CN110608740A - Unmanned ship path planning method - Google Patents
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- 230000004888 barrier function Effects 0.000 claims description 12
- 238000012546 transfer Methods 0.000 claims description 6
- 238000013519 translation Methods 0.000 claims description 6
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
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Abstract
An unmanned ship path planning method comprises the steps of firstly carrying out rasterization modeling on an environment from a starting point to a terminal point of a USV, then carrying out global path planning, then carrying out obstacle avoidance on a static obstacle, and then carrying out obstacle avoidance on a dynamic obstacle; the invention adds hardware COMPASS deflection information, sets corresponding threshold values, and adjusts the direction in real time in a self-adaptive manner, so that the direction always points to the terminal point, thereby greatly improving the algorithm, and greatly improving the convergence although the complexity is increased.
Description
Technical Field
The invention relates to an unmanned ship path planning method, and belongs to the technical field of intelligent control systems and artificial intelligence.
Background
At present, land resources are relatively deficient due to the rapid economic growth speed and the large population pressure in the world, but oceans have abundant resources which cannot be estimated, and most of oceans are not developed, so that the eyes of the development of various countries are gradually projected to the oceans from the land. China has a long coastline, is a developing marine kingdom, and has more marine interests disputes with neighboring marine kingdoms, so that the development of marine science and technology is required continuously. An Unmanned Surface Vessel (USV) is a small-sized surface vessel that can realize autonomous navigation in the ocean and complete corresponding mission, and is currently recognized as a small-sized surface vessel that will play an important role in future ocean development, and is recently paid much attention from navy of various countries in the world, and is often used to perform tasks that are special and not suitable for manned vessels, and has functions of information collection, environment monitoring, maritime search and rescue, hydrographic geographic survey and the like, and has a very wide application range.
Most of the current USV path planning is based on pure theory research, and the obtained simulation result does not fully consider mechanical and electrical characteristics of the USV and external variable marine environment, so that the practicability is not strong.
Disclosure of Invention
In order to solve the problems in the prior art, the unmanned ship path planning method is based on the global path planning of the improved ant colony algorithm and the obstacle avoidance scheme algorithm design of various local dynamic and static obstacles, and has high practicability.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an unmanned ship path planning method comprises the following steps:
the method comprises the following steps: firstly, performing rasterization modeling on an environment from a starting point to an end point of the USV, rasterizing the environment into a certain number of grids, and encoding the grids in a binary format: 1 represents that the current grid has an obstacle, and 0 represents no obstacle; the attribute storage of each grid includes information such as obstacle position, wave amplitude, current flow rate, and wind speed. And corroding and expanding the barrier grid by utilizing the bionic principle, wherein the corrosion and expansion of A relative to B are respectively defined as follows:
(A)-bis a translation set of the mapping for B, (A)bIs a set of translations for a with respect to B; in morphology, erosion operations delete certain pixels at the boundary of an obstacle in an image, making the range of a target area smaller, and can be used to eliminate relatively small and meaningless objects; the expansion operation adds pixels to the boundary of the image object to enlarge the range of the target area, and combines background points contacted with the target area into the target to expand the boundary of the target to the outside so as to fill up the holes in the object;
the expand-first then-corrode operation is referred to as a close operation, and the expand-first then-corrode operation is referred to as an open operation, which are respectively expressed as follows:
wherein, close operation connects the adjacent objects, and the area of the adjacent objects is not obviously changed while the boundary of the adjacent objects is smoothed; the opening operation separates the object at a fine point, smoothes a larger object and changes the area of the larger object unobviously, the text performs the opening operation on the barrier, and after the operation, the feasible space and the barrier space are finely divided;
step two: after the operation of the first step, global path planning is firstly carried out, firstly, an electronic chart is combined, a high-precision GPS is used for positioning the current position of the current USV in the sea, a COMPASS is combined, an ant colony algorithm is used for roughly planning a global route from a starting point to an end point, the COMPASS self-defines the direction from the current USV position to the end point target position as a zero degree direction, the COMPASS deviation angle is, anticlockwise is positive, clockwise is negative, and the specific ant colony algorithm adopts the following steps:
1. initializing ant colony parameters, and assuming that the ant colony scale is N and the maximum iteration number is NmaxInitializing pheromone magnitude values for each edge: tau isij(0)＝1，△τij(0)＝0；
