CN106406311B - Robot ambulation barrier-avoiding method based on information fusion and environment sensing - Google Patents
Robot ambulation barrier-avoiding method based on information fusion and environment sensing Download PDFInfo
- Publication number
- CN106406311B CN106406311B CN201610898505.3A CN201610898505A CN106406311B CN 106406311 B CN106406311 B CN 106406311B CN 201610898505 A CN201610898505 A CN 201610898505A CN 106406311 B CN106406311 B CN 106406311B
- Authority
- CN
- China
- Prior art keywords
- robot
- information
- distance
- walking
- moment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
Abstract
The invention discloses a kind of robot ambulation barrier-avoiding method based on information fusion and environment sensing, mainly solve the problems, such as that robot is difficult to carry out avoidance walking according to global information and environmental form in the prior art.Its implementation is: 1. are arranged sensor in each orientation of robot, measure range information and obliquity information, 2. judging robot local environment safe condition and environment-identification type by information fusion technology, 3. setting fuzzy membership function according to sensor and ranging range, 4. calculating avoidance direction and travel time according to multi-source range information and environmental form, the speed of travel is set；6. direction of travel, time, speed are inputted robot dynamical system, so that avoidance is walked.The present invention is based on information fusion technologies, carry out fuzzy avoidance in conjunction with specific environment type, improve the flexibility and safety of robot obstacle-avoiding walking, can be used for domestic robot control.
Description
Technical field
The invention belongs to robotic technology fields, are specifically designed a kind of robot ambulation barrier-avoiding method, can be used for household machine
Device people control.
Background technique
With the progress of science and technology, people's lives level is gradually increased.Meanwhile the appearance of intelligent domestic concept, promote
Keep domestic appliance man-based development more ardent.Nowadays, domestic robot in terms of home services, amusement, cleaning application
Through very extensive.However to realize above functions, inevitably, robot must have autonomous in moving process and keep away
The ability of barrier.Due to the limitation of robot receptor type and the environment of an activation, existing robot be normally based on known environment map or
Preset path realizes walking or even random walk.These walking manners are not only inflexible, low efficiency, have to robot fuselage
There is destructiveness, and cannot be guaranteed intelligently to make walking decision in the environment of dynamically changeable, thus under the circumstances not known needed
Path planned.
Currently, common method has genetic algorithm, Artificial Potential Field Method, A* for the path planning problem under circumstances not known
Algorithm etc., in which: genetic algorithm need to repeatedly search for calculating to global context data, and search space cost is big；Artificial Potential Field Method exists
During robot ambulation, direction of travel is adjudicated by calculating environment potential field matrix, is easily trapped into local optimum；A* algorithm is one
The searching method of kind static path carries out path judgement based on estimation function, and algorithm complexity is high.
Meanwhile there is also following common drawbacks for these above-mentioned conventional methods:
1. robot is in the process of walking, the court verdict of each step only considers current state, ignores history walking states,
Cause state discrete；
2. during robot ambulation, the calculating of the error result long lasting effect later step of back judges, so that accidentally
Difference is cumulative, causes the reduction of avoidance precision.
Summary of the invention
It is an object of the invention to be directed to the deficiency of above-mentioned existing method, propose a kind of based on information fusion and environment sensing
Robot ambulation barrier-avoiding method reduce One-step error so that walking motion coherentization, improve avoidance precision.
To achieve the goals above, technical solution of the present invention includes the following:
(1) by being deployed in the sensor of robot fuselage, the multi-source distance in robot ambulation environment is obtained
Information: D0={ d0,d1,...,dk,...,dn, wherein dkIndicate the surveyed range information of k-th of sensor, the value of k be from 0 to
N, n are number of probes；
(2) according to multi-source range information D0, the global weighted average distance information D of calculating robot；
(3) hazardous environment state recognition:
The global safety state threshold β of (3a) calculating robotDWith fault condition threshold βS；
βD=r
βS=Δ tv+r
Wherein, r indicates that robot geometric radius, Δ t indicate the robot single step iteration time, and v indicates robot ambulation speed
Degree；
(3b) is by robot global weighted average distance information D and global safety state threshold βDIt is compared, identification ring
Border precarious position: if D < βD, then it is judged as precarious position, robot stops walking, if D >=βD, then it is judged as safe condition, holds
Row step (3c)；
(3c) is by robot ambulation orientation measurement distance S and fault condition threshold βSIt is compared, identifies walking failure shape
State: if S < βS, then it is judged as malfunction, robot stops walking, if S >=βS, then it is judged as non-faulting state, executes step
(4)；
(4) general environment state recognition:
(4a) utilizes multi-source range information D0, obtain along wall environment status information e1；
(4b) utilizes multi-source range information D0, obtain topography mutation environmental state information e2；
(4c) obtains the gradient and changes environmental state information e using gyro sensor metrical information θ at the top of robot3；
(5) walking avoidance direction is calculated:
(5a) divides sensor measurement distance in proportion, control domain U needed for obtaining fuzzy obstacle avoidance algorithmSWith control
Domain threshold value set TS；
(5b) chooses minimum operation method function as subordinating degree function R (d), calculates multi-source range information D0With step (4)
Three kinds of obtained environmental state information e1,e2,e3, obtain membership vector R0；
(5c) is with gravity model appoach to membership vector R0Ambiguity solution obtains the output valve F (t) of fuzzy obstacle avoidance algorithm；
(5d) obtains robot ambulation avoidance direction M to output valve F (t) round of fuzzy obstacle avoidance algorithm；
(6) mapping walking variable:
(6a) is according to the output valve F (t) for obscuring obstacle avoidance algorithm, the time ratio r of calculating robot's walkingt:
(6b) is according to time ratio rtWith unit driving time Δ t0, the time T of calculating robot's next step walkingr:
Tr=rtΔt0
(7) robot ambulation is driven:
Robot ambulation speed v is arranged in (7a)；
(7b) is by travel time Tr, walking avoidance direction M, speed of travel v input robot dynamical system, drive robot
Walking.
