CN115071926A - Petri network-based autonomous underwater vehicle anchoring and bottoming task control method - Google Patents
Petri network-based autonomous underwater vehicle anchoring and bottoming task control method Download PDFInfo
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Abstract
The invention relates to an autonomous underwater vehicle anchoring and bottoming task control method based on a Petri network, and belongs to the technical field of automatic control. The method comprises the steps of establishing an autonomous underwater vehicle anchoring and decubitus task discrete model based on a Petri network; after reaching the bed bottom area, switching to a fixed angle diving state; after submerging to a set height, switching to a posture adjustment state; after the attitude adjustment is finished, switching to a constant-height cruising state, and detecting the terrain flatness; after a suitable terrain is detected, the AUV is stopped and decelerated, and the anchor chain is released to grab the bottom; after the anchor chain touches the bottom, the AUV posture is adjusted to complete the lying task; and executing fault detection in the bedding task all the time, and if a fault occurs, performing corresponding operation according to the fault type. The mode of the invention has lower cost, can realize the posture adjustment after the bed is laid, and is beneficial to secondary starting.
Description
Technical Field
The invention belongs to the technical field of automatic control, and relates to a control method for an anchor berthing and sole-lying task of an autonomous underwater vehicle. The specific task is to control the AUV to stably submerge to a certain height from the seabed for navigation and to freely sink to the seabed after anchoring.
Background
The Autonomous Underwater Vehicle (AUV) has the advantages of good concealment, high operation accuracy, strong task reconstruction capability and the like, and can complete tasks such as ocean monitoring, seabed detection, Underwater operation and the like without manual intervention and large-scale water surface support. However, the marine monitoring task is executed in a long period, and cannot meet the long-time fixed-point observation task for the AUV with limited power energy.
The research institute of navy in 1992 firstly proposes the concept that after the AUV sails to a set water area, the drive motor is turned off to save energy, and the AUV is located at the bottom of the ocean by changing buoyancy and gravity to perform long-time ocean environment detection and monitoring and the like. In view of this concept, researchers at home and abroad propose ballast water tank type, hydraulic support type, anchor chain type and other bedding modes. In order for the AUV to safely perform the bedding action, a special bedding control strategy needs to be designed.
The existing bedding control strategies are mainly divided into the following steps of freely sinking the bedding by water injection, controlling the water injection bedding, and controlling the water injection landing by diving and sailing in place. In the prior relevant literature, the submergence in-place navigation and the control of water filling landing adopt a mode of controlling a water ballast tank for water filling. This kind of mode cost is higher, can not adjust the gesture behind the bedding, and is unfavorable for the secondary start.
Disclosure of Invention
Technical problem to be solved
The invention provides an autonomous underwater vehicle anchoring and bottom lying task control method based on a Petri network, which aims to solve the problems of high cost, complex water injection control, difficult code implementation, slow secondary starting and the like in the prior art.
Technical scheme
An autonomous underwater vehicle anchoring and bottoming task control method based on a Petri network is characterized by comprising the following steps:
s1: establishing an autonomous underwater vehicle anchoring and decubitus task discrete model based on a Petri network;
s2: after reaching the bedridden area, switching to a fixed angle diving state;
s3: after submerging to a set height, switching to a posture adjustment state;
s4: after the attitude adjustment is finished, switching to a constant-height cruising state, and detecting the terrain flatness;
s5: after a suitable terrain is detected, the AUV is stopped and decelerated, and the anchor chain is released to grab the bottom;
s6: after the anchor chain touches the bottom, the AUV posture is adjusted to complete the lying task;
s7: and executing fault detection in the bedding task all the time, and if a fault occurs, performing corresponding operation according to the fault type.
