CN110501020A - A kind of multiple target three-dimensional path planning method - Google Patents
A kind of multiple target three-dimensional path planning method Download PDFInfo
- Publication number
- CN110501020A CN110501020A CN201910803728.0A CN201910803728A CN110501020A CN 110501020 A CN110501020 A CN 110501020A CN 201910803728 A CN201910803728 A CN 201910803728A CN 110501020 A CN110501020 A CN 110501020A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- distance
- point
- node
- adjoint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
-
- 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3469—Fuel consumption; Energy use; Emission aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of multiple target three-dimensional path planning methods, by establishing overall estimate cost function F (n), F (n) is made of the estimate cost function H (n) of the estimate cost function G (n) of current point to adjoint point and adjoint point to terminal summation, in which: G (n) is used to calculate in three-dimensional space current point to the energy consumption and distance cost of adjoint point；H (n) includes total energy consumption and distance, and total energy consumption is obtained by calculating adjoint point to the energy consumption between destination node, distance by calculating adjoint point between target linear distance and curve distance obtain；Finally, the multiple-objection optimization Solve problems of the paths planning method are solved using multi-objective chaotic optimization algorithm, it is able to achieve the three-dimensional planning for taking into account energy consumption and distance, the path optimizing for reducing energy consumption is found in the shape of mountainous region, improves the course continuation mileage of Intelligent mobile equipment.
Description
Technical field
The invention belongs to path planning fields, are related to a kind of multiple target three-dimensional path planning method, and this method can be used for intelligence
It can navigate, the path planning in unmanned equal fields.
Background technique
Current battery energy storage is extremely limited, and the cruising ability for improving battery is important research topic.Correlative study is main
Include two aspects: improving the charge storage ability of battery；Reduce the specific energy consumption of battery work.Since upward slope coefficient of energy dissipation is noticeably greater than
Coefficient of energy dissipation on level road, for the city on more mountains, a road flat and that appropriateness is remote may be upper and lower more than relatively short for this
The more road in slope will save energy, and there is no significant more consuming time costs.
Be currently suggested many paths planning methods: as artificial force field algorithm, neural network method, genetic algorithm,
Random tree method, A star algorithm.They can obtain preferable planning effect under the conditions of various disorders.However they are nearly all bases
It is unfolded to study in two-dimensional map, the paths planning method under three-dimensional map study few.Comparatively, either machine
In the case of energy consumption is limited, research considers that the paths planning method of energy loss will for people, space probe or electric car
It is very valuable.
Summary of the invention
In view of this, it is a primary object of the present invention to propose in one on the basis of taking into account energy consumption and two targets of distance
Multiple target three-dimensional path planning method finds the path optimizing for reducing energy consumption to realize in the shape of mountainous region, improves intelligent mobile
The course continuation mileage of equipment.
On the one hand, the present invention provides a kind of multiple target three-dimensional path planning method, includes the following steps:
Step 1, multiple target three-dimensional path planning model is constructed, and establishes overall estimate cost function F (n):
F (n)=G (n)+H (n) (1)
In formula, G (n) is estimate cost function of the current point to adjoint point, and H (n) is estimate cost function of the adjoint point to terminal,
Wherein:
G (n) is obtained by energy consumption between current point and adjoint point and linear distance；
H (n) includes total energy consumption and distance, and total energy consumption is obtained by calculating the energy consumption between adjoint point and destination node, distance
It is obtained by calculating linear distance between adjoint point and destination node and curve distance；
Step 2, the value for calculating overall estimate cost function F (n), it is the smallest to find overall estimate cost function F (n) in adjoint point
Value is stored in path list Lane；
Step 3, using the smallest node of the overall estimate cost function F (n) found in step 2 as current point, repeat
Path planning；
Step 4, when path planning to current point is terminal, stop planning, at this point, what is stored in Lane is multiple target
Three-dimensional path planning model cooks up the path come.
Preferably, step 1 specific manifestation are as follows:
Step 1.1, it initializes map altitude data: obtaining (x, y, z) coordinate data of each feasible node, and being stored in can
In row node listing Path, wherein coordinate (x, y) is longitude and latitude, and coordinate (x, y) is mapped as actual range coordinate；
Step 1.2, initialization path projecting parameter: definition planning beginning and end position, set search radius as
Factor is planned for searching for the adjoint point around present node using starting point as current point；
Step 1.3, all adjoint points around current point are found using search radius factor, and calculates current point to adjoint point
Estimate cost function G (n) and adjoint point to terminal estimate cost function H (n) value, in which:
G (n)=D (Pn,Pn+1)+EG(Pn,Pn+1) (2)
In formula, EG(Pn,Pn+1) energy consumption between current point and adjoint point, D (Pn,Pn+1) between current point and adjoint point
Euclidean distance, PnFor current point, Pn+1For adjoint point；
H (n)=η1*EH+η2*D′H+η3*D″H (3)
In formula, EHIt is total energy consumption of the adjoint point to destination node, D 'HLinear distance between adjoint point and destination node, D "H
Curve distance between adjoint point and destination node, η1、η2And η3Respectively total energy consumption EH, linear distance D 'HWith curve distance D "H
Weight.
