CN111123913A - Multi-unmanned-vessel double-layer path planning and collaborative obstacle avoidance method - Google Patents
Multi-unmanned-vessel double-layer path planning and collaborative obstacle avoidance method Download PDFInfo
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Abstract
The invention discloses a double-layer path planning and collaborative obstacle avoidance method for multiple unmanned boats, which comprises the steps of applying double-layer planning, firstly obtaining the topographic and geomorphic conditions from a starting point to the periphery of a destination through equipment, detecting the positions of obstacles and targets, applying an improved ant colony algorithm to plan and construct a static course, then obtaining the parameter information of other unmanned boats in real time, and constructing a dynamic balance model; compared with the traditional single-layer planning, dynamic obstacle avoidance only needs to be calculated in time, so that the iteration time is shorter, and the obstacle avoidance response time is faster. Furthermore, the unmanned ship damage scene is introduced, the unmanned ship obstacle avoidance model under the military warfare condition is considered, the curve interpolation fitting and the expansion model are carried out on the air route, the air route of the unmanned ship is smooth, and the actual application capability of the model is stronger. Still be, through the damaged condition analysis of unmanned ship, open the inflation model for other unmanned ships avoid damaged unmanned ship rapidly, avoid resulting in by the secondary destruction that damages unmanned ship and cause.
Description
Technical Field
The invention relates to the technical field of unmanned ship obstacle avoidance coordination, in particular to a multi-unmanned ship double-layer path planning and coordination obstacle avoidance method.
Background
In recent years, the safety problem of the peripheral sea areas of the country is more obvious, and a set of offshore attack and defense combat system taking unmanned boats as the leading strength needs to be established as soon as possible.
At present, the research of unmanned ship groups in tactical application scenes is still in a starting stage, the tactics of unmanned ship bee groups are researched earlier, China can get a lead in the aspect of unmanned ship tactics, and the defense capability on the sea is effectively improved. Therefore, a drone swarm (a marine formation formed by a plurality of drones) tactics is provided, but considering that the drones fight on a two-dimensional marine plane, secondary damage can be caused to the drone swarm, and the unmanned plane cannot be damaged, so that a set of obstacle avoidance scheme for drone swarm fighting is provided extremely necessarily.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multi-unmanned-boat double-layer path planning and cooperative obstacle avoidance method which is strong in actual application capability of a model, fast in obstacle avoidance response time and capable of solving the problem of secondary damage caused by single boat damage.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a multi-unmanned-boat double-layer path planning and collaborative obstacle avoidance method comprises the following steps:
s1: acquiring the landform situation from a departure point to the periphery of a destination, and detecting the positions of an obstacle and a target;
s2: planning a static route of the unmanned ship group by adopting an improved ant algorithm according to the information acquired in the step S1;
s3: the unmanned ship group navigates along a planned static airline, each unmanned ship acquires other surrounding unmanned ship parameters in real time, and a system dynamic balance model of the unmanned ship group is constructed;
s4: when the unmanned ship is marked to be damaged, the potential force field of the damaged unmanned ship disappears, the repulsive force field under the expansion model is started by the damaged unmanned ship, and then the repulsive force field is constructed according to the expansion model of the damaged unmanned ship;
s5: calculating the resultant force of each unmanned ship according to the repulsive force and the attractive force;
s6: solving a navigation angle of the unmanned ship and fitting a route by a Lagrange interpolation method;
s7: judging whether the unmanned ship is far away from the damaged unmanned ship, if so, entering the step S8, otherwise, returning to the step S5;
s8: judging whether the unmanned ship navigates along a static airline and the system reaches dynamic balance, if not, navigating along a double-layer planning route combining the static airline and the potential force field until the unmanned ship arrives at a destination; otherwise, the process returns to step S3.
