CN113110464A - Intelligent full-electric ship path planning method capable of reducing energy consumption - Google Patents
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Abstract
The invention discloses an intelligent full-electric ship path planning method for reducing energy consumption, which comprises the following steps: constructing an intelligent all-electric ship navigation route map based on a PRM algorithm; the method comprises the steps that an energy consumption model of the full electric ship is established by analyzing a resistance model of the ship in an ocean current environment and combining ship actual parameters; according to the constructed navigation route map and the established energy consumption model, an energy consumption searching algorithm designed by the invention is adopted to obtain a path with optimal energy consumption. The method obtains a more economic, environment-friendly and practical and feasible path, and effectively improves the cruising ability of the intelligent all-electric ship.
Description
Technical Field
The invention relates to the field of ship path planning, in particular to an intelligent all-electric ship path planning method capable of reducing energy consumption.
Background
In recent years, with the rapid development of artificial intelligence, automation technology, and navigation technology. Meanwhile, the development of the intelligent ship is accelerated, the intelligent ship has great potential application value as an offshore intelligent platform, and can be used for executing a plurality of tasks, such as scientific research, offshore resource exploration, water quality and offshore meteorological monitoring, military application and other fields. Meanwhile, energy and environment are always concerned by all countries in the world, the intelligent all-electric ship becomes an effective way for relieving the fossil energy crisis and environmental pollution, and compared with the traditional ship, the intelligent all-electric ship realizes energy conservation and emission reduction and greatly improves the operating performance.
The path planning is an important component of an intelligent ship system, and marks the development level of autonomous navigation of the intelligent ship. The purpose of the path planning algorithm is to obtain a feasible path with safety, energy conservation and shortest time, and to obtain an energy-saving path, at present, many path planning algorithms are applied to path planning of an intelligent ship, and are mainly divided into two categories, namely a traditional algorithm and an intelligent algorithm, wherein the traditional algorithms are most representative of an artificial potential field method (APF), a tabu search algorithm (TS), a simulated annealing algorithm (SA) and a Fuzzy Logic Algorithm (FLA). The intelligent algorithm mainly comprises the following steps: genetic algorithms, particle swarm algorithms, neural network algorithms, and ant colony algorithms. However, in most cases, these planning algorithms do not fully consider the influence of current and other factors in the ocean on the planning of the ship path and the energy consumption of the ship. They only consider deriving a path that is the safest and shortest in the ideal situation. However, the intelligent ship has limited energy, so that the time and range of the intelligent all-electric ship sailing on the sea are limited, and the method has important research significance for finding a path with optimal energy consumption in the sailing process of the intelligent all-electric ship.
Disclosure of Invention
The invention provides an intelligent all-electric ship path planning method for reducing energy consumption to solve the problems.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent all-electric ship path planning method for reducing energy consumption comprises the following steps:
s1, establishing an ocean current environment model;
s2, constructing a route map by using a PRM algorithm according to the environment model;
s3, establishing a resistance model of the full electric ship in an ocean current environment, acquiring actual parameters of the full electric ship, and establishing an energy consumption model of the full electric ship by combining the resistance model and the actual parameters;
and S4, acquiring the actual situation of ocean current and the energy consumption model, and acquiring the energy consumption minimum path by adopting an energy consumption search algorithm.
Preferably, the S1 includes:
s11, establishing a two-dimensional changed ocean current environment model, wherein a two-dimensional plane of the environment model is set as a xoy two-dimensional plane;
and S12, establishing a flow field in the xoy two-dimensional plane, and equally dividing the xoy two-dimensional plane into a plurality of grids.
Preferably, each of the grids is a square with a side length set to 10 km.
Preferably, the sea current states in each of the grids are arranged identically, and the flow field arrangement of the sea current states is generated as a flow function of east-west flow direction and north-south meandering flow direction.
Preferably, the S2 includes:
s21, converting the working global environment of the full-electric ship into a discrete space by adopting the PRM algorithm;
s22, connecting the sampling points to generate a plurality of paths from the starting point to the end point to form a route map;
and S23, searching the route map by adopting a search algorithm to obtain a feasible path from the starting point to the target point.
Preferably, the S22 includes:
s221, detecting and acquiring the position of the sampling point by using a collision detection program, and if the position is located in a threat body, abandoning; if the position is located in the free space, adding a corresponding sampling point into the route map to form one node;
s222, connecting the corresponding sampling points with nodes in the route map;
and S223, continuously circulating the step S221 and the step S222, and constructing and completing the route pattern.