Preferably, the energy consumption in the G (n) between current point and adjoint point is obtained by establishing gradient energy consumption model, described
Gradient energy consumption model is established as follows:
In formula, g1(Pn,Pn+1) and g2(Pn,Pn+1) it is respectively to be in the gradientWhen unit distance descending energy consumption letter
Several and unit distance upward slope energy consumption function, in which:
In formula, (xn,yn,zn) it is respectively current point PnCoordinate data, (xn+1,yn+1,zn+1) it is respectively adjoint point Pn+1(
Coordinate data；
Preferably, the unit distance descending energy consumption function g1(Pn,Pn+1) establish by the following method:
Choose one section of level road descending road different with the multistage gradient；
It is measured produced by the robot of same model or the level road of running car same distance and multistage descending road respectively
Energy consumption；
Repeatedly test acquires the energy consumption average value of level road and every section of descending road respectively；
Find out the ratio of different gradient descending road average energy consumption and level road average energy consumption；
By the way of cubic polynomial fitting, unit distance descending energy consumption function g is obtained1(Pn,Pn+1):
In formula, a1,b1And c1It is undetermined coefficient.
Preferably, the unit distance upward slope energy consumption function g2(Pn,Pn+1) establish by the following method:
Choose one section of level road upward slope road different with the multistage gradient；
It is measured produced by the robot of same model or the level road of running car same distance and multistage upward slope road respectively
Energy consumption；
Repeatedly test acquires the energy consumption average value of level road and every section of upward slope road respectively；
Find out the ratio of different gradient upward slope road average energy consumption and level road average energy consumption；
By the way of cubic polynomial fitting, unit distance upward slope energy consumption function g is obtained2(Pn,Pn+1):
In formula, a2,b2And c2It is undetermined coefficient.
Preferably, it is added by the energy consumption of adjacent node, so that algorithm simulation is gone out the landform in mountainous region, and estimate high mountain low ebb
Position, the energy consumption for obtaining the adjacent node is calculated by gradient energy consumption model, as follows:
Wherein, EHIt is total energy consumption of the adjoint point to destination node, PiAnd Pi+1For the intermediate node of node to destination node, ωi
For i-th of node to the weight of i+1 node, and ωiIt is acquired by the curve that sigmoid function declines,
Wherein, b is curves, and c is bias, and exp (- b*x (i)) is-b*x (i) power of e, and x (i) is i-th
Distance of the node to current point.
Preferably, distance is obtained by calculating linear distance between adjoint point and destination node and curve distance in the H (n)
It arrives, formula is as follows:
D=D 'H+D″H (11)
In formula, D is distance, wherein D 'HIt is calculated by following formula:
In formula, PgoalFor destination node, (xgoal,ygoal,zgoal) it is PgoalCoordinate data；
D″HIt is calculated by following formula:
In formula, D (Pi,Pi+1) it is linear distance of i-th of node to i+1 node.
Preferably, two targets of total energy consumption in H (n) and distance are optimized by multi-objective chaotic optimization algorithm, is asked
Solution meets the minimum solution with most short two targets of distance of total energy consumption, and to wherein parameter η1、η2、η3It optimizes, obtains with c
To the optimal value of each parameter.
Preferably, specific step is as follows for the multi-objective chaotic optimization algorithm:
A) initialization population: for above-mentioned 4 optimized variable η1、η2、η3And c, the initial value of fine difference is assigned respectively, is obtained
It is amplified to corresponding optimized variable value range respectively to 4 Chaos Variables, and by the range of Chaos Variable, meanwhile, for kind
Pop solution in group is initialized with the mode of chaos sequence respectively；
B population scale) is selected；
C) the calculating of objective function: obtaining path Lane by multiple target three-dimensional path planning model, obtains every in population
The target function value of a solution；
D) non-dominant relationship and crowding sort: carrying out non-dominant relationship sequence to the solution of objective function, only retain first
The solution in non-dominant forward position, and to the solution that first dominates forward position, crowding sequence is carried out, the lower solution of crowding is chosen；
E) chaotic mutation generates filial generation: it is made a variation by the method for chaos sequence to parent, calculation formula are as follows:
Coffspring=4*Cparent*(1-Cparent) (18)
Wherein, CparentIt is the disaggregation of parent population, CoffspringIt is the disaggregation of progeny population；
F) loop iteration: obtained progeny population is continued to the calculating of objective function in step C, obtains progeny population
The target function value of each solution carries out loop iteration；
G) stop criterion: when algorithm, which goes to the target function value in maximum algebra or group, to be stablized, algorithm is terminated,
Wherein, the first all solutions for dominating forward position are all required solutions, and obtain parameter [η1, η2,η3, c] solution.
In conclusion the present invention is firstly, establish overall estimate cost function F (n), F (n) by current point to adjoint point estimation generation
The estimate cost function H (n) of valence function G (n) and adjoint point to terminal, which sums, to be constituted, in which: G (n) pass through current point and adjoint point it
Between energy consumption and linear distance obtain；H (n) includes total energy consumption and distance, and total energy consumption is by calculating adjoint point between destination node
Energy consumption obtains, distance by calculate adjoint point between target linear distance and curve distance obtain；Then, always estimated by calculating
The minimum value of meter cost function F (n) finds current point；Then, repeat path planning stopping when current point is terminal,
These make the smallest node of overall estimate cost function F (n) ultimately form the path of planning.The above method compared with prior art, energy
It realizes the three-dimensional planning for taking into account energy consumption and distance, the path optimizing for reducing energy consumption is found in the shape of mountainous region, improve intelligent mobile and set
Standby course continuation mileage.
