Unmanned ship group high-maneuverability cooperative control method based on communication network optimization

Abstract

The invention discloses an unmanned ship group high-maneuverability cooperative control method based on communication network optimization, and relates to the technical field of unmanned ship cluster cooperative control, and the method comprises the steps: building an inertial coordinate system; the shore-based terminal obtains a cluster original undirected communication network of the unmanned surface vehicle in real time, and determines input of a navigation unmanned surface vehicle and a virtual leader; the shore-based terminal establishes a simplified communication network based on the network feature vector; establishing a second-order kinematics equation model of the unmanned ship, and establishing a collaborative formation model based on a virtual leader; the shore-based terminal calculates collaborative formation consistency control input according to the simplified communication network, the position information and the speed information; and the unmanned surface vehicle performs state evolution according to the cooperative formation consistency control input until the state consistency is achieved. According to the topology communication network based on the network feature vector, communication redundancy can be reduced, efficient transmission of information from the piloting unmanned surface vehicle to the following unmanned surface vehicle is ensured, and the maneuvering capability of an unmanned surface vehicle cluster is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat control, and in particular to a high-maneuverability collaborative control method for an unmanned boat group based on communication network optimization. Background Art

[0002] The development of unmanned boats plays an important role in the fields of marine science, resource exploration and military. However, it is a challenging task to realize the coordinated process maneuvering enhanced control of multiple unmanned boats in a complex underwater environment. The current unmanned boat system faces many problems in the collaborative process, such as path planning, obstacle avoidance, communication protocol, etc. These problems directly affect the collaborative operation efficiency and safety of the unmanned boats. In the collaborative control process of the unmanned boat cluster, the interaction between the unmanned boats forms a topological structure that can be described by the network at the macro level. This structure greatly determines the maneuverability of the unmanned boat cluster system. Under the leader-follower architecture based on graph theory, the unmanned boats need to achieve good collaborative maneuverability to adapt to different mission requirements and complex underwater environments.

[0003] However, the redundancy of the topological communication structure based on the unidirectional communication network brings a huge burden to the communication cost of the maritime unmanned boat cluster.

[0004] At present, unmanned boat systems mostly use a two-way communication topology network in the collaborative process. Since the two-way communication network in the cluster does not distinguish the status information of collaborative control, the collaborative control input is mixed with a large amount of non-pilot information from within the cluster, which directly affects the collaborative operation efficiency and safety of the unmanned boat. It not only creates a burden on communication costs, but also makes it difficult to ensure the high efficiency of collaborative control.

[0005] Therefore, technicians in this field are committed to developing a high-maneuverability collaborative control method for unmanned boat swarms based on communication network topology optimization. Under the leader-follower architecture based on graph theory, the unmanned boats can achieve good collaborative maneuverability to adapt to different mission requirements and complex underwater environments. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to simplify the cluster communication network and improve the coordination efficiency of the unmanned boat cluster while ensuring effective information transmission.

[0007] To achieve the above object, the present invention provides a high-maneuverability cooperative control method for an unmanned boat group based on communication network optimization, the method comprising the following steps:

[0008] Step 1: Establish an inertial coordinate system based on the environment where the unmanned boat is located;

[0009] Step 2: The shore-based terminal obtains the original undirected communication network of the unmanned boat cluster in real time;

[0010] Step 3: The shore-based terminal determines the original undirected communication network of the cluster, and determines the input of the pilot unmanned boat and the virtual leader;

[0011] Step 4: The shore-based terminal establishes a simplified communication network based on the network feature vector;

[0012] Step 5: Establish a second-order kinematic equation model of the unmanned boat and a collaborative formation model based on a virtual leader;

[0013] Step 6: The shore-based terminal calculates the coordinated formation consistency control input according to the simplified communication network, location information, and speed information, and transmits the coordinated formation consistency control input to each unmanned boat; the coordinated formation consistency control input includes cluster consistency control input and pilot-follower consistency control input;

[0014] Step 7: The unmanned boat evolves its state according to the collaborative formation consistency control input until state consistency is achieved.

[0015] Furthermore, each unmanned boat includes an unmanned boat control unit, a bottom-level control unit, an inertial navigation module, and a GPS positioning module; the unmanned boat control unit serves as a server, and the shore-based terminal serves as a client; information is transmitted between the unmanned boat and the shore-based terminal via a local area network through a socket communication protocol; the unmanned boat control unit is used to send the collaborative formation consistency control input to the bottom-level control unit, and the bottom-level control unit performs state evolution according to the collaborative formation consistency control input; the inertial navigation module is used to obtain speed information, and the GPS positioning module is used to obtain position information; the speed information and the position information are shared between unmanned boat clusters through a local area network, and the speed information and the position information are sent to the shore-based terminal.

