unmanned ship according to external environment data, an electronic chart, navigation data and photoelectric data;
if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area;
if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
Referring to fig. 7, fig. 7 is a schematic diagram illustrating an embodiment of a computer-readable storage medium according to the present invention. As shown in fig. 7, the present embodiment provides a computer-readable storage medium 600 having a computer program 611 stored thereon, the computer program 611, when executed by a processor, implementing the steps of:
loading external environment data, an electronic chart, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
determining a task area and a boundary thereof according to task information, and determining the current position of the unmanned ship according to external environment data, an electronic chart, navigation data and photoelectric data;
if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area;
if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such 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 such modifications and variations.
Claims (10)
1. An intelligent route planning method for an unmanned ship is characterized by comprising the following steps:
loading external environment data, an electronic chart, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
determining a task area and a boundary thereof according to task information, and determining the current position of the unmanned ship according to external environment data, an electronic chart, navigation data and photoelectric data;
if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area;
if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
2. The intelligent routing method for unmanned boats of claim 1, further comprising: and updating the navigation point on the planned route according to the barrier information in the process that the unmanned ship advances according to the planned route.
3. The intelligent route planning method for unmanned surface vehicle of claim 2, wherein the updating of the waypoints on the planned route based on the obstacle information comprises:
judging whether the obstacle is a static obstacle or not,
if the obstacle is a static obstacle, calculating a navigation safety angle of the unmanned ship, planning an obstacle avoidance point, and inserting the obstacle avoidance point serving as a next navigation point into a navigation point sequence of an original route;
and if the obstacle is a dynamic obstacle, constructing a collision model according to the unmanned ship motion state information and the obstacle motion state information, dynamically planning obstacle avoidance points according to the collision model, and inserting the obstacle avoidance points into a navigation point sequence of the original route as next navigation points.
4. The intelligent route planning method for unmanned surface vehicle of claim 3, wherein the collision model is represented by the following formula:
the model is based on a rectangular coordinate system established by taking the current position of the unmanned ship A as an origin, wherein V isAIs the speed of the unmanned ship A, alpha is the initial angle of the unmanned ship, VBIs the navigational speed of the obstacle B, beta is the initial angle of the obstacle, theta is the included angle between the AB connecting line and the X axis of the rectangular coordinate system, and VsIs a VAAnd VBThe resultant velocity Δ V of (A) is resolved into a velocity in the AB direction, VθIs a VAAnd VBThe resultant velocity Δ V is resolved to a velocity perpendicular to the AB direction;
and adjusting the navigation attitude of the unmanned ship in real time according to the collision model and the navigation safety angle mu of the unmanned ship relative to the AB connection line.
5. The intelligent route planning method for the unmanned ship according to claim 1, further comprising rasterizing the electronic chart after loading the electronic chart, and binarizing the rasterized electronic chart according to the electronic chart information, the navigation information and the length of the unmanned ship, that is, setting a navigable area to be 1 and a non-navigable area to be 0.
6. The intelligent routing method for unmanned boats of claim 1, further comprising: and in the process that the unmanned ship advances according to the planned route, the unmanned ship is subjected to track planning by combining the ship speed, the ship direction, the steering angular speed and the minimum turning radius of the unmanned ship between adjacent navigation points on the planned route.
7. The intelligent route planning method for unmanned ships according to claim 6, wherein the trajectory planning comprises:
constructing a dynamic model of the unmanned ship according to the dynamic characteristics of the unmanned ship and determining the dynamic constraint of the model;
and according to the dynamic constraint of the unmanned ship, performing track smoothing treatment on the original route by using a B-spline curve.
8. An intelligent route planning system of unmanned ship is characterized by comprising
The data loading module is used for loading external environment data, electronic charts, navigation data, photoelectric data and task information; the task information comprises task categories, task point coordinates and task working range information;
the task planning module is used for determining a task area and a boundary thereof according to the task information and determining the current position of the unmanned ship according to external environment data, the electronic chart, navigation data and photoelectric data;
the route planning module is used for judging the relative position of the unmanned ship and the task area and planning the route; if the current position of the unmanned ship is outside the task area, performing global route planning; the global route is a route from the current position of the unmanned ship to the nearest boundary point of the task area; if the current position of the unmanned ship is located in the task area, local route planning is carried out; the local air route is an air route which is planned in a task area by adopting different strategies according to different task types.
9. An electronic device, comprising:
a memory for storing a computer software program;
a processor for reading and executing the computer software program stored in the memory, thereby implementing an unmanned ship intelligent route planning method according to any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored therein a computer software program for implementing an unmanned boat intelligent route planning method according to any one of claims 1-7.
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