CN113917930A - Unmanned ship navigation state control method based on sensing data - Google Patents
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Abstract
The invention discloses a method for controlling the navigation state of an unmanned ship based on sensing data, which comprises the steps of obtaining the sensing data of the unmanned ship in the navigation process, and calculating the state data of the unmanned ship at the current moment according to the sensing data; generating a local chart according to the unmanned ship state data; extracting the environment target characteristics to match with the known environment target characteristics, and updating the generated local chart by using the matched optimal environment target characteristics; and controlling the navigation state of the unmanned ship by using the updated local chart. According to the unmanned ship navigation control method, the sensing data of the multi-sensor information is fused, and the sensing data is used for generating and updating the local chart, so that the navigation state of the unmanned ship is controlled, and the instantaneity and accuracy of autonomous navigation of the unmanned ship are improved; and the autonomous navigation performance of the unmanned ship is evaluated by adopting the course keeping performance and the track tracking performance, so that the autonomous navigation and the accuracy of the unmanned ship are further improved.
Description
Technical Field
The invention relates to the technical field of autonomous navigation of unmanned ships, in particular to a method for controlling navigation state of an unmanned ship based on sensing data.
Background
With the increasing emphasis of the country on marine resources, the continuous frequency of marine exploration, mining and transportation activities, and the continuous development and progress of science and technology, the intelligent, systematized and unmanned ship system becomes a new development direction. In recent years, a novel research subject, namely a water surface unmanned ship, is developed by combining a ship with an advanced control technology, and the unmanned ship is a small water surface unmanned platform which can complete tasks such as target detection and the like through autonomous perception planning and autonomous navigation. Unmanned ships have wide and good development prospects in various fields, and the technology of the unmanned ships gradually becomes the focus of attention and the key object of research.
With the rapid development of artificial intelligence and deep learning, unmanned and intelligent development becomes one of the main directions of ship development. The unmanned ship as a full-automatic water surface robot can independently navigate in a complex marine environment so as to replace human beings to complete important tasks. The autonomous navigation capability is realized by depending on the accurate sensing of the ship to the environment, but the sensing of the existing unmanned ship to the surrounding navigation environment cannot meet the requirements of autonomous navigation real-time performance and accuracy in complex sea conditions and high-speed navigation.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for controlling the navigation state of an unmanned ship based on sensing data.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
an unmanned ship navigation state control method based on perception data comprises the following steps:
acquiring sensing data of the unmanned ship in the process of sailing, and calculating the state data of the unmanned ship at the current moment according to the sensing data;
generating a local chart according to the unmanned ship state data;
extracting the environment target characteristics to match with the known environment target characteristics, and updating the generated local chart by using the matched optimal environment target characteristics;
and controlling the navigation state of the unmanned ship by using the updated local chart.
Further, the perception data comprises multi-sensor data of the unmanned ship in the process of sailing.
Further, the multi-sensor data specifically includes: navigation environment parameters, water surface ship parameters, restricted area and obstructive object parameters, ship navigation parameters and marine environment parameters; wherein the content of the first and second substances,
the navigation environment parameters specifically comprise information of seacoasts, ports, wharfs and navigation aid facilities;
the water surface ship parameters specifically comprise water surface ship classification, shape, speed and azimuth information;
the parameters of the restricted area and the navigation obstacle specifically comprise the shape, speed and direction information of an island reef, a water surface floater, a sea-crossing cable, a dangerous shallow point, a sunken ship and a submerged reef;
the boat navigation parameters specifically comprise navigation position, navigation speed, course and pose information of the boat;
the marine environmental parameters specifically include wind direction, wind speed, water flow, surge, water temperature, and salinity information.
Further, the generating of the local chart according to the unmanned ship state data specifically includes:
and calculating to obtain the unmanned ship state data at the next moment according to the unmanned ship state data at the current moment and the set system input control parameters.