[0016] Furthermore, the step 2 also includes:

[0017] The original communication structure of the unmanned boat cluster is represented by an undirected communication network; the shore-based terminal obtains the original undirected communication network of the cluster of multiple unmanned boats in real time in, Represents a collection of nodes. represents the edge set, W = [w ij ]∈R n×n Represents the network adjacency matrix; the neighbor set of node i in the network is defined as When two nodes i and j in the network are neighbors, the adjacency matrix w ij =1, otherwise w ij =0.

[0018] Furthermore, the step 3 further includes: determining the pilot unmanned boats v1, ..., v p , used to receive the information of the virtual leader input v0 in the collaborative control determined by the shore-based terminal.

[0019] Furthermore, the step 4 also includes:

[0020] The cluster original undirected communication network The corresponding perturbation Laplace matrix is:

[0021] L B =L+diag(B1 m )

[0022] Where L = [l ij ]∈R n×n is the graph Laplacian matrix corresponding to w, which is calculated as:

[0023] For i≠j, we have For i=j, we have l ij =-w ij ;

[0024] diag(B1 m ) is a diagonal matrix, except for the network Except for the position corresponding to the Zhongling Unmanned Boat, which is 1, the other positions are all 0;

[0025] Assume φ1(L B ) i represents the item at the i-th position in the item-wise positive eigenvector φ1 corresponding to the minimum eigenvalue of the perturbation Laplace matrix, φ1(L B ) ij Indicates φ1(L B ) i With φ1(L B ) j The ratio of

[0026] The shore-based terminal performs network optimization based on the network feature vector based on the pilot unmanned boat of the original undirected communication network of the cluster, and determines each edge w in the adjacency matrix ij Whether to keep, and finally get the simplified communication network

[0027] Furthermore, each edge w in the adjacency matrix is ​​judged ij Whether to keep includes: for each communication edge w ij , calculate its corresponding φ1(L B ) ij ; When φ1(L B ) ij<1, and unmanned boat i and unmanned boat j are neighbors of each other, then It is expressed as:

[0028]

[0029] Furthermore, the step 5 further includes: the second-order kinematic equation model of each unmanned boat is expressed as:

[0030]

[0031] Among them, x i (t)∈R d ,v i (t)∈R d and u i (t)∈R d They represent the position information, velocity information and acceleration control input of the unmanned boat i in the inertial coordinate system of the d-dimensional space at time t, respectively. The acceleration is output by the bottom control unit in the control protocol;

[0032] The second-order kinematic model of the virtual leader is expressed in the inertial coordinate system as follows:

[0033]

[0034] in, v0(t) is the position state and speed state at time t corresponding to the virtual leader, and v0(t)=v0, that is, the speed of the virtual leader is always a constant.

[0035] Furthermore, step 6 also includes: the cluster consistency control input u of the i-th unmanned boat 1i (t) is calculated as follows:

[0036]

[0037] The design parameters are coupling gain parameters α>0 and β>0, and the cluster consistency control input of the unmanned boat is the local information.

[0038] Furthermore, the pilot-follower consistency control input u of the i-th unmanned boat is 2i(t) The calculation is as follows:

[0039]

[0040] Among them, when i is the pilot unmanned boat that receives information from the virtual leader, there is b il =1, otherwise b il =0.

[0041] Furthermore, the coordinated formation consistency control input u of the i-th unmanned boat isi (t) is calculated as follows:

[0042] u i (t) = u i1 (t)+u i2 (t).

[0043] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0044] 1. The present invention significantly reduces the cluster communication cost of the unmanned boat cluster, reduces communication redundancy and ensures efficient transmission of information from the leader to the follower unmanned boat, and significantly optimizes the topology of the unmanned boat cluster communication network;

[0045] 2. The optimized unmanned boat cluster communication topology network provided by the present invention eliminates a large amount of redundancy caused by two-way communication in the network structure, and at the same time ensures that the virtual leader information and the pilot unmanned boat information are propagated along the monotonically increasing direction of the eigenvector of the communication disturbance Laplace matrix under each reachable path. This improvement greatly improves the maneuverability and collaborative control capability of the unmanned boat cluster.

