CN108319276B - Underwater robot posture adjusting and controlling device and method based on Boolean network - Google Patents
Underwater robot posture adjusting and controlling device and method based on Boolean network Download PDFInfo
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Abstract
The invention relates to a Boolean network-based attitude adjustment control device and method for an underwater robot, wherein the device comprises: the temperature sensor is used for respectively acquiring the real-time temperatures of the seawater and the propulsion motor; the pressure sensor is used for monitoring the water depth and the pressure of the working point; the electronic compass is used for monitoring the heading and the attitude of the submersible vehicle; the sonar is used for detecting obstacles and measuring relative distance; the system comprises a Boolean network control module, a motor driver, a motor, a driving motor, a pressure sensor, an electronic compass, a sonar, a temperature sensor, a pressure sensor, a motor driver, a Boolean network control module and a motor driver, wherein input nodes respectively correspond to the temperature sensor, the pressure sensor; and the propelling motor is used for realizing the posture adjustment control of the underwater robot. Compared with the prior art, the control device provided by the invention realizes the autonomous attitude adjustment control of the underwater robot, and the control method is based on the Boolean network model and has the advantages of logic determination, flexible network construction and strong control robustness.
Description
Technical Field
The invention relates to the underwater robot attitude regulation control technology, in particular to an underwater robot attitude regulation control device and method based on a Boolean network.A Boolean network model is constructed for an underwater robot by mapping a sensor as an input node and mapping a propeller as an output node; and a Boolean network is used as a controller core, the state of the output node is obtained, and a control signal is output by a decoding driver to adjust the working state of a propulsion motor, so that the attitude of the underwater robot is adjusted and controlled.
Background
The underwater robot overcomes the diving depth limit of human, can assist or even replace human to carry out underwater operation in some high-risk environments, and becomes an important tool for exploring ocean resources, protecting ocean ecology, developing ocean economy and maintaining ocean rights and interests. Because the underwater robot has complex operation environment and is influenced by external factors such as ocean currents, obstacles and the like, the attitude adjustment control such as dynamic positioning, tracking, obstacle avoidance and the like is very difficult. At present, the underwater robot is mostly operated manually to adjust and control the posture. Since the propulsion system of underwater robots is usually under-actuated and there are delays in communication, not only are high demands placed on the skills of the operator, but the real-time and accuracy requirements of the control are often difficult to meet. Therefore, it is necessary to establish an effective control method to realize autonomous attitude adjustment and control of the underwater robot, so as to improve adaptability to a complex underwater environment and reduce the working intensity of personnel.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide the gesture adjusting and controlling device and method of the underwater robot based on the Boolean network.
The purpose of the invention can be realized by the following technical scheme:
an underwater robot posture adjusting and controlling device based on a Boolean network comprises:
the temperature sensors are arranged at different positions inside and outside the submersible shell and are used for respectively collecting the real-time temperatures of the seawater and the propulsion motor;
the pressure sensor is used for monitoring the water depth and the pressure of the working point;
the electronic compass is used for monitoring the heading and the attitude of the submersible vehicle;
the sonar is used for detecting obstacles and measuring relative distance;
the input nodes of the Boolean network control module are respectively connected with the temperature sensor, the pressure sensor, the electronic compass and the sonar and used for receiving the acquired data, the output nodes of the Boolean network control module correspond to the motor driver, and the output state of the Boolean network control module is coded and used for driving the propulsion motor;
the motor driver is connected with an output node of the Boolean network control module and used for driving the propulsion motor;
and the propelling motor is connected with the motor driver and used for realizing the posture adjustment control of the underwater robot.
Preferably, the temperature sensor is used for ensuring that the glass window on the submersible vehicle shell is not damaged due to sudden cooling and heating of water temperature when the hydrothermal solution abnormal area is detected, and the motor is prevented from stopping or even being damaged due to overheating; and once the set threshold value is exceeded, an alarm is given out and the protection measures of motor deceleration or reverse thrust are executed.
