CN108319572A - A kind of mixed self-adapting particle filter method of mobile robot fault diagnosis - Google Patents
A kind of mixed self-adapting particle filter method of mobile robot fault diagnosis Download PDFInfo
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- CN108319572A CN108319572A CN201711095126.1A CN201711095126A CN108319572A CN 108319572 A CN108319572 A CN 108319572A CN 201711095126 A CN201711095126 A CN 201711095126A CN 108319572 A CN108319572 A CN 108319572A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
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Abstract
The present invention relates to a kind of mixed self-adapting particle filter methods of mobile robot fault diagnosis, and state space is adaptively combined with the adaptive two kinds of mechanism of population.The adaptive core concept of state space is that sample space is tied to a certain fuzzy subset in " total state space " according to the domain knowledge of mobile robot fault diagnosis, number of particles is then adjusted by the KL distances between the APPROXIMATE DISTRIBUTION of the particle set representations of two different number of particles of synchronization, domain knowledge is used to portray the mobile robot probability that various failures occur under different motion pattern, since domain knowledge uncertainty caused by driving and executing agency is indicated by fuzzy set.
Description
Technical field
The present invention relates to a kind of mixed self-adapting particle filter methods of mobile robot fault diagnosis, belong to mobile machine
People's fault diagnosis field.
Background technology
Fault diagnosis is most important for localization for Mobile Robot, modeling, navigation or even safety.Fault diagnosis is an allusion quotation
The random hybrid system state estimation problem of type.Particle filter is the Monte Carlo method for monitoring dynamical system, passes through cum rights
Sample (i.e. particle) with collecting imparametrization approximation probability distribution.Particle filter is in the complicated higher-dimension problem of processing, main problem
If overcoming the contradiction between precision and efficiency：In order to improve precision, it is necessary to increase number of particles；And in order to improve efficiency,
Number of particles must be reduced.
Document is (referring to " remaining cleverness, Cai Zixing, Tan Ping, Duan Zhuohua：《Based on multi-modal evolution Rao-Blackwellized
The fault diagnosis of the mobile robot dead reckoning system of particle filter》, control and decision, the 12nd phase in 2010 ") be directed to one
The troubleshooting issue of class mobile robot dead reckoning system proposes a kind of multi-modal evolution Rao-Blackwellized
Subfilter, to solve by the poor caused problem of inconsistency of particle, using intersection and variation population policy optimization, according to grain
Discontinuous Factors are added in sub- diversity, and using the MERBPF corresponding to Expert Rules judgement robot motion state, construction complexity is patrolled
Collect table.The defect of this method is to use multiple particles filter for different modalities so that turns between particle filter
More time and memory headroom will be expended by changing；In addition, this method can not dynamically adjust number of particles.
Invention content
The present invention relates to a kind of mixed self-adapting particle filter methods of mobile robot fault diagnosis, certainly by state space
It adapts to and the adaptive two kinds of mechanism of population combines.The adaptive core concept of state space is according to mobile machine
Sample space is tied to a certain fuzzy subset in " total state space " by the domain knowledge of people's fault diagnosis, and number of particles then passes through
KL distances between the APPROXIMATE DISTRIBUTION of the particle set representations of the different number of particles of synchronization two adjust, and domain knowledge is used for
The mobile robot probability that various failures occur under different motion pattern is portrayed, due to being led caused by driving and executing agency
Domain knowledge uncertainty is indicated by fuzzy set.It is as follows：
Step 1 initialization step:
Defective space S, model space M, number of particles are set as N0, arrange parameter c, γ, α, β initialize particle collectionWherein
Step 2 determines the characteristic function C of each mode m in model space M according to domain knowledgem:S → { 0,1 }, it is each
The membership function μ of a mode mm(uL,uR), wherein uL,uRMobile robot left and right wheels actuating speed is indicated respectively；
Step 3 is made iteratively step 4 to step 9 for each time step t,
It is respectively u that step 4, which is calculated according to formula (1) in mobile robot left and right wheels actuating speed,L,uRUnder conditions of failure shape
The conditional probability p that state s occursa(s|uL,uR)
WhereinFor normalization factors；
Step 5 is according to formula (2) design conditions transition probability p (st|st-1,uL,uR)
p(st|st-1,uL,uR)=η2pa(st|uL,uR)pb(st|st-1)(2)
Wherein st、st-1The malfunction of t moment and t-1 moment, p are indicated respectivelyb(st|st-1) indicate malfunction transfer
Equation,For constant of standardizing；Step 6 calculates importance sampling