Further, the specific process of step S2 is as follows:
s2-1: initializing parameters according to the information obtained in step S1;
s2-2: randomly placing the total m ants on the starting point, and emptying a taboo list;
s2-3: calculating the state transition probability, and calculating the next node of the ant according to a state transition equation;
s2-4: recording the next node of each ant in a path table, and adding the next node into a taboo table of each ant;
s2-5: repeating until m ants move to the next node;
s2-6: updating pheromone information, adding 1, N to iteration numberc＝Nc+1；
ΔτijIndicates the increment of pheromone on the path (i, j) in the current cycle, and the initial time delta tauij(t)＝0，
Representing the information quantity of the kth ant left on the path (i, j) in the current cycle;
Lkthe path length of the kth ant in the cycle, Q is pheromone intensityDegree;
s2-7: the conditions are met or the number of iterations reaches the maximum;
s2-8: obtaining a static planning route of the unmanned ship;
s2-9: the curve was fitted by interpolation.
Further, in step S2-3, the specific process of calculating the state transition probability is as follows:
the state transition equation is:
wherein ,
indicates the probability, tau, of the unmanned boat k transferring from node i to node jijRepresents the pheromone quantity of ants on the path (i, j); allowedk＝{C-tabukExpressing the nodes allowed to be selected by the ant k next step, and using tabuk(k is 1, 2, …, m) to record the node that ant k currently walks through, α is an pheromone heuristic factor reflecting the relative importance of pheromone accumulation in the ant colony search, β is an expected heuristic factor reflecting the relative importance of the distance of the next target point in the ant colony search, and the larger the β value is, the closer the state transition probability is to the greedy rule, therefore, the β and gamma values cannot be too large, otherwise, the local optimum is easily trapped, thus leading to stagnation, ηijIs a heuristic function;
[λ(i，j)]γthe size of the guidance factor is inversely proportional to the distance between the node and the target node, so that blindness of ant search in the state transition process is avoided, and the ant searches for the next node towards the target node;
the cost of the elicitation factors is mainly the threat of obstacles and the threat of enemy weapons, missiles and radars, and the elicitation factors are arranged in the visible range of the unmanned boatThe threats of the obstacle, radar and missile are respectively KO、KR、KMThe coordinates of the jth threat point are respectively (x)Oj，yOj)、(xRj，yRj) and (xMj，yMj) The threat cost from node i to threat point j is expressed as:
in the above formula, the first and second carbon atoms are,
δ is the proportion of each cost, and the heuristic function of the node is equal to the reciprocal of the total threat cost of the node, i.e. the sum of the cost of each node is equal to the sum of the cost of each node
Therefore, when the node threat cost is low, the visibility is higher if the heuristic function is larger; conversely, the lower.
Further, the specific process of constructing the dynamic balance model of the unmanned ship fleet system in step S3 is as follows:
the attraction of the static sailing boat to the unmanned boat is as follows:
Fatt＝KA×d(Xunc，Xr)；
in the above formula, KADenotes the gravitational field coefficient, d (X)unc，Xo) Representing the Euclidean distance between the unmanned boat and a point on the nearest static air route, and the direction points to the nearest point on the air route;
the unmanned boat system is in a dynamic equilibrium, each boat is subjected to the potential field forces of the other boats:
in the above formula, KεRepresenting the potential force field coefficient, sigma is the distance between two boats with the interaction where the potential field force is just zero, and α parameters are determined by fitting the results of known experimental data;
when the distance between the unmanned boats is sigma and the geometric center of the unmanned boat group system is positioned on the static air line, the unmanned boat system is in a dynamic balance state at the moment, and the advancing direction is the tangential direction of the point on the air line.
Further, the specific process of constructing the repulsive force field according to the expansion model of the damaged unmanned ship in step S4 is as follows:
to damage the coordinate position X of the unmanned ship0(x0，y0) Navigation speed V0And establishing an expansion model by using the unmanned ship step length time T:
the repulsion force of the damaged unmanned ship to other unmanned ships is as follows:
KRdenotes the coefficient of repulsive force field, d (X)unc，Xo) Euclidean distance, d, that damages unmanned boats for other unmanned boat distancesmFor damaging the farthest influence range of the repulsive field of the unmanned boat, i.e. if the distance between the unmanned boat and the damaged unmanned boat is more than dmIt is not affected by the repulsive force.
