CN112124537B - Intelligent control method for underwater robot for autonomous absorption and fishing of benthos - Google Patents

Intelligent control method for underwater robot for autonomous absorption and fishing of benthos Download PDF

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CN112124537B
CN112124537B CN202011009529.1A CN202011009529A CN112124537B CN 112124537 B CN112124537 B CN 112124537B CN 202011009529 A CN202011009529 A CN 202011009529A CN 112124537 B CN112124537 B CN 112124537B
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黄海
徐明浩
李冀永
鲍轩
靳佰达
李忻阳
万兆亮
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Harbin Engineering University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63C—LAUNCHING, HAULING-OUT, OR DRY-DOCKING OF VESSELS; LIFE-SAVING IN WATER; EQUIPMENT FOR DWELLING OR WORKING UNDER WATER; MEANS FOR SALVAGING OR SEARCHING FOR UNDERWATER OBJECTS
    • B63C11/00—Equipment for dwelling or working underwater; Means for searching for underwater objects
    • B63C11/48—Means for searching for underwater objects
    • B63C11/49—Floating structures with underwater viewing devices, e.g. with windows ; Arrangements on floating structures of underwater viewing devices, e.g. on boats
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63C—LAUNCHING, HAULING-OUT, OR DRY-DOCKING OF VESSELS; LIFE-SAVING IN WATER; EQUIPMENT FOR DWELLING OR WORKING UNDER WATER; MEANS FOR SALVAGING OR SEARCHING FOR UNDERWATER OBJECTS
    • B63C11/00—Equipment for dwelling or working underwater; Means for searching for underwater objects
    • B63C11/52—Tools specially adapted for working underwater, not otherwise provided for

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Abstract

The invention belongs to the technical field of intelligent control of underwater robots, and particularly relates to an intelligent control method of an underwater robot for autonomous absorption and fishing of benthos. The invention is mainly used for completing the detection and identification of target organisms in a complex underwater environment, guiding the operation of a robot and realizing the accurate absorption of a specified target. During operation, the suction robot firstly identifies and tracks an operation target through an underwater vision and reinforcement learning algorithm, and then guides and completes autonomous suction and fishing operation of benthos through self pose feedback regulation and an intelligent control system of platform motion of the robot to deduce and optimize fuzzy rules. The invention can realize continuous and stable tracking and autonomous suction of targets based on advanced achievements in artificial intelligence research, has the advantages of accurate identification, high intelligence degree, high fishing efficiency, low operation cost and the like, is practically applied to underwater robot system design, and has important significance for efficient autonomous suction and fishing of marine organisms.

Description

Intelligent control method for underwater robot for autonomous absorption and fishing of benthos
Technical Field
The invention belongs to the technical field of intelligent control of underwater robots, and particularly relates to an intelligent control method of an underwater robot for autonomous absorption and fishing of benthos.
Background
With the development of marine economy and the improvement of the living standard of Chinese people, the dietary demand of people on high-protein marine organisms is higher and higher, and the current fishing of the marine organisms such as sea cucumbers, scallops, sea urchins and the like in a marine ranch is mainly completed by a diver. The manual fishing not only easily causes life and injury threats to divers, but also has less divers in the aspect recently, so that the autonomous fishing robot for marine organisms improves the fishing economy and greatly reduces the industrial crisis of marine organism fishing.
In recent years, researchers have conducted some research on marine life fishing robots, and for example, patent document "precious marine life fishing robot (cn201410686861. x)" mainly walks through the sea floor, and a manually-controlled robot observes and sucks marine life underwater to complete fishing of marine life. Patent documents "autonomous navigation and man-machine cooperative fishing operation system of benthos fishing robot (CN 201210553378.5)" and "a bionic benthos fishing robot (CN 201210553365.8)" have a certain autonomy in navigation and walking of underwater operation, but still rely on certain intervention of an operator on path planning, target searching, fishing instructions, and the like, and the autonomy is limited. The above invention is difficult to realize the intelligent control of the marine life and the autonomous absorption and capture, is difficult to use and popularize by common fishermen, and needs to invest certain manpower support and use training. In view of these problems, there is a high necessity for a control method of a marine organism fishing robot capable of realizing autonomous capturing in the field of aquaculture.
Disclosure of Invention
The invention aims to provide an intelligent control method of an underwater robot for the autonomous suction and fishing of benthos, which can realize the continuous tracking and the autonomous suction of targets.