Preferably, the PRM algorithm includes two stages, which are respectively the route map construction and the route query, and the route map construction includes:
constructing a path network R of an undirected graph as (N, E), wherein N is a random point set, and E is a possible path set between any two points;
randomly scattering points, namely putting the scattered points into the random point set N to form new nodes c, wherein the new nodes c are random points in free space, and each new node c has no collision with an obstacle;
selecting a series of nodes N adjacent to the new node c from the random point set N, and planning a path by using a local path planner;
and adding the boundary (c, n) of the drivable paths into the possible path set E, and deleting the infeasible paths.
Preferably, the S3 includes:
s31, calculating the longitudinal flow load of the full electric ship according to a slice theory calculation method;
s32, calculating the transverse flow load of the full electric ship according to a slice theory calculation method;
and S33, calculating the total energy consumption of the full electric ship according to the longitudinal flow load and the transverse flow load.
Preferably, the S4 includes:
s41, calculating and obtaining energy consumption values among all sampling points through an energy consumption function of the full electric ship to obtain a node energy consumption matrix F;
and S42, inquiring the matrix F through an energy consumption A-algorithm to obtain the energy consumption minimum path.
Preferably, the S42 includes:
creating an open list and a close list, wherein nodes of the two lists are obtained from the matrix F; the open list stores nodes needing to be expanded, and the close list stores expanded nodes;
s421, putting a starting point S and an end point G into the open list;
s422, checking whether the open list is empty, if so, failing to search, otherwise, executing S423;
s423, selecting the node with the minimum f' value in the open list as the current expansion node, processing the nodes around the expansion node, and when the nodes around the expansion node are already in the close list, no action is taken; when the peripheral nodes of the extension node are not in the open list, adding the peripheral nodes into the open list, and calculating f' values and energy consumption values of the peripheral nodes; when the surrounding nodes are already in the open list, if the current expansion node is taken as a father node and the energy consumption value of the surrounding nodes is lower than that of the current expansion node, recalculating the energy consumption value of the surrounding nodes by taking the current expansion node as the father node, otherwise, not changing.
S424, putting the current expansion node into the close list, checking whether the end point is in an open list, if not, jumping back to S423 to continue searching, otherwise, finding the optimal path and ending the searching;
and S425, backtracking each father node from the end point to generate a final path.
The invention has the beneficial effects that:
1) according to the method, firstly, the PRM algorithm is adopted to convert the working global environment of the intelligent all-electric ship into a discrete space, so that an all-electric ship navigation route map is constructed, and each grid is not required to be inquired during route searching, so that the searching speed of the algorithm is greatly improved.
2) According to the invention, the energy consumption model of the full electric ship is established by analyzing the resistance model of the ship in the ocean current environment and combining the actual parameters of the ship, and the actual energy consumption of the ship can be effectively calculated through the established energy consumption model.
3) Compared with the traditional algorithm, the algorithm can save more energy by using the distance optimal algorithm, obtain a more economic, environment-friendly and practical and feasible path, and effectively improve the cruising ability of the intelligent all-electric ship.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram comparing the lowest energy consumption effect and the shortest distance effect of the intelligent all-electric ship path planning method for reducing energy consumption.
Fig. 2 is a flow chart of a path planning algorithm with the lowest energy consumption of the intelligent all-electric ship path planning method for reducing energy consumption.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1 to 2, a method for intelligent full-electric ship path planning with reduced energy consumption according to a preferred embodiment of the present invention includes:
s1, establishing an ocean current environment model;
s2, constructing a route map by using a PRM algorithm according to the environment model;
s3, establishing a resistance model of the full electric ship in an ocean current environment, acquiring actual parameters of the full electric ship, and establishing an energy consumption model of the full electric ship by combining the resistance model and the actual parameters;
and S4, acquiring the actual situation of ocean current and the energy consumption model, and acquiring the energy consumption minimum path by adopting an energy consumption search algorithm.
The detailed steps of the invention are as follows:
and S1, establishing an ocean current environment model.
In order to simulate a real marine environment and verify the effectiveness of the constructed energy consumption function algorithm in the changed ocean current conditions, the invention establishes a two-dimensional changed ocean current environment and simultaneously provides ocean current data for the following energy consumption function.