Preferably, the electronic compass is a full-attitude three-dimensional electronic compass heading sensor used for monitoring the heading and the underwater attitude of the submersible vehicle in real time and providing reference for attitude adjustment.
Preferably, the temperature sensor, the pressure sensor, the electronic compass and the sonar perform discretization processing on the acquired data through respective encoders, convert the data into one or a group of binary data according to different threshold settings, and send the binary data to an input node of the boolean network control module.
Preferably, an output control signal of an output node of the boolean network control module is converted into a PWM wave by a motor driver for controlling the propulsion motor.
Preferably, the boolean network control module takes a boolean network model as a dynamic model, trains the boolean network with an optimization algorithm to obtain corresponding parameters according to different task types, constructs corresponding boolean logic rules, and trains with an intelligent algorithm such as, but not limited to, a genetic algorithm.
Preferably, the input node and each sensor of the boolean network control module, and the output node and the motor driver are not limited to a simple 1-to-1 mapping, but may also be a many-to-many mapping relationship as required.
The method for adjusting and controlling the attitude of the underwater robot based on the Boolean network comprises the following steps:
the method comprises the following steps: establishing a Boolean network, mapping sensors and a propulsion motor to input nodes and output nodes of the Boolean network, wherein each sensor corresponds to one input node, and the propulsion motor is mapped to the output node of the Boolean network;
step two: the input value of the sensor is coded, and the state of each input node in the Boolean network is converted into a binary form after being coded;
step three: decoding the binary state of the output node of the Boolean network into a control signal of the propulsion motor;
step four: the Boolean network is trained according to different tasks, and after the node composition of the Boolean network is determined, the dynamic relationship among the nodes is obtained through intelligent algorithm training.
Compared with the prior art, the invention realizes the autonomous attitude adjustment control of the underwater robot based on the proposed control device, replaces the conventional manual operation mode, improves the precision and efficiency during fine operation, and reduces the working intensity of personnel and the risk of personnel and equipment. The control method is based on the Boolean network model and has the advantages of logic determination, flexible network construction and strong control robustness.
The control device and the control method are very suitable for attitude adjustment control of the underwater robot and are also suitable for control of other Boolean dynamic systems after slight modification.
Drawings
FIG. 1 is a schematic view of a control apparatus according to the present invention;
FIG. 2 is a schematic diagram of a Boolean network of the present invention;
fig. 3 is a boolean network diagram corresponding to equation (3) of the dynamics.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The invention is realized by the following technical scheme. Firstly, a control device consisting of a sensor, a Boolean network control module, a motor driver and a propulsion motor is provided; then, a mapping relation is established between the physical equipment and the input/output nodes in the Boolean network; and finally, constructing a Boolean network model by using an intelligent optimization algorithm to realize the posture adjustment of the underwater robot. The innovation point is that the provided control device realizes the autonomous attitude control of the underwater robot and reduces unnecessary human intervention. The method has the advantages that the Boolean network is adopted as a model of the control algorithm, so that the control target is achieved, and meanwhile, the robustness is good.
The attitude control device provided by the invention comprises a temperature sensor, a pressure (depth) sensor, an electronic compass, a sonar, a sensor encoder, a motor decoding driver, a propulsion motor and the like. When underwater fine operation such as dynamic positioning, tracking, obstacle avoidance and the like is carried out, the underwater robot is switched to an autonomous working mode to take over own attitude control, and corresponding motor thrust and sensor working threshold values are set according to different task types. Temperature sensors arranged at different positions inside and outside the submersible shell respectively collect the real-time temperatures of the seawater and the propulsion motor; the pressure (depth) sensor is used for monitoring the water depth and the pressure of the working point; the electronic compass is used for monitoring the heading and the attitude of the submersible vehicle; the sonar is used for detecting obstacles and measuring relative distance; the data of the sensors are converted into binary data (0/1) required by a Boolean network controller through respective sensor encoders, and the binary data are accessed to the input nodes of the Boolean network; the binary data output of the Boolean network output node is converted into control parameters of a propulsion motor through a motor coding driver, so that the attitude adjustment control of the underwater robot is realized.