Further, in step S4, whether the expansion model is opened or not is determined by analyzing the damage condition of the unmanned ship, and the specific analysis process is as follows:
the unmanned ship can receive sea wind disturbance when sailing on the sea, the sailing speed can not be in an absolute stable state, and the sailing speed is recorded as a fluctuation interval [ -v ]w，vw]，vwValue ofThe real-time sea surface wind speed can be determined by a specific application scene;
because the unmanned ship group is in a dynamic balance state, the speed difference of each unmanned ship is not large, the unmanned ship group is considered to be relatively static at the moment, but the speed range of the unmanned ship is vs±vwAt the moment, the unmanned boat is in a normal state, vsThe average speed of the unmanned ship group system at the last moment;
when an unmanned boat encounters an enemy strike, three situations are distinguished:
the first method comprises the following steps: the unmanned boat is damaged, but the power system is normal;
the power system of the unmanned ship is normal, and the unmanned ship can normally operate at the moment, so that the unmanned ship is considered as normal;
and the second method comprises the following steps: the unmanned boat is fried;
at the moment, the unmanned ship is fried into fragments by an enemy and sinks, and the influence of the fragments on other unmanned ships can be regarded as no effect, so that the unmanned ships are regarded as nonexistent;
and the third is that: damage to the unmanned boat power system;
the power system of the unmanned boat is damaged, the speed of the unmanned boat is gradually reduced, and when the speed of the unmanned boat is not in the normal speed range vs±vwAnd at the moment, the unmanned ship is regarded as an expansion model, and repulsion force is applied to other unmanned ships, so that the normal unmanned ship group is far away from the damaged unmanned ship.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
1. the method comprises the steps of using double-layer planning, firstly obtaining the landform conditions from a starting point to the periphery of a destination through equipment, detecting the positions of obstacles and targets, using an improved ant colony algorithm to plan and construct a static air route, then obtaining parameter information of other unmanned boats in real time, and constructing a dynamic balance model; compared with the traditional single-layer planning, dynamic obstacle avoidance only needs to be calculated in time, so that the iteration time is shorter, and the obstacle avoidance response time is faster.
2. An unmanned ship damage scene is introduced, an unmanned ship obstacle avoidance model under the military warfare condition is considered, interpolation fitting of curves and an expansion model are carried out on the air route, the air route of the unmanned ship is smooth, and the actual application capability of the model is stronger.
3. Aiming at the cluster motion scene of the unmanned ship, the expansion model is started through the analysis of the damage condition of the unmanned ship, so that other unmanned ships can rapidly avoid the damaged unmanned ship, and the secondary damage caused by the damaged unmanned ship is avoided.