2. Placing ants on the initial point of a track planning space within the error range of GPS positioning, and setting a taboo table as a corresponding vertex;
3. calculating the probability according to the transfer rule and the transfer probability of the ants:
k- -the kth ant;
ψ0(0≤ψ0≦ 1) - - -a fixed threshold constant that may be empirically derived;
psi-a random number uniformly distributed between [0,1 ];
τij(t) -pheromone concentration on the path from node i to node j at the tth iteration;
α (α >0) -pheromone significance index;
ηijdistance heuristic on the path from node i to node j at the (t) -th iteration, of size ηij(t)＝1/Lij；
Lij-energy consumption heuristic information on the path from node i to node j;
β (β >0) -distance heuristic information importance index;
-the heuristic information of the compass deflection angle from node i to node j at the t-th iteration is 1/| θ |;
gamma (gamma >0) -compass deflection angle heuristic information importance index;
selecting the next node according to the calculated probability, carrying out local updating on the pheromone after moving each ant once by one step, and updating the taboo table; continuously calculating the probability;
τij(t+1)＝(1-ρ)τij(t)+△τij，τij(t +1) -pheromone concentration after one step shift;
τij(t) -pheromone concentration of the current node;
ρ (0< ρ <1) - -pheromone local update volatility coefficient;
△τij(t) -pheromone concentration retained by each ant on the path (i → j) in the current cycle,
-the change value of the pheromone;
q- -a constant set empirically;
LS- -the total path track comprehensive value that ant k took in this cycle;
selecting nodes and updating the tabu table according to the calculated probability until all the nodes are traversed once; performing pheromone global updating on the optimal path obtained after all ants complete one-time iteration, and continuously calculating the probability;
rho-pheromone global update volatility coefficient;
q- -empirical constant;
OP-optimal path obtained in natural loop;
Λ — the current optimal solution obtained;
at the moment, updating a taboo table simultaneously according to the probability of the time;
4. calculating the pheromone quantity of the ant left on each side, and discarding the ant;
5. repeating the steps from 3 to 4 until all the n ants are traversed;
6. calculating pheromone increment and pheromone total amount of each edge, simultaneously recording the path of the iteration, updating the current optimal path, and clearing the tabu list;
7. judging whether a preset iteration step number N is reachedmaxOr judging whether the stagnation phenomenon occurs or not, if so, finishing the algorithm and outputting the current optimal path; otherwise, turning to the step two, and performing next iteration;
the local path planning adopts a laser radar (LIdar) to use a PRLIDAR, the PRLIDAR is provided with a small motor with adjustable speed, and a radar detector can rotate by 360 degrees to measure the distance of obstacles around the USV and is used for local dynamic obstacle avoidance and static obstacle avoidance; when the external marine environment changes transiently, and a global path is planned in front of the external marine environment, the related nodes cannot be guaranteed to be still all the time, and if an obstacle with slowly changing speed or a static obstacle temporarily appearing outside is encountered, local emergency path planning must be realized;
step three: the method for avoiding the static obstacle comprises the following steps:
1. based on the system, the connecting direction of the USV and the destination is set to be a 0-degree direction in a self-defined mode, as shown in the direction of FIG. 2, the current advancing direction of the USV is 1 direction, the tangential direction of the USV and the obstacle is 3, and the included angle between the direction 1 and the direction 2 is theta1Can be detected in real time through an electronic compass, and the included angle between the direction 2 and the direction 3 is theta2The distance between the USV and the obstacle is L, the distance can be calculated through a laser radar, and the radius of the obstacle is r;
2, controlling a steering engine to rotate clockwise theta by the USV to avoid the obstacle1+θ2So that static obstacles can be avoided;
3. when the USV avoids the obstacle, the USV quickly returns to the original global planning path;
step four: and then the following steps are adopted for avoiding the dynamic barrier:
the method comprises the following steps: and the dynamic obstacle avoidance adopts a GUI remote control interface designed by an upper computer, the USV is manually controlled to temporarily avoid the obstacle, and then the USV continuously returns to the original global planned path until the destination is reached.
The beneficial effect of adopting above-mentioned technical scheme is:
the environment is rasterized by using bionics, the space with obstacles and the space without obstacles can be finely distinguished, and the open operation of the method can simulate the natural marine environment to the maximum extent, so that the distance from a starting point to a terminal point of a subsequent algorithm can be reduced. The existing ant colony algorithm is not accurate enough to be sent from the same point for the initial starting point, the invention properly improves the algorithm, uses GPS to position the starting point of the USV, and places ants in the error range, which is very suitable for the actual situation; secondly, for the average distribution of the initial information, the invention is improved properly, and the iteration times are reduced; the method adds the hardware COMPASS deflection information, sets a corresponding threshold value, and adjusts the direction in real time in a self-adaptive manner to enable the direction to always point to the end point, thereby greatly improving the algorithm, and greatly improving the convergence although the complexity is increased.