[0046] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the system structure of a preferred embodiment of the present invention;

[0048] Figure 2 is a method flow chart of a preferred embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram of a cluster original undirected communication network of a preferred embodiment of the present invention;

[0050] Figure 4 is a simplified communication network diagram of a preferred embodiment of the present invention;

[0051] Figure 5 is a flow chart of a collaborative control method of a preferred embodiment of the present invention;

[0052] Figure 6 is a position evolution comparison diagram of a preferred embodiment of the present invention;

[0053] Figure 7 It is a speed evolution comparison diagram of a preferred embodiment of the present invention;

[0054] Figure 8 It is a trajectory evolution curve diagram of a cluster original undirected communication network of a preferred embodiment of the present invention;

[0055] Fig. 9 It is a trajectory evolution curve diagram of a simplified communication network according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0057] In the drawings, components with the same structure are indicated by the same numerical reference numerals, and components with similar structures or functions are indicated by similar numerical reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of the components is appropriately exaggerated in some places in the drawings.

[0058] This embodiment provides a high-mobility cooperative control system for unmanned boat groups based on communication network optimization, such as Figure 1 As shown, it includes n unmanned boats and shore-based terminals; each unmanned boat includes an unmanned boat control unit, a bottom control unit, an inertial navigation module, and a GPS positioning module; the unmanned boat control unit serves as a server, and the shore-based terminal serves as a client, and information is transmitted between the unmanned boat and the shore-based terminal via a local area network through a socket communication protocol; the shore-based terminal is connected to the unmanned boat control unit through the IP port of the unmanned boat control unit; the unmanned boat control unit is used to send control input to the bottom control unit, and the bottom control unit evolves the state according to the control input; the inertial navigation module is used to obtain the speed information of the unmanned boat, and the GPS positioning module is used to obtain the position information of the unmanned boat; the unmanned boat clusters share speed information and position information, and send speed information and position information to the shore-based terminal through the local area network.

[0059] like Figure 2 As shown, this embodiment also provides a high-maneuverability collaborative control method for an unmanned boat cluster, comprising the following steps:

[0060] Step 1: Establish an inertial coordinate system. The inertial coordinate system is established based on the environment where the unmanned boat is located.

[0061] Step 2: The shore-based terminal obtains the original undirected communication network of the unmanned boat cluster in real time.

[0062] The original communication structure of the unmanned boat swarm can be represented by an undirected communication network, such as Figure 3As shown in the figure, in the unmanned boat cluster system, nodes are used to replace the actual unmanned boats, and the communication topology between the unmanned boats is described by the graph data structure. Each unmanned boat can be regarded as a node, and the edges between nodes represent the constraints between the unmanned boats. Therefore, the original undirected communication network of the cluster is expressed as in, Represents a collection of nodes. represents the edge set, W = [w ij ]∈R n×n represents the network adjacency matrix. The neighbor set of node i in the network is defined as When two nodes i and j in the network are neighbors, the adjacency matrix w ij =1, otherwise w ij =0.

[0063] Step 3: The shore-based terminal determines the original undirected communication network of the cluster and determines the pilot unmanned boats v1,...,v in the network p , used to receive the information of the virtual leader input v0 in the collaborative control determined by the shore-based terminal.

[0064] Step 4: The shore-based terminal establishes a simplified communication network based on the network feature vector according to the original undirected communication network of the cluster.

[0065] Cluster primitive undirected communication network The corresponding perturbation Laplace matrix is:

[0066] L B =L+diag(B1 m )

[0067] Where L = [l ij ]∈R n×n is the graph Laplacian matrix corresponding to w, which is calculated as:

[0068] For i≠j, we have For i=j, we have l ij =-w ij .

[0069] diag(B1 m ) is a diagonal matrix, except for the network Except for the position corresponding to the pilot unmanned boat, the other positions are all 0. Let φ1(L B ) i represents the item at the i-th position in the item-wise positive eigenvector φ1 corresponding to the minimum eigenvalue of the perturbation Laplace matrix, φ1(L B ) ij Indicates φ1(L B ) i With φ1(L B ) jThe ratio of, that is:

[0070] The shore-based terminal is based on the pilot unmanned boat and the network feature vector in the multi-unmanned boat communication network, and simplifies the network construction according to the multi-unmanned boat topology communication network. ij , whether to keep depends on the following conditions: for each communication edge w ij , calculate its corresponding When φ1(L B ) ij <1, and unmanned boat i and unmanned boat j are neighbors of each other, then The result is a simplified communication network like Figure 4 As shown, the network simplification process follows the following formula:

[0071]

[0072] Step 5: Establish the second-order kinematic equation model of the unmanned boat and establish a collaborative formation model based on the virtual leader, that is, through various consistency control inputs, make the unmanned boat cluster consistent in multiple d-dimensional states, and design a collaborative consistency control method, such as Figure 5 shown.