In further technical solution, using multi-objective chaotic optimization algorithm to two mesh of total energy consumption in H (n) and distance
Mark optimizes, and solves and meets the minimum solution with most short two targets of distance of total energy consumption, and to wherein parameter η1、η2、η3With c into
Row Optimization Solution obtains the optimal value of each parameter, to solve the multiple-objection optimization Solve problems of the paths planning method.
Detailed description of the invention
Fig. 1 is a kind of flow chart of multiple target three-dimensional path planning method of the present invention；
Fig. 2 is a kind of multiple target three-dimensional path planning illustraton of model of the present invention；
Fig. 3 is the flow chart of multi-objective chaotic optimization algorithm of the present invention；
Fig. 4 is the road network figure of one embodiment of the invention Mountainous City；
Fig. 5 is that the present invention is based on the path main views that the Road Fig. 4 network planning marks；
Fig. 6 is that the present invention is based on the path top views that the Road Fig. 4 network planning marks.
Specific embodiment
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
It is a primary object of the present invention to provide a kind of scheme for saving energy from the angle of path planning.Due to electronic vapour
Vehicle upward slope coefficient of energy dissipation is noticeably greater than the coefficient of energy dissipation on level road, and for the city on more mountains, one flat and appropriate remote for this
Road may the road more more than relatively short but climb and fall to save energy, and expend time costs there is no significant more.It is different from
The single shortest angle design paths planning method of consideration distance of tradition, the present invention is in the base for taking into account energy consumption Yu two targets of distance
A kind of multiple target three-dimensional path planning method is proposed on plinth.
Referring to Fig. 1, Fig. 1 is a kind of flow chart of multiple target three-dimensional path planning method, is specifically comprised the following steps:
Step 1.1, it initializes map altitude data: obtaining (x, y, z) coordinate data of each feasible node, and being stored in can
In row node listing Path, wherein coordinate (x, y) is longitude and latitude, and coordinate (x, y) is mapped as actual range coordinate；
Step 1.2, initialization path projecting parameter: definition planning beginning and end position, set search radius as
Factor is planned for searching for the adjoint point around present node using starting point as current point；
It should be noted that adjoint point is defined as the point of the certain radius around current point；
Step 1.3, all adjoint points around current point are found using search radius factor, and calculates current point to adjoint point
Estimate cost function G (n) and adjoint point to terminal estimate cost function H (n) value, in which:
Since upward slope coefficient of energy dissipation is noticeably greater than the coefficient of energy dissipation on level road, and descending coefficient of energy dissipation is also different from upward slope
Coefficient of energy dissipation, for planning path more for reasonability, this cooks up the path come can be to avoid continuous by the way of energy consumption model
Upward slope descending, and because motor is in half locked rotor condition when going up a slope, it allows electric motor operation electric current to increase, keeps motor a large amount of
Fever, endangers the service life of motor, the calculation of the estimate cost function G (n) of above-mentioned current point to adjoint point is as follows as a result:
G (n)=D (Pn,Pn+1)+EG(Pn,Pn+1) (2)
In formula, EG(Pn,Pn+1) energy consumption between current point and adjoint point and unit be kilojoule (kJ), D (Pn,Pn+1) it is to work as
Euclidean distance between preceding point and adjoint point, Pn(for current point, Pn+1For adjoint point；
Meanwhile estimate cost function H (n) foundation of adjoint point to terminal need to consider two sides of energy consumption and distance in the present invention
Face, i.e. estimate cost function H (n) include total energy consumption EHWith distance D, wherein total energy consumption EHBy calculate adjoint point and destination node it
Between energy consumption obtain, distance D is by calculating the linear distance D ' between adjoint point and destination nodeHWith curve distance D "HIt obtains.This
Kind method makes electric car or robot etc. in mountain topography traveling while guaranteeing that path is shorter and saves energy, and one
As H (n) function only consider the influence of distance, in Complex Mountain when planning path, can make algorithm that mountain topography can not be identified, make
At climb and fall repeatedly, increase energy consumption, reduce the course continuation mileage of battery, the foundation of H (n) function is established by following formula as a result:
H (n) H=η1*EH+η2*D′H+η3*D″H (3)
In formula, EHIt is total energy consumption of the adjoint point to destination node, D 'HLinear distance between adjoint point and destination node, D "H
Curve distance between adjoint point and destination node, η1、η2And η3Respectively total energy consumption EH, linear distance D 'HWith curve distance D "H
Weight.