Conditional transition probability p (the s obtained are calculated according to formula (2)t|st-1,uL,uR) sampling generation particle collection
Wherein WhereinT moment is indicated respectively
Malfunction, continuous state and the weight that i-th particle indicates, Nt-1Indicate the number of particles at t-1 moment, ztWhen indicating t
The measurement of sensor is carved,Expression is nonserviceabledUnder from continuous state metastasis model；It indicates in event
Barrier stateUnder measurement model；Step 7 calculates maximum a-posteriori estimation
Step 7.1 calculates edge distribution
Step 7.2 state estimation
Wherein δ indicates dirac functions
Step 8 number of particles adjusts
Step 8.1 is from particle collectionMiddle extraction Nt-1A particle generates new particle collectionSo thatIt is equal toProbability beWherein
Step 8.2 enablesGenerating population as stated above isParticle collectionWherein
Step 8.3 above-mentioned two particle collection shows respectively two kinds of approximations of posterior probability density, about discrete state
Edge distribution be expressed as：
Particle collection is indicated respectivelyWithThe determining edge distribution about discrete state；
Step 8.4 calculates its KL distance：
Step 8.5if ρ ＞ γ Nt=α Nt-1else Nt=β Nt-1
Step 9 resampling：FromResampling NtA particle generates new particle collection
From particle collectionMiddle extraction NtA particle generates new particle collectionSo thatIt is equal toProbability beWherein
For each particle i, enable
Specific implementation mode
The specific implementation method of the present invention is described in detail below.The present invention relates to a kind of mobile robot failures to examine
Disconnected mixed self-adapting particle filter method adaptively organically combines state space with the adaptive two kinds of mechanism of population
Come.The adaptive core concept of state space is to be tied to sample space according to the domain knowledge of mobile robot fault diagnosis
The a certain fuzzy subset in " total state space ", number of particles then pass through the particle set representations of two different number of particles of synchronization
APPROXIMATE DISTRIBUTION between KL distances adjust, domain knowledge for portray mobile robot under the different motion pattern it is various therefore
Hinder the probability occurred, since domain knowledge uncertainty caused by driving and executing agency is indicated by fuzzy set.
It is as follows：
Step 1 initialization step:
Defective space S, model space M, number of particles are set as N0=100, arrange parameter c=2, γ=0.1, α=
1.2, β=0.9, initialize particle collectionWherein
Step 2 determines the characteristic function C of each mode m in model space M according to domain knowledgem:S → { 0,1 }, it is each
The membership function μ of a mode mm(uL,uR), wherein uL,uRMobile robot left and right wheels actuating speed is indicated respectively；
Step 3 is made iteratively step 4 to step 9 for each time step t,
It is respectively u that step 4, which is calculated according to formula (1) in mobile robot left and right wheels actuating speed,L,uRUnder conditions of failure shape
The conditional probability p that state s occursa(s|uL,uR)
WhereinFor normalization factors；
Step 5 is according to formula (2) design conditions transition probability p (st|st-1,uL,uR)
p(st|st-1,uL,uR)=η2pa(st|uL,uR)pb(st|st-1) (2)
Wherein st、st-1The malfunction of t moment and t-1 moment, p are indicated respectivelyb(st|st-1) indicate malfunction transfer
Equation,For constant of standardizing；
Step 6 calculates importance sampling
Conditional transition probability p (the s obtained are calculated according to formula (2)t|st-1,uL,uR) sampling generation particle collection
Wherein WhereinT moment the is indicated respectively
Malfunction, continuous state and the weight that i particle indicates, Nt-1Indicate the number of particles at t-1 moment, ztIndicate t moment
The measurement of sensor,Expression is nonserviceabledUnder from continuous state metastasis model；It indicates in failure
StateUnder measurement model；
Step 7 calculates maximum a-posteriori estimation
Step 7.1 calculates edge distribution
Step 7.2 state estimation
Wherein δ indicates dirac functions
Step 8 number of particles adjusts
Step 8.1 is from particle collectionMiddle extraction Nt-1A particle generates new particle collectionSo thatIt is equal toProbability beWherein
Step 8.2 enablesGenerating population as stated above isParticle collectionWherein
Step 8.3 above-mentioned two particle collection shows respectively two kinds of approximations of posterior probability density, about discrete state
Edge distribution be expressed as：
Particle collection is indicated respectivelyWithThe determining edge distribution about discrete state；
Step 8.4 calculates its KL distance：
Step 8.5if ρ ＞ γ Nt=α Nt-1else Nt=β Nt-1
Step 9 resampling：FromResampling NtA particle generates new particle collection
From particle collectionMiddle extraction NtA particle generates new particle collectionSo thatIt is equal toProbability beWherein
For each particle i, enable
The above, only best mode for carrying out the invention, any one skilled in the art is in the present invention
In the technical scope of disclosure, the simple change or equivalence replacement of the technical solution that can be become apparent to each fall within the present invention's
In protection domain.
Claims (1)
1. a kind of mixed self-adapting particle filter method of mobile robot fault diagnosis, by state space is adaptive and population
Adaptive two kinds of mechanism combines；The adaptive core concept of state space is according to mobile robot fault diagnosis
Sample space is tied to a certain fuzzy subset in " total state space " by domain knowledge, and number of particles is then by synchronization two
KL distances between the APPROXIMATE DISTRIBUTION of the particle set representations of different number of particles adjust, and domain knowledge is for portraying mobile machine
People's probability that various failures occur under different motion pattern, since domain knowledge caused by driving and executing agency is uncertain
Property is indicated by fuzzy set；It is as follows：
Step 1：Initialization step:Defective space S, model space M, number of particles are set as, arrange parameter c,
, initialize particle collection, wherein,,；
Step 2：The characteristic function of each mode m in model space M is determined according to domain knowledge, often
The membership function of one mode m, whereinMobile robot left and right wheels actuating speed is indicated respectively；
Step 3：For each time stept, it is made iteratively step 4 to step 9,
Step 4：According to formula（1）It calculates in mobile robot left and right wheels actuating speed and is respectivelyUnder conditions of malfunction s
The conditional probability of generation
（1）
WhereinFor normalization factors;
Step 5：According to formula（2）Design conditions transition probabilityp(s t |s t-1 ,u L,u R)
p(s t |s t-1 ,u L,u R)= p a(s t |u L,u R)p b(s t |s t-1 ) (2)
Whereins t 、s t-1 It indicates respectivelytMoment andtThe malfunction at -1 moment,p b(s t |s t-1 ) indicate malfunction equation of transfer,For constant of standardizing;
Step 6：Calculate importance sampling：According to formula（2）Calculate the conditional transition probability obtainedp(s t | s t-1,u L,u R) sampling life
Granulating subset, wherein,,,；WhereinThe failure that i-th of particle of t moment indicates is indicated respectively
State, continuous state and weight,Indicate the number of particles at t-1 moment,Indicate the measurement of t moment sensor,Expression is nonserviceabledUnder from continuous state metastasis model；It indicates in failure shape
StateUnder measurement model；
Step 7：Calculate maximum a-posteriori estimation：
Step 7.1 calculates edge distribution；
Step 7.2 state estimation；
WhereinIndicate dirac functions
Step 8：Number of particles adjusts
Step 8.1 is from particle collectionMiddle extractionA particle generates new particle collection,
So thatIt is equal toProbability be, wherein。
Step 8.2 enables, generating population as stated above isParticle collection,
Wherein；
Step 8.3 above-mentioned two particle collection shows respectively two kinds of approximations of posterior probability density, the side about discrete state
Fate cloth is expressed as：
Particle collection is indicated respectivelyWithIt is determining about discrete state
Edge distribution；
Step 8.4 calculates its KL distance：
Step 8.5 if else
Step 9：Resampling：FromResamplingA particle generates new particle collection
From particle collectionMiddle extractionA particle generates new particle collectionSo thatIt is equal toProbability be, wherein
For each particlei, enable。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108972553A (en) * | 2018-08-06 | 2018-12-11 | 北京邮电大学 | A kind of space manipulator fault detection method based on particle filter algorithm |
| CN113375658A (en) * | 2021-06-15 | 2021-09-10 | 电子科技大学中山学院 | Method and system for simultaneously FDD and SLAM under mobile robot fault |
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| US20110054689A1 (en) * | 2009-09-03 | 2011-03-03 | Battelle Energy Alliance, Llc | Robots, systems, and methods for hazard evaluation and visualization |
| CN103795373A (en) * | 2013-11-29 | 2014-05-14 | 电子科技大学中山学院 | Particle filter generating method for incomplete system fault diagnosis |
| CN107038459A (en) * | 2017-04-07 | 2017-08-11 | 电子科技大学中山学院 | A kind of incomplete system method for diagnosing faults merged based on model and data-driven |
| CN107246873A (en) * | 2017-07-03 | 2017-10-13 | 哈尔滨工程大学 | A kind of method of the mobile robot simultaneous localization and mapping based on improved particle filter |
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| US20110054689A1 (en) * | 2009-09-03 | 2011-03-03 | Battelle Energy Alliance, Llc | Robots, systems, and methods for hazard evaluation and visualization |
| CN103795373A (en) * | 2013-11-29 | 2014-05-14 | 电子科技大学中山学院 | Particle filter generating method for incomplete system fault diagnosis |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108972553A (en) * | 2018-08-06 | 2018-12-11 | 北京邮电大学 | A kind of space manipulator fault detection method based on particle filter algorithm |
| CN108972553B (en) * | 2018-08-06 | 2021-08-13 | 北京邮电大学 | Space manipulator fault detection method based on particle filter algorithm |
| CN113375658A (en) * | 2021-06-15 | 2021-09-10 | 电子科技大学中山学院 | Method and system for simultaneously FDD and SLAM under mobile robot fault |
| CN113375658B (en) * | 2021-06-15 | 2023-05-09 | 电子科技大学中山学院 | Method and system for simultaneously FDD and SLAM under fault of mobile robot |
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