CN105606103A - Method for planning operation route of robot in mine - Google Patents

Abstract

The invention discloses a method for planning an operation route of a robot in a mine and relates to the technical field of mine operation route planning. The method comprises the steps that freely moving space modeling of the robot is performed, and a planning algorithm of particle swarm optimization in a grid space is executed. In addition, the invention further relates to a basic principle of a particle swarm and an implementation method of the particle swarm optimization. The method has the advantages that the method utilizes a traditional grid strategy to conduct the modeling on a surrounding environment, and a more optimized global route is found out through the particle swarm optimization.

Description

A kind of robot mine operation paths planning method

Technical field

The present invention relates to mine operation Path Planning Technique field, be specifically related to a kind of robot mine operation path planningMethod.

Background technology

Coal mine internal structure complexity, condition is arduous. Some operating environment is not suitable for the long-term operation of miner, in addition in ore depositAfter difficult generation, due to often situation the unknown to down-hole, can bring very large rescue difficulty to rescue personnel, can not arrive atRescue place, even if arrive accident spot, self is also comparatively dangerous. And the development of Robotics will be underground work bandFacilitate. In the research field of robot, path planning is one of important research direction. For a best is found by robotPath planning problem, be exactly according to some or certain some optimize require and constraint (as the shortest in track route, travel timeThe shortest, the aspect such as energy resource consumption is minimum), in the working space of robot, find one to meet to optimize and require and constraintsBest path. From the angle of optimizing, this is a function optimization problem with Prescribed Properties. In whole process, roadBarrier is planned, located, keeps away in footpath is a problem that needs consider. Between how from starting point to destination, search out oneHow excellent collisionless path, avoid external object robot to be impacted or made impact minimum, how to utilize known letterBreath builds mental map, and for robot navigates, thereby the behaviour decision making of more optimizing is to need in robot navigationThe problem solving.

Summary of the invention

The object of this invention is to provide a kind of robot mine operation paths planning method, it utilizes traditional grid strategySurrounding environment is carried out to modeling, find out a global path of comparatively optimizing by particle cluster algorithm.

In order to solve the existing problem of background technology, applying step of the present invention is as follows:

Step 1, robot move freely spatial modeling

Robot mobile space is carried out to modeling with raster based method, the size of grid can generate as follows:

(1) barrier of selection of order in optimizing space;

(2) if this barrier is a convex polygon, this polygon is divided into mutually disjoint triangle; If this barrierNot convex polygon, build a rectangle with this polygonal maximum and minimum transverse and longitudinal coordinate, and rectangle is divided intoTwo disjoint triangles;

(3) calculate each leg-of-mutton area according to following formulaWherein a, b is respectively leg-of-mutton twoLimit, α is a, the angle that b is folded;

(4) the next barrier of the processing of order in optimizing space, if also have other barrier, jumps to (2) step;

(5) calculate the area summation Sab of all barriers;

(6) according to following formula computation grid granularity $l=\left\{\begin{array}{c}{l}_t,l{>}_t{l}_{\min }\\ l_{\min },{\mathrm{other}}_t\end{array}\right.$ Wherein $l_t=\frac{S_{ab}}{S_{total}}\×l_{mzx},l_{mzx}$ For maximum grid limitLong, l_minFor the minimum grid length of side, l is last definite grid length of side;

(7) algorithm stops

What above algorithm obtained is the grid size of calculating according to barrier size, and calculating robot again uses the same methodThe size of itself, final grid size is the maximum in both;

Step 2, the particle cluster algorithm planning algorithm in grid space

1. according to the big or small computation grid size of barrier and robot self, set up the environmental model of robot;

2. the design parameter that initializes particle cluster algorithm, comprises population scale, initial position and primary speed, inertia powerHeavy, acceleration factor and maximum number of iterations;

3. the validity of each particle is checked;

4. calculate the fitness of each particle, the relatively fitness of each particle and historical optimum fitness, according to needWant more new historical adaptive optimal control degree;

5. the more speed of new particle and position;

6. check particle validity, if invalid, need to regenerate new particle;

7. upgrade coefficient, as inertia weight etc.;

8. judge whether to meet end condition, if met, program stops; If do not meet, jump to step 4.

Adopt after said structure, beneficial effect of the present invention is: it utilizes traditional grid strategy to build surrounding environmentMould, finds out a global path of comparatively optimizing by particle cluster algorithm.

Brief description of the drawings

Fig. 1 is particle shifting principle figure in particle cluster algorithm;

Fig. 2 is robot real work ring;

Fig. 3 is the working model that grid shape map grid method obtains.

Detailed description of the invention

Below in conjunction with accompanying drawing, the present invention is further illustrated.

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing and specifically enforcementMode, is further elaborated to the present invention. Should be appreciated that detailed description of the invention described herein is only in order to explain thisInvention, is not intended to limit the present invention.

As Figure 1-3, this detailed description of the invention adopts following technical scheme: applying step of the present invention is as follows:

Step 1, robot move freely spatial modeling

Robot mobile space is carried out to modeling with raster based method, wherein, the size of grid is by the density of barrier in map,The factors such as robot self size decide, and the size of grid can generate as follows:

(1) barrier of selection of order in optimizing space;

(2) if this barrier is a convex polygon, this polygon is divided into mutually disjoint triangle; If this barrierNot convex polygon, build a rectangle with this polygonal maximum and minimum transverse and longitudinal coordinate, and rectangle is divided intoTwo disjoint triangles;

(3) calculate each leg-of-mutton area according to following formulaWherein a, b is respectively leg-of-muttonBoth sides, α is a, the angle that b is folded;

(4) the next barrier of the processing of order in optimizing space, if also have other barrier, jumps to (2) step;

(5) calculate the area summation Sab of all barriers;