The temperature sensors are respectively used for acquiring the real-time temperatures of seawater and the motor, and are used for ensuring that the fragile components such as a glass window and the like on the submersible shell are not damaged due to shock cooling and shock heating of water temperature when the hydrothermal solution abnormal area is detected, and the motor is prevented from being shut down or even damaged due to overheating. And once the set threshold value is exceeded, an alarm is given out and protective measures such as motor speed reduction or reverse thrust are executed.
The pressure (water depth) sensor is used for measuring water depth and provides support for depth setting and dynamic positioning of the submersible vehicle.
The electronic compass is a full-attitude three-dimensional electronic compass heading sensor and is used for monitoring the bow and the underwater attitude in real time and providing reference for attitude adjustment.
The sonar sensor is used for detecting the external environment and searching the target, and provides reference for obstacle avoidance and positioning.
Different sensors are accessed to respective sensor encoders through IIC, RS232 serial ports and the like, discretization processing is carried out, and the sensor encoders are converted into one or a group of binary data (1/0) according to different threshold settings of the sensors and used as input of the Boolean network.
The control signal output by the output node of the Boolean network is converted into PWM wave by the motor decoding drive circuit to realize the control of the propulsion motor.
The Boolean network control module takes an embedded processor as a core, receives binary data state data from a sensor, outputs a control signal to control a motor, and realizes a Boolean network control algorithm.
The invention also comprises a control method for underwater robot posture adjustment based on the Boolean network, wherein the network structure mainly comprises input nodes corresponding to the sensors and output nodes corresponding to the propulsion motors, and each node state has an independent logic updating function. In a boolean network, each node has only two states, "on" and "off", and each node can only be in one of these two states at a time. The state of each node at the next moment is determined by the state of the adjacent node, the state of the adjacent node is input, and the new state of the node is obtained through a series of logical operations. Logical operators used in the operation include AND (AND), OR (OR), NOT, exclusive OR (XOR), etc.
The Boolean network as a discrete dynamic model not only has logic certainty, but also has globally convergent state evolution, that is, no matter from which initial state the Boolean network evolves, the state of the Boolean network always enters a certain attractor (limit ring or fixed point), and after the Boolean network system enters the attractor state, the system is difficult to jump out of the attractor state unless there is external control or disturbance. Therefore, the method has strong robustness and is beneficial to the autonomous adjustment of the posture.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
As shown in fig. 1, the underwater robot posture adjustment control device based on the boolean network mainly includes three parts, a sensor 1 is provided in a left dashed line frame, a boolean network control module 2 is provided in a middle solid line frame, and an actuator 3 is provided in a right dashed line frame.
The sensor 1 comprises a water temperature sensor T1 arranged outside the submersible vehicle shell, a temperature sensor T2 arranged on a propulsion motor, a water depth (pressure) sensor H arranged at the bottom of the submersible vehicle shell, a full-attitude three-dimensional electronic compass heading sensor C and a sonar S arranged on the bow; the actuator 3 comprises 5 special direct current motors with 48VDC thrusters, wherein, 2 vertical thrusters are arranged at the left and right, 2 front and back and 1 vertical thruster; the boolean network control module has a strong computing and storing capability with an embedded processor as a core, and an input part 21, a boolean network control algorithm part 22, an output part 23, and an ethernet port 24 are mainly drawn in the schematic diagram.
The control module also provides various interfaces, for example, it can be seen that the IIC interfaces are accessed by the temperature sensors T1 and T2 in a cascade connection manner, and the 485 interfaces are also accessed by the water depth (pressure) sensor, sonar S and electronic compass C in a cascade connection manner because they are serial inputs. In addition, a RJ45 port in the figure leads out a network controller in the embedded processor, and is mainly used for data communication between the control device and other parts of the underwater robot and a mother ship. It should be noted that the reference numeral 1x in the figure indicates other sensors that need to be expanded, and the 232 interface can be used for serial communication between circuit boards besides a sensor that can be connected in a serial manner.