The further technical scheme of the invention is as follows: s1 specifically includes: establishing an autonomous underwater vehicle decision layer anchoring and lying task model, wherein the model is described by a PERTI network as follows: u ═ X, Σ, B, S 0 ) (ii) a Wherein X is P 0 ,P 1 ,…P 9 The methods are respectively as follows: the method comprises the following steps of P0 initial state, P1 fixed angle diving, P2 posture adjustment, P3 fixed height cruising, P4 free sinking, P5 anchor chain adjustment, P6 bottom lying completion, P7 fault detection, P8 fault waiting and P9 emergency floating; the set of transitions sigma ═ T 1 ,T 2 ,...T 9 The methods are respectively as follows: the method comprises the following steps that T1 arrives at a decubitus area, T2 navigation depth meets, T3 posture meets, T4 terrain detection is smooth, T5 bottoming detection, T6 posture detection, T7 navigation fault, T8 dangerous fault and T9 general fault; the system flow relation is expressed by calculating B ═ XxSigma ∑ XX as the directed arc between the library and the transition, S 0 ＝[000000000]To be aThe initial state of the system indicates the state of the system.
The further technical scheme of the invention is as follows: s2 specifically includes: when the AUV enters the area in place, namely T1 reaches the lying area, the constant-depth/depth control mode is switched to execute a constant-angle diving trajectory, namely garage P1 constant-angle diving, the AUV is controlled to dive at a specified angle, and the AUV is decelerated in the diving process to ensure the lowest-speed navigation of AUV maneuverability.
The further technical scheme of the invention is as follows: s3 specifically includes: when the AUV submerges to a position which is less than H + delta from the sea bottom, namely the T2 navigation depth is met, switching to an attitude adjustment mode, namely P2 attitude adjustment; h is a constant-height cruising height, and delta is the advance of switching; finally, the AUV is adjusted from the negative pitch angle to the pitch angle of 0 degrees, and the leveling of the aircraft is completed; this phase requires no overshoot in the longitudinal control, ensuring that the AUV does not bottom out.
The further technical scheme of the invention is as follows: s4 specifically includes: when the AUV attitude is adjusted to the pitch angle within 0 +/-2 degrees, namely the T3 attitude is satisfied; and the mode is switched to the constant-high cruise mode, namely P3 constant-high cruise.
The further technical scheme of the invention is as follows: s5 specifically includes: during the constant-height cruising process, the AUV detects the flatness of the submarine topography constantly, the difference between the maximum value and the minimum value is judged by using the current and historical multiple Doppler altimeter data, and when the flat submarine topography is detected, the T4 flatness detection is carried out; AUV power stop reduces speed, switches to free sinking, namely P4 free sinking.
The further technical scheme of the invention is as follows: s6 specifically includes: detecting the AUV anchor chain bottom, namely T5 bottom detection; adjusting the self-attitude of the AUV by adjusting the lengths of the front anchor chain and the rear anchor chain according to the self-attitude sensor information, namely adjusting the P5 anchor chain; finally, the bedding task is completed, namely P6 bedding is completed.
The further technical scheme of the invention is as follows: s7 specifically includes: in the whole task process, if a navigation fault occurs, namely a T7 navigation fault; the AUV will perform fault detection, namely P7 fault detection; dividing the fault into a general fault and a dangerous fault according to the detected fault risk degree, namely a T9 general fault and a T8 dangerous fault; and (4) determining to carry out fault waiting or emergency floating, namely P8 fault waiting or P9 emergency floating.
A computer system, comprising: one or more processors, a computer readable storage medium, for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the above-described method.
A computer-readable storage medium having stored thereon computer-executable instructions for performing the above-described method when executed.
Advantageous effects
Compared with the prior art, the Petri network-based autonomous underwater vehicle anchoring and bottoming task control method has the following beneficial effects:
1. adjustable bed bottom posture
In the invention, after the AUV is stabilized, the bottoming posture of the AUV can be adjusted by adjusting the lengths of the two anchor chains to realize the expected posture, but the posture adjustment after the bottoming can not be realized by using a ballast water tank mode.
2. Convenient for secondary start
In the invention, after the AUV is stably landed, if secondary work or a target attack task needs to be carried out, only the anchor chain system needs to be separated, and the AUV can work after freely floating to be stable by virtue of self positive buoyancy, and does not need to wait for long-time water injection/water discharge and miss the task opportunity.