The purpose of the invention is realized by the following technical scheme: the underwater robot for autonomous suction and catching of the benthos comprises a motion module, a suction and catching module, an underwater vision perception module and a II-type fuzzy approximation control module based on reinforcement learning; the motion module comprises a horizontal propeller and a vertical propeller; the underwater vision perception module comprises an absorption tracking observation camera arranged on the absorption fishing module; the method comprises the following steps:
step 1: setting a desired region in the shooting range of the absorption tracking observation camera, and selecting n desired points in the desired region; obtaining a suction fishing target relative to a desired areaPosition state error set St ═ St for each desired point in the domainx,Sty}; wherein StxSet of horizontal position state errors, Stx＝{pex1,pex2,...,pexn}；StySet of errors for vertical position states, Sty＝{pey1,pey2,...,peyn}；
Step 2: inputting the position state error set St of the suction-catching target relative to the reference point into a II-type fuzzy approximation control module based on reinforcement learning, wherein the II-type fuzzy approximation control module based on reinforcement learning comprises nxFuzzy rule of horizontal motion and nyFuzzy rules of vertical motion; set horizontal position state errors StxInputting the motion information into all horizontal motion fuzzy rules to obtain the output result of each horizontal motion fuzzy rule
Figure BDA0002697108580000021
ix＝1,2,...,nxAnd calculating the standard deviation sigma of the output results of all horizontal motion blur rulesx(ii) a Set of vertical position state errors StyInputting the motion information into all the vertical motion fuzzy rules to obtain the output result of each vertical motion fuzzy rule
Figure BDA0002697108580000022
iy＝1,2,...,nyAnd calculating the standard deviation sigma of the output results of all horizontal motion blur rulesy；
And step 3: calculating an upper bound for activation strength for all horizontal motion blur rules
Figure BDA0002697108580000023
And lower bound
Figure BDA0002697108580000024
Construction of a type II fuzzy set of horizontal motion
Figure BDA0002697108580000025
Calculating an upper bound for activation strength for all vertical motion blur rules
Figure BDA0002697108580000026
And lower bound
Figure BDA0002697108580000027
Constructing type II fuzzy sets of vertical motion
Figure BDA0002697108580000028
Step 3.1: calculating a set of horizontal position state errors Stx＝{pex1,pex2,...,pexnState error pe of each horizontal position in thexjCorresponding to each horizontal motion blur rule ixUpper bound of the gaussian primary membership function of
Figure BDA0002697108580000029
And lower bound
Figure BDA00026971085800000210
Figure BDA00026971085800000211
Figure BDA00026971085800000212
Wherein,
Figure BDA00026971085800000213
and
Figure BDA00026971085800000214
is a set of constants set, and
Figure BDA00026971085800000215
j＝1,2,...,n；
step 3.2: calculating a set of vertical position state errors Sty＝{pey1,pey2,...,peynAt each vertical positionAttitude error peyjCorresponding to each vertical motion blur rule iyUpper bound of the gaussian primary membership function of
Figure BDA00026971085800000216
And lower bound
Figure BDA00026971085800000217
Figure BDA0002697108580000031
Figure BDA0002697108580000032
Wherein,
Figure BDA0002697108580000033
and
Figure BDA0002697108580000034
is a set of constants set, and
Figure BDA0002697108580000035
step 3.3: calculating an upper bound for activation strength for all horizontal motion blur rules
Figure BDA0002697108580000036
And lower bound
Figure BDA0002697108580000037
Construction of a type II fuzzy set of horizontal motion
Figure BDA0002697108580000038
Figure BDA0002697108580000039
Figure BDA00026971085800000310
Step 3.4: calculating an upper bound for activation strength for all vertical motion blur rules
Figure BDA00026971085800000311
And lower bound
Figure BDA00026971085800000312
Constructing type II fuzzy sets of vertical motion
Figure BDA00026971085800000313
Figure BDA00026971085800000314
Figure BDA00026971085800000315
And 4, step 4: assembling horizontally moving II-type fuzzy sets
Figure BDA00026971085800000316
And vertically moving type II fuzzy sets
Figure BDA00026971085800000317
Converting the model reduction link into a linear fuzzy set, and calculating the voltage control value u of the horizontal thrusterxAnd a voltage control value u of the vertical thrustery；
Figure BDA0002697108580000041
Figure BDA0002697108580000042
Figure BDA0002697108580000043
Wherein, LcxAnd RcxSets of type II fuzzy moving horizontally respectively
Figure BDA0002697108580000044
The left and right intersection points of the Gaussian main membership function; lcyAnd RcySet of II types of blur, each in vertical motion
Figure BDA0002697108580000045
The left and right intersection points of the Gaussian main membership function;
and 5: II-type fuzzy approximation control module based on reinforcement learning is used for controlling voltage u of horizontal thrusterxAnd a voltage control value u of the vertical thrusteryThe target is transmitted to a motion module to ensure that the target can approach and always keep in a safe area of the visual field; judging whether the underwater robot which is automatically sucked and caught by the benthos finishes sucking the target or not; and if not, returning to the step 1 to perform intelligent control at the next moment.