4. The existing ant algorithm is improved, which is mainly reflected in the improvement of a state transition equation, so that the convergence speed of the algorithm is improved, and the optimal or suboptimal flight path is converged more quickly.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a working flow chart of a double-layer path planning and cooperative obstacle avoidance method for multiple unmanned boats according to the present invention;
fig. 2 is a schematic diagram of a system matched with the multi-unmanned-vessel double-layer path planning and collaborative obstacle avoidance method of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
a system principle matched with the method of the embodiment is shown in figure 2, and the method steps are shown in figure 1, and specifically include the following steps:
s1: acquiring the landform situation from a departure point to the periphery of a destination by using a radar and a satellite, and detecting the positions of an obstacle and a target;
s2: planning a static route of the unmanned ship group by adopting an improved ant algorithm according to the information acquired in the step S1; the specific process of the step is as follows:
s2-1: initializing parameters according to the information obtained in step S1;
s2-2: randomly placing the total m ants on the starting point, and emptying a taboo list;
s2-3: calculating the state transition probability, and calculating the next node of the ant according to a state transition equation; the specific process of calculating the state transition probability is as follows:
the state transition equation is:
in the above formula, the first and second carbon atoms are,
indicates the probability, tau, of the unmanned boat k transferring from node i to node jijRepresents the pheromone quantity of ants on the path (i, j); allowedk＝{C-tabukExpressing the nodes allowed to be selected by the ant k next step, and using tabuk(k is 1, 2, …, m) to record the node that ant k currently walks through, α is an pheromone heuristic factor reflecting the relative importance of pheromone accumulation in the ant colony search, β is an expected heuristic factor reflecting the relative importance of the distance of the next target point in the ant colony search, and the larger the β value is, the closer the state transition probability is to the greedy rule, therefore, the β and gamma values cannot be too large, otherwise, the local optimum is easily trapped, thus leading to stagnation, ηijIs a heuristic function;
[λ(i，j)]γthe size of the guidance factor is inversely proportional to the distance between the node and the target node, so that blindness of ant search in the state transition process is avoided, and the ant searches for the next node towards the target node;
the cost of the heuristic factor is mainly the threat of the barrier and the threat of enemy weapons, missiles and radars which are arranged in the visible range of the unmanned ship, and the barrier, the radar and the missile threats are respectively KO、KR、KMCoordinates of the jth threat point thereofAre respectively (x)Oj，yOj)、(xRj，yRj) and (xMj，yMj) Node i to threat pointjThe threat cost of (c) is expressed as:
in the above formula, the first and second carbon atoms are,
δ is the proportion of each cost, and the heuristic function of the node is equal to the reciprocal of the total threat cost of the node, i.e. the sum of the cost of each node is equal to the sum of the cost of each node
Therefore, when the node threat cost is low, the visibility is higher if the heuristic function is larger; conversely, the lower.
S2-4: recording the next node of each ant in a path table, and adding the next node into a taboo table of each ant;
s2-5: repeating until m ants move to the next node;
s2-6: updating pheromone information, adding 1, N to iteration numberc＝Nc+1；
ΔτijIndicates the increment of pheromone on the path (i, j) in the current cycle, and the initial time delta tauij(t)＝0，
Representing the information quantity of the kth ant left on the path (i, j) in the current cycle;
Lkthe path length of the kth ant in the cycle is taken, and Q is the pheromone strength;
s2-7: the conditions are met or the number of iterations reaches the maximum;
s2-8: obtaining a static planning route of the unmanned ship;
s2-9: the curve was fitted by interpolation.
S3: the unmanned ship group navigates along a planned static airline, each unmanned ship acquires other surrounding unmanned ship parameters in real time, and a system dynamic balance model of the unmanned ship group is constructed;
in this step, the attraction of the static sailing boat to the unmanned boat is:
Fatt＝KA×d(Xunc，Xr)；
in the above formula, KADenotes the gravitational field coefficient, d (X)unc，Xo) Representing the Euclidean distance between the unmanned boat and a point on the nearest static air route, and the direction points to the nearest point on the air route;
the unmanned boat system is in a dynamic equilibrium, each boat is subjected to the potential field forces of the other boats:
in the above formula, KεRepresenting the potential force field coefficient, sigma is the distance between two boats with the interaction where the potential field force is just zero, and α parameters are determined by fitting the results of known experimental data;
when the distance between the unmanned boats is sigma and the geometric center of the unmanned boat group system is positioned on the static air line, the unmanned boat system is in a dynamic balance state at the moment, and the advancing direction is the tangential direction of the point on the air line.