The control method of the underwater robot posture adjustment control device based on the Boolean network comprises the following specific steps:
the method comprises the following steps: establishing a Boolean network, namely mapping sensors and actuators as input and output nodes of the Boolean network, wherein each sensor in FIG. 1 corresponds to one input node, and in FIG. 1, a propulsion motor is also mapped as an output node in the Boolean network, it should be noted that the nodes in the Boolean network are not limited to the input and output nodes, and some common nodes can be added according to the complexity of tasks to obtain a better training effect;
step two: to encode the sensor input values, the state of each input node in the boolean network must be encoded and converted to a binary form (1/0). For example, as for the seawater temperature sensor T1, the function is mainly to monitor the seawater temperature and avoid damage to fragile components such as portholes due to passing through an abnormal water temperature region in a short time, so that upper and lower limits of the temperature can be set, the state is set to 1 when the upper and lower limits are exceeded, and the state is set to 0 by default at normal water temperature. Similarly, the sensor T2 for monitoring the temperature of the propulsion motor may be correspondingly coded according to given constraints. By analogy, the measured values of the water depth (pressure) sensor H, the sonar S and the electronic compass C can also be set according to the parameter requirements of depth fixing, obstacle avoidance and attitude, wherein the table is the mapping relation between the seawater temperature sensor and the Boolean network input node;
TABLE 1
| T1 State | X1 |
| The temperature exceeds the upper and |
1 |
| Normal temperature | 0 |
Step three: the binary state of the output node of the boolean network is decoded into a control signal for the propulsion motor. For example, for a propulsion motor, the start and stop of the motor may simply be coded as (1/0). It should be noted that if different output powers are desired, one motor may be mapped to multiple output nodes, and then corresponding control effects are obtained through different combinations of node states, table 2 is a mapping relationship between a propulsion motor and multiple input nodes in a boolean network, and table 2 demonstrates that 1 motor is mapped to 3 output nodes (x2, x3, x4), and the control of 8 different operation modes can be implemented through the boolean network at most;
TABLE 2
| Working mode | | X3 | X4 | |
| 1 | 0 | 0 | 0 | |
| 2 | 0 | 0 | 1 | |
| 3 | 0 | 1 | 0 | |
| 4 | 0 | 1 | 1 | |
| 5 | 1 | 0 | 0 | |
| 6 | 1 | 0 | 1 | |
| 7 | 1 | 1 | 0 | |
| 8 | 1 | 1 | 1 |
Step four: the Boolean network is trained according to different tasks, and after the node composition of the Boolean network is determined, the dynamic relationship among the nodes is obtained through intelligent algorithm training. The process of training is essentially an optimization process, which obtains minimum or maximum values according to the merit function. Common meta-heuristic optimization algorithms include tabu search algorithm, simulated annealing algorithm, genetic algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, artificial neural network algorithm and the like. Genetic algorithms are mainly used in our practice to train boolean networks.
For a node x comprising n nodes1,x2,...xnThe dynamic equation of which can be described as
Wherein f isi:＝Dn→ D, i ═ 1, 2.., n is a logic function. After training according to different tasks, a specific form of the logistic function can be obtained. Based on matrix half tensor product theory, matrix representation directed quantity of logic
The dynamic equations of the system (1) can be converted into component algebraic form
Wherein M isiIs a node structure matrix corresponding to the node 'i'.
Fig. 2 shows a schematic boolean network diagram, and it can be seen that a sensor is coded as an input node, an actuator is coded as an output node, a plurality of common nodes are further added according to task requirements, after network nodes are determined, training parameters (such as evaluation functions) are set according to different tasks, and a boolean network kinetic equation corresponding to an optimal solution is found through an optimization algorithm.