Drawings
FIG. 1 is a diagram of a rasterized environment model;
FIG. 2 is a USV next-step motion rule model diagram;
FIG. 3 is a USV simulation diagram for ant colony algorithm route planning of the present invention;
FIG. 4 is an iterative convergence diagram of the ant colony algorithm of the present invention;
fig. 5 is a USV obstacle avoidance diagram.
Detailed Description
The invention is described in further detail below:
an unmanned ship path planning method comprises the following steps:
the method comprises the following steps: firstly, performing rasterization modeling on an environment from a starting point to an end point of the USV, rasterizing the environment into a certain number of grids, and encoding the grids in a binary format: 1 represents that the current grid has an obstacle, and 0 represents no obstacle; the attribute storage of each grid includes information such as obstacle position, wave amplitude, current flow rate, and wind speed. And corroding and expanding the barrier grid by utilizing the bionic principle, wherein the corrosion and expansion of A relative to B are respectively defined as follows:
(A)-bis a translation set of the mapping for B, (A)bIs a set of translations for a with respect to B; in morphology, erosion operations delete certain pixels at the boundary of an obstacle in an image, making the range of a target area smaller, and can be used to eliminate relatively small and meaningless objects; the expansion operation adds pixels to the boundary of the image object to enlarge the range of the target area, and combines background points contacted with the target area into the target to expand the boundary of the target to the outside so as to fill up the holes in the object;
the expand-first then-corrode operation is referred to as a close operation, and the expand-first then-corrode operation is referred to as an open operation, which are respectively expressed as follows:
wherein, close operation connects the adjacent objects, and the area of the adjacent objects is not obviously changed while the boundary of the adjacent objects is smoothed; the opening operation separates the object at a fine point, smoothes a larger object and changes the area of the larger object unobviously, the text performs the opening operation on the barrier, and after the operation, the feasible space and the barrier space are finely divided;
step two: after the operation of the first step, global path planning is firstly carried out, firstly, an electronic chart is combined, a high-precision GPS is used for positioning the current position of the current USV in the sea, a COMPASS is combined, an ant colony algorithm is used for roughly planning a global route from a starting point to an end point, the COMPASS self-defines the direction from the current USV position to the end point target position as a zero degree direction, the COMPASS deviation angle is, anticlockwise is positive, clockwise is negative, and the specific ant colony algorithm adopts the following steps:
1. initializing ant colony parameters, and assuming that the ant colony scale is N and the maximum iteration number is NmaxInitializing pheromone magnitude values for each edge: tau isij(0)＝1，△τij(0)＝0；
2. Placing ants on the initial point of a track planning space within the error range of GPS positioning, and setting a taboo table as a corresponding vertex;
3. calculating the probability according to the transfer rule and the transfer probability of the ants:
k- -the kth ant;
ψ0(0≤ψ0≦ 1) - - -a fixed threshold constant that may be empirically derived;
psi-a random number uniformly distributed between [0,1 ];
τij(t) -pheromone concentration on the path from node i to node j at the tth iteration;
α (α >0) -pheromone significance index;
ηijdistance heuristic on the path from node i to node j at the (t) -th iteration, of size ηij(t)＝1/Lij；
Lij-energy consumption heuristic information on the path from node i to node j;
β (β >0) -distance heuristic information importance index;
-the heuristic information of the compass deflection angle from node i to node j at the t-th iteration is 1/| θ |;
gamma (gamma >0) -compass deflection angle heuristic information importance index;
selecting the next node according to the calculated probability, carrying out local updating on the pheromone after moving each ant once by one step, and updating the taboo table; continuously calculating the probability;
τij(t+1)＝(1-ρ)τij(t)+△τij，τij(t +1) -pheromone concentration after one step shift;
τij(t) -pheromone concentration of the current node;
ρ (0< ρ <1) - -pheromone local update volatility coefficient;
△τij(t) -pheromone concentration retained by each ant on the path (i → j) in the current cycle,
-the change value of the pheromone;
q- -a constant set empirically;
LS- -the total path track comprehensive value that ant k took in this cycle;
selecting nodes and updating the tabu table according to the calculated probability until all the nodes are traversed once; performing pheromone global updating on the optimal path obtained after all ants complete one-time iteration, and continuously calculating the probability;
rho-pheromone global update volatility coefficient;
q- -empirical constant;
OP-optimal path obtained in natural loop;
Λ — the current optimal solution obtained;
at the moment, updating a taboo table simultaneously according to the probability of the time;
4. calculating the pheromone quantity of the ant left on each side, and discarding the ant;
5. repeating the steps from 3 to 4 until all the n ants are traversed;
6. calculating pheromone increment and pheromone total amount of each edge, simultaneously recording the path of the iteration, updating the current optimal path, and clearing the tabu list;
7. judging whether a preset iteration step number N is reachedmaxOr judging whether the stagnation phenomenon occurs or not, if so, finishing the algorithm and outputting the current optimal path; otherwise, turning to the step two, and performing next iteration;
the local path planning adopts a laser radar (LIdar) to use a PRLIDAR, the PRLIDAR is provided with a small motor with adjustable speed, and a radar detector can rotate by 360 degrees to measure the distance of obstacles around the USV and is used for local dynamic obstacle avoidance and static obstacle avoidance; when the external marine environment changes transiently, and a global path is planned in front of the external marine environment, the related nodes cannot be guaranteed to be still all the time, and if an obstacle with slowly changing speed or a static obstacle temporarily appearing outside is encountered, local emergency path planning must be realized;
step three: the method for avoiding the static obstacle comprises the following steps:
1. based on the system, the connecting direction of the USV and the destination is set to be a 0-degree direction in a self-defined mode, as shown in the direction of FIG. 2, the current advancing direction of the USV is 1 direction, the tangential direction of the USV and the obstacle is 3, and the included angle between the direction 1 and the direction 2 is theta1Can be detected in real time through an electronic compass, and the included angle between the direction 2 and the direction 3 is theta2The distance between the USV and the obstacle is L, the distance can be calculated through a laser radar, and the radius of the obstacle is r;
2, controlling a steering engine to rotate clockwise theta by the USV to avoid the obstacle1+θ2So that static obstacles can be avoided;
3. when the USV avoids the obstacle, the USV quickly returns to the original global planning path;
step four: and then the following steps are adopted for avoiding the dynamic barrier:
1. and the dynamic obstacle avoidance adopts a GUI remote control interface designed by an upper computer, the USV is manually controlled to temporarily avoid the obstacle, and then the USV continuously returns to the original global planned path until the destination is reached.
Claims (1)
1. An unmanned ship path planning method is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: firstly, performing rasterization modeling on an environment from a starting point to an end point of the USV, rasterizing the environment into a certain number of grids, and encoding the grids in a binary format: 1 represents that the current grid has an obstacle, and 0 represents no obstacle; the attribute storage of each grid includes information such as obstacle position, wave amplitude, current flow rate, and wind speed. And corroding and expanding the barrier grid by utilizing the bionic principle, wherein the corrosion and expansion of A relative to B are respectively defined as follows:
(A)-bis a translation set of the mapping for B, (A)bIs a set of translations for a with respect to B; in morphology, erosion operations delete certain pixels at the boundary of an obstacle in an image, making the range of a target area smaller, and can be used to eliminate relatively small and meaningless objects; the expansion operation adds pixels to the boundary of the image object to enlarge the range of the target area, and combines background points contacted with the target area into the target to expand the boundary of the target to the outside so as to fill up the holes in the object;
the expand-first then-corrode operation is referred to as a close operation, and the expand-first then-corrode operation is referred to as an open operation, which are respectively expressed as follows:
A·B＝(A⊕B)ΘB (3)
wherein, close operation connects the adjacent objects, and the area of the adjacent objects is not obviously changed while the boundary of the adjacent objects is smoothed; the opening operation separates the object at a fine point, smoothes a larger object and changes the area of the larger object unobviously, the text performs the opening operation on the barrier, and after the operation, the feasible space and the barrier space are finely divided;
step two: after the operation of the first step, global path planning is firstly carried out, firstly, an electronic chart is combined, a high-precision GPS is used for positioning the current position of the current USV in the sea, a COMPASS is combined, an ant colony algorithm is used for roughly planning a global route from a starting point to an end point, the COMPASS self-defines the direction from the current USV position to the end point target position as a zero degree direction, the COMPASS deviation angle is, anticlockwise is positive, clockwise is negative, and the specific ant colony algorithm adopts the following steps:
1) initializing ant colony parameters, and assuming that the ant colony scale is N and the maximum iteration number is NmaxInitializing pheromone magnitude values for each edge: tau isij(0)＝1，△τij(0)＝0；
2) Placing ants on the initial point of a track planning space within the error range of GPS positioning, and setting a taboo table as a corresponding vertex;
3) calculating the probability according to the transfer rule and the transfer probability of the ants:
k- -the kth ant;
ψ0(0≤ψ0≦ 1) - - -a fixed threshold constant that may be empirically derived;
psi-a random number uniformly distributed between [0,1 ];
τij(t) -pheromone concentration on the path from node i to node j at the tth iteration;
α (α >0) -pheromone significance index;
ηijdistance heuristic on the path from node i to node j at the (t) -th iteration, of size ηij(t)＝1/Lij；
Lij-energy consumption heuristic information on the path from node i to node j;
β (β >0) -distance heuristic information importance index;
-the heuristic information of the compass deflection angle from node i to node j at the t-th iteration is 1/| θ |;
gamma (gamma >0) -compass deflection angle heuristic information importance index;
selecting the next node according to the calculated probability, carrying out local updating on the pheromone after moving each ant once by one step, and updating the taboo table; continuously calculating the probability;
τij(t+1)＝(1-ρ)τij(t)+△τij，
τij(t +1) -pheromone concentration after one step shift;
τij(t) -pheromone concentration of the current node;
ρ (0< ρ <1) - -pheromone local update volatility coefficient;
△τij(t) -pheromone concentration retained by each ant on the path (i → j) in the current cycle,
-the change value of the pheromone;
q- -a constant set empirically;
LS- -the total path track comprehensive value that ant k took in this cycle;
selecting nodes and updating the tabu table according to the calculated probability until all the nodes are traversed once; performing pheromone global updating on the optimal path obtained after all ants complete one-time iteration, and continuously calculating the probability;
rho-pheromone global update volatility coefficient;
q- -empirical constant;
OP-optimal path obtained in natural loop;
Λ — the current optimal solution obtained;
at the moment, updating a taboo table simultaneously according to the probability of the time;
4) calculating the pheromone quantity of the ant left on each side, and discarding the ant;
5) repeating the steps from 3 to 4 until all the n ants are traversed;
6) calculating pheromone increment and pheromone total amount of each edge, simultaneously recording the path of the iteration, updating the current optimal path, and clearing the tabu list;
7) judging whether a preset iteration step number N is reachedmaxOr judging whether the stagnation phenomenon occurs or not, if so, finishing the algorithm and outputting the current optimal path; otherwise, turning to the step two, and performing next iteration;
the local path planning adopts a laser radar (LIdar) to use a PRLIDAR, the PRLIDAR is provided with a small motor with adjustable speed, and a radar detector can rotate by 360 degrees to measure the distance of obstacles around the USV and is used for local dynamic obstacle avoidance and static obstacle avoidance; when the external marine environment changes transiently, and a global path is planned in front of the external marine environment, the related nodes cannot be guaranteed to be still all the time, and if an obstacle with slowly changing speed or a static obstacle temporarily appearing outside is encountered, local emergency path planning must be realized;
step three: the method for avoiding the static obstacle comprises the following steps:
1) based on the system, the connecting direction of the USV and the destination is set to be a 0-degree direction in a self-defined mode, as shown in the direction of FIG. 2, the current advancing direction of the USV is 1 direction, the tangential direction of the USV and the obstacle is 3, and the included angle between the direction 1 and the direction 2 is theta1Can be detected in real time through an electronic compass, and the included angle between the direction 2 and the direction 3 is theta2The distance between the USV and the obstacle is L, the distance can be calculated through a laser radar, and the radius of the obstacle is r;
2) when the USV needs to avoid the obstacle, the steering engine is controlled to rotate clockwise by theta1+θ2So that static obstacles can be avoided;
3) when the USV avoids the obstacle, the USV quickly returns to the original global planning path;
step four: and then the following steps are adopted for avoiding the dynamic barrier:
1) and the dynamic obstacle avoidance adopts a GUI remote control interface designed by an upper computer, the USV is manually controlled to temporarily avoid the obstacle, and then the USV continuously returns to the original global planned path until the destination is reached.
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