And establishing a flow field in a xoy two-dimensional plane, rasterizing the two-dimensional plane, wherein each grid is a square with the side length of 10km, the sea current state in each grid is the same, and the flow field is generated by an east-west flow direction meandering flow function and a south-north meandering flow function. Flow function over time t
The mathematical expression of (a) is:
wherein B (t) ═ B0+εcos(ωt+θ)，B0＝1.2,c＝0.12,k＝0.84,ω＝0.4,ε＝0.3,θ＝π/2
The velocity field of the ocean current is a vector field, which can be composed of
Obtaining:
where U (X, Y, t), V (X, Y, t) are the velocity components in the X-axis direction and the Y-axis direction, respectively, at time t, and (X, Y) is the location.
And S2, constructing a route map by using a PRM algorithm according to the environment model.
In the invention, a PRM algorithm is firstly used for converting the working global environment of the intelligent full-electric ship into a discrete space, and a plurality of paths from a starting point to an end point are generated by connecting sampling points. And searching a feasible path from the starting point to the target point on a given route map by utilizing a searching algorithm, thereby improving the searching efficiency of the path.
The PRM algorithm is divided into two phases: constructing a route map and inquiring a route. The construction of the roadmap is a step-by-step iterative process that requires a certain number of points to be sampled in the planning environment. Firstly, selecting a point from a configuration space, detecting the position of a sampling point by using a collision detection program, and abandoning if the sampling point is positioned in a threat body; if the point is located in free space, the point is added to the route map as a node. Subsequently, an attempt is made to join the newly obtained nodes with nodes in the roadmap, and the process is repeated until the construction of the roadmap is completed.
The PRM algorithm carries out the steps of road map construction:
the method comprises the following steps: and constructing a path network R of an undirected graph as (N, E), wherein N replaces a random point set, and E represents a possible path set between any two points.
Step two: and (2) randomly scattering points, namely putting the scattered points into N, wherein the randomly scattered points have to meet two requirements, and 1, the randomly scattered points have to be random points in free space. 2. Each point is guaranteed to be collision free with obstacles.
Step three: for each new node c, we select a series of neighboring nodes N from the current N and use a local path planner for path planning.
Step four: the boundaries (c, n) of the traversable paths are added to the set E, eliminating the unfeasible paths.
S3, establishing a resistance model of the full electric ship in an ocean current environment, obtaining actual parameters of the full electric ship, and establishing an energy consumption model of the full electric ship by combining the resistance model and the actual parameters.
The intelligent all-electric ship is inevitably influenced by ocean currents in the sailing process, the ocean currents can push the intelligent all-electric ship to move forwards sometimes, and therefore energy consumption is reduced, and sometimes the ocean currents can block the movement of the intelligent all-electric ship to increase energy consumption. The ship resistance is the most important index for determining the effective power of the ship, namely the actual power of the propeller for propelling the ship. So we can analyze the energy consumption of the path by analyzing the resistance that the ocean current generates to the ship. The longitudinal flow load and the transverse flow load of the ship can be respectively calculated according to a slice theory calculation method, and the specific calculation method is as follows.
1) Calculation of longitudinal flow load
Longitudinal force FcuxMainly caused by friction, the specific calculation formula is as follows:
in the formula: rho is the density of the seawater; ccuxIs a coefficient of resistance, Ccux＝k1*(1+k)*CfWherein k is1For ship model conversion coefficients, it is usual to take
k is a shape factor; cfFor flat friction coefficient, by ITTC formula
ReIs Reynolds number;
s is the wet surface area of the ship, usSpeed of the vessel relative to the ground, UcThe velocity of the ocean current. And alpha is phi-omega, phi is a flow direction angle, and omega is a ship azimuth angle.
2) Calculation of transverse flow load
Since the vessel needs to perform certain actions to maintain a given position or path, the forces exerted on the vessel in the sway direction should also be taken into account. The lateral flow forces acting on the vessel are calculated and can be estimated using the lateral flow principle. Transverse flow force F on a shipcuyCan be expressed as:
in the formula: l is the length of the ship; d (x) is the draft of the section; cD(x) Is the ship cross section resistance coefficient at the ordinate x; vS is the rolling speed of the ship.
3) Total energy consumption of ship
The energy is calculated as follows:
t is the slave node of the intelligent full electric shipPoint NiTo node NjThe time taken for the user to take the product,
is node NiTo node NjEnergy consumption of (2).
The total energy consumption E is then:
and S4, acquiring the actual situation of ocean current and the energy consumption model, and acquiring the energy consumption minimum path by adopting an energy consumption search algorithm.
According to the actual situation of ocean current and the constructed energy consumption model, finding a path with the minimum energy consumption by adopting an energy consumption searching algorithm specifically comprises the following steps: when energy consumption is taken as an optimization target, the most energy-saving full-electric ship path is planned by considering the size and the flow direction of ocean current, so that the Euclidean distance cannot be simply used as a heuristic function, the heuristic function is improved by adopting the concept of longitudinal flow load, and a new evaluation function is shown as an expression (3-3)
Wherein
The energy consumption value of two adjacent nodes is obtained; fcuxFor vertical flow loading, f' (n) is the energy consumption value of the current node. Through the PRM algorithm, the construction of an environment route map is completed, and the sampling point number of the PRM algorithm is assumed to be N, the starting point is S, and the end point is G. Energy consumption values among all sampling points can be calculated through the established ship energy consumption function, and finally, a node energy consumption matrix F of (N +2) × (N +2) is obtained.
And querying the matrix F by adopting an energy consumption A-search algorithm, and finally generating an energy consumption path, wherein the energy consumption optimal A-search algorithm is based on the following steps:
firstly, two lists, namely an Open list and a close list, are created, nodes needing to be stored in the two lists are in a matrix F, nodes needing to be expanded are stored in the Open list, and nodes which are already expanded are stored in the close list.
Step1, put the starting point S and the end point G into the open list.
Step2, check if the open list is empty, if so, then the search fails, otherwise, execute the next Step.
Step3, selecting the node with the minimum f' value in the open list as the current expansion node, and processing the nodes around the expansion node as follows: there is no action when nodes around the extended node are already in the close list. When the nodes around the node are not in the open list, adding the surrounding nodes into the open list, and calculating the f' values and the energy consumption of the surrounding nodes
The value of (c). When the nodes around the node are already in the open list, if the current extension node is taken as the father node, the nodes around the node
If the value is lower than the original value, the current expansion node is used as a father node to recalculate the surrounding nodes
Value, otherwise, no change is made.
And Step4, putting the current expansion node into a close list, checking whether the end point is in an open list, if not, jumping back to the Step3 to continue searching, and if not, finding the optimal path and ending the searching.
Step 5. starting from the end point, each parent node is traced back to generate the final path.
According to the method, firstly, the PRM algorithm is adopted to convert the working global environment of the intelligent all-electric ship into a discrete space, so that an all-electric ship navigation route map is constructed, and each grid is not required to be inquired during route searching, so that the searching speed of the algorithm is greatly improved; the method comprises the steps that a resistance model of a ship in the ocean current environment is analyzed, an energy consumption model of the full electric ship is established by combining actual parameters of the ship, and the actual energy consumption of the ship can be effectively calculated through the established energy consumption model; compared with the traditional algorithm, the algorithm can save more energy by using the distance optimal algorithm, obtain a more economic, environment-friendly and practical and feasible path, and effectively improve the cruising ability of the intelligent all-electric ship. The method combines a PRM algorithm, a ship energy consumption function and an energy consumption search algorithm, utilizes the PRM algorithm to randomly sample the ship working environment, generates a plurality of paths from a starting point to a terminal point by connecting sampling points, constructs a path network diagram of the all-electric ship, constructs an energy consumption model of the intelligent all-electric ship by considering the resistance of ocean current to the ship in the marine environment, and finally plans a path with optimal energy consumption by utilizing the energy consumption search algorithm designed by the invention. Compared with the conventional algorithm, the algorithm saves more energy, thereby being beneficial to improving the cruising ability of the intelligent all-electric ship.
While the foregoing description shows and describes the preferred embodiments of the present invention, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as described herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An intelligent all-electric ship path planning method for reducing energy consumption is characterized by comprising the following steps:
s1, establishing an ocean current environment model;
s2, constructing a route map by using a PRM algorithm according to the environment model;
s3, establishing a resistance model of the full electric ship in an ocean current environment, acquiring actual parameters of the full electric ship, and establishing an energy consumption model of the full electric ship by combining the resistance model and the actual parameters;
and S4, acquiring the actual situation of ocean current and the energy consumption model, and acquiring the energy consumption minimum path by adopting an energy consumption search algorithm.
2. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 1, wherein the S1 includes:
s11, establishing a two-dimensional changed ocean current environment model, wherein a two-dimensional plane of the environment model is set as a xoy two-dimensional plane;
and S12, establishing a flow field in the xoy two-dimensional plane, and equally dividing the xoy two-dimensional plane into a plurality of grids.
3. The intelligent all-electric ship path planning method with reduced energy consumption according to claim 2, wherein each grid is a square with a side length set to 10 km.
4. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 3, wherein the ocean current states in each grid are arranged identically, and the flow field arrangement of the ocean current states is generated as a flow function with an east-west flow direction and a south-north winding direction.
5. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 1, wherein the S2 includes:
s21, converting the working global environment of the full-electric ship into a discrete space by adopting the PRM algorithm;
s22, connecting the sampling points to generate a plurality of paths from the starting point to the end point to form a route map;
and S23, searching the route map by adopting a search algorithm to obtain a feasible path from the starting point to the target point.
6. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 5, wherein the S22 includes:
s221, detecting and acquiring the position of the sampling point by using a collision detection program, and if the position is located in a threat body, abandoning; if the position is located in the free space, adding a corresponding sampling point into the route map to form one node;
s222, connecting the corresponding sampling points with nodes in the route map;
and S223, continuously circulating the step S221 and the step S222, and constructing and completing the route pattern.
7. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 5, wherein the PRM algorithm includes two stages, namely, construction of the route map and path query, and the construction of the route map includes:
constructing a path network R of an undirected graph as (N, E), wherein N is a random point set, and E is a possible path set between any two points;
randomly scattering points, namely putting the scattered points into the random point set N to form new nodes c, wherein the new nodes c are random points in free space, and each new node c has no collision with an obstacle;
selecting a series of nodes N adjacent to the new node c from the random point set N, and planning a path by using a local path planner;
and adding the boundary (c, n) of the drivable paths into the possible path set E, and deleting the infeasible paths.
8. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 1, wherein the S3 includes:
s31, calculating the longitudinal flow load of the full electric ship according to a slice theory calculation method;
s32, calculating the transverse flow load of the full electric ship according to a slice theory calculation method;
and S33, calculating the total energy consumption of the full electric ship according to the longitudinal flow load and the transverse flow load.
9. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 1, wherein the S4 includes:
s41, calculating and obtaining energy consumption values among all sampling points through an energy consumption function of the full electric ship to obtain a node energy consumption matrix F;
and S42, inquiring the matrix F through an energy consumption A-algorithm to obtain the energy consumption minimum path.
10. The intelligent all-electric ship path planning method for reducing energy consumption according to claim 9, wherein the S42 includes:
creating an open list and a close list, wherein nodes of the two lists are obtained from the matrix F; the open list stores nodes needing to be expanded, and the close list stores expanded nodes;
s421, putting a starting point S and an end point G into the open list;
s422, checking whether the open list is empty, if so, failing to search, otherwise, executing S423;
s423, selecting the node with the minimum f' value in the open list as the current expansion node, processing the nodes around the expansion node, and when the nodes around the expansion node are already in the close list, no action is taken; when the peripheral nodes of the extension node are not in the open list, adding the peripheral nodes into the open list, and calculating f' values and energy consumption values of the peripheral nodes; when the surrounding nodes are already in the open list, if the current expansion node is taken as a father node and the energy consumption value of the surrounding nodes is lower than that of the current expansion node, recalculating the energy consumption value of the surrounding nodes by taking the current expansion node as the father node, otherwise, not changing.
S424, putting the current expansion node into the close list, checking whether the end point is in an open list, if not, jumping back to S423 to continue searching, otherwise, finding the optimal path and ending the searching;
and S425, backtracking each father node from the end point to generate a final path.
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| CN114217637A (en) * | 2021-12-03 | 2022-03-22 | 北京理工大学 | Environment self-adaptive cruise airship control method |
| CN114764251A (en) * | 2022-05-13 | 2022-07-19 | 电子科技大学 | Energy-saving method for multi-agent collaborative search based on energy consumption model |
| CN115979275A (en) * | 2023-03-20 | 2023-04-18 | 中国船舶集团有限公司第七〇七研究所 | Sea area full-coverage energy consumption optimal route planning method |
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