Fig. 3 shows a boolean network corresponding to the kinetic equation (3), in which there are 14 nodes, where S1 to S5 correspond to 5 input nodes being mappings of sensors, M1 to M5 correspond to 5 output nodes being mappings of actuators, and N1 to N4 are general nodes added as needed. The state of the executor M1 at time t +1 is determined by the state of the node N1 at time t, the state of the node N1 at time t +1 is determined by the states of the nodes N4, S1, S3, and S4 at time t, where "+" represents a logical or relationship, and S1(t) S3(t) S4(t) represents a logical and relationship of the states of the nodes S1, S3, and S4 at time t. The dynamic relationships between other node states are also described in terms of similar logical relationships as follows
Step five: the trained Boolean network is used for adjusting and controlling the attitude of the underwater robot, the output binary signals are converted into corresponding PWM waves through the motor decoding driver to drive the actuator, and the adjustment and control of the attitude of the underwater robot are realized.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The utility model provides an underwater robot gesture adjustment control device based on boolean network which characterized in that includes:
the temperature sensors are arranged at different positions inside and outside the underwater robot shell and are used for respectively acquiring the real-time temperatures of the seawater and the propulsion motor;
the pressure sensor is used for monitoring the water depth and the pressure of the working point;
the electronic compass is used for monitoring the heading and the attitude of the underwater robot;
the sonar is used for detecting obstacles and measuring relative distance;
the input node of the Boolean network control module is respectively connected with the temperature sensor, the pressure sensor, the electronic compass and the sonar and receives the data acquired by the temperature sensor, the output node corresponds to the motor driver, and the output state of the output node is used for driving the propulsion motor after being coded;
the motor driver is connected with an output node of the Boolean network control module and used for driving the propulsion motor;
the propulsion motor is connected with the motor driver and used for realizing the attitude adjustment control of the underwater robot;
the Boolean network control module takes a Boolean network model as a dynamic model, trains the Boolean network by using an optimization algorithm according to different task types to obtain corresponding parameters, constructs a corresponding Boolean logic rule, and adopts a genetic algorithm during training; the Boolean network control module adopts 1-to-1 simple mapping or many-to-many mapping between the input node and each sensor, and adopts 1-to-1 simple mapping or many-to-many mapping between the output node and the motor driver.
2. The device of claim 1, wherein the temperature sensor is used for protecting a glass window on the underwater robot shell from being damaged due to sudden cooling and heating of water temperature when detecting a hydrothermal abnormal area, and avoiding the motor from being stopped or even damaged due to overheating; and once the set threshold value is exceeded, an alarm is given out and the protection measures of motor deceleration or reverse thrust are executed.
3. The device of claim 1, wherein the electronic compass is a full-attitude three-dimensional electronic compass heading sensor for monitoring heading and underwater attitude of the underwater robot in real time and providing a reference for attitude adjustment.
4. The device according to claim 1, characterized in that the temperature sensor, the pressure sensor, the electronic compass and the sonar perform discretization processing on the acquired data through respective encoders, convert the data into one or a group of binary data according to different threshold settings, and send the binary data to an input node of the Boolean network control module.
5. The apparatus of claim 1, wherein the output control signal of the output node of the boolean network control module is converted into a PWM wave by a motor driver for controlling the propulsion motor.
6. A method for using the boolean network-based attitude adjustment control device for underwater robots according to claim 1, characterized by comprising the following steps:
the method comprises the following steps: establishing a Boolean network, mapping sensors and a propulsion motor to input nodes and output nodes of the Boolean network, wherein each sensor corresponds to one input node, and the propulsion motor is mapped to the output node of the Boolean network;
step two: the input value of the sensor is coded, and the state of each input node in the Boolean network must be converted into a binary form after being coded;
step three: decoding the binary state of the output node of the Boolean network into a control signal of the propulsion motor;
step four: the Boolean network is trained according to different tasks, and after the node composition of the Boolean network is determined, the dynamic relationship among the nodes is obtained through genetic algorithm training.
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| CN103823471A (en) * | 2014-03-13 | 2014-05-28 | 北京理工大学 | Vector-propelled small four-axis underwater robot control system |
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