S4: when the unmanned ship is marked to be damaged, the potential force field of the damaged unmanned ship disappears, the damaged unmanned ship opens the repulsive force field under the expansion model, whether the expansion model is opened or not is determined through the damaged condition analysis of the unmanned ship, and the specific analysis process is as follows:
the unmanned ship can receive sea wind disturbance when sailing on the sea, the sailing speed can not be in an absolute stable state, and the sailing speed is recorded as a fluctuation interval [ -v ]w，vw]，vwThe value of (a) can be determined by the real-time sea surface wind speed of a specific application scene;
because the unmanned ship group is in a dynamic balance state, the speed difference of each unmanned ship is not large, the unmanned ship group is considered to be relatively static at the moment, but the speed range of the unmanned ship is vs±vwAt the moment, the unmanned boat is in a normal state, vsThe average speed of the unmanned ship group system at the last moment;
when an unmanned boat encounters an enemy strike, three situations are distinguished:
the first method comprises the following steps: the unmanned boat is damaged, but the power system is normal;
the power system of the unmanned ship is normal, and the unmanned ship can normally operate at the moment, so that the unmanned ship is considered as normal;
and the second method comprises the following steps: the unmanned boat is fried;
at the moment, the unmanned ship is fried into fragments by an enemy and sinks, and the influence of the fragments on other unmanned ships can be regarded as no effect, so that the unmanned ships are regarded as nonexistent;
and the third is that: damage to the unmanned boat power system;
the power system of the unmanned boat is damaged, the speed of the unmanned boat is gradually reduced, and when the speed of the unmanned boat is not in the normal speed range vs±vwAnd at the moment, the unmanned ship is regarded as an expansion model, and repulsion force is applied to other unmanned ships, so that the normal unmanned ship group is far away from the damaged unmanned ship.
Then constructing a repulsive force field according to an expansion model of the damaged unmanned ship, and the specific process is as follows:
to damage the coordinate position X of the unmanned ship0(x0，y0) Navigation speed V0And step length time T of unmanned ship builds expansion model：
The repulsion force of the damaged unmanned ship to other unmanned ships is as follows:
KRdenotes the coefficient of repulsive force field, d (X)unc，Xo) Euclidean distance, d, that damages unmanned boats for other unmanned boat distancesmFor damaging the farthest influence range of the repulsive field of the unmanned boat, i.e. if the distance between the unmanned boat and the damaged unmanned boat is more than dmIt is not affected by the repulsive force.
S5: and (3) calculating the resultant force of each unmanned boat according to the repulsive force and the attractive force:
Ft(Xuc)＝Frep(Xuc)+Fatt(Xuc)；
resultant force FtThe direction of the planned route and the next navigation point of the unmanned ship at the current position.
S6: solving a navigation angle of the unmanned ship and fitting a route by a Lagrange interpolation method;
s7: judging whether the unmanned ship is far away from the damaged unmanned ship, if so, entering the step S8, otherwise, returning to the step S5;
s8: judging whether the unmanned ship navigates along a static airline and the system reaches dynamic balance, if not, navigating along a double-layer planning route combining the static airline and the potential force field until the unmanned ship arrives at a destination; otherwise, the process returns to step S3.
