CN104407616A - Dynamic path planning method for mobile robot based on immune network algorithm - Google Patents
Dynamic path planning method for mobile robot based on immune network algorithm Download PDFInfo
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Abstract
The invention discloses a dynamic path planning method for a mobile robot based on an immune network algorithm. The method comprises the following steps: firstly, acquiring information of working environment of the mobile robot, processing the acquired information, and expressing a static obstacle in the surrounding environment by using a rectangular bounding box; secondly, planning a global path by applying an improved immune network algorithm on the basis of the collected static environment information; thirdly, allowing the robot to move forward along the planned path, if the unknown obstacle is found, predicting whether the unknown obstacle hampers normal running of the robot or not, if the unknown obstacle influences the normal running of the robot, predicting a dangerous area with possible collision; fourthly, forming a new local environment view according to the predicated result, and planning a new local path by applying the immune network algorithm; fifthly, when the robot reaches a temporary target point of local planning, allowing the robot to return to the planned global path for continuous advancement. According to the dynamic path planning method disclosed by the invention, a global optimal path can be solved under the condition that a complex environment is guaranteed, and the optimum of the local dynamic area path planning can also be guaranteed.
Description
Technical field: the present invention relates to a kind of mobile robot's dynamic path planning method based on Immune network algorithm.The present invention can be widely used in solve relative complex dynamic environment under mobile robot's path planning problem.
Background technology: path planning is one of mobile robot's core content learning research, its objective is in the working environment with barrier, find a suitable safety movement path from given starting point to impact point, make robot can walk around all barriers without collision in motion process.In the relation technological researching of mobile robot, Path Planning Technique is an important field of research.
In reality, under most cases, mobile robot is in dynamic uncertain environments, and to the dynamic barrier in this kind of environment, mobile robot is difficult to be possessed priori and only has component environment information.In this case, mobile robot can only carry out local paths planning according to the environmental information real-time detected.In order to solve this kind of path planning problem, normally traditional global path planning algorithm is combined with the method for prediction of collision, as the algorithm that a kind of pre-collision sensing method combines with Artificial Potential Field Method, the travelling speed that this algorithm changes mobile robot according to the position that prediction collides with dynamic barrier is hidden, but it does not consider the global path planning ability of mobile robot under complex environment.For solving above-mentioned deficiency, the mobile robot path planning under the present invention uses a kind of Immune network algorithm of improvement to be applied to part circumstances not known.
Immune network algorithm is the important method of in Artificial Immune Algorithm.The distinct network proposing immune system internal regulation based on Jerne is theoretical, de Castro and Timmis proposed Immune network algorithm (opt-aiNet) in 2002, it has fast convergence rate, solving precision is high, stability is good, and effectively can overcome the feature of " precocity ", make it in global optimizing and optimal speed, have advantage, be therefore suitable for solving the complexity many obstacles Global motion planning problem in mobile robot path planning and quick dynamic obstacle avoidance planning problem.
Summary of the invention:
Goal of the invention: in order to improve the problem solving quality and solution efficiency of mobile robot path planning problem, the invention provides a kind of mobile robot's dynamic path planning method based on Immune network algorithm, the method not only can solve mobile robot global path planning in circumstance complication situation, is also applicable to the local paths planning under component environment unknown situation.
Technical scheme:
A kind of mobile robot's dynamic path planning method based on Immune network algorithm, it is characterized in that: first based on Visual Graph method, pre-service is carried out to overall static environment space, apply Immune network algorithm afterwards and cook up global path, ambient condition information is detected in real time in the process of advancing, risk prediction is carried out to the unknown dynamic disorder entering sensing range, form new local environment Visual Graph according to the result of prediction and apply Immune network algorithm and plan the local path made new advances, continue to advance in the global path of the preplanning got back to after robot arrives the transient target point of sector planning.
(1), the implementation procedure of Immune network algorithm:
Through to the improvement of Immune network algorithm and and the combination of Visual Graph, the concrete performing step of algorithm is as follows:
(1) pre-service is carried out to the environment space that mobile robot runs, build Visual Graph;
(2) according to the Establishment and analysis of Visual Graph, sum up priori and also extract vaccine, not there is not setback in the bounding box of penetrate thing and the antibody of generation or path to comprise the antibody of generation or path;
(3) initialization of population: first vaccine inoculation, in search volume, produce m antibody and m paths by implanting vaccine, the generating mode in path progressively extends to impact point from starting point, forms initial parent population by this m antibody and m paths
wherein subscript i represented for the i-th generation, and subscript m represents antibody number;
(4) stop condition: the affinity finally determining antibody according to the concentration between the fitness between antibody and antigen and antibody, calculate current parent for population genetic algebra and
in the affinity of all antibody, if satisfy condition, then stop computing and Output rusults, otherwise continue;
(5) clonal expansion: produce n clone body respectively to each antibody (path) in above-mentioned antibody population, now amplification is m*n clonal antibody (path), and now antibody population becomes
every paths all copies n doubly, and next step path variation will be more abundant, add often for the diversity of antibody;
(6) high frequency closedown: clonal antibody group
in the antibody of each clone all to experience high frequency closedown and obtain
here refer to antagonist path to carry out increasing or deletion of node;
(7) recalculate adaptation value: all individualities in antagonist group, comprise each father's antibody and variant clone antibody thereof, recalculate its affinity;
(8) memory antibody group upgrades: from each father's antibody and clonal antibody thereof
in, select to have the variant clone antibody of the highest affinity, elite's antibody variants group of what common composition m was new have most high-affinity
and it can be used as the candidate upgrading memory antibody group, then from
with
in select the new elite's antibody variants group of m the composition that affinity is the highest
calculate the average affinity in memory antibody group afterwards again;
(9) immune self-regulation and receptor editing: the individual brand-new antibody of stochastic generation r (r<m)
replace now antibody population
the individual antibody with minimum affinity of middle r, to simulate the receptor editing process in Immune System, increase the diversity of antibody population, thus, namely algorithm creates follow-on antibody population
return step 4 afterwards;
(2), based on mobile robot's active path planning step of Immune network algorithm:
Send out for mobile the situation that robot working space is the unknown of environmental information part, concrete path planning step is as follows:
(1) global path planning is carried out to the Immune network algorithm of known static context information application enhancements;
(2) robot advances along the path cooked up;
(3) judge whether to arrive impact point, arrive and then terminate, do not arrive the path of then continuing along cooking up and advance;
Because the information of the static-obstacle thing in environment space is known in advance, so whenever robot ambulation one step will judge whether the distance between robot and each known static-obstacle thing is less than scrolling windows, the i.e. radius of the sensing range of sensor, so just can show whether these static-obstacle things have appeared in the scope of scrolling windows;
(4) judge whether dyskinesia hinders the normal traveling of robot;
(5) dope mark unknown dynamic barrier can with along the preplanning good path robot of advancing next certain T moment collides, then the position in this Unknown Motion barrier T moment is recorded, be used as " static-obstacle thing " process, consider the uncertain factor in the error of prediction and actual environment, the region of this " static-obstacle thing " is expanded, is denoted as hazardous location;
(6) when hazardous location enters after within scrolling windows completely, robot using the path intersection point of current time scrolling windows preplanning with it as interim localized target point, the position of current robot is as starting point, static-obstacle thing in perceived window and hazardous location are as local environmental information, application Immune network algorithm plans the path made new advances, robot, along the route of new planning, returns step 3.
(2) in (4) step of step, robot first need not consider scrolling windows in the process of advancing, until sensor finds that new unknown barrier enters within scrolling windows, namely, when the distance of unknown barrier and robot is less than the radius of scrolling windows, marks this unknown dynamic barrier and start to predict the movement locus of unknown barrier; The result of prediction is divided into following two kinds of situations: one is the normal traveling that unknown barrier can not hinder robot, then robot continue along the good path of preplanning advance, namely return to step 2; Two is dope the normal traveling that dyskinesia may hinder robot, then forward (5) step of (two) step to.
The design of antibody in Immune network algorithm:
After Visual Graph method is to the process of mobile work robot environment space, form antibody path only to need to consider the element in the set V on each barrier summit, do not need to consider any point outside in environment space, such hunting zone will significantly be reduced, and efficiency of algorithm is improved.The structure of each node is as follows:
| IDpoint | IDobject | x | y |
Wherein IDpoint represents node serial number, and IDobject represents barrier numbering belonging to node, and x, y represent the transverse and longitudinal coordinate of node respectively, in mobile robot path planning problem, antigen represents problem to be solved and optimal path, the application is set as the Euclidean distance of starting point s and impact point g, antibody represents the solution of required problem, namely the feasible path searched out, it is by the starting point of mobile robot, the broken line that impact point and a series of intermediate node connect into is formed, for describing path trend clearly, through the order of node when in antibody, the order of element represents moveable robot movement, antibody adopts string encoding mode, its length can change with the number of node, the data structure of antibody enter lower shown in:
| Affinity | Density | Fitness | Length | s | … | v | … | g |
Wherein Affinity represents the affinity of antibody, evaluates the quality of antibody here with affinity; Fitness represents the fitness of antibody, has and has higher fitness compared with the antibody of short path; Density represents the concentration of antibody, the ratio namely in antibody population shared by same antibody, and Length represents the length of antibody by s to g, and remainder is form the node in path, this storage organization when antibody changes, the length of flexibly changing antibody;
The calculating of affinity in Immune network algorithm:
The application, in order to suppress and avoid the appearance of precocious phenomenon, adds the calculating of antibody concentration here, and the computing formula of concrete affinity is as follows:
Wherein Affinity (i) represents the affinity of antibody i; Fitness (i) represents the fitness of antibody i, and distance dependent, and the distance in antibody path is shorter, and the fitness of its antibody is higher; Density (i) represents the concentration of antibody i, and namely identical antibody path is more, and the concentration of antibody is larger; α is a positive constant, and can find out that fitness is larger by this formula, affinity is larger; Concentration is higher, and affinity is less.
Advantage and effect: the invention provides a kind of mobile robot's dynamic path planning method based on Immune network algorithm, path length as weighing the whether excellent main standard in path, be have studied the paths planning method that the opt-aiNet algorithm in Idiotype immune network theory combines with Visual Graph by the application under known at static context information, unknown dynamic barrier information circumstances not known.First based on Visual Graph method, overall static environment space is described, applies Immune network algorithm afterwards and cook up global path.Ambient condition information is detected in real time in robot moving process, risk prediction is carried out to the unknown dynamic disorder entering sensing range, form new local environment Visual Graph according to the result of prediction and apply Immune network algorithm and plan the local path made new advances, thus realize the combination of local dynamic station planning and overall static programming.In addition, the problem such as evolution precocity, antibody deficiency diversity easily occurred when immunological network carries out robot path planning, introduces the computing method of antibody concentration.The present invention also can ensure the optimum that local dynamic station zone routing is planned on the basis ensureing global optimum path.
Accompanying drawing illustrates:
Fig. 1 robot working environment schematic diagram;
Fig. 2 local paths planning process flow diagram;
Fig. 3 Immune network algorithm process flow diagram;
The path planning of Fig. 4 working environment relative complex;
The statistics of Fig. 5 evolutionary generation;
The analysis of Fig. 6 population antibody affinity;
There is the path planning of unknown dynamic barrier in Fig. 7.
Embodiment: the present invention is described further below in conjunction with accompanying drawing:
Fig. 1 represents mobile work robot environment space, the dolly of advancing along path represents mobile robot, the dashed circle of its periphery is the local sensing scope of mobile robot, i.e. effective sensing range (as laser sensor, sonac or panoramic vision sensor etc.) of sensor, s represents the starting point of mobile robot, g represents the final goal point of mobile robot, and the static-obstacle thing that black rectangle represents in environment space (uses O
jrepresent), in environment space, another dolly represents dynamic barrier, and arrow represents its direction of motion, and four summits of the Rectangular Bounding Volume of each barrier have fixing numbering (to use v
irepresent), the corresponding relation of the numbering i on its summit and the numbering j of barrier is i=1,2 ..., j*4.
Consider the physical size of mobile robot herein, the dotted line frame of certain safe distance namely barrier bounding box periphery is reserved when building barrier bounding box, the all end points satisfied condition in Fig. 1 are connected, namely form Visual Graph, in figure, the line of every bar point g from starting point s to final goal is a paths (also representing an antibody).Through the order on summit when the method for expressing in path is moveable robot movement, as path1=s → v
9→ v
2→ g.
Through the pre-service of Visual Graph method to environment space, the point all from working environment of the scope of algorithm search tapers to node and the impact point of each barrier, make the hunting zone huge compression of algorithm, significantly improve the speed of convergence of optimized algorithm, enable to adapt to real-time local paths planning.
A kind of mobile robot path planning step based on Immune network algorithm:
Send out for mobile the situation that robot working space is the unknown of environmental information part, as shown in Figure 2, concrete path planning step is as follows:
Step 1: obtain the information of mobile work robot environment and it is processed, the static-obstacle thing in surrounding environment is represented by the mode of Rectangular Bounding Volume.
The complexity of algorithm search and the requirement of real-time caused by the polytrope considering mobile work robot environment, the application adopts Visual Graph method to describe the working environment at mobile robot place.Note Visual Graph VG=(V, L), wherein V is by each summit of each barrier bounding box and starting point s, impact point g and each barrier Rectangular Bounding Volume, the set that the application adopts each summit of Rectangular Bounding Volume to form, L represents " visual " line between each summit, if namely these lines are not crossing with barrier, think that line segment is " visual ", the combination of so all " visual " lines is the Visual Graph in current environment space, and what every bar line also represented mobile robot can operating path.
Utilize the working environment space of Visual Graph method to mobile robot to carry out planning process, the problem of core is construction cost matrix, i.e. the matrix of the size formation of the spacing of each node.With node serial number, four summits of each barrier Rectangular Bounding Volume and starting point, impact point represent the row and column in Cost matrix, the Euclidean distance of content in row and column between two nodes that can directly be communicated with represents, wherein ∞ represents that the line between two nodes passes barrier, and namely cost is infinitely great.Utilize Cost matrix, the cost needed for arbitrary path can be calculated fast; After Cost matrix construction complete, solve the extraction of mobile robot path planning problem time vaccines and the determination of antibody coding mode by affecting Immune network algorithm to a certain extent.
Step 2: apply Immune network algorithm and carry out global path planning on the static context information basis of having collected.
Problem to be solved and optimal path are considered as antigen, the solution (optimal path obtained) of problem is considered as antibody, stochastic generation is to the initial path of determined number (antibody), adopt selects, copy, the operator such as transform path carries out evolutional operation to population, generation is better than the filial generation path in parent path.Here the simulation of interphase interaction to antibody in network or immune self-regulating function is added, by the similarity degree between calculating antibody, the dynamically number of antibody in balance antibody group.
The coded system of this step to the account form of the affinity in Immune network algorithm and antibody and gene thereof is improved.
As shown in Figure 3, its algorithm steps is as follows for the process flow diagram of application Immune network algorithm realizing route planning:
(1) according to the Establishment and analysis of Visual Graph, sum up priori and also extract vaccine, not there is not setback in the bounding box of penetrate thing and the antibody (path) of generation to comprise the antibody (path) of generation.
After Visual Graph method is to the process of mobile work robot environment space, form antibody path only to need to consider the element in the set V on each barrier summit, do not need to consider any point outside in environment space, such hunting zone will significantly be reduced, and efficiency of algorithm is improved.The structure of each node is as shown in table 1:
| IDpoint | IDobject | x | y |
Table 1 nodes encoding form
Wherein IDpoint represents node serial number, and IDobject represents barrier numbering belonging to node, and x, y represent the transverse and longitudinal coordinate of node respectively.In mobile robot path planning problem, antigen represents problem to be solved and optimal path, and the application is set as the Euclidean distance of starting point s and impact point g.Antibody represents the solution of required problem, the feasible path namely searched out, and the broken line that it is connected into by the starting point of mobile robot, impact point and a series of intermediate node is formed.For describing path trend clearly, through the order of node when in antibody, the order of element represents moveable robot movement.Antibody adopts string encoding mode, and its length can change with the number of node, shown in the data structure table 2 of antibody:
| Affinity | Density | Fitness | Length | s | … | v | … | g |
Table 2 antibody coding form
Wherein Affinity represents the affinity of antibody, evaluates the quality of antibody here with affinity; Fitness represents the fitness of antibody, has and has higher fitness compared with the antibody of short path; Density represents the concentration of antibody, the ratio namely in antibody population shared by same antibody, and Length represents the length of antibody by s to g, and remainder is form the node in path, this storage organization when antibody changes, the length of flexibly changing antibody.
In Immune network algorithm, the affinity of antibody plays an important role, and especially upgrade memory antibody group and weigh the value of antibody in immune self-regulation step with affinity, thus antagonist screens.
The affinity of antibody refers to the bond strength of antibody and antigen, and the application's affinity evaluates the quality of antibody.In order to suppress and avoid the appearance of precocious phenomenon, add the calculating of antibody concentration here.The computing formula of concrete affinity is as follows:
Wherein Affinity (i) represents the affinity of antibody i; Fitness (i) represents the fitness of antibody i, and distance dependent, and the distance in antibody path is shorter, and the fitness of its antibody is higher; Density (i) represents the concentration of antibody i, and namely identical antibody path is more, and the concentration of antibody is larger; α is a positive constant.Can find out that fitness is larger by this formula, affinity is larger; Concentration is higher, and affinity is less.
(2) initialization of population: first vaccine inoculation, m antibody (m paths) is produced by implanting vaccine in search volume, the generating mode in path progressively extends to impact point from starting point, forms initial parent population by this m antibody (m paths)
wherein subscript i represented for the i-th generation, and subscript m represents antibody number.
(3) stop condition: the affinity finally determining antibody according to the concentration between the fitness between antibody and antigen and antibody, calculate current parent for population genetic algebra and
in the affinity of all antibody, if satisfy condition, then stop computing and Output rusults, otherwise continue.
(4) clonal expansion: produce n clone body respectively to each antibody (path) in above-mentioned antibody population, now amplification is m*n clonal antibody (path), and now antibody population becomes
every paths all copies n doubly, and next step path variation will be more abundant, add often for the diversity of antibody.
(5) high frequency closedown: clonal antibody group
in the antibody of each clone all to experience high frequency closedown and obtain
here refer to antagonist path to carry out increasing or deletion of node.
(6) recalculate adaptation value: all individualities in antagonist group, comprise each father's antibody and variant clone antibody thereof, recalculate its affinity.
(7) memory antibody group upgrades: from each father's antibody and clonal antibody thereof
in, select to have the variant clone antibody of the highest affinity, elite's antibody variants group of what common composition m was new have most high-affinity
and it can be used as the candidate upgrading memory antibody group, then from
with
in select the new elite's antibody variants group of m the composition that affinity is the highest
calculate the average affinity in memory antibody group afterwards again.
(8) immune self-regulation and receptor editing: the individual brand-new antibody of stochastic generation r (r<m)
replace now antibody population
the individual antibody with minimum affinity of middle r, to simulate the receptor editing process in Immune System, increases the diversity of antibody population.Thus, namely algorithm creates follow-on antibody population
Return afterwards (3).
Step 3: robot advances along the path cooked up.
Step 4: judge whether to arrive impact point, arrive and then terminate, does not arrive the path of then continuing along cooking up and advances.
Because the information of the static-obstacle thing in environment space is known in advance, so whenever robot ambulation one step will judge whether the distance between robot and each known static-obstacle thing is less than the radius of scrolling windows (sensing range of sensor), so just can show whether these static-obstacle things have appeared in the scope of scrolling windows.
Step 5: judge whether dyskinesia hinders the normal traveling of robot.
Robot first need not consider scrolling windows in the process of advancing, until when sensor finds that new unknown barrier enters into (namely the distance of unknown barrier and robot is less than the radius of scrolling windows) within scrolling windows, mark this unknown dynamic barrier and start to predict the movement locus of unknown barrier.The result of prediction is divided into following two kinds of situations: one is the normal traveling that unknown barrier can not hinder robot, then robot continue along the good path of preplanning advance, namely return to step 3.Two is dope the normal traveling that dyskinesia may hinder robot, then forward step 6 to.
Step 6: dope mark unknown dynamic barrier can with along the preplanning good path robot of advancing next certain T moment collides, then the position in this Unknown Motion barrier T moment is recorded, be used as " static-obstacle thing " process, consider the uncertain factor in the error of prediction and actual environment, the region of this " static-obstacle thing " is expanded, is denoted as hazardous location.
Step 7: when hazardous location enters after within scrolling windows completely, robot using the path intersection point of current time scrolling windows preplanning with it as interim localized target point, the position of current robot is as starting point, static-obstacle thing in perceived window and hazardous location are as local environmental information, application Immune network algorithm plans the path made new advances, robot, along the route of new planning, returns step 4.
Describe through above-mentioned steps, draw the present invention can not only more efficient realize mobile robot global path planning under complex environment, the local paths planning that there is the unknown dynamic disorder in local can also be completed.
Fig. 4 represents the global path planning of environment space relative complex, is provided with multiple intensive obstacle in the movement environment of robot.In figure, 2 lines of each any not penetrate represent the feasible path line segment of robot, and any paths of s to g is all the combination of these feasible path line segments.Along with increasing of barrier, the path from s to g is increase at double.Can find out that path number feasible in Fig. 4 is very many, algorithm finally will select that paths of cost minimization from the various path of this number, from s to g, the line of overstriking line segment just represents the final path that Algorithm for Solving goes out, and the path that can solve as seen from Figure 4 is optimum.Fig. 5 represents the variation relation of path planning number of times and evolutionary generation.500 planning has been carried out at this working environment, owing to planning that initial antibodies (path) group is random generation at every turn, the randomness adding high frequency closedown, with per generation has new antibody (path) to add, makes to plan that the evolutionary generation obtained required for optimal path has uncertainty at every turn.Through showing that average evolutionary generation only had for 248 generations to the statistics of 500 planning, be starkly lower than because the excessive traditional immunization network algorithm causing evolutionary generation obviously higher in search volume.Have selected the comparatively close data of evolutionary generation (245 generation) and average evolutionary generation (248 generation) as analysis from these 500 times planning, in current planning that Fig. 6 is given, successive dynasties antibody population affinity is along with the ever-increasing situation of change of evolutionary generation, horizontal ordinate represents evolutionary generation, and ordinate represents affinity.As can be seen from Figure 6, the optimum antibody affinity of population presents progressively increasing trend along with the increase of evolutionary generation, although 500 generations of algorithm evolution, just reaches the highest affinity when evolving to for 245 generation; When optimum affinity reaches mxm., also there is certain fluctuation in average affinity always, and this is due to the diversity in order to keep antibody in population, and per generation has new random antibody to generate the antibody that in replacement population, equal number affinity is minimum.
Local paths planning be based upon global path planning basis on to there is the planning of dynamic unknown uncertain barrier for working environment, Fig. 7 shows final planning effect, the direction of motion setting unknown barrier is unknown, but it does at the uniform velocity one-way movement along straight line.Can find out that the global path cooked up and local path are optimum.In overwhelming majority situation, the complaint message that local environment comprises is less than or is significantly less than the quantity of information of the overall situation, like this from calculated amount local paths planning be just equivalent to environment simple time global path planning, its convergence of algorithm speed can be competent at the requirement of real-time.
Claims (4)
1. the mobile robot's dynamic path planning method based on Immune network algorithm, it is characterized in that: first based on Visual Graph method, pre-service is carried out to overall static environment space, apply Immune network algorithm afterwards and cook up global path, ambient condition information is detected in real time in the process of advancing, risk prediction is carried out to the unknown dynamic disorder entering sensing range, form new local environment Visual Graph according to the result of prediction and apply Immune network algorithm and plan the local path made new advances, continue to advance in the global path of the preplanning got back to after robot arrives the transient target point of sector planning.
2. the mobile robot's dynamic path planning method based on Immune network algorithm according to claim 1, is characterized in that:
(1), the implementation procedure of Immune network algorithm:
Through to the improvement of Immune network algorithm and and the combination of Visual Graph, the concrete performing step of algorithm is as follows:
(1) pre-service is carried out to the environment space that mobile robot runs, build Visual Graph;
(2) according to the Establishment and analysis of Visual Graph, sum up priori and also extract vaccine, not there is not setback in the bounding box of penetrate thing and the antibody of generation or path to comprise the antibody of generation or path;
(3) initialization of population: first vaccine inoculation, in search volume, produce m antibody and m paths by implanting vaccine, the generating mode in path progressively extends to impact point from starting point, forms initial parent population by this m antibody and m paths
, wherein subscript i represented for the i-th generation, and subscript m represents antibody number;
(4) stop condition: the affinity finally determining antibody according to the concentration between the fitness between antibody and antigen and antibody, calculate current parent for population genetic algebra and
in the affinity of all antibody, if satisfy condition, then stop computing and Output rusults, otherwise continue;
(5) clonal expansion: produce n clone body respectively to each antibody (path) in above-mentioned antibody population, now amplification is m*n clonal antibody (path), and now antibody population becomes
every paths all copies n doubly, and next step path variation will be more abundant, add often for the diversity of antibody;
(6) high frequency closedown: clonal antibody group
in the antibody of each clone all to experience high frequency closedown and obtain
here refer to antagonist path to carry out increasing or deletion of node;
(7) recalculate adaptation value: all individualities in antagonist group, comprise each father's antibody and variant clone antibody thereof, recalculate its affinity;
(8) memory antibody group upgrades: from each father's antibody and clonal antibody thereof
in, select to have the variant clone antibody of the highest affinity, elite's antibody variants group of what common composition m was new have most high-affinity
and it can be used as the candidate upgrading memory antibody group, then from
with
in select the new elite's antibody variants group of m the composition that affinity is the highest
calculate the average affinity in memory antibody group afterwards again;
(9) immune self-regulation and receptor editing: the individual brand-new antibody of stochastic generation r (r<m)
replace now antibody population
the individual antibody with minimum affinity of middle r, to simulate the receptor editing process in Immune System, increase the diversity of antibody population, thus, namely algorithm creates follow-on antibody population
return step 4 afterwards;
(2), based on mobile robot's active path planning step of Immune network algorithm:
Send out for mobile the situation that robot working space is the unknown of environmental information part, concrete path planning step is as follows:
(1) global path planning is carried out to the Immune network algorithm of known static context information application enhancements;
(2) robot advances along the path cooked up;
(3) judge whether to arrive impact point, arrive and then terminate, do not arrive the path of then continuing along cooking up and advance;
Because the information of the static-obstacle thing in environment space is known in advance, so whenever robot ambulation one step will judge whether the distance between robot and each known static-obstacle thing is less than scrolling windows, the i.e. radius of the sensing range of sensor, so just can show whether these static-obstacle things have appeared in the scope of scrolling windows;
(4) judge whether dyskinesia hinders the normal traveling of robot;
(5) dope mark unknown dynamic barrier can with along the preplanning good path robot of advancing next certain T moment collides, then the position in this Unknown Motion barrier T moment is recorded, be used as " static-obstacle thing " process, consider the uncertain factor in the error of prediction and actual environment, the region of this " static-obstacle thing " is expanded, is denoted as hazardous location;
(6) when hazardous location enters after within scrolling windows completely, robot using the path intersection point of current time scrolling windows preplanning with it as interim localized target point, the position of current robot is as starting point, static-obstacle thing in perceived window and hazardous location are as local environmental information, application Immune network algorithm plans the path made new advances, robot, along the route of new planning, returns step 3.
3. the mobile robot's local paths planning method based on Immune network algorithm according to claim 2, it is characterized in that: in (4) step of (two) step, robot first need not consider scrolling windows in the process of advancing, until sensor finds that new unknown barrier enters within scrolling windows, namely, when the distance of unknown barrier and robot is less than the radius of scrolling windows, marks this unknown dynamic barrier and start to predict the movement locus of unknown barrier; The result of prediction is divided into following two kinds of situations: one is the normal traveling that unknown barrier can not hinder robot, then robot continue along the good path of preplanning advance, namely return to step 2; Two is dope the normal traveling that dyskinesia may hinder robot, then forward (5) step of (two) step to.
4. the mobile robot's local paths planning method based on Immune network algorithm according to claim 2, is characterized in that:
The design of antibody in Immune network algorithm:
After Visual Graph method is to the process of mobile work robot environment space, form antibody path only to need to consider the element in the set V on each barrier summit, do not need to consider any point outside in environment space, such hunting zone will significantly be reduced, and efficiency of algorithm is improved.The structure of each node is as follows:
Wherein IDpoint represents node serial number, and IDobject represents barrier numbering belonging to node, and x, y represent the transverse and longitudinal coordinate of node respectively, in mobile robot path planning problem, antigen represents problem to be solved and optimal path, the application is set as the Euclidean distance of starting point s and impact point g, antibody represents the solution of required problem, namely the feasible path searched out, it is by the starting point of mobile robot, the broken line that impact point and a series of intermediate node connect into is formed, for describing path trend clearly, through the order of node when in antibody, the order of element represents moveable robot movement, antibody adopts string encoding mode, its length can change with the number of node, the data structure of antibody enter lower shown in:
Wherein Affinity represents the affinity of antibody, evaluates the quality of antibody here with affinity; Fitness represents the fitness of antibody, has and has higher fitness compared with the antibody of short path; Density represents the concentration of antibody, the ratio namely in antibody population shared by same antibody, and Length represents the length of antibody by s to g, and remainder is form the node in path, this storage organization when antibody changes, the length of flexibly changing antibody;
The calculating of affinity in Immune network algorithm:
The application, in order to suppress and avoid the appearance of precocious phenomenon, adds the calculating of antibody concentration here, and the computing formula of concrete affinity is as follows:
Wherein Affinity (i) represents the affinity of antibody i; Fitness (i) represents the fitness of antibody i, and distance dependent, and the distance in antibody path is shorter, and the fitness of its antibody is higher; Density (i) represents the concentration of antibody i, and namely identical antibody path is more, and the concentration of antibody is larger; α is a positive constant, and can find out that fitness is larger by this formula, affinity is larger; Concentration is higher, and affinity is less.
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