The invention has the following advantages over the prior art:
First, the present invention is based on the angles of Multi-source Information Fusion, carry out fusion treatment to the Global obstacle information of robot,
Avoid defect unknown to global state caused by information independent process in walking process；
Second, the present invention analyzes environmental state information, may recognize that precarious position and starts emergency stopping, and passes through knowledge
These ambient conditions are not mutated along wall, slope change, topography, overcome the single disadvantage of prior art avoidance environment；
Third, the present invention carry out time ratio calculating to the output of fuzzy obstacle avoidance algorithm, fuzzy domain are moved with robot
Force system control variable effectively connects, and result and the robot dynamical system controling parameter for overcoming fuzzy reasoning are uncomfortable
With the shortcomings that, improve the flexibility and accuracy of robot ambulation；
Simulation result shows data volume of the invention and practices complexity to be able to satisfy robot requirement of real-time, simultaneously
Can efficiently, accurately solve the problems, such as robot obstacle-avoiding walking.
Detailed description of the invention
Fig. 1 is realization general flow chart of the invention；
Fig. 2 is the sub-process figure of environment-identification state in the present invention；
Fig. 3 is the sub-process figure that fuzzy avoidance is carried out in the present invention；
Fig. 4 is the sensor placement figure of robot；
Fig. 5 is the route comparison diagram of the robot obstacle-avoiding walking carried out with the method for the present invention and conventional method.
Specific implementation method
Present invention is further described in detail with reference to the accompanying drawing.
Shenfu Fig. 1, steps are as follows for realization of the invention.
Step 1, sensor is set.
Sensor by being deployed in robot fuselage different location obtains range information.Common ranging sensing
Device includes optics sensor and acoustic category sensor, and the present invention selects infrared distance sensor and ultrasonic distance-measuring sensor.
Referring to Fig. 4, the present invention is according to ranging demand, setting such as lower sensor with robot:
Left bit ultrasonic sensor is set on front side of robot fuselage respectively, ultrasonic sensor, the right side is arranged in direct bearing
Ultrasonic sensor is arranged in orientation, and infrared sensor is arranged in left bit, and infrared sensor is arranged in right bit, is used for robot measurement
Front side all angles distance signal；
In the front side of robot fuselage to height ultrasonic sensor is had, for perceiving robot traveling front topography
Altitude signal, i.e. height of the robot chassis apart from ground；
Left side ultrasonic sensor, right side setting ultrasonic sensor is respectively set in the two sides of robot fuselage, is used for
The range information of robot measurement two sides；
On the upside of robot fuselage, it is horizontally mounted gyro sensor, is existed for measuring robot center of gravity in walking process
The angulation change amount of three-dimensional space.
Step 2, Multi-source Information Fusion ranging.
2.1) it using the sensor for being deployed in robot fuselage in step 1, obtains in robot ambulation environment
Multi-source range information D0={ d0,d1,...,dk,...,dn, wherein dkIndicate the surveyed range information of k-th of sensor, the value of k
For from 0 to n, n is number of probes；
2.2) in moment T, with ultrasonic sensor measure equation deMeasurement obtain left bit, direct bearing, right bit and
Robot fuselage two sides apart from letter information be { 133.77,140.71,13.71,4.81,114.39,16.30 }, wherein
In formula, teIndicate ultrasonic reflections high-frequency signal cut-off time, tsWhen indicating that ultrasonic reflections high-frequency signal starts
It carves, dvThe spread speed of ultrasonic wave in air medium is represented, d is takenv=3.4*102(m/s)；
2.3) in moment T, with infrared sensor measure equation: dr=SId0Measurement obtains left bit, the information of right bit is
{ 5,5 }, in which:
In formula, d0Indicate infrared sensor measured information, dthIndicate the activation threshold of default infrared sensor measurement distance,
D is set in the present inventionth=5cm, SIIndicate sign function.