Step 2, the value for calculating overall estimate cost function F (n), it is the smallest to find overall estimate cost function F (n) in adjoint point
Value is stored in path list Lane, and the calculation formula of F (n) is as follows:
F (n)=G (n)+H (n) (1)；
Fig. 2 is a kind of multiple target three-dimensional path planning illustraton of model.It also can be seen that from Fig. 2, overall estimate cost function F (n) packet
Include estimate cost function G (n) and adjoint point estimate cost function H (n) to terminal of the current point to adjoint point, and estimate cost function
G (n) and estimate cost function H (n) are separately modeled, this is because the estimate cost of current point to adjoint point is determining, and adjoint point
It is uncertain to the path of terminal, can only estimate adjoint point to terminal cost.
Step 3, using the smallest node of the overall estimate cost function F (n) found in step 2 as current point, repeat
Path planning.
Step 4, when path planning to current point is terminal, stop planning, at this point, what is stored in Lane is multiple target
Three-dimensional path planning model cooks up the path come.
It should be noted that step 1.1 is to step 1.4 it is believed that for constructing multiple target three-dimensional path in above-mentioned flow chart
Plan model, and establish overall estimate cost function F (n).
Further, the estimate cost function G (n) of current point to adjoint point is being calculated by gradient energy consumption model, preceding
It is as follows to state the foundation of gradient energy consumption model:
In formula, g1(Pn,Pn+1) and g2(Pn,Pn+1) it is respectively to be in the gradientWhen unit distance descending energy consumption letter
Several and unit distance upward slope energy consumption function, wherein the Euclidean distance D (P between current point and adjoint pointn,Pn+1) calculating it is public
Formula is as follows:
In formula, (xn,yn,zn) it is respectively current point PnCoordinate data, (xn+1,yn+1,zn+1) it is respectively adjoint point Pn+1(
Coordinate data；
Meanwhile the calculation method of the gradient is shown below:
In addition, in further technical solution, above-mentioned unit distance descending energy consumption function g1(Pn,Pn+1) by such as lower section
Method is established:
Choose one section of level road descending road different with the multistage gradient；
It is measured produced by the robot of same model or the level road of running car same distance and multistage descending road respectively
Energy consumption；
Repeatedly test acquires the energy consumption average value of level road and every section of descending road respectively；
Find out the ratio of different gradient descending road average energy consumption and level road average energy consumption；
By the way of cubic polynomial fitting, unit distance descending energy consumption function g is obtained1(Pn,Pn+1):
In formula, a1,b1And c1It is undetermined coefficient.
By taking the automobile of the robot of a certain model or automobile as an example, respectively gradient α=[0 °, -5 °, -10 °, -15 °, -
20 °, -25 °, -30 °, -35 °, -40 °] under, energy consumption caused by its 100 meters of traveling is measured, acquires energy consumption by many experiments
Average value [E1,E2,E3,E4,E5,E6,E7,E8,E9], find out the ratio of different gradient energy consumption Yu level road energy consumptionIt show that the gradient is the principal element of energy consumption variation by analysis of experimental data, uses
The mode of cubic polynomial fitting, finds out undetermined coefficient a by experimental data above1,b1And c1Value, can be obtained above-mentioned
Unit distance descending energy consumption function.
Similarly, unit distance upward slope energy consumption function g2(Pn,Pn+1) method for building up and above-mentioned unit distance descending energy consumption letter
Number g1(Pn,Pn+1) method for building up is similar:
Choose one section of level road descending road different with the multistage gradient；
It is measured produced by the robot of same model or the level road of running car same distance and multistage descending road respectively
Energy consumption；
Repeatedly test acquires the energy consumption average value of level road and every section of descending road respectively；
Find out the ratio of different gradient descending road average energy consumption and level road average energy consumption；
By the way of cubic polynomial fitting, unit distance descending energy consumption function g is obtained1(Pn,Pn+1):
In formula, a1,b1And c1It is undetermined coefficient.
Equally, by taking the robot of a certain model or automobile as an example, respectively gradient α=[0 °, 5 °, 10 °, 15 °, 20 °,
25 °, 30 °, 35 °, 40 °] under, energy consumption caused by its 100 meters of traveling is measured, the average value of energy consumption is acquired by many experiments
[E1′,E2′,E3′,E4′,E5′,E6′,E7′,E8′,E9'], and find out the ratio of different gradient energy consumption Yu level road energy consumptionIt show that the gradient is the principal element of energy consumption variation by analysis of experimental data, uses
The mode of cubic polynomial fitting, finds out undetermined coefficient a by experimental data above2,b2And c2Value, can be obtained above-mentioned
Unit distance upward slope energy consumption function.
Further, total energy consumption EHIt is calculated by gradient energy consumption model, as follows:
In formula, energy consumption estimates model EH, it is added by the energy consumption of adjacent node, algorithm simulation can be made to go out the ground in mountainous region
Shape, and the position of high mountain low ebb is estimated, make algorithm more intelligent；PiAnd Pi+1For the intermediate node of node to destination node,
ωiFor i-th of node to the weight of i+1 node, since the habit of people is the mountain peak more taken notice of nearby, to electronic vapour
When vehicle or mountainous region robot etc. carry out path planning, nearby influence of the mountain peak to path is first considered, then this weights omegaiSetting
Big for nearby weight, with the increase of distance, weight is gradually decreased, so, weights omegaiCan by sigmoid function (neuron
Nonlinear interaction function) decline curve acquire,
Wherein, b is curves, and c is bias, and exp (- b*x (i)) is-b*x (i) power of e, and x (i) is i-th
Distance of the node to current point.