The invention has the beneficial effects that:
the invention is mainly used for completing the detection and identification of target organisms in a complex underwater environment, guiding the operation of a robot and realizing the accurate absorption of a specified target. During operation, the suction robot firstly identifies and tracks an operation target through an underwater vision and reinforcement learning algorithm, and then guides and completes autonomous suction and fishing operation of benthos through self pose feedback regulation and an intelligent control system of platform motion of the robot to deduce and optimize fuzzy rules. The invention can realize continuous and stable tracking and autonomous suction of targets based on advanced achievements in artificial intelligence research, has the advantages of accurate identification, high intelligence degree, high fishing efficiency, low operation cost and the like, is practically applied to underwater robot system design, and has important significance for efficient autonomous suction and fishing of marine organisms.
Drawings
Fig. 1 is a main body diagram of an underwater robot for autonomous sucking and catching of benthos.
Fig. 2 is a general block diagram of a control system of an underwater robot for autonomous suction and fishing of benthos.
Fig. 3 is a schematic diagram of a type II fuzzy learning controller in accordance with the present invention.
Fig. 4 is a schematic diagram of a discrete grid of 2048 × 1536CCD image planes.
FIG. 5 is a schematic of the S-plane function.
Fig. 6 is a schematic diagram of a video frame in scene II of an autonomous capture process.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention provides an intelligent control method of an underwater robot for autonomous absorption and fishing of benthos, which is mainly used for completing detection and identification of target creatures in a complex underwater environment, guiding the robot to operate and realizing accurate absorption of a specified target. The invention comprises the following steps: the underwater robot carrier for the absorption type marine organism fishing, the underwater robot intelligent motion control system, the underwater vision perception system based on deep learning and the like. During operation, the suction robot firstly identifies and tracks an operation target through an underwater vision and reinforcement learning algorithm, and then guides and completes autonomous suction and fishing operation of benthos through self pose feedback regulation and an intelligent control system of platform motion of the robot to deduce and optimize fuzzy rules. The invention can realize continuous and stable tracking and autonomous suction of targets based on advanced achievements in artificial intelligence research, has the advantages of accurate identification, high intelligence degree, high fishing efficiency, low operation cost and the like, is practically applied to underwater robot system design, and has important significance for efficient autonomous suction and fishing of marine organisms.
An intelligent control method for an underwater robot for autonomous absorption and fishing of benthos, wherein the underwater robot for autonomous absorption and fishing of benthos comprises a motion module, an absorption and fishing module, an underwater vision perception module and a II-type fuzzy approximation control module based on reinforcement learning; the motion module comprises a horizontal propeller and a vertical propeller; the underwater vision perception module comprises an absorption tracking observation camera arranged on the absorption fishing module; the specific control method comprises the following steps:
step 1: setting a desired region in the shooting range of the absorption tracking observation camera, and selecting n desired points in the desired region; acquiring a position state error set St ═ St { St } of the suction fishing target relative to each expected point in the expected areax,Sty}; wherein StxSet of horizontal position state errors, Stx＝{pex1,pex2,...,pexn}；StySet of errors for vertical position states, Sty＝{pey1,pey2,...,peyn}；
Step 2: inputting the position state error set St of the suction-catching target relative to the reference point into a II-type fuzzy approximation control module based on reinforcement learning, wherein the II-type fuzzy approximation control module based on reinforcement learning comprises nxFuzzy rule of horizontal motion and nyFuzzy rules of vertical motion; set horizontal position state errors StxInputting the motion information into all horizontal motion fuzzy rules to obtain the output result of each horizontal motion fuzzy rule
Figure BDA0002697108580000051
ix＝1,2,...,nxAnd calculating the standard deviation sigma of the output results of all horizontal motion blur rulesx(ii) a Set of vertical position state errors StyInputting the motion information into all the vertical motion fuzzy rules to obtain the output result of each vertical motion fuzzy rule
Figure BDA0002697108580000052