3. Easy code conversion
In the invention, the discrete system modeling method based on the Petri network is used in the AUV bedding task control strategy, and the asynchronous, synchronous and parallel logical relations of the system can be described. Through clear description of dynamic change of discrete events in the bedding process, a control strategy can be effectively converted into an actual code to be applied to the AUV, and then the bedding task is realized in engineering.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 depicts a bedding task flow model;
FIG. 2 library and transition meanings of the Petri Net;
FIG. 3AUV lying trajectory;
FIG. 4 is a schematic diagram of an artificial potential field in a seafloor elevation tracking control mode;
FIG. 5 is a vertical plane depth variation curve;
FIG. 6 is a pitch angle variation curve;
fig. 7 speed profile.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
According to the invention, an autonomous underwater vehicle decision layer anchoring and bottom lying task model is established according to the requirements of the anchoring and bottom lying task of the underwater vehicle. The model may be described by a PERTI net as: u ═ X, Σ, B, S 0 ). Wherein X is P 0 ,P 1 ,...P 8 T, a transition set ∑ T 1 ,T 2 ,...T 8 }. The system flow relation is expressed by calculating B ═ XxSigma ∑ XX as the directed arc between the library and the transition, S 0 ＝[00000000000]The initial state of the system represents the state of the system.
An autonomous underwater vehicle anchoring and decubitus task control method based on a Petri network is shown in a schematic diagram 1 and comprises the following steps:
s1: establishing an anchor berth decubitus task model of an autonomous underwater vehicle decision layer, wherein the model can be described as follows by a PERTI network: u ═ X, Σ, B, S 0 ). Wherein X is P 0 ,P 1 ,...P 9 Are respectively: p0 initial state, P1 fixed angle diving, P2 attitude adjustment, P3 fixed height cruising, P4 free sinking, P5 anchor chain adjustment, P6 bottom finishing,P7 fault detection, P8 fault waiting and P9 emergency floating. The set of transitions sigma ═ T 1 ,T 2 ,...T 9 The methods are respectively as follows: t1 arrives at a decubitus area, T2 navigation depth is met, T3 attitude is met, T4 terrain detection is smooth, T5 bottoming detection, T6 attitude detection, T7 navigation fault, T8 dangerous fault and T9 general fault. The system flow relation is expressed by calculating B ═ XxSigma ∑ XX as the directed arc between the library and the transition, S 0 ＝[000000000]The initial state of the system represents the state of the system.
S2: when the AUV enters the area to be in place (T1 reaches the lying area), the constant-depth/depth control mode is switched to execute a constant-angle diving trajectory (Kuchkusho P1 constant-angle diving), namely, the AUV is controlled to dive at a specified angle, and the AUV is decelerated during the diving process to ensure the lowest speed navigation of the maneuverability of the AUV.
S3: and when the AUV submerges to a position which is less than H + delta from the sea bottom (the T2 navigation depth is satisfied), switching to an attitude adjustment mode (P2 attitude adjustment). H is the constant-height cruising height, delta is the advance of switching, and delta is selected to ensure the stability of the AUV when the AUV is switched to the constant-height cruising. And finally, adjusting the AUV from the negative pitch angle to the vicinity of the 0-degree pitch angle to finish the leveling of the aircraft. This phase requires no overshoot in the longitudinal control, ensuring that the AUV does not bottom out.
S4: when the AUV attitude is adjusted to within 0 DEG + -2 DEG of the pitch angle (the T3 attitude is satisfied), the cruise mode is switched to the constant-altitude cruise mode (P3 constant-altitude cruise). The selection of the constant-height cruising height H ensures that the AUV has enough time and distance to unfold the anchor chain.
S5: during the constant-height cruising process, the AUV detects the flatness of the submarine topography constantly, the difference between the maximum value and the minimum value is taken for judgment by using the current and historical data of multiple Doppler altimeters, and when the flat submarine topography is detected (T4 flatness detection), the AUV is powered off and decelerated, and the switch is switched to free sinking (P4 free sinking). In order to avoid the winding condition caused by premature release of the anchor chain, the anchor chain is released to grab the bottom when the anchor chain is close to the seabed (when the distance is 3m from the seabed), and the AUV floats upwards to tighten the anchor chain to finish bottoming.
S6: after detecting that the AUV anchor chain is bottomed (T5 bottoming detection), adjusting the self-posture of the AUV (P5 anchor chain adjustment) by adjusting the lengths of the front and rear anchor chains in combination with the self-posture sensor information, and finally completing the bedding task (P6 bedding completion).
S7: during the whole task, if a navigation fault (T7 navigation fault) occurs, the AUV carries out fault detection (P7 fault detection), and the AUV is divided into a general fault (T9 general fault) and a dangerous fault (T8 dangerous fault) according to the detected fault danger degree, and decides to carry out fault waiting (P8 fault waiting) or emergency floating (P9 emergency floating).
After the bottom-lying is finished, if secondary work is needed, only the anchor chain system needs to be separated, and the AUV floats upwards by means of positive buoyancy.
The embodiment of the invention provides an autonomous underwater vehicle anchoring and background task control method based on a Petri network, and a DEDS (digital underwater vehicle detection system) model of an autonomous underwater vehicle determining layer anchoring and background task is established. In order to verify the effectiveness of the task control strategy, the following embodiments are also provided in the present invention.
And selecting appearance parameters of the brave and persevere AUV fluid to simulate the decubitus tasks.
The simulation initial state is that the bed area is reached and the bed task parameters are loaded. And at the moment, the AUV is switched to a fixed-angle submergence mode, and the AUV is controlled to submerge stably according to a set pitch angle parameter of-10 degrees. Meanwhile, the rotating speed of the propeller is reduced, the AUV is guaranteed to dive at the lowest speed meeting the maneuverability, and the control algorithm adopts self-adaptive sliding mode control.
Submerging to a position 3m above the set depth, wherein the parameter is related to the AUV gyration radius and is larger than the gyration radius according to the experiment. And at the moment, switching to an attitude adjustment mode, and leveling the pitch angle of the AUV. And after the attitude sensor detects that the AUV has run stably, switching to a constant-height cruise mode.
And starting to track the submarine topography according to the height from the sea bottom detected by the AUV Doppler and the depth information obtained by the depth sensor. In the fixed-height navigation mode, the self-guiding AUV is required to keep high navigation with the sea bottom. In this mode, the restriction of the navigation depth must be considered while tracking the altitude, that is, the pressure resistance of the hull of the aircraft determines that the navigation depth cannot exceed a certain range, and if the navigation depth is too small, the possibility of water outflow is increased, especially in the case of large sea surface waves, and the stability of navigation is damaged, where a control algorithm based on an artificial potential field is adopted, a motion planning layer calculates a reference instruction, and dynamics are responsible for driving a horizontal rudder, as shown in fig. 4.
During the constant-height cruising process, the AUV detects the flatness of the submarine topography constantly, and when the flat submarine topography is detected, the AUV is powered to stop and decelerate to switch to freely sinking. In order to avoid the winding condition caused by premature release of the anchor chain, the anchor chain is released to grab the bottom when the AUV is close to the seabed, the AUV is integrally changed into positive buoyancy, and the floating and tightening of the anchor chain are completed to lay the bottom.
Fig. 5, 6 and 7 show the depth change curve of the longitudinal plane, the pitch angle change curve and the speed change curve of the whole process of the bed bottom respectively. Under the dynamic control based on sliding mode self-adaptive control, the AUV can keep a corresponding pitch angle and speed to dive; under the control of the motion planning based on the artificial potential field, the sailing track of the UUV is basically consistent with the submarine topography profile, and the performance requirements are met.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present disclosure.
Claims (10)
1. An autonomous underwater vehicle anchoring and bottoming task control method based on a Petri network is characterized by comprising the following steps:
s1: establishing an autonomous underwater vehicle anchoring and decubitus task discrete model based on a Petri network;
s2: after reaching the bed bottom area, switching to a fixed angle diving state;
s3: after submerging to a set height, switching to a posture adjustment state;
s4: after the attitude adjustment is finished, switching to a constant-height cruising state, and detecting the terrain flatness;
s5: after a suitable terrain is detected, the AUV is stopped and decelerated, and the anchor chain is released to grab the bottom;
s6: after the anchor chain touches the bottom, the AUV posture is adjusted to complete the lying task;
s7: and executing fault detection in the bedding task all the time, and if a fault occurs, performing corresponding operation according to the fault type.
2. The Petri net-based autonomous underwater vehicle mooring nadir task control method according to claim 1, wherein S1 is specifically: establishing an autonomous underwater vehicle decision layer anchoring and lying task model, wherein the model is described by a PERTI network as follows: u ═ X, Σ, B, S 0 ) (ii) a Wherein X is P 0 ,P 1 ,...P 9 The methods are respectively as follows: the method comprises the following steps of P0 initial state, P1 fixed angle diving, P2 posture adjustment, P3 fixed height cruising, P4 free sinking, P5 anchor chain adjustment, P6 bottom lying completion, P7 fault detection, P8 fault waiting and P9 emergency floating; the set of transitions sigma ═ T 1 ,T 2 ,...T 9 The methods are respectively as follows: t1 arrives at a decubitus area, T2 navigation depth meets, T3 posture meets, T4 terrain detection is smooth, T5 bottoming detection, T6 posture detection, T7 navigation fault, T8 dangerous fault and T9 general fault; the system flow relation is expressed by calculating B ═ XxSigma ∑ XX as the directed arc between the library and the transition, S 0 ＝[000000000]The initial state of the system represents the state of the system.
3. The Petri net-based autonomous underwater vehicle mooring nadir task control method according to claim 2, wherein S2 is specifically: when the AUV enters the area in place, namely T1 reaches the lying area, the constant-depth/depth control mode is switched to execute a constant-angle diving trajectory, namely garage P1 constant-angle diving, the AUV is controlled to dive at a specified angle, and the AUV is decelerated in the diving process to ensure the lowest-speed navigation of AUV maneuverability.
4. The Petri net-based autonomous underwater vehicle mooring decubitus task control method according to claim 3, wherein S3 is specifically: when the AUV submerges to a position which is less than H + delta from the sea bottom, namely the T2 navigation depth is met, switching to an attitude adjustment mode, namely P2 attitude adjustment; h is a constant-height cruising height, and delta is the advance of switching; finally, the AUV is adjusted from the negative pitch angle to the pitch angle of 0 degrees, and the leveling of the aircraft is completed; this phase requires no overshoot in the longitudinal control, ensuring that the AUV does not bottom out.
5. The Petri net-based autonomous underwater vehicle mooring decubitus task control method according to claim 4, wherein S4 is specifically: when the AUV attitude is adjusted to the pitch angle within 0 +/-2 degrees, namely the T3 attitude is satisfied; and the mode is switched to the constant-high cruise mode, namely P3 constant-high cruise.
6. The Petri net-based autonomous underwater vehicle mooring decubitus task control method according to claim 5, wherein S5 is specifically: during the constant-height cruising process, the AUV detects the flatness of the submarine topography constantly, the difference between the maximum value and the minimum value is judged by using the current and historical multiple Doppler altimeter data, and when the flat submarine topography is detected, the T4 flatness detection is carried out; AUV power stop reduces speed, switches to free sinking, namely P4 free sinking.
7. The Petri net-based autonomous underwater vehicle mooring undercover task control method according to claim 6, wherein S6 is specifically: detecting the AUV anchor chain bottom, namely T5 bottom detection; adjusting the self-attitude of the AUV by adjusting the lengths of the front anchor chain and the rear anchor chain according to the self-attitude sensor information, namely adjusting the P5 anchor chain; finally, the bedding task is completed, namely P6 bedding is completed.
8. The Petri net-based autonomous underwater vehicle mooring nadir task control method according to claim 7, wherein S7 is specifically: during the whole task, if a navigation fault occurs, namely T7 navigation fault; the AUV will perform fault detection, namely P7 fault detection; dividing the fault into a general fault and a dangerous fault according to the detected fault risk degree, namely a T9 general fault and a T8 dangerous fault; and (4) determining to carry out fault waiting or emergency floating, namely P8 fault waiting or P9 emergency floating.
9. A computer system, comprising: one or more processors, a computer readable storage medium, for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of claim 1.
10. A computer-readable storage medium having stored thereon computer-executable instructions for, when executed, implementing the method of claim 1.
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