In the embodiment, double-layer planning is applied, the topographic and geomorphic conditions from a starting point to the periphery of a destination are obtained through equipment, the positions of obstacles and targets are detected, a static air route is planned and constructed by using an improved ant colony algorithm, and then parameter information of other unmanned boats is obtained in real time to construct a dynamic balance model; compared with the traditional single-layer planning, dynamic obstacle avoidance only needs to be calculated in time, so that the iteration time is shorter, and the obstacle avoidance response time is faster. Furthermore, the unmanned ship damage scene is introduced, the unmanned ship obstacle avoidance model under the military warfare condition is considered, the curve interpolation fitting and the expansion model are carried out on the air route, the air route of the unmanned ship is smooth, and the actual application capability of the model is higher. Still be, through the damaged condition analysis of unmanned ship, open the inflation model for other unmanned ships avoid damaged unmanned ship rapidly, avoid resulting in by the secondary destruction that damages unmanned ship and cause.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (6)
1. A multi-unmanned-boat double-layer path planning and collaborative obstacle avoidance method is characterized by comprising the following steps:
s1: acquiring the landform situation from a departure point to the periphery of a destination, and detecting the positions of an obstacle and a target;
s2: planning a static route of the unmanned ship group by adopting an improved ant algorithm according to the information acquired in the step S1;
s3: the unmanned ship group navigates along a planned static airline, each unmanned ship acquires other surrounding unmanned ship parameters in real time, and a system dynamic balance model of the unmanned ship group is constructed;
s4: when the unmanned ship is marked to be damaged, the potential force field of the damaged unmanned ship disappears, the repulsive force field under the expansion model is started by the damaged unmanned ship, and then the repulsive force field is constructed according to the expansion model of the damaged unmanned ship;
s5: calculating the resultant force of each unmanned ship according to the repulsive force and the attractive force;
s6: solving a navigation angle of the unmanned ship and fitting a route by a Lagrange interpolation method;
s7: judging whether the unmanned ship is far away from the damaged unmanned ship, if so, entering the step S8, otherwise, returning to the step S5;
s8: judging whether the unmanned ship navigates along a static airline and the system reaches dynamic balance, if not, navigating along a double-layer planning route combining the static airline and the potential force field until the unmanned ship arrives at a destination; otherwise, the process returns to step S3.
2. The multi-unmanned-boat double-deck path planning and collaborative obstacle avoidance method according to claim 1, wherein the specific process of the step S2 is as follows:
s2-1: initializing parameters according to the information obtained in step S1;
s2-2: randomly placing the total m ants on the starting point, and emptying a taboo list;
s2-3: calculating the state transition probability, and calculating the next node of the ant according to a state transition equation;
s2-4: recording the next node of each ant in a path table, and adding the next node into a taboo table of each ant;
s2-5: repeating until m ants move to the next node;
s2-6: updating pheromone information, adding 1, N to iteration numberc＝Nc+1；
ΔτijIndicates the increment of pheromone on the path (i, j) in the current cycle, and the initial time delta tauij(t)＝0，
Representing the information quantity of the kth ant left on the path (i, j) in the current cycle;
Lkthe path length of the kth ant in the cycle is taken, and Q is the pheromone strength;
s2-7: the conditions are met or the number of iterations reaches the maximum;
s2-8: obtaining a static planning route of the unmanned ship;
s2-9: the curve was fitted by interpolation.
3. The multi-unmanned-boat double-deck path planning and collaborative obstacle avoidance method according to claim 2, wherein in the step S2-3, a specific process of calculating the state transition probability is as follows:
the state transition equation is:
wherein ,
indicates the probability, tau, of the unmanned boat k transferring from node i to node jijRepresents the pheromone quantity of ants on the path (i, j); allowedk＝{C-tabukExpressing the nodes allowed to be selected by the ant k next step, and using tabuk(k is 1, 2, …, m) to record the node that ant k currently walks through, α is an pheromone heuristic factor reflecting the relative importance of pheromone accumulation in the ant colony search, β is an expected heuristic factor reflecting the relative importance of the distance of the next target point in the ant colony search, and the larger the β value is, the closer the state transition probability is to the greedy rule, therefore, the β and gamma values cannot be too large, otherwise, the local optimum is easily trapped, thus leading to stagnation, ηijIs a heuristic function;
[λ(i，j)]γthe size of the guidance factor is inversely proportional to the distance between the node and the target node, so that blindness of ant search in the state transition process is avoided, and the ant searches for the next node towards the target node;
the cost of the heuristic factor is mainly the threat of the barrier and the threat of enemy weapons, missiles and radars which are arranged in the visible range of the unmanned ship, and the barrier, the radar and the missile threats are respectively KO、KR、KMThe coordinates of the jth threat point are respectively (x)Oj，yOj)、(xRj，yRj) and (xMj，yMj) The threat cost from node i to threat point j is expressed as:
in the above formula, the first and second carbon atoms are,
δ is the proportion of each cost, and the heuristic function of the node is equal to the reciprocal of the total threat cost of the node, i.e. the sum of the cost of each node is equal to the sum of the cost of each node
Therefore, when the node threat cost is low, the visibility is higher if the heuristic function is larger; conversely, the lower.