[0073] The second-order kinematic model of the i-th unmanned boat is expressed as follows in the inertial coordinate system:

[0074]

[0075] Among them, x i (t)∈R d ,v i (t)∈R d and u i (t)∈R d They respectively represent the position, velocity and acceleration control input of the unmanned boat i in the inertial coordinate system of the d-dimensional space at time t. The acceleration is output by the unmanned boat control unit in the control protocol, the position information is obtained by the GPS positioning module, and the velocity information is obtained by the inertial navigation module.

[0076] Among them, the second-order kinematic model of the virtual leader is expressed as follows in the inertial coordinate system:

[0077]

[0078] in, v0(t) is the position state and speed state at time t corresponding to the virtual leader, and v0(t)=v0, that is, the speed of the virtual leader is always a constant.

[0079] Step 6: For each unmanned boat, the shore-based terminal calculates the cluster consistency control input and the pilot-follower consistency control input according to the simplified communication network, location information, and speed information, and transmits them to each unmanned boat via the local area network through the socket communication protocol.

[0080] The cluster consistency control input u of the i-th unmanned boat 1i (t) is calculated as follows:

[0081]

[0082] The design parameters are coupling gain parameters α>0 and β>0. The cluster consistency control input of the unmanned boats is the local information. Each unmanned boat communicates with the unmanned boats in the neighboring set through the local area network based on socket, and shares the position information and speed information obtained by the GPS positioning module and inertial navigation module on the boat. The cluster consistency control input of the single boat is determined based on the local information.

[0083] The pilot-follower consistency control input u of the i-th unmanned boat 2i(t) The calculation is as follows:

[0084]

[0085] When i is an unmanned boat that receives information from a virtual leader, there is b il =1, otherwise b il =0.

[0086] The coordinated formation consistency control input u of the i-th unmanned boat i (t) is calculated as follows:

[0087] u i (t) = u i1 (t)+u i2 (t)

[0088] Step 7. In the simplified communication network, each unmanned boat only receives the speed and posture broadcast information of the remaining neighboring unmanned boats after the network optimization strategy based on the network feature vector of the unmanned boat cluster. The shore-based terminal uses the simplified neighbor information to calculate the consistent collaborative control input. The multi-unmanned boat system evolves the consistent collaborative process according to the collaborative algorithm. The unmanned boat control unit of each unmanned boat parses the collaborative formation consistency control input obtained by the collaborative control method and sends it to the underlying control unit. The underlying control unit evolves the state according to the collaborative formation consistency control input until the state consistency is achieved, thereby realizing the high-maneuverability collaborative control of the unmanned boat cluster.

[0089] like Figure 6 , Figure 7As shown in the figure, the consistency state convergence performance of the unmanned boat cluster in the original undirected communication network and the simplified communication network of the cluster is shown. Figure 6 (a) (b) shows the original network, where each boat in the unmanned boat cluster is in the two-dimensional plane, at x i1 With x i2 Position convergence in two directions; Figure 6 (c) (d) shows the position of each boat in the unmanned boat cluster in the two-dimensional plane at x when the system switches from the original network to the simplified communication network at the time corresponding to the red line. i1 With x i2 Position convergence in two directions. Figure 7 (a) (b) shows the original network, where each boat in the unmanned boat cluster is in a two-dimensional plane, at v i1 With v i2 Convergence of velocities in two directions; Figure 7 (c) (d) shows the position of each boat in the unmanned boat cluster in the two-dimensional plane at v when the system switches from the original network to the simplified communication network at the time corresponding to the red line. i1 With v i2 Speed ​​convergence in two directions. It can be seen that in the simplified communication network, the speed convergence efficiency and position convergence efficiency of the unmanned boat cluster are significantly improved.

[0090] like Figure 8 , Fig. 9 As shown in the figure, the trajectory evolution curves of the unmanned boat cluster on the two-dimensional plane of the sea surface in the original undirected communication network of the cluster and the simplified communication network respectively. It can be seen that the simplified communication network not only greatly improves the maneuverability of the unmanned boat cluster in the collaborative control process, but also makes the node trajectory of the unmanned boat cluster smoother and easier to implement, so that the unmanned boat cluster can rely on low-cost underlying control units to achieve high-maneuverability collaborative control process.