In addition, distance estimates that model D estimates for path length, model D " is estimated by curve distanceHAnd linear distance mould
Type D 'HIt constitutes, it may be assumed that
D=D 'H+D″H (11)
Due to linear distance model D 'HH (n) function is added, so that this planning algorithm is had more directionality, is not in that path is circuitous
The state returned.Linear distance D 'HIt is obtained using Euclid's calculation formula, unit is rice (m), i.e.,
In formula, PgoalFor destination node, xgoal,ygoal,zgoalFor PgoalCoordinate data；
Curve distance estimation model is calculated using following formula, i.e.,
Wherein, D (Pi,Pi+1) it is linear distance of i-th of node to i+1 node.Here weights omega is usediTo curve away from
It from being weighted, lead to planning path will not excessive directly across mountain nearby because of the mountain weighing factor of distant place.
In addition, as mentioned before adjoint point to terminal estimate cost function H (n) foundation, including energy consumption estimate, straight line away from
From the weight η with curve distance1、η2And η3, while the foundation of the energy consumption model, the calculating including bias c；Using more mesh
Calibration method is minimum to energy consumption and most short two targets of distance optimize, and above-mentioned coefficient optimizes.
Specifically, the calculation method of two targets of energy consumption and distance, using A star algorithm cook up come path Lane, meter
Energy consumption and distance numerical value, calculation formula in calculation path between each consecutive points is as follows:
Wherein, n is the total node number of planning path.
The method of the chaos optimization does not repeat ergodic by the pseudorandom overall situation of chaos, compared to heredity, PSO etc.
Optimization method be better able to overcome the problems, such as to converge on it is local, meanwhile, the method for chaos optimization includes two steps:
A, the Chaos Variable in entire space is successively investigated, the current optimum point for meeting performance indicator, Chaos Variable are found
Calculation method are as follows:
x(k+1)=η * x(k)*(1-x(k)) (16)
Wherein, η is control parameter, and as η=1, system is in chaos state, and output is equivalent to random between [0,1]
Number, and there is ergodic in [0,1], any state therein will not all repeat；
B, in optimum point current after searching for several times without changing, then the son defined near current optimum point is empty
Between, then the Chaos Variable in entire space is investigated, find the optimum point for meeting performance indicator.
Preferably, the present invention is by multi-objective chaotic optimization algorithm to total energy consumption EHIt is optimized with two targets of distance D
It solves, using multi-objective optimization algorithm, adjoint point can be made more accurate to the foundation of the estimate cost function H (n) of terminal, obtain institute
The expected path needed, and simultaneously to optimized variable η1、η2、η3Optimal value is solved with c.The constraint condition of the optimization method is to make
The value of following objective function is minimum:
Specifically, the detailed step of above-mentioned multi-objective chaotic optimization algorithm is as follows:
A) initialization population: for above-mentioned 4 optimized variable η1、η2、η3And c, the initial value of fine difference is assigned respectively, is obtained
It is amplified to corresponding optimized variable value range respectively to 4 Chaos Variables, and by the range of Chaos Variable, meanwhile, for kind
Pop solution in group is initialized with the mode of chaos sequence respectively；
B population scale) is selected；
Preferably, the present embodiment sets population scale as pop=50, maximum number of iterations g=200；
C) the calculating of objective function: obtaining path Lane by multiple target three-dimensional path planning model, obtains every in population
The target function value E of a solutionLaneAnd DLane；
D) non-dominant relationship and crowding sort: carrying out non-dominant relationship sequence to the solution of objective function, only retain first
The solution in non-dominant forward position, the i.e. solution in the forward position are not dominated by other any solutions, and to the solution that first dominates forward position, carry out crowding
The lower solution of crowding is chosen in sequence, to make the solution obtained be evenly distributed as far as possible and have diversity；
E) chaotic mutation generates filial generation: it is made a variation by the method for chaos sequence to parent, calculation formula are as follows:
Coffspring=4*Cparent*(1-Cparent) (18)
Wherein, CparentIt is the disaggregation of parent population, CoffspringIt is the disaggregation of progeny population；
F) loop iteration: obtained progeny population is continued to the calculating of objective function in step C, obtains progeny population
The target function value of each solution carries out loop iteration；
G) stop criterion: when algorithm, which goes to the target function value in maximum algebra or group, to be stablized, algorithm is terminated,
Wherein, the first all solutions for dominating forward position are all required solutions, and obtain parameter [η1, η2,η3, c] solution.
Further, as shown in figure 3, step F loop iteration specific manifestation are as follows: after entering next algebra, chaotic mutation is raw
After filial generation and needing father and son's generation integration, the calculating of step C objective function is entered back into, the non-dominant relationship of step D is subsequently entered and gathers around
Degree sequence is squeezed, then, chaotic mutation generates filial generation and selects new population parent, until entering step G.
Meanwhile referring to fig. 4 to Fig. 6, Fig. 4 is the road network figure in certain city, and Fig. 5 is that the present invention is based on the paths that railway network planning goes out
Main view, Fig. 6 are that the present invention is based on the path top views that railway network planning goes out.