4. The multi-unmanned-boat double-layer path planning and collaborative obstacle avoidance method according to claim 1, wherein the specific process of constructing the unmanned boat group system dynamic balance model in the step S3 is as follows:
the attraction of the static sailing boat to the unmanned boat is as follows:
Fatt＝KA×d(Xunc，Xr)；
in the above formula, KADenotes the gravitational field coefficient, d (X)unc，Xo) Representing the Euclidean distance between the unmanned boat and a point on the nearest static air route, and the direction points to the nearest point on the air route;
the unmanned boat system is in a dynamic equilibrium, each boat is subjected to the potential field forces of the other boats:
in the above formula, KεRepresenting the potential force field coefficient, sigma is the distance between two boats with the interaction where the potential field force is just zero, and α parameters are determined by fitting the results of known experimental data;
when the distance between the unmanned boats is sigma and the geometric center of the unmanned boat group system is positioned on the static air line, the unmanned boat system is in a dynamic balance state at the moment, and the advancing direction is the tangential direction of the point on the air line.
5. The multi-unmanned-boat double-deck path planning and collaborative obstacle avoidance method according to claim 1, wherein the specific process of constructing the repulsive field according to the expansion model of the damaged unmanned boat in the step S4 is as follows:
to damage the coordinate position X of the unmanned ship0(x0，y0) Navigation speed V0And establishing an expansion model by using the unmanned ship step length time T:
the repulsion force of the damaged unmanned ship to other unmanned ships is as follows:
KRdenotes the coefficient of repulsive force field, d (X)unc，Xo) Euclidean distance, d, that damages unmanned boats for other unmanned boat distancesmFor damaging the farthest influence range of the repulsive field of the unmanned boat, i.e. if the distance between the unmanned boat and the damaged unmanned boat is more than dmIt is not affected by the repulsive force.
6. The method for double-deck path planning and collaborative obstacle avoidance of multiple unmanned boats according to claim 1, wherein in step S4, whether the expansion model is opened or not is determined by analyzing damage conditions of the unmanned boats, and the specific analysis process is as follows:
the unmanned ship can receive sea wind disturbance when sailing on the sea, the sailing speed can not be in an absolute stable state, and the sailing speed is recorded as a fluctuation interval [ -v ]w，vw]，vwThe value of (a) can be determined by the real-time sea surface wind speed of a specific application scene;
because the unmanned ship group is in a dynamic balance state, the speed difference of each unmanned ship is not large, the unmanned ship group is considered to be relatively static at the moment, but the speed range of the unmanned ship is vs±vwAt the moment, the unmanned boat is in a normal state, vsThe average speed of the unmanned ship group system at the last moment;
when an unmanned boat encounters an enemy strike, three situations are distinguished:
the first method comprises the following steps: the unmanned boat is damaged, but the power system is normal;
the power system of the unmanned ship is normal, and the unmanned ship can normally operate at the moment, so that the unmanned ship is considered as normal;
and the second method comprises the following steps: the unmanned boat is fried;
at the moment, the unmanned ship is fried into fragments by an enemy and sinks, and the influence of the fragments on other unmanned ships can be regarded as no effect, so that the unmanned ships are regarded as nonexistent;
and the third is that: damage to the unmanned boat power system;
the power system of the unmanned boat is damaged, the speed of the unmanned boat is gradually reduced, and when the speed of the unmanned boat is not in the normal speed range vs±vwAnd at the moment, the unmanned ship is regarded as an expansion model, and repulsion force is applied to other unmanned ships, so that the normal unmanned ship group is far away from the damaged unmanned ship.
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