Further, the extracting of the environment target features is matched with the known environment target features, and the generated local chart is updated by using the matched optimal environment target features, which specifically comprises the following sub-steps:
converting an environmental target detected by a radar at the current moment and radar detection parameters into a radar detection image in a polar coordinate mode based on a radar detection model;
performing feature extraction on the radar detection image by adopting a scale invariant feature conversion algorithm to obtain environmental target features;
and matching the extracted environment target features with the known environment target features, and selecting the environment target features with the maximum significance as new environment target features to update the generated local chart.
Further, the new environmental target characteristics are represented as:
in the formula (I), the compound is shown in the specification,
features representing the environmental target i detected at the current time t, (x)i，yi) Global coordinates representing the environmental target i, (x)r，yr) Global coordinates representing the unmanned ship at the current time t,
representing the angle between the heading of the unmanned ship at the current moment t and the x axis in the chart,
representing the range noise of the environmental object i detected at the current time instant t,
representing the azimuth noise of the environmental target i detected at the current time t.
Further, the method further comprises:
estimating the course keeping performance and the track tracking performance of the unmanned ship under the control of the autonomous navigation system;
judging whether the heading keeping performance evaluation value and the track tracking performance evaluation value are both smaller than a set threshold value; if so, continuing to control the navigation state of the unmanned ship by using the updated local chart; otherwise, the sensing data of the unmanned ship in the sailing process is obtained again.
Further, the method for evaluating the course keeping performance of the unmanned ship under the control of the autonomous navigation system specifically comprises the following steps:
respectively calculating course deviation smoothness, navigation time and energy consumption values of the unmanned ship under the control of the autonomous navigation system;
respectively carrying out normalization processing on the calculated course deviation smoothness, the calculated navigation time and the calculated energy consumption value;
and calculating a course keeping performance evaluation value of the unmanned ship under the control of the autonomous navigation system by adopting a linear weighted summation method.
Further, the method for evaluating the track tracking performance of the unmanned ship under the control of the autonomous navigation system specifically comprises the following steps:
respectively calculating track smoothness, path point deviation, navigation time and energy consumption values of the unmanned ship under the control of the autonomous navigation system;
respectively carrying out normalization processing on the calculated flight path smoothness, the calculated path point deviation, the calculated flight time and the calculated energy consumption value;
and calculating a track tracking performance evaluation value of the unmanned ship under the control of the autonomous navigation system by adopting a linear weighted summation method.
The invention has the following beneficial effects:
according to the unmanned ship navigation control method, the sensing data of the multi-sensor information is fused, and the sensing data is used for generating and updating the local chart, so that the navigation state of the unmanned ship is controlled, and the instantaneity and accuracy of autonomous navigation of the unmanned ship are improved; and the autonomous navigation performance of the unmanned ship is evaluated by adopting the course keeping performance and the track tracking performance, so that the autonomous navigation and the accuracy of the unmanned ship are further improved.
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FIG. 1 is a schematic flow chart of a method for controlling the navigation state of an unmanned ship based on sensing data according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating the substep of step S3 according to the embodiment of the present invention;
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, an embodiment of the present invention provides a method for controlling a navigation state of an unmanned ship based on sensed data, including the following steps S1 to S4:
s1, obtaining sensing data of the unmanned ship in the sailing process, and calculating the state data of the unmanned ship at the current moment according to the sensing data;
in the step, the sensing data of the unmanned ship in the process of sailing is obtained, wherein the sensing data comprises multi-sensor data of the unmanned ship in the process of sailing.
The multi-sensor data acquired by the invention specifically comprises the following steps: navigation environment parameters, water surface ship parameters, restricted area and obstructive object parameters, ship navigation parameters and marine environment parameters; wherein the content of the first and second substances,
the navigation environment parameters specifically comprise information of seacoasts, ports, wharfs and navigation aid facilities;
the water surface ship parameters specifically comprise water surface ship classification, shape, speed and azimuth information;
the parameters of the restricted area and the navigation obstacle specifically comprise the shape, speed and direction information of an island reef, a water surface floater, a sea-crossing cable, a dangerous shallow point, a sunken ship and a submerged reef;
the boat navigation parameters specifically comprise navigation position, navigation speed, course and pose information of the boat;
the marine environmental parameters specifically include wind direction, wind speed, water flow, surge, water temperature, and salinity information.
After the multi-sensor data of the unmanned ship in the process of sailing are obtained, the multi-sensor data are subjected to fusion calculation, and therefore unmanned ship state data X (t) at the current time t are obtained.
S2, generating a local chart according to the unmanned ship state data;
in this step, the invention calculates the unmanned ship state data X (t +1| t) at the next time t +1 according to the unmanned ship state data X (t) at the current time t and the preset system input control parameter u (t).
S3, extracting the environment target characteristics to match with the known environment target characteristics, and updating the generated local chart by using the matched optimal environment target characteristics;
in this step, the method extracts the environment target features to match with the known environment target features, and updates the generated local chart by using the matched optimal environment target features, which specifically includes the following substeps S3-1 to S3-3:
s3-1, converting the environmental target and the radar detection parameters detected by the radar at the current moment into radar detection images in a polar coordinate mode based on the radar detection model;
specifically, the present invention is based on a radar detection model, and an environmental target i detected at a current time t is characterized by:
in the formula (I), the compound is shown in the specification,
representing the characteristics of the environmental object i detected at the current time t,
indicating the distance of the environmental object i detected at the current time instant t,
the position of the environmental object i detected at the current time t,
representing the system noise.
S3-2, performing feature extraction on the radar detection image by adopting a scale invariant feature conversion algorithm to obtain environmental target features;
specifically, the invention adopts the feature of unchanged dimensionThe conversion algorithm carries out feature extraction on the radar detection image to obtain the feature of the environment target, namely the position of the central point of the environment target i
And S3-3, matching the extracted environment target features with known environment target features, and selecting the environment target features with the largest significance as new environment target features to update the generated local chart.
Specifically, the method adopts a scale invariant feature transformation algorithm to search for the known environment target feature matched with the current environment target feature, performs the significance test on the current environment target feature by calculating the mahalanobis distance between the current environment target feature and the matched known environment target feature, and updates the generated local chart by taking the current environment target feature passing the significance test as a new environment target feature. Wherein the new environmental target characteristics are represented as:
in the formula (I), the compound is shown in the specification,
features representing the environmental target i detected at the current time t, (x)i，yi) Global coordinates representing the environmental target i, (x)r，yr) Global coordinates representing the unmanned ship at the current time t,
representing the angle between the heading of the unmanned ship at the current moment t and the x axis in the chart,
representing the range noise of the environmental object i detected at the current time instant t,
indicating the current time tpyeAnd measuring the azimuth noise of the environmental target i.
In particular, when no matching known environmental target feature is found, the current environmental target feature is added as a new environmental target feature to the environmental target feature library.
And S4, controlling the navigation state of the unmanned ship by using the updated local chart.
In this step, the present invention controls the navigation state of the unmanned ship by using the updated local chart, and further includes:
estimating the course keeping performance and the track tracking performance of the unmanned ship under the control of the autonomous navigation system;
judging whether the heading keeping performance evaluation value and the track tracking performance evaluation value are both smaller than a set threshold value; if so, continuing to control the navigation state of the unmanned ship by using the updated local chart; otherwise, the sensing data of the unmanned ship in the sailing process is acquired again, and the step S1 is returned.
The method specifically comprises the following steps of:
respectively calculating course deviation smoothness, navigation time and energy consumption values of the unmanned ship under the control of the autonomous navigation system;
specifically, the following formula is adopted to calculate and calculate the heading deviation smoothness of the unmanned ship under the control of the autonomous navigation system,
where CDM represents heading deviation smoothness, θtIndicating the heading of the unmanned ship at the current time t, teIndicating the time, t, at which the unmanned ship enters the sailing areasRepresenting the time when the unmanned ship drives out of the navigation area;
the navigation time of the unmanned ship under the control of the autonomous navigation system is calculated by adopting the following formula,
T＝te-ts
wherein T represents a voyage time;
calculating and calculating the energy consumption value of the unmanned ship under the control of the autonomous navigation system by adopting the following formula,
E＝Ee-Es
wherein E represents an energy consumption value, EeIndicating the amount of diesel fuel that the unmanned ship enters the sailing area, EsIndicating the diesel quantity of the unmanned ship which drives out of the sailing area;
respectively normalizing the calculated course deviation smoothness, the calculated navigation time and the calculated energy consumption value, and expressing the values as
Wherein X represents CDM, respectivelyi、Ti、Ei(i＝1，2，...，n)；
Normalizing to obtain normalized value of course deviation smoothness, navigation time and energy consumption value as CDMi *、Ti *、Ei *；
The course keeping performance evaluation value of the unmanned ship under the control of the autonomous navigation system is calculated by adopting a linear weighted summation method, and the calculation formula is expressed as
In the formula, CKiRepresents the heading maintenance performance rating of the ith autonomous navigation system,
k1，k2，k3index weights representing heading deviation smoothness, voyage time, and energy consumption values, respectively.
The method for evaluating the track tracking performance of the unmanned ship under the control of the autonomous navigation system specifically comprises the following steps:
respectively calculating track smoothness, path point deviation, navigation time and energy consumption values of the unmanned ship under the control of the autonomous navigation system;
calculating and calculating the track smoothness of the unmanned ship under the control of the autonomous navigation system by adopting the following formula,
where TSM denotes track smoothness, θtIndicating the heading of the unmanned ship at the current time t,
teindicating the time, t, at which the unmanned ship enters the sailing areasRepresenting the time when the unmanned ship drives out of the navigation area;
calculating the deviation degree of the path point of the unmanned ship under the control of the autonomous navigation system by adopting the following formula,
wherein MD represents a degree of deviation of a path point, DiThe deviation degree of the ith path point is shown, and n is the number of path points in the polygonal path;
specifically, the following formula is adopted to calculate and calculate the navigation time of the unmanned ship under the control of the autonomous navigation system,
T＝te-ts
wherein T represents a voyage time;
calculating and calculating the energy consumption value of the unmanned ship under the control of the autonomous navigation system by adopting the following formula,
E＝Ee-Es
wherein E represents an energy consumption value, EeIndicating the amount of diesel fuel that the unmanned ship enters the sailing area, EsIndicating the diesel quantity of the unmanned ship which drives out of the sailing area;
respectively carrying out normalization processing on the calculated track smoothness, the calculated path point deviation, the calculated navigation time and the calculated energy consumption value, and obtaining the normalized values of the track smoothness, the calculated path point deviation, the calculated navigation time and the calculated energy consumption value as TSMi *、MDi *、Ti *、Ei *；
Calculating the track tracking performance evaluation value of the unmanned ship under the control of the autonomous navigation system by adopting a linear weighted summation method, wherein a calculation formula is expressed as
In the formula, TTiRepresents the track-following performance evaluation value of the ith autonomous navigation system,
k4，k5，k6，k7index weights representing track smoothness, waypoint deviation, travel time, and energy consumption value, respectively.
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 processor, 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.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (9)
1. A unmanned ship navigation state control method based on sensing data is characterized by comprising the following steps:
acquiring sensing data of the unmanned ship in the process of sailing, and calculating the state data of the unmanned ship at the current moment according to the sensing data;
generating a local chart according to the unmanned ship state data;
extracting the environment target characteristics to match with the known environment target characteristics, and updating the generated local chart by using the matched optimal environment target characteristics;
and controlling the navigation state of the unmanned ship by using the updated local chart.
2. The unmanned ship navigation state control method based on perception data of claim 1, wherein the perception data comprises multi-sensor data of the unmanned ship during navigation.