Using the road network figure in certain city, elevation information and road network information are therefrom obtained, foundation is suitable for the invention map
Model, carries out path planning in matlab (matrix labotstory), and obtained path planning effect is as shown in Figure 5, it can be seen that
Algorithm proposed by the present invention can be quickly obtained planning path in mountain topography, and path can avoid high mountain and low ebb into
Professional etiquette is drawn, and the driving habit of people is met.
So saving electric energy energy to obtain, a kind of multiple target three-dimensional road for considering energy loss and distance is proposed
Diameter planing method, this method by establishing overall estimate cost function F (n), F (n) by current point to adjoint point estimate cost function G
(n) and adjoint point to terminal estimate cost function H (n) sum constitute, in which: estimate cost function G (n), by current point with
Energy consumption and linear distance between adjoint point obtain；Estimate cost function H (n), includes total energy consumption and distance, and total energy consumption passes through calculating
Adjoint point is obtained to the energy consumption between destination node, distance by calculate adjoint point between target linear distance and curve distance obtain
It arrives；Finally, solving the multiple-objection optimization Solve problems of the paths planning method, using multi-objective chaotic optimization algorithm with certain
It is tested for city, it was demonstrated that algorithm can acquire the path for taking into account energy loss and time cost.Based on experiment and principle
Can inference, this method is applicable to all dimensional topographies, can visit for electric car, military mountain reconnaissance robot, space celestial body
It surveys device (Jade Hare number, Mars probes) etc. and considers that the equipment of energy loss provides path planning scheme.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (9)
1. a kind of multiple target three-dimensional path planning method, which comprises the steps of:
Step 1, multiple target three-dimensional path planning model is constructed, and establishes overall estimate cost function F (n):
F (n)=G (n)+H (n) (1)
In formula, G (n) is estimate cost function of the current point to adjoint point, and H (n) is estimate cost function of the adjoint point to terminal,
In:
G (n) is obtained by energy consumption between current point and adjoint point and linear distance；
H (n) includes total energy consumption and distance, and total energy consumption is obtained by calculating the energy consumption between adjoint point and destination node, and distance passes through
The linear distance and curve distance calculated between adjoint point and destination node obtains；
Step 2, the value for calculating overall estimate cost function F (n), finds the smallest value of overall estimate cost function F (n) in adjoint point, deposits
Enter in path list Lane；
Step 3, using the smallest node of the overall estimate cost function F (n) found in step 2 as current point, repeat path
Planning；
Step 4, when path planning to current point is terminal, stop planning, at this point, what is stored in Lane is that multiple target is three-dimensional
Path planning model cooks up the path come.
2. multiple target three-dimensional path planning method according to claim 1, which is characterized in that step 1 specific manifestation
Are as follows:
Step 1.1, it initializes map altitude data: obtaining (x, y, z) coordinate data of each feasible node, and be stored in feasible section
In point list Path, wherein coordinate (x, y) is longitude and latitude, and coordinate (x, y) is mapped as actual range coordinate；
Step 1.2, initialization path projecting parameter: definition planning beginning and end position sets search radius as factor, uses
Adjoint point around search present node, is planned starting point as current point；
Step 1.3, all adjoint points around current point are found using search radius factor, and calculates current point estimating to adjoint point
Count cost function G (n) and adjoint point to terminal estimate cost function H (n) value, in which:
G (n)=D (Pn,Pn+1)+EG(Pn,Pn+1) (2)
In formula, EG(Pn,Pn+1) energy consumption between current point and adjoint point, D (Pn,Pn+1) Europe between current point and adjoint point is several
In distance, PnFor current point, Pn+1For adjoint point；
H (n) H=η1*EH+η2*D′H+η3*D″H (3)
In formula, EHIt is total energy consumption of the adjoint point to destination node, D 'HLinear distance between adjoint point and destination node, D "HFor neighbour
Curve distance between point and destination node, η1、η2And η3Respectively total energy consumption EH, linear distance D 'HWith curve distance D "HPower
Weight.
3. multiple target three-dimensional path planning method according to claim 2, which is characterized in that in the G (n) current point and
Energy consumption between adjoint point is obtained by establishing gradient energy consumption model, and the gradient energy consumption model is established as follows:
In formula, g1(Pn,Pn+1) and g2(Pn,Pn+1) it is respectively to be in the gradientWhen unit distance descending energy consumption function and
Unit distance upward slope energy consumption function, in which:
In formula, (xn,yn,zn) it is respectively current point PnCoordinate data, (xn+1,yn+1,zn+1) it is respectively adjoint point Pn+1Number of coordinates
According to；
4. multiple target three-dimensional path planning method according to claim 3, which is characterized in that the unit distance descending energy
Consume function g1(Pn,Pn+1) establish by the following method:
Choose one section of level road descending road different with the multistage gradient；
Energy caused by the robot of same model or the level road of running car same distance and multistage descending road is measured respectively
Consumption；
Repeatedly test acquires the energy consumption average value of level road and every section of descending road respectively；
Find out the ratio of different gradient descending road average energy consumption and level road average energy consumption；
By the way of cubic polynomial fitting, unit distance descending energy consumption function g is obtained1(Pn,Pn+1):
In formula, a1,b1And c1It is undetermined coefficient.