[0091] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A high-maneuverability cooperative control method for unmanned boat swarm based on communication network optimization, characterized in that: The method comprises the following steps: Step 1: Establish an inertial coordinate system based on the environment where the unmanned boat is located; Step 2: The shore-based terminal obtains the original undirected communication network of the unmanned boat cluster in real time; Step 3: The shore-based terminal determines the original undirected communication network of the cluster, and determines the input of the pilot unmanned boat and the virtual leader; Step 4: The shore-based terminal establishes a simplified communication network based on the network feature vector; Step 5: Establish a second-order kinematic equation model of the unmanned boat and a collaborative formation model based on a virtual leader; Step 6: The shore-based terminal calculates the coordinated formation consistency control input according to the simplified communication network, location information, and speed information, and transmits the coordinated formation consistency control input to each unmanned boat; the coordinated formation consistency control input includes cluster consistency control input and pilot-follower consistency control input; Step 7: The unmanned boat evolves its state according to the collaborative formation consistency control input until state consistency is achieved.

2. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 1 is characterized in that: Each unmanned boat includes an unmanned boat control unit, a bottom-level control unit, an inertial navigation module, and a GPS positioning module; the unmanned boat control unit serves as a server, and the shore-based terminal serves as a client; information is transmitted between the unmanned boat and the shore-based terminal via a local area network using a socket communication protocol; the unmanned boat control unit is used to send the collaborative formation consistency control input to the bottom-level control unit, and the bottom-level control unit performs state evolution according to the collaborative formation consistency control input; the inertial navigation module is used to obtain speed information, and the GPS positioning module is used to obtain position information; the speed information and the position information are shared between unmanned boat clusters via a local area network, and the speed information and the position information are sent to the shore-based terminal.

3. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 2 is characterized in that: The step 2 also includes: The original communication structure of the unmanned boat cluster is represented by an undirected communication network; the shore-based terminal obtains the original undirected communication network of the cluster of multiple unmanned boats in real time in, Represents a collection of nodes. represents the edge set, represents the network adjacency matrix; the neighbor set of node i in the network is defined as When two nodes i and j in the network are neighbors, the adjacency matrix w ij =1, otherwise w ij =0.

4. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 3 is characterized in that: The step 3 also includes: determining the pilot unmanned boats v1, ..., v p , used to receive the information of the virtual leader input v0 in the collaborative control determined by the shore-based terminal.

5. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 4 is characterized in that: The step 4 also includes: The cluster original undirected communication network The corresponding perturbation Laplace matrix is: L B =L+diag(B1 m ) Where L = [l ij ]∈R n×n is the graph Laplacian matrix corresponding to w, which is calculated as: For i≠j, we have For i=j, we have l ij =-w ij ; diag(B1 m ) is a diagonal matrix, except for the network Except for the position corresponding to the Zhongling Unmanned Boat, which is 1, the other positions are all 0; Assume φ1(L B ) i represents the item at the i-th position in the item-wise positive eigenvector φ1 corresponding to the minimum eigenvalue of the perturbation Laplace matrix, φ1(L B ) ij Indicates φ1(L B ) i With φ1(L B ) j The ratio of The shore-based terminal performs network optimization based on the network feature vector based on the pilot unmanned boat of the original undirected communication network of the cluster, and determines each edge w in the adjacency matrix ij Whether to keep, and finally get the simplified communication network 6. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 5, characterized in that: Each edge w in the adjacency matrix is ​​judged ij Whether to keep includes: for each communication edge w ij , calculate its corresponding φ1(L B ) ij ; When φ1(L B ) ij <1, and unmanned boat i and unmanned boat j are neighbors of each other, then It is expressed as:

7. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 6, characterized in that: The step 5 also includes: the second-order kinematic equation model of each unmanned boat is expressed as: Among them, x i (t)∈R d ,v i (t)∈R d and u i (t)∈R d They represent the position information, velocity information and acceleration control input of the unmanned boat i in the inertial coordinate system of the d-dimensional space at time t, respectively. The acceleration is output by the bottom control unit in the control protocol; The second-order kinematic model of the virtual leader is expressed in the inertial coordinate system as follows: in, v0(t) is the position state and speed state at time t corresponding to the virtual leader, and v0(t)=v0, that is, the speed of the virtual leader is always a constant.

8. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 7, characterized in that: The step 6 also includes: the cluster consistency control input u of the i-th unmanned boat 1i (t) is calculated as follows: The design parameters are coupling gain parameters α>0 and β>0, and the cluster consistency control input of the unmanned boat is the local information.

9. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 8, characterized in that: The pilot-follower consistency control input u of the i-th unmanned boat 2i(t) The calculation is as follows: Among them, when i is the pilot unmanned boat that receives information from the virtual leader, there is b il =1, otherwise b il =0.

10. The high-maneuverability cooperative control method of unmanned boat swarm based on communication network optimization according to claim 9, characterized in that: The coordinated formation consistency control input u of the i-th unmanned boat i (t) is calculated as follows: u i (t)=u i1 (t)+u i2 (t)。

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