iy＝1,2,...,nyAnd calculating the standard deviation sigma of the output results of all horizontal motion blur rulesy；
And step 3: calculating an upper bound for activation strength for all horizontal motion blur rules
Figure BDA0002697108580000053
And lower bound
Figure BDA0002697108580000054
Construction of a type II fuzzy set of horizontal motion
Figure BDA0002697108580000061
Calculating an upper bound for activation strength for all vertical motion blur rules
Figure BDA0002697108580000062
And lower bound
Figure BDA0002697108580000063
Constructing type II fuzzy sets of vertical motion
Figure BDA0002697108580000064
Step 3.1: calculating a set of horizontal position state errors Stx＝{pex1,pex2,...,pexnState error pe of each horizontal position in thexjCorresponding to each horizontal motion blur rule ixUpper bound of the gaussian primary membership function of
Figure BDA0002697108580000065
And lower bound
Figure BDA0002697108580000066
Figure BDA0002697108580000067
Figure BDA0002697108580000068
Wherein,
Figure BDA0002697108580000069
and
Figure BDA00026971085800000610
is a set of constants set, and
Figure BDA00026971085800000611
j＝1,2,...,n；
step 3.2: calculating a set of vertical position state errors Sty＝{pey1,pey2,...,peynThe state error pe of each vertical position in theyjCorresponding to each vertical motion blur rule iyUpper bound of the gaussian primary membership function of
Figure BDA00026971085800000612
And lower bound
Figure BDA00026971085800000613
Figure BDA00026971085800000614
Figure BDA00026971085800000615
Wherein,
Figure BDA0002697108580000071
and
Figure BDA0002697108580000072
is a set of constants set, and
Figure BDA0002697108580000073
step 3.3: calculating an upper bound for activation strength for all horizontal motion blur rules
Figure BDA0002697108580000074
And lower bound
Figure BDA0002697108580000075
Construction of a type II fuzzy set of horizontal motion
Figure BDA0002697108580000076
Figure BDA0002697108580000077
Figure BDA0002697108580000078
Step 3.4: calculating an upper bound for activation strength for all vertical motion blur rules
Figure BDA0002697108580000079
And lower bound
Figure BDA00026971085800000710
Constructing type II fuzzy sets of vertical motion
Figure BDA00026971085800000711
Figure BDA00026971085800000712
Figure BDA00026971085800000713
And 4, step 4: assembling horizontally moving II-type fuzzy sets
Figure BDA00026971085800000714
And vertically moving type II fuzzy sets
Figure BDA00026971085800000715
Converting the model reduction link into a linear fuzzy set, and calculating the voltage control value u of the horizontal thrusterxAnd a voltage control value u of the vertical thrustery；
Figure BDA00026971085800000716
Figure BDA00026971085800000717
Figure BDA00026971085800000718
Wherein, LcxAnd RcxSets of type II fuzzy moving horizontally respectively
Figure BDA00026971085800000719
The left and right intersection points of the Gaussian main membership function; lcyAnd RcySet of II types of blur, each in vertical motion
Figure BDA00026971085800000720
The left and right intersection points of the Gaussian main membership function;
and 5: II-type fuzzy approximation control module based on reinforcement learning is used for controlling voltage u of horizontal thrusterxAnd a voltage control value u of the vertical thrusteryThe target is transmitted to a motion module to ensure that the target can approach and always keep in a safe area of the visual field; judging whether the underwater robot which is automatically sucked and caught by the benthos finishes sucking the target or not; and if not, returning to the step 1 to perform intelligent control at the next moment.
Example 1:
the invention relates to an underwater robot for automatically sucking and catching benthos, which mainly comprises the following parts: the underwater robot carrier for the absorption type marine organism fishing, the underwater robot intelligent motion control system, the underwater vision perception system based on deep learning and the like. An underwater robot for catching marine organisms is taken as a carrier, and a deep learning method for underwater visual perception is combined with an underwater robot intelligent motion control method for control and detection. The carrier platform consists of a propeller, a pumping structure and a storage box; the underwater visual perception method based on deep learning jointly completes biological density detection and establishment of a kinematic relationship between an image and an absorbed target on the basis of two sets of fisheye cameras; the underwater robot intelligent motion control method takes a cabin PC104 hardware circuit as a core, and intelligent suction capture of marine organism targets is completed through an intelligent suction motion control method based on reinforcement learning optimization. The method establishes fuzzy rules by determining a reasoning system of II-type fuzzy, designs an S-surface adaptive function by the rules derived by subdivision, and finally ensures that the robot finishes accurate absorption of biological targets appearing in a formulated area by a reinforcement learning method.
Constructing behavior rules according to the current and expected states of the depth and direction of the marine organism pumping suction pipe, and determining fuzzy rules, degradation types and deblurring output by the following methods: determining a fuzzy rule:
Figure BDA0002697108580000081
Figure BDA0002697108580000082
Figure BDA0002697108580000083
Figure BDA0002697108580000084
in the formula, mijIs an uncertain mean value
Figure BDA0002697108580000085
σijIs a fixed standard deviation;
Figure BDA0002697108580000086
representing a degree of membership, which is a bounded set,
Figure BDA0002697108580000087
and
Figure BDA0002697108580000088
respectively represent
Figure BDA0002697108580000089
Upper and lower bounds off i；
Figure BDA00026971085800000810
Upper and lower bounds for activation intensity, respectively; pe (t) represents the target position state error within the camera range; n is the number of states input.
Type reduction:
Figure BDA0002697108580000091
wherein mr is a rule number; lc and Rc are the left and right intersections, respectively; a isiIs the corresponding behavior of the fuzzy rule; n number of states input.
And (3) resolving fuzzy output to a thruster voltage:
Figure BDA0002697108580000092
enabling the target to appear in a proper suction area in a distance camera coordinate system according to a fuzzy rule, and then selecting the optimal values of the particles and the particle swarm to be updated in a non-stage mode, wherein the method is realized by the following fitness function:
Figure BDA0002697108580000093
in the formula, keAnd kuIs an adjustable parameter; k is a radical ofpIs a proportionality coefficient; k is a radical ofdIs a differential coefficient; e.g. of the typeiAnd aiThe error in a certain direction and the corresponding behavior of the fuzzy rule.
The underwater robot for automatically sucking and fishing the benthos mainly comprises: the underwater robot carrier for the absorption type marine organism fishing, the underwater robot intelligent motion control system, the underwater vision perception system based on deep learning and the like. The absorption type marine organism fishing underwater robot carrier comprises a carrier frame, four horizontal propellers, two vertical propellers, a marine organism suction pump device, an absorption storage tank and the like, wherein the four horizontal propellers are arranged in a vector mode. The underwater vision perception system based on the deep learning comprises a fisheye camera arranged in front of the robot and a suction tracking observation camera arranged on a suction pipe; the fish-eye camera detects the existence of marine life and determines the density of the marine life through deep learning and distortion processing, and the suction tracking observation camera on the suction pipe establishes the kinematic relationship between the camera image and the suction target based on the kinematic modeling of the image. The underwater robot intelligent motion control system mainly takes a PC104 control computer in a pressure-resistant cabin as a core, and controls the motion and the suction of the underwater robot by an intelligent suction motion control method based on reinforcement learning according to the feedback of a magnetic compass, a depth meter and an underwater camera, thereby realizing the intelligent suction and capture of marine organisms.
An intelligent control method of an underwater robot for autonomous absorption and fishing of benthos comprises the following steps: firstly, establishing a II-type fuzzy inference system based on the depth and the angle of a target relative to an image, and initially establishing a fuzzy rule; secondly, designing an improved particle optimization method to subdivide and optimize the derivative fuzzy rule, particularly designing an S-surface-based adaptive function for the adaptive judgment of the derivative fuzzy, and determining and judging the fuzzy rule; and finally, further training fuzzy rule output by combining the current suction state and the external environment state of the robot by using a reinforcement learning method, thereby ensuring that the target can approach and always keep in a safe area of the visual field, and controlling a suction pipe of the underwater robot to gradually approach the target to finish suction of marine life.
And the intelligent approach controller of the suction robot enables the pumping pipeline to be continuously close to the target to finish suction according to the visual characteristics of the image. The advantage of this controller is that the control performance is improved by the context-specific automatic selection strategy. In addition, for low cost robots without horizontal velocity or position sensors, the present invention can stabilize the target within a safe suction area, with the pumping tubing constantly approaching the target. Type II fuzzy systems help to minimize the uncertain effects of unknown environments compared to type I fuzzy systems. The invention realizes the stable and accurate motion control of the ROV through the intelligent motion control system, and ensures that the target is in a safe range and is continuously close to the target.
The type II fuzzy inference system aims at constructing action rules and determining action set output ranges according to current and expected states of the depth and the direction of a pipeline. Wherein the action set indicates that a group of action robots of the robot will perform actions such as heading, sideways and heave through state evaluation to stabilize their pose, hold the target in the camera, control the ROV to approach the target and be ready for absorption. Referring to fig. 4 of the drawings, a 2048 × 1536 pixel CCD image plane may be divided into 32 × 24 discrete grids each containing 64 × 64 pixels, with the different shaded areas in the figure representing the desired reach, approach and danger areas, respectively. The definition of the type II fuzzy set in the invention is designed as follows:
Figure BDA0002697108580000101
in the formula,
Figure BDA0002697108580000102
pe (t) represents the target position state error within the camera range; μ (t) represents a fuzzy membership function; st represents the state set of pe (t); js denotes the members in pe (t).
Its primary gaussian membership function can be expressed as:
Figure BDA0002697108580000103
in the formula, mijIs an uncertain mean value
Figure BDA0002697108580000104
σijIs a fixed standard deviation;
Figure BDA0002697108580000105
representing the degree of membership, which is a bounded set,
Figure BDA0002697108580000106
and
Figure BDA0002697108580000107
respectively represent
Figure BDA0002697108580000108
Upper and lower bounds of (1):
Figure BDA0002697108580000109
Figure BDA00026971085800001010
the operation of fuzzy t norm in the inference machine is realized by algebraic product, the rule activation strength Fi is interval type I fuzzy set, and the activation strength of the suction fishing target corresponding to the ith rule at present can be obtained by continuous multiplication of fuzzy operation:
Figure BDA0002697108580000111
wherein,f i；
Figure BDA0002697108580000112
the activation intensity is respectively the upper bound and the lower bound, because the output of the system inference engine is a type II fuzzy set, the fuzzy set is converted into a type I fuzzy set through a model reduction link, and then the fuzzy solution operation can be carried out, the invention uses the following method to carry out the model reduction operation:
Figure BDA0002697108580000113
wherein mr is a rule number; lc and Rc are the left and right intersections, respectively;f i；
Figure BDA0002697108580000114
upper and lower bounds for activation intensity, respectively; a isiIs the result deduced by the ith fuzzy rule; n is the number of states input. The voltage value u finally output to the propeller0Comprises the following steps:
Figure BDA0002697108580000115
a learning-based Particle Swarm Optimization (PSO) fuzzy rule optimizer is developed for uncertain environment and dynamic changes and factors in the capture process. The purpose of the fuzzy rule is to select an ROV action to approach the aspiration target based on the current and expected state of the depth and orientation of the ROV tube. The subdivision and derivation optimization method is as follows: the adaptive function of the rule enables the target to quickly reach a desired region when the target is far away from a suction region in a camera coordinate system through a fuzzy rule, and enables a suction pipe of the underwater robot to be stabilized in the suction region of the camera coordinate system through the fuzzy rule subdivision control quantity when the target is near the suction region in the camera coordinate system, and suction is completed. Considering that different field environments exist in the capturing process, the fixed fuzzy rule is lack of universality, and the fuzzy rule is trained and optimized through a particle swarm optimization algorithm. The fuzzy rule optimization algorithm process is described as follows:
(1) initialization: fuzzy rules for actions and input states have been randomly generated before PSO optimization.
(2) Fitness function determination: for each fuzzy rule test, the fitness function is important to determine the optimal action of the controller. Because the designed learning-based II-type fuzzy approximation controller is accurate to realize online, the selection of an effective and intelligent fitness function is of great significance for realizing the quick iteration and optimization of the fuzzy rule in each step length released by the control command. To achieve rapid target access and absorption maintenance, it is desirable to control the motion loosely when the deviation is large and tightly when the deviation is small. The S-plane function shown in FIG. 5 of the specification is one of these functions, and the adaptive function is designed as follows:
Figure BDA0002697108580000116
in the formula, keAnd kuIs an adjustable parameter; k is a radical ofpIs a proportionality coefficient; k is a radical ofdIs a differential coefficient; e.g. of the typeiAnd aiThe error in a certain direction and the corresponding behavior of the fuzzy rule.
(3) Particle memory and selection: each rule particle will be evaluated by remembering its own fitness value and selecting the largest one as the fitness.
Each particle is further modified based on the above method. The above steps are repeatedly performed until a significant improvement is achieved. The particle having the best adaptability is a globally optimal particle, and thus, an optimal fuzzy rule adaptability value is obtained to cope with environmental disturbance and load variation during the work.