5. multiple target three-dimensional path planning method according to claim 4, which is characterized in that the unit distance upward slope energy
Consume function g2(Pn,Pn+1) establish by the following method:
Choose one section of level road upward slope road different with the multistage gradient；
Energy caused by the robot of same model or the level road of running car same distance and multistage upward slope road is measured respectively
Consumption；
Repeatedly test acquires the energy consumption average value of level road and every section of upward slope road respectively；
Find out the ratio of different gradient upward slope road average energy consumption and level road average energy consumption；
By the way of cubic polynomial fitting, unit distance upward slope energy consumption function g is obtained2(Pn,Pn+1):
In formula, a2,b2And c2It is undetermined coefficient.
6. multiple target three-dimensional path planning method according to claim 5, which is characterized in that pass through the energy consumption of adjacent node
It is added, so that algorithm simulation is gone out the landform in mountainous region, and estimate the position of high mountain low ebb, the energy consumption for obtaining the adjacent node passes through
Gradient energy consumption model is calculated, as follows:
Wherein, EHIt is total energy consumption of the adjoint point to destination node, PiAnd Pi+1For the intermediate node of node to destination node, ωiIt is i-th
Weight of a node to i+1 node, and ωiIt is acquired by the curve that sigmoid function declines,
Wherein, b is curves, and c is bias, and exp (- b*x (i)) is-b*x (i) power of e, and x (i) is i-th of node
To the distance of current point.
7. multiple target three-dimensional path planning method according to claim 6, which is characterized in that distance passes through in the H (n)
The linear distance and curve distance calculated between adjoint point and destination node obtains, and formula is as follows:
D=D 'H+D″H (11)
In formula, D is distance, wherein D 'HIt is calculated by following formula:
In formula, PgoalFor destination node, (xgoal,ygoal,zgoal) it is PgoalCoordinate data；
D″HIt is calculated by following formula:
In formula, D (Pi,Pi+1) it is linear distance of i-th of node to i+1 node.
8. multiple target three-dimensional path planning method according to claim 7, which is characterized in that optimized by multi-Objective Chaotic
Algorithm optimizes two targets of total energy consumption in H (n) and distance, and solution meets that total energy consumption is minimum and most short two targets of distance
Solution, and to wherein parameter η1、η2、η3It is optimized with c, obtains the optimal value of each parameter.
9. multiple target three-dimensional path planning method according to claim 8, which is characterized in that the multi-Objective Chaotic optimization
Specific step is as follows for algorithm:
A) initialization population: for above-mentioned 4 optimized variable η1、η2、η3And c, the initial value of fine difference is assigned respectively, obtains 4
Chaos Variable, and the range of Chaos Variable is amplified to corresponding optimized variable value range respectively, meanwhile, in population
Pop solution is initialized with the mode of chaos sequence respectively；
B population scale) is selected；
C) the calculating of objective function: path Lane is obtained by multiple target three-dimensional path planning model, obtains each solution in population
Target function value；
D) non-dominant relationship and crowding sort: carrying out non-dominant relationship sequence to the solution of objective function, only retain the first non-branch
Solution with forward position, and to the solution that first dominates forward position, crowding sequence is carried out, the lower solution of crowding is chosen；
E) chaotic mutation generates filial generation: it is made a variation by the method for chaos sequence to parent, calculation formula are as follows:
Coffspring=4*Cparent*(1-Cparent) (18)
Wherein, CparentIt is the disaggregation of parent population, CoffspringIt is the disaggregation of progeny population；
F) loop iteration: obtained progeny population is continued to the calculating of objective function in step C, it is each to obtain progeny population
The target function value of solution carries out loop iteration；
G) stop criterion: when algorithm, which goes to the target function value in maximum algebra or group, to be stablized, algorithm is terminated,
In, the first all solutions for dominating forward position are all required solutions, and obtain parameter [η1, η2,η3, c] solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910803728.0A CN110501020B (en) | 2019-08-28 | 2019-08-28 | Multi-target three-dimensional path planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910803728.0A CN110501020B (en) | 2019-08-28 | 2019-08-28 | Multi-target three-dimensional path planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110501020A true CN110501020A (en) | 2019-11-26 |
| CN110501020B CN110501020B (en) | 2022-12-02 |
Family
ID=68590156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910803728.0A Active CN110501020B (en) | 2019-08-28 | 2019-08-28 | Multi-target three-dimensional path planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110501020B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111174793A (en) * | 2020-01-17 | 2020-05-19 | 北京市商汤科技开发有限公司 | Path planning method and device and storage medium |
| CN112082558A (en) * | 2020-09-14 | 2020-12-15 | 哈尔滨工程大学 | UUV submarine topography tracking path rolling generation method based on polynomial fitting |