3. The unmanned ship navigation state control method based on perception data according to claim 2, wherein the multi-sensor data specifically includes: navigation environment parameters, water surface ship parameters, restricted area and obstructive object parameters, ship navigation parameters and marine environment parameters; wherein the content of the first and second substances,
the navigation environment parameters specifically comprise information of seacoasts, ports, wharfs and navigation aid facilities;
the water surface ship parameters specifically comprise water surface ship classification, shape, speed and azimuth information;
the parameters of the restricted area and the navigation obstacle specifically comprise the shape, speed and direction information of an island reef, a water surface floater, a sea-crossing cable, a dangerous shallow point, a sunken ship and a submerged reef;
the boat navigation parameters specifically comprise navigation position, navigation speed, course and pose information of the boat;
the marine environmental parameters specifically include wind direction, wind speed, water flow, surge, water temperature, and salinity information.
4. The unmanned ship voyage state control method based on perception data as claimed in claim 1, wherein the generating of the local chart according to the unmanned ship state data specifically includes:
and calculating to obtain the unmanned ship state data at the next moment according to the unmanned ship state data at the current moment and the set system input control parameters.
5. The unmanned ship navigation state control method based on perceptual data as defined in claim 1, wherein the extracting of the environmental target feature is matched with a known environmental target feature, and the generated local sea chart is updated by using a matched optimal environmental target feature, specifically comprising the following sub-steps:
converting an environmental target detected by a radar at the current moment and radar detection parameters into a radar detection image in a polar coordinate mode based on a radar detection model;
performing feature extraction on the radar detection image by adopting a scale invariant feature conversion algorithm to obtain environmental target features;
and matching the extracted environment target features with the known environment target features, and selecting the environment target features with the maximum significance as new environment target features to update the generated local chart.
6. The unmanned ship navigation state control method based on perceptual data of claim 5, wherein the new environmental target characteristic is expressed as:
in the formula (I), the compound is shown in the specification,
features representing the environmental target i detected at the current time t, (x)i，yi) Global coordinates representing the environmental target i, (x)r，yr) Global coordinates representing the unmanned ship at the current time t,
representing the angle between the heading of the unmanned ship at the current moment t and the x axis in the chart,
representing the range noise of the environmental object i detected at the current time instant t,
representing the azimuth noise of the environmental target i detected at the current time t.
7. The unmanned ship voyage state control method based on perceptual data of any one of claims 1 to 6, wherein the method further comprises:
estimating the course keeping performance and the track tracking performance of the unmanned ship under the control of the autonomous navigation system;
judging whether the heading keeping performance evaluation value and the track tracking performance evaluation value are both smaller than a set threshold value; if so, continuing to control the navigation state of the unmanned ship by using the updated local chart; otherwise, the sensing data of the unmanned ship in the sailing process is obtained again.
8. The unmanned ship navigation state control method based on perceptual data as defined in claim 7, wherein the heading maintenance performance of the unmanned ship under the control of the autonomous navigation system is evaluated, comprising the following sub-steps:
respectively calculating course deviation smoothness, navigation time and energy consumption values of the unmanned ship under the control of the autonomous navigation system;
respectively carrying out normalization processing on the calculated course deviation smoothness, the calculated navigation time and the calculated energy consumption value;
and calculating a course keeping performance evaluation value of the unmanned ship under the control of the autonomous navigation system by adopting a linear weighted summation method.
9. The unmanned ship navigation state control method based on perceptual data as defined in claim 7, wherein the evaluation of the track following performance of the unmanned ship under the control of the autonomous navigation system comprises the following sub-steps:
respectively calculating track smoothness, path point deviation, navigation time and energy consumption values of the unmanned ship under the control of the autonomous navigation system;
respectively carrying out normalization processing on the calculated flight path smoothness, the calculated path point deviation, the calculated flight time and the calculated energy consumption value;
and calculating a track tracking performance evaluation value of the unmanned ship under the control of the autonomous navigation system by adopting a linear weighted summation method.
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