With reference to fig. 1, the designed marine organism autonomous sucking robot based on the intelligent control method comprises a carrier platform, a control cabin, a pumping suction pipe, a propeller of a propeller, a depth meter and a doppler velocimeter; the control cabin comprises hardware such as a PC104 core module, an I/O board, a CAN data acquisition board, an isolation serial port board, a data acquisition board, a direct current servo motor control board, a propeller motor driver, a magnetic compass and a marine organism capturing and storing cabin.
With reference to fig. 2, the core of the unmanned underwater organism suction robot control system is a PC104 core module, and the PC104 module acquires data of a doppler velocimeter and a magnetic compass through an isolation serial port plate and performs dead reckoning to obtain the current pose of the unmanned underwater vehicle; and the PC104 module sends a control instruction to the motor driver through the direct-current servo motor control panel according to the result of the coordinated motion controller, and controls the suction robot propeller to move so as to realize the stable and accurate motion of the carrier platform in the suction fishing operation process.
With reference to fig. 3, the unmanned underwater organism suction robot utilizes a II-type fuzzy approximation controller based on reinforcement learning to retain the visual characteristics of the marine organisms to be caught in the image, and can simultaneously approximate the pumping pipeline to the suction target. Defining a set of target position state error sets St ═ { pe) within a camera region1 pe2 ... penAnd inputting a clear accurate value by the fuzzy controller, and inputting a deviation amount between the position of the robot and an expected arrival area by an input state layer. Fuzzification operation fuzzifies an input accurate value into a fuzzy set, each node corresponding to input in the layer defines an interval type II Gaussian membership function, the rule of the type II fuzzy system adopts an If-Then form, and the rule is as follows: if pe1(t)is St1 and,…,pen(t)is Stn,then u1(t)is a1(t)and,…,um(t)is am(t). A fuzzy rule may determine a type ii output fuzzy set, from which the inference engine infers to determine the action to be taken next. Fuzzy inference carries out fuzzy operation through continuous multiplication to obtain activation strength, a degrader maps a type II fuzzy set to a type I fuzzy set, and a defuzzifier converts fuzzy output defuzzification into a clear set.
Referring to fig. 4, a CCD image plane of 2048 × 1536 pixels in the unmanned underwater bioabsorption robot underwater vision system may be divided into 32 × 24 discrete grids each including 64 × 64 pixels. Fig. 4 can show the current state and the expected state of the depth and the orientation of the pumping pipeline, the visual field of the camera comprises a desired area, an approaching area, a dangerous area, a safe area and the like which are distinguished by different colors according to the graph, different areas correspond to different sucking success rates, and the control system aims to control the submarine organism sucking robot suction pipe to be stabilized in the desired area of a red square.
With reference to fig. 5, the determination of the fitness function in the fuzzy rule of the reinforcement learning of the unmanned underwater organism suction robot plays a role in determining the optimal action of the controller. The II-type fuzzy approximation controller based on reinforcement learning aims to ensure accurate online realization and complete stable absorption capture. Therefore, the invention selects an effective and intelligent fitness function to realize the quick iteration and optimization of the fuzzy rule in each step of the release of the control command. In order to achieve rapid target approach and absorption maintenance, it is desirable to have a relaxed control action when the deviation is large and a strict control action when the deviation is small, so the S-plane function shown in fig. 5 is selected to achieve the above control purpose.
With reference to fig. 6, six frame images show the process of switching the autonomous recognition target from the environmental disturbance to the biological target block during the operation of the unmanned underwater bioabsorption robot. The invention obtains the relation between the pixel distance and the actual distance through underwater height-setting calibration measurement, and meanwhile, the II-type fuzzy controller based on reinforcement learning can optimize the tracking rule and continuously adapt to the environment, thereby realizing the continuous tracking of the target in the complex underwater environment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (1)

1. An intelligent control method for an underwater robot for autonomous absorption and fishing of benthos, wherein the underwater robot for autonomous absorption and fishing of benthos comprises a motion module, an absorption and fishing module, an underwater vision perception module and a II-type fuzzy approximation control module based on reinforcement learning; the motion module comprises a horizontal propeller and a vertical propeller; the underwater vision perception module comprises an absorption tracking observation camera arranged on the absorption fishing module; the method is characterized by comprising the following steps:
step 1: setting a desired region in the shooting range of the absorption tracking observation camera, and selecting n desired points in the desired region; obtaining the position of the suction fishing target relative to each expected point in the expected areaSet of state errors St ═ Stx,Sty}; wherein StxSet of horizontal position state errors, Stx＝{pex1,pex2,...,pexn}；StySet of errors for vertical position states, Sty＝{pey1,pey2,...,peyn}；
Step 2: inputting the position state error set St of the suction fishing target relative to each expected point in the expected area into a II type fuzzy approximation control module based on reinforcement learning, wherein the II type fuzzy approximation control module based on reinforcement learning comprises nxFuzzy rule of horizontal motion and nyFuzzy rules of vertical motion; set horizontal position state errors StxInputting the motion information into all horizontal motion fuzzy rules to obtain the output result a of each horizontal motion fuzzy ruleix，ix＝1,2,...,nxAnd calculating the standard deviation sigma of the output results of all horizontal motion blur rulesx(ii) a Set of vertical position state errors StyInputting the motion information into all the vertical motion fuzzy rules to obtain the output result a of each vertical motion fuzzy ruleiy，iy＝1,2,...,nyAnd calculating the standard deviation sigma of the output results of all horizontal motion blur rulesy；
And step 3: calculating an upper bound for activation strength for all horizontal motion blur rules
Figure FDA0003038199550000011
And lower bound
Figure FDA0003038199550000012
Construction of a type II fuzzy set of horizontal motion
Figure FDA0003038199550000013
Calculating an upper bound for activation strength for all vertical motion blur rules
Figure FDA0003038199550000014
And lower bound
Figure FDA0003038199550000015
Constructing type II fuzzy sets of vertical motion
Figure FDA0003038199550000016
Step 3.1: calculating a set of horizontal position state errors Stx＝{pex1,pex2,...,pexnState error pe of each horizontal position in thexjCorresponding to each horizontal motion blur rule ixUpper bound of the gaussian primary membership function of
Figure FDA0003038199550000017
And lower bound
Figure FDA0003038199550000018
Figure FDA0003038199550000019
Figure FDA0003038199550000021
Wherein,
Figure FDA0003038199550000022
and
Figure FDA0003038199550000023
is a set of constants set, and
Figure FDA0003038199550000024
step 3.2: calculating a set of vertical position state errors Sty＝{pey1,pey2,...,peynThe state error pe of each vertical position in theyjCorresponding to each vertical motion blur rule iyDegree of gaussian principal membershipUpper bound of function
Figure FDA0003038199550000025
And lower bound
Figure FDA0003038199550000026
Figure FDA0003038199550000027
Figure FDA0003038199550000028
Wherein,
Figure FDA0003038199550000029
and
Figure FDA00030381995500000210
is a set of constants set, and
Figure FDA00030381995500000211
step 3.3: calculating an upper bound for activation strength for all horizontal motion blur rules
Figure FDA00030381995500000212
And lower bound
Figure FDA00030381995500000213
Construction of a type II fuzzy set of horizontal motion
Figure FDA00030381995500000214
Figure FDA00030381995500000215
Figure FDA00030381995500000216
Step 3.4: calculating an upper bound for activation strength for all vertical motion blur rules
Figure FDA00030381995500000217
And lower bound
Figure FDA00030381995500000218
Constructing type II fuzzy sets of vertical motion
Figure FDA00030381995500000219
Figure FDA0003038199550000031
Figure FDA0003038199550000032
And 4, step 4: assembling horizontally moving II-type fuzzy sets
Figure FDA0003038199550000033
And vertically moving type II fuzzy sets
Figure FDA0003038199550000034
Converting the model reduction link into a linear fuzzy set, and calculating the voltage control value u of the horizontal thrusterxAnd a voltage control value u of the vertical thrustery；
Figure FDA0003038199550000035
Figure FDA0003038199550000036
Figure FDA0003038199550000037
Wherein, LcxAnd RcxSets of type II fuzzy moving horizontally respectively
Figure FDA0003038199550000038
The left and right intersection points of the Gaussian main membership function; lcyAnd RcySet of II types of blur, each in vertical motion
Figure FDA0003038199550000039
The left and right intersection points of the Gaussian main membership function;
and 5: II-type fuzzy approximation control module based on reinforcement learning is used for controlling voltage u of horizontal thrusterxAnd a voltage control value u of the vertical thrusteryThe target is transmitted to a motion module to ensure that the target can approach and always keep in a safe area of the visual field; judging whether the underwater robot which is automatically sucked and caught by the benthos finishes sucking the target or not; and if not, returning to the step 1 to perform intelligent control at the next moment.
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