| CN114491755A (en) * | 2022-01-24 | 2022-05-13 | 阳光新能源开发股份有限公司 | Mountain land photovoltaic power station road planning method, device, equipment and storage medium |
| CN116105741A (en) * | 2023-04-07 | 2023-05-12 | 南京航天宏图信息技术有限公司 | Multi-target three-dimensional dynamic path planning method and device |
| CN116125995A (en) * | 2023-04-04 | 2023-05-16 | 华东交通大学 | Path planning method and system for high-speed rail inspection robot |
| CN118366334A (en) * | 2024-06-17 | 2024-07-19 | 天津华慧智能科技有限公司 | Accurate parking method and system under visual synchronous guidance |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004100148A (en) * | 2002-09-04 | 2004-04-02 | Mitsubishi Research Institute Inc | Road linear design method, device, and program |
| JP2004326363A (en) * | 2003-04-23 | 2004-11-18 | Mitsubishi Heavy Ind Ltd | Four-dimensional navigation object, four-dimensional navigation device, and four-dimensional control method of navigation object |
| CN106444835A (en) * | 2016-10-11 | 2017-02-22 | 哈尔滨工程大学 | Underwater vehicle three-dimensional path planning method based on Lazy Theta satellite and particle swarm hybrid algorithm |
-
2019
- 2019-08-28 CN CN201910803728.0A patent/CN110501020B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004100148A (en) * | 2002-09-04 | 2004-04-02 | Mitsubishi Research Institute Inc | Road linear design method, device, and program |
| JP2004326363A (en) * | 2003-04-23 | 2004-11-18 | Mitsubishi Heavy Ind Ltd | Four-dimensional navigation object, four-dimensional navigation device, and four-dimensional control method of navigation object |
| CN106444835A (en) * | 2016-10-11 | 2017-02-22 | 哈尔滨工程大学 | Underwater vehicle three-dimensional path planning method based on Lazy Theta satellite and particle swarm hybrid algorithm |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111174793A (en) * | 2020-01-17 | 2020-05-19 | 北京市商汤科技开发有限公司 | Path planning method and device and storage medium |
| CN111174793B (en) * | 2020-01-17 | 2021-11-30 | 北京市商汤科技开发有限公司 | Path planning method and device and storage medium |
| CN112082558A (en) * | 2020-09-14 | 2020-12-15 | 哈尔滨工程大学 | UUV submarine topography tracking path rolling generation method based on polynomial fitting |
| CN114491755A (en) * | 2022-01-24 | 2022-05-13 | 阳光新能源开发股份有限公司 | Mountain land photovoltaic power station road planning method, device, equipment and storage medium |
| CN116125995A (en) * | 2023-04-04 | 2023-05-16 | 华东交通大学 | Path planning method and system for high-speed rail inspection robot |
| CN116105741A (en) * | 2023-04-07 | 2023-05-12 | 南京航天宏图信息技术有限公司 | Multi-target three-dimensional dynamic path planning method and device |
| CN118366334A (en) * | 2024-06-17 | 2024-07-19 | 天津华慧智能科技有限公司 | Accurate parking method and system under visual synchronous guidance |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110501020B (en) | 2022-12-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110501020A (en) | A kind of multiple target three-dimensional path planning method | |
| Yi et al. | Optimal stochastic eco-routing solutions for electric vehicles | |
| CN104331743B (en) | Electric vehicle travel planning method based on multi-target optimization | |
| Halabi et al. | Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation | |
| CN110160526B (en) | Air route planning method based on genetic algorithm | |
| CN109886473B (en) | Watershed wind-solar water system multi-objective optimization scheduling method considering downstream ecology | |
| CN105091889B (en) | A kind of determination method and apparatus of hotspot path | |
| CN111178619A (en) | Multi-objective optimization method considering distributed power supply and charging station joint planning | |
| CN109034465A (en) | Consider the charging station bi-level optimization method that charging station addressing is coupled with trip route | |
| CN103208034B (en) | A kind of track traffic for passenger flow forecast of distribution model is set up and Forecasting Methodology | |
| CN104951834A (en) | LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization) | |
| Sun et al. | Integrating traffic velocity data into predictive energy management of plug-in hybrid electric vehicles | |
| CN109239603A (en) | A kind of extreme learning machine under manifold regularization frame predicts power battery SOC method | |
| CN110866636A (en) | Microgrid planning method comprehensively considering electric vehicle charging station and distributed energy | |
| CN109685274A (en) | The method of high-speed rail path planning design based on maklink figure Multi-node link | |
| CN110350517A (en) | A kind of grid-connected Distribution Network Reconfiguration of electric car based on operation risk | |
| CN115856633A (en) | Lithium ion battery capacity estimation method based on graph neural network | |
| CN105262167A (en) | Intra-regional electric vehicle ordered charging control method | |
| CN104199884A (en) | Social networking service viewpoint selection method based on R coverage rate priority | |
| JP2023175992A (en) | Energy supply system and information processing device | |
| CN110543967B (en) | Electric vehicle waiting time distribution short-time prediction method in network connection charging station environment | |
| CN104009472A (en) | Power distribution network state estimation method based on cooperative particle swarms | |
| Grubwinkler et al. | A modular and dynamic approach to predict the energy consumption of electric vehicles | |
| CN113031647B (en) | Power supply type unmanned aerial vehicle optimal path planning method based on fuzzy comprehensive evaluation | |
| Wang et al. | Forecast of urban EV charging load and smart control concerning uncertainties |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |