CN107168324A - A kind of robot path planning method based on ANFIS fuzzy neural networks - Google Patents
A kind of robot path planning method based on ANFIS fuzzy neural networks Download PDFInfo
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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Abstract
The invention discloses a kind of robot path planning method based on ANFIS fuzzy neural networks, the problem of mainly solving reciprocal complicated trap path in conventional reactive type navigation and path redundancy.Its planning step is to set up kinematics model to mobile robot first；By the autonomous learning function and the fuzzy reasoning ability of fuzzy theory of neutral net, a kind of Mobile Robotics Navigation controller of fuzzy neural network is proposed；Based on Adaptive Fuzzy Neural-network structure, Takagi Sugeno types fuzzy inference systems are built and as the reference model of robot local reaction control；The real-time output offset angle of the fuzzy neural network controller and the speed of service, the offset direction of on-line tuning mobile robot, enable mobile robot collisionless to automatically adjust speed and tend to target；Using improving virtual target method, selection robot can catch the optimal path of state.
Description
Technical field
The invention belongs to robotic technology field, particularly a kind of path planning for being related to mobile robot, available for each
The independent navigation of class mobile robot.
Background technology
Path planning problem is one of key technology of Mobile Robotics Navigation, and main task is that having the environment of barrier
In, according to certain performance indications, find one between starting point to target point one it is optimal or close to optimal collisionless
Path.The difference of degree is perceived to environmental information according to robot, path planning is divided into two kinds:Fully known complete of environmental information
Office's path planning and the local paths planning that environmental information is totally unknown or part is unknown.Global path planning typically enters offline
OK, conventional method mainly has Visual Graph method, Grid Method, structure space method, topological approach, simulated annealing, genetic algorithm and ant
The intelligent algorithms such as group's algorithm.The conventional method of local paths planning has Artificial Potential Field Method, fuzzy logic algorithm and neural network
Deng.Neutral net because of fault-tolerance by force with adaptive learning the characteristics of, preferably can be felt under unstructured moving grids
Know the analysis of information with merging, and fuzzy control has logical reasoning ability, more effective to processing structure knowledge however it is anti-
Answer formula navigation to lack to recognize the overall situation of environment, robot is absorbed in local trap and can not be reached home.Asked for this
Topic, the effective ways proposed at present have action amalgamation, empty target, along methods such as Contour extractions, but action amalgamation method needs meter
The weights of each behavior are calculated, system complexity is added；Influenceed larger by barrier shape, size along contour tracing method；Empty mesh
It is marked under complex environment and is difficult to remove virtual sub-goal and is also easy to produce redundant path.
The content of the invention
Goal of the invention：It is reciprocal and path redundancy ask in order to solve in reactive navigation in the prior art complicated trap path
Topic, the present invention provides a kind of robot path planning method based on ANFIS fuzzy neural networks, and this method can not only be reduced
Reasoning from logic workload, and the trapping state in robot trend object run can be broken away from.
Technical scheme：To achieve the above object, the technical solution adopted by the present invention is：
A kind of robot path planning method based on ANFIS fuzzy neural networks, sets up to mobile robot transport first
It is dynamic to learn model；By the autonomous learning function and the fuzzy reasoning ability of fuzzy theory of neutral net, a kind of fuzzy neural is proposed
The fuzzy neural network controller of the Mobile Robotics Navigation of network；It is based on Adaptive Fuzzy Neural-network structure, builds
Takagi-Sugeno types fuzzy inference system is simultaneously used as the reference model of robot local reaction control；By the distance of barrier
Inputted with the relevant information of position as two of fuzzy neural network controller, fuzzy neural network controller exports machine in real time
Device people deviation angle and the speed of service, by the offset direction of fuzzy neural network controller on-line tuning mobile robot, make
Mobile robot can collisionless automatically adjust speed tend to target.
It is preferred that：Robot move angle and speed are represented by fuzzy neural network controller output valve, closer to barrier
Output angle absolute value is bigger when hindering thing, and speed absolute value is smaller；When all is clear ahead, the presetting direction of Robot
Advance；When there is a barrier in front, robot moves closer to barrier, within the specific limits in real time change deviation angle and
Speed, makes robot slowly drive towards target around from barrier；When there are two and its above barrier in front, mobile robot exists
Virtual target is adjusted in real time in traveling process, i.e., last barrier that Robot is recognized advances and avoids removing
All barriers outside this, select an optimal path away from obstacle to tend to target.
It is preferred that：Fuzzy neural network controller completes input/output data pair using LMS algorithm and least square method
Modeling so that Takagi-Sugeno types fuzzy inference system can simulate wish or reality input/output relation.
It is preferred that：Fuzzy neural network controller is calculated in study according to system real output value and desired output
Go out learning error, then the deviation angle and speed of system are adjusted by LMS algorithm.
The method that kinematics model is set up to mobile robot is as follows：
Step 101, the distance measuring sensor that mobile robot is carried by body measures the distance of barrier, wherein, machine
People's changing coordinates are (xr,yr), coordinate of ground point is (xt,yt), E is robot current location (xr,yr) arrive target point (xt,yt)
Vector, its mould length and vectorial angle are expressed as：
EnFor potential energy of the robot in target range potential field,The angle of current robot and target point, according to machine
People is constantly corrected current location, and target location is pointed to all the time, and subscript n represents the specific moment；
Step 102, rate pattern, speed of the mobile robot in navigation task is between robot and peripheral obstacle
Distance determines that, when clear stops, robot full speed aheads, the Reduced Speed Now when running into barrier, it then follows below equation：
Wherein, v is robot translational speed, d1It is robot far from obstacle distance, d2For emergent stopping distance, β is speed
Proportionality coefficient, maxV is the robot maximum travelling speed of setting；
Step 103, rule is offset, in reactive navigation, mobile robot carries out local path according to sensor information
Planning, is generally divided into trend goal behavior and avoid-obstacle behavior, if surrounding is without barrier, robot towards target point withBefore angle
Enter, when there is barrier in front, then need artificially to add a migration noise δ, robot needs collisionless to tend to target, thus sets up such as
Lower equation：
ΦnFor the pre- sighted direction of mobile robot, φnFor n moment angles, δnFor n instants offset noises；K is proportionality coefficient
The migration noise size of addition, the environment that its value is presently in by fuzzy neural network controller according to robot determines, whenWhen, device people advances towards target location；WhenWhen, mobile robot is by according to the target added after deviation angle
Advance in direction.
The present invention compared with prior art, has the advantages that：
1. neutral net is combined with the advantage of fuzzy control, the self-learning capability of fused neural network and fuzzy control
Fuzzy reasoning ability, reduce reasoning from logic workload.
2. modified void goal approach, using simple virtual target method, breaks away from robot and tends in object run
Trapping state.
Brief description of the drawings
Fig. 1 is schematic flow sheet of the invention；
Fig. 2 is ANFIS structural representations.
Embodiment
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is furture elucidated, it should be understood that these examples are merely to illustrate this
Invention rather than limitation the scope of the present invention, after the present invention has been read, those skilled in the art are various to the present invention's
The modification of the equivalent form of value falls within the application appended claims limited range.
A kind of robot path planning method based on ANFIS fuzzy neural networks, sets up to mobile robot transport first
It is dynamic to learn model；By the autonomous learning function and the fuzzy reasoning ability of fuzzy theory of neutral net, a kind of fuzzy neural is proposed
The fuzzy neural network controller of the Mobile Robotics Navigation of network；It is based on Adaptive Fuzzy Neural-network structure, builds
Takagi-Sugeno types fuzzy inference system is simultaneously used as the reference model of robot local reaction control；By the distance of barrier
Inputted with the relevant information of position as two of fuzzy neural network controller, fuzzy neural network controller exports machine in real time
Device people deviation angle and the speed of service, by the offset direction of fuzzy neural network controller on-line tuning mobile robot, make
Mobile robot being capable of collisionless adjust automatically speed trend target.
Robot move angle and the speed of service are represented by fuzzy neural network controller output valve, closer to barrier
When output angle absolute value it is bigger, speed absolute value is smaller；When all is clear ahead, before the presetting direction of Robot
Enter；When there is a barrier in front, robot moves closer to barrier, changes deviation angle and speed in real time within the specific limits
Degree, makes robot slowly drive towards target around from barrier；When there are two and its above barrier in front, mobile robot is expert at
Virtual target is adjusted in real time during entering, i.e., last barrier that Robot is recognized advances and avoids removing this
Outside all barriers, select one away from obstacle optimal path tend to target.
Fuzzy neural network controller completes the modeling of input/output data pair using LMS algorithm and least square method,
Takagi-Sugeno type fuzzy inference systems are simulated to wish or actual input/output relation.Fuzzy neural network
Controller calculates learning error in study according to system real output value and desired output, then by LMS algorithm to being
The deviation angle and the speed of service of system are adjusted.
For Mobile Robotics Navigation practical problem under location circumstances, build nerve network controller, by barrier away from
From two inputs of the relevant information with position as controller, robot deviation angle and the speed of service are realized as output
Local paths planning, and the method for combining virtual sub-goal, can strengthening system solve trap in tradition reaction navigation problem
Path complexity and path redundancy problem under state.Path complexity and path redundancy in tradition reaction navigation problem is solved to ask
Topic, planning department one catch state collisionless tend to target optimal path.
1. obstacle distance is measured by the sensor around robot, and to the position of robot, speed is modeled
And set up avoidance rule.
(1) distance measuring sensor that mobile robot is carried by body measures the distance of barrier.Robot changing coordinates
For (xr,yr), coordinate of ground point is (xt,yt), E is robot current location (xr,yr) arrive target point (xt,yt) vector, its mould
Long and vectorial angle is expressed as
EnFor potential energy of the robot in target range potential field；The angle of current robot and target point, according to robot
Current location is constantly corrected, and target location is pointed to all the time；Subscript n represents the specific moment.
(2) rate pattern
Speed of the mobile robot in navigation task distance between robot and peripheral obstacle is determined.When accessible
When thing stops, robot full speed aheads, the Reduced Speed Now when running into barrier.Follow below equation：
V is robot translational speed；d1It is robot far from obstacle distance；d2For emergent stopping distance；β speed proportionals system
Number；MaxV is the robot maximum travelling speed of setting.
(3) rule is offset
In reactive navigation, mobile robot carries out local paths planning according to sensor information.It is generally divided into trend
Goal behavior and avoid-obstacle behavior.If enclosing no barrier, device people towards target point withAngle is advanced, when there is barrier in front, then
A migration noise δ need to be artificially added, robot needs collisionless to tend to target, thus sets up following equation
For the pre- sighted direction of mobile robot；K is the migration noise size that proportionality coefficient is added, and its value is by fuzznet
The environment that network controller is presently according to robot determines that works asWhen, device people advances towards target location；When
When, mobile robot will advance according to the target direction added after deviation angle.
2. based on Adaptive Fuzzy Neural-network ANFIS networks, Takagi-Sugeno type fuzzy inference systems are built, are carried
Go out nerve network controller.
The distance of barrier and the relevant information of position are inputted as two of controller, robot deviation angle and fortune
Scanning frequency degree is used as output.Fuzzy Neural Network System completes input/output data pair using LMS algorithm and least square method
Modeling so that Takagi-Sugeno types fuzzy inference system, which can be simulated, wishes or actual input/output relation.Fuzzy god
Through system in study, learning error can be calculated according to system real output value and desired output, then pass through LMS algorithm
The deviation angle and the speed of service of system are adjusted.
Study mechanism is introduced using neutral net, is that fuzzy controller automatically extracts fuzzy rule and fuzzy membership functions,
Whole system is set to turn into Fuzzy Neural Network System.Its sample data is the data based on hands-on, the adaptive mode of use
The ANFIS networks of neutral net are pasted, Takagi-Sugeno type fuzzy inference systems are built.
Typical ANFIS structures, as shown in Fig. 2 wherein, x1,x2It is the input of system, y is the input of inference system,
There is provided according to right；Each node of network same layer has similar function, uses O1+iThe output of i-th of node of first layer is represented, according to
This analogizes.
First layer：This node layer is by input signal obfuscation
O1+i=μ Ai(xi), i=1,2 (5)
Oi+j=μ Bj-2(x2), j=3,4 (6)
Wherein, AiOr Bj-2.It is fuzzy set, such as " many ", " few " etc.；μAi(xi) be fuzzy set membership function.
The second layer：This node layer is used for the relevance grade w for calculating each rulei, i.e.,：By the degree of membership phase of each defeated people's signal
Multiply, and regard product as this rule relevance grade.
O2+i=wi=μ Ai(x1)μBi(x2), i=1,2 (7)
Third layer：The normalization that this node layer carries out each rule relevance grade is calculated, i.e.,：Calculate the i-th rule with it is complete
Portion's rule is applicable
O3,i=w1'=wi/(w1+w2), i=1,2 (8)
4th layer：This node layer is used for the output for calculating each rule
Ok,i=wi'fi=wi'(pixi+qix2+ri), i=1,2 (9)
Wherein, it is consequent (conclusion) output function of Sugeno fuzzy systems, when it is that linear function is then referred to as " single order
System "；If constant is then referred to as " 0 level system ".
Layer 5：This layer is single node, total output for computing system
The system frequently be error back propagation algorithm or train correlation with hybrid algorithm that least square is combined
Parameter so that system being capable of the given sample data of simulation very well.The characteristics of Adaptive Neuro-fuzzy Inference is maximum is just
It is that the system is the modeling method based on data.
Fuzzy Neural Network System completes the modeling of input/output data pair using LMS algorithm and least square method.Make
The Takagi-Sugeno types fuzzy inference system come must be generated and can be simulated and wished or actual input/output relation.Mould
Nervous system is pasted in study, learning error can be calculated according to system real output value and desired output, then pass through LMS
Algorithm is adjusted to systematic parameter.
3. path planning is carried out using virtual target method
Using improving virtual target method, selection robot can catch the optimal path of state, by adaptive
The real-time output offset angle of fuzzy neural network controller and the speed of service, the direction of advance of on-line tuning mobile robot, make
Mobile robot being capable of collisionless adjust automatically speed trend target.
Robot move angle and the speed of service are represented by Fuzzy Neural Network System output valve, during closer to barrier
Output angle absolute value is bigger, and speed absolute value is smaller.When all is clear ahead, the presetting direction of Robot is advanced；
When there is a barrier in front, robot moves closer to barrier, changes deviation angle and speed in real time within the specific limits,
Robot is set slowly to drive towards target around from barrier；When there are two and its above barrier in front, to avoid proposed void
Intend the challenge (being absorbed in trapping state) of path redundancy in target, mobile robot is needed to virtual target during traveling
Adjusted, i.e., last barrier that Robot is recognized advances and avoids in addition all barriers, selected in real time
Select an optimal path away from obstacle and tend to target, be finally completed the navigation to target point.
Described above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (5)
1. a kind of robot path planning method based on ANFIS fuzzy neural networks, it is characterised in that：First to mobile machine
People sets up kinematics model；By the autonomous learning function and the fuzzy reasoning ability of fuzzy theory of neutral net, propose a kind of
The fuzzy neural network controller of the Mobile Robotics Navigation of fuzzy neural network；It is based on Adaptive Fuzzy Neural-network knot
Structure, builds Takagi-Sugeno types fuzzy inference system and as the reference model of robot local reaction control；By barrier
Distance and position relevant information as two of fuzzy neural network controller inputs, fuzzy neural network controller is real-time
Output device people deviation angle and the speed of service, pass through the skew side of fuzzy neural network controller on-line tuning mobile robot
To, enable mobile robot collisionless automatically adjust speed tend to target；Robot is broken away from using virtual target method to tend to
Trapping state in object run.
2. the robot path planning method according to claim 1 based on ANFIS fuzzy neural networks, it is characterised in that：
Robot move angle and speed are represented by fuzzy neural network controller output valve, output angle is exhausted during closer to barrier
Bigger to being worth, speed absolute value is smaller；When all is clear ahead, the presetting direction of Robot is advanced；When front has one
During individual barrier, robot moves closer to barrier, changes deviation angle and speed in real time within the specific limits, delays robot
Slowly target is driven towards around from barrier；When there are two and its above barrier in front, mobile robot is during traveling to void
Intend target to be adjusted in real time, i.e., last barrier that Robot is recognized advances and avoids in addition all obstacles
Thing, selects an optimal path away from obstacle to tend to target.
3. the robot path planning method according to claim 1 based on ANFIS fuzzy neural networks, it is characterised in that：
Fuzzy neural network controller completes the modeling of input/output data pair using LMS algorithm and least square method so that
Takagi-Sugeno types fuzzy inference system, which can be simulated, wishes or actual input/output relation.
4. the robot path planning method according to claim 1 based on ANFIS fuzzy neural networks, it is characterised in that：
Fuzzy neural network controller calculates learning error, then lead in study according to system real output value and desired output
LMS algorithm is crossed to be adjusted the deviation angle and the speed of service of system.
5. the robot path planning method according to claim 2 based on ANFIS fuzzy neural networks, it is characterised in that：
The method that kinematics model is set up to mobile robot is as follows：
Step 101, the distance measuring sensor that mobile robot is carried by body measures the distance of barrier, wherein, robot works as
Preceding coordinate is (xr,yr), coordinate of ground point is (xt,yt), E is robot current location (xr,yr) arrive target point (xt,yt) arrow
Amount, its mould length and vectorial angle are expressed as：
<mrow>
<msub>
<mi>E</mi>
<mi>n</mi>
</msub>
<mo>=</mo>
<msqrt>
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<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
EnFor potential energy of the robot in target range potential field,The angle of current robot and target point, it is current according to robot
Position is constantly corrected, and target location is pointed to all the time, and subscript n represents the specific moment；
Step 102, rate pattern, speed of the mobile robot in navigation task distance between robot and peripheral obstacle
Determine, when clear stops, robot full speed aheads, the Reduced Speed Now when running into barrier, it then follows below equation：
<mrow>
<mi>v</mi>
<mo>=</mo>
<mrow>
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<mtable>
<mtr>
<mtd>
<mrow>
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Wherein, v is robot translational speed, d1It is robot far from obstacle distance, d2For emergent stopping distance, β is speed proportional
Coefficient, maxV is the robot maximum travelling speed of setting；
Step 103, rule is offset, in reactive navigation, mobile robot carries out local paths planning according to sensor information,
Be generally divided into trend goal behavior and avoid-obstacle behavior, if surrounding is without barrier, robot towards target point withAngle is advanced, preceding
When there is barrier side, then need artificially to add a migration noise δ, robot needs collisionless to tend to target, thus set up as inferior
Formula：
ΦnFor the pre- sighted direction of mobile robot, φnFor n moment angles, δnFor n instants offset noises；K adds for proportionality coefficient
Migration noise size, the environment that its value is presently in by fuzzy neural network controller according to robot determines, when
When, device people advances towards target location；WhenWhen, mobile robot by according to add deviation angle after target direction before
Enter.
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| CN107727100A (en) * | 2017-10-16 | 2018-02-23 | 广东智爱机器人科技有限公司 | A kind of Mobile Robotics Navigation method for planning track based on apart from type fuzzy reasoning |
| CN108053067A (en) * | 2017-12-12 | 2018-05-18 | 深圳市易成自动驾驶技术有限公司 | Planing method, device and the computer readable storage medium of optimal path |
| CN109242294A (en) * | 2018-08-29 | 2019-01-18 | 国网河南省电力公司电力科学研究院 | Improve the power communication performance method for early warning and device of fuzzy neural network |
| CN109597404A (en) * | 2017-09-30 | 2019-04-09 | 徐工集团工程机械股份有限公司 | Road roller and its controller, control method and system |
| CN109782757A (en) * | 2018-12-30 | 2019-05-21 | 芜湖哈特机器人产业技术研究院有限公司 | A kind of path dispatching method of more AGV systems based on subsection scheduling |
| CN110196588A (en) * | 2019-03-28 | 2019-09-03 | 陕西理工大学 | Method for planning path for mobile robot based on networks decomposition |
| CN110262512A (en) * | 2019-07-12 | 2019-09-20 | 北京机械设备研究所 | A kind of mobile robot is detached from the barrier-avoiding method and system of U-shaped obstacle trap |
| CN110426950A (en) * | 2019-07-06 | 2019-11-08 | 大国重器自动化设备(山东)股份有限公司 | A kind of intelligent robot based on fuzzy logic |
| CN110672101A (en) * | 2019-09-20 | 2020-01-10 | 北京百度网讯科技有限公司 | Navigation model training method and device, electronic equipment and storage medium |
| CN111443603A (en) * | 2020-03-31 | 2020-07-24 | 东华大学 | Robot sharing control method based on self-adaptive fuzzy neural network system |
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