(6) according to following formula computation grid granularity $l=\left\{\begin{array}{c}{l}_t,l{>}_t{l}_{\min }\\ l_{\min },{\mathrm{other}}_t\end{array}\right.$ Wherein $l_t=\frac{S_{ab}}{S_{total}}\×l_{mzx},l_{mzx}$ For maximum gridThe length of side, l_minFor the minimum grid length of side, l is last definite grid length of side;

(7) algorithm stops

What above algorithm obtained is the grid size of calculating according to barrier size, and calculating robot again uses the same methodThe size of itself, final grid size is the maximum in both;

Step 2, the particle cluster algorithm planning algorithm in grid space

1. according to the big or small computation grid size of barrier and robot self, set up the environmental model of robot;

2. the design parameter that initializes particle cluster algorithm, comprises population scale, initial position and primary speed, inertia powerHeavy, acceleration factor and maximum number of iterations;

3. the validity of each particle is checked;

4. calculate the fitness of each particle, the relatively fitness of each particle and historical optimum fitness, according to needWant more new historical adaptive optimal control degree;

5. the more speed of new particle and position;

6. check particle validity, if invalid, need to regenerate new particle;

7. upgrade coefficient, as inertia weight etc.;

8. judge whether to meet end condition, if met, program stops; If do not meet, jump to step 4.

In this application, the general principle of population is: nineteen ninety-five, doctor JamesKennedy and RussellEberhartDoctor has proposed the motion model of simple flock of birds. By Computer Simulation, famous particle swarm optimization algorithm is proposed(ParticleSwarmOptimization, PSO). This is a kind of groups optimized algorithm, the motion of establishing a particle in colonyBe described [3] by several comparatively simple rules. This makes the effectively simple and easy realization of PSO algorithm. About particleGroup's algorithm can do following description, and making target search space is a N dimension space, and number of particles is m, i particle whereinPosition in n-dimensional space is just like giving a definition: suppose, in the target search space of a N dimension, to form a group by m particleFall, wherein i particle position in n-dimensional space is X_i＝(x_i1，…，x_im), i=1,2 ..., m; In population, eachThe speed of individual particle is V_i＝(v_i1，…，v_in), i=1,2 ..., n; The following v of more new formula of its d dimension component_id＝w×v_id+c₁r₁(x_id ^Pbest-x_id)+c₂r₂×(x_id ^Pbest-x_id)，x_id＝x_id+v_id；

Wherein, v_idIt is the d dimension component of the velocity vector of particle i; x_idIt is the d dimension component of particle i position vector;BeThe best position that the d dimension component of particle i position vector experiences;That all particles traveled through in solution spaceGood position. R1, r2 is the random number between 0,1; C1, c2 is acceleration factor; W is inertia weight. Particle shifting principle figureAs shown in Figure 1.

The performing step of particle cluster algorithm is:

(l) initialization of population: this one-phase initializes population, individual amount in definition population, and each exampleInitial position and speed;

(2) fitness function of each particle in calculating population, sorts to the particle in population;

(3) fitness function of each particle of comparison, relatively each particle fitness function and Pbest_i. If the shape that particle is newThe fitness function of state is better than Pbest_i, upgrade Pbest_i。

(4) compare each particle fitness function and gbest. If the fitness function of the state that particle is new is better thanPbest_i, upgrade gbest.

(5), according to (3), (4) formula is upgraded new position and the speed of each particle of calculating;

(6) if end condition meets, program stops, otherwise returns to (2) step.

The above, only, in order to illustrate that technical scheme of the present invention is and unrestricted, those of ordinary skill in the art are to thisOther amendment that bright technical scheme is made or be equal to replacement, only otherwise depart from the spirit and scope of technical solution of the present invention,All should be encompassed in the middle of claim scope of the present invention.

Claims (1)

1. a robot mine operation paths planning method, is characterized in that comprising the steps:

Step 1, robot move freely spatial modeling

Robot mobile space is carried out to modeling with raster based method, the size of described grid can generate as follows:

(1) barrier of selection of order in optimizing space;

(2) if this barrier is a convex polygon, this polygon is divided into mutually disjoint triangle; If this barrierNot convex polygon, build a rectangle with this polygonal maximum and minimum transverse and longitudinal coordinate, and rectangle is divided intoTwo disjoint triangles;

(3) calculate each leg-of-mutton area according to following formulaWherein a, b is respectively leg-of-mutton twoLimit, α is a, the angle that b is folded;

(4) the next barrier of the processing of order in optimizing space, if also have other barrier, jumps to (2) step;

(5) calculate the area summation Sab of all barriers;

(6) according to following formula computation grid granularityWherein $l_t=\frac{S_{ab}}{S_{total}}\×l_{mzx},l_{mzx}$

For the maximum grid length of side, l_minFor the minimum grid length of side, l is last definite grid length of side;

(7) algorithm stops

What above algorithm obtained is the grid size of calculating according to barrier size, and calculating robot again uses the same methodThe size of itself, final grid size is the maximum in both;

Step 2, the particle cluster algorithm planning algorithm in grid space

This algorithm comprises step:

1. according to the big or small computation grid size of barrier and robot self, set up the environmental model of robot;

2. the design parameter that initializes particle cluster algorithm, comprises population scale, initial position and primary speed, inertia powerHeavy, acceleration factor and maximum number of iterations;

3. the validity of each particle is checked;

4. calculate the fitness of each particle, the relatively fitness of each particle and historical optimum fitness, according to needWant more new historical adaptive optimal control degree;

5. the more speed of new particle and position;

6. check particle validity, if invalid, need to regenerate new particle;

7. upgrade coefficient, as inertia weight etc.;

8. judge whether to meet end condition, if met, program stops; If do not meet, jump to step 4.