2.4) the surveyed range information of both sensors in combining step (2.2) and step (2.3), obtains T moment multi-source
Range information D0:
D0={ 133.77,140.71,13.71,4.81,5,5,114.39,16.30 }
Wherein, parasang is centimetre；
2.5) according to multi-source range information D0, global weighted average distance information D is calculated；
Wherein, wiFor the weight of i-th of sensor, diRange information is surveyed by i-th of sensor, the value of i be from 0 to
N, n=8 are range information number.
Referring to Fig. 2, subordinate's step 3 to step 6 is described as follows；
Step 3, hazardous environment state recognition.
Robot ambulation local environment state constantly changes, and in order to protect robot fuselage safe, improves walking avoidance
Accuracy, the present invention identify the risk of current ambient conditions in each step of robot ambulation.
3.1) the global safety state threshold β of calculating robotDWith fault condition threshold βS；
βD=r
βS=Δ tv+r
Wherein, the present embodiment setting r=9.5cm indicates that robot geometric radius, Δ t=1.5S indicate that robot single step changes
For the time, v=10cm/s indicates robot ambulation speed, β is calculatedD=9.5, βS=24.5；
3.2) by robot global weighted average distance information D and global safety state threshold βDIt is compared, identification ring
Border precarious position: at this point, D=53.21 > βD=9.5, so being judged as safe condition, execute step (3.3)；
3.3) by robot ambulation orientation measurement distance S and fault condition threshold βSIt is compared, identifies walking failure shape
State:
S=140.71 > β at this timeS=24.5, so being judged as non-faulting state, execute step 4.
Step 4, ambient condition identification is walked along wall.
Walking ambient condition along wall is common one of ambient condition during robot ambulation.Eliminate danger ambient condition
Afterwards, if accurately identifying robot local environment, and using environmental form as the input of subsequent fuzzy avoidance step, avoidance is improved
The accuracy of walking.
4.1) T moment and its p=4 moment before, metrical information are as follows:
Walked along wall be a continuous time period motion state, can not only judge well from a time point, therefore
This example assesses the walking environment of robot with above-mentioned 5 moment, not only can judge the environment shape at continuous moment
State can also judge direction of travel next time on this basis.
In order to identify environment locating for these moment robots, body orientation measurement distance letter on the right side of robot is removed respectively
Cease l1,l2,...,li,...,ljMinimum range lmin=15.77 and maximum distance lmax=16.73, it calculates separately export-oriented average
Fluctuate Weighted distance LNWeighted distance L is averagely fluctuated with introversiveM:
Wherein, liIndicate that i-th of moment leans to one side direction range information, the value of i is from 1 to j, and j is time of measuring number；
ρn=0.3 is export-oriented coefficient of variation, for indicating that Robot wall walks state parameter farthest apart from wall in environment, ρm=
1.5 is interior to coefficient of variation, for indicating that Robot wall walks state parameter nearest apart from wall in environment；
It is calculated in T moment, LN=4.893, LM=24.105
4.2) by minimum range lminWeighted distance L is averagely fluctuated with export-orientedNCompare: if lmin<LN, then e is set1=0, it uses
It is in non-in expression robot and walks ambient condition along wall, if lmin≥LN, execute step (4.3), l in the present embodimentmin=
15.77>LN=4.893, it executes step (4.3)；
4.3) by maximum distance lmaxWeighted distance L is averagely fluctuated with introversiveMCompare, if lmax>LM, then e is set1=0, if
lmax≤LM, then e is set1=1, ambient condition is walked along wall for indicating that robot is in, in the present embodiment, lmax=16.73 <
LM=24.105, therefore e is set1=1, for indicating that robot is in the ambient condition walked along right side wall.
Step 5, topography mutation ambient condition identification.
Topography is mutated ambient condition, is a kind of uncontrollable ambient condition of burst；Robot is in this environment with protection
Based on fuselage safety, real-time judge environment high knots modification is needed；Meanwhile by height fluctuation coefficient measure robot fuselage with
The ability of height change is born on chassis, achievees the purpose that safe avoidance walking.
5.1) the T moment and its before 4 moment machine is measured by downward ultrasonic sensor on front side of robot
Device people's chassis height range information (h(T),h(T-1),h(T-2),h(T-3),h(T-4))=(4.81,4.45,4.44,3.98), it calculates
To robot the T moment chassis height apart from knots modification Δ h:
5.2) maximum height coefficient of variation μ is setd=13cm, for indicating most deep height of the robot chassis apart from ground
Minimum constructive height coefficient of variation μ is arranged in distance state parameteru=-2cm, for indicating robot chassis apart from the most shallow high of ground
Spend distance state parameter；
5.3) by chassis height apart from knots modification Δ h and maximum height coefficient of variation μdIt is compared: if Δ h >=μd, then set
Set e2=-1, for indicating that T moment robot is in topography bust ambient condition, if Δ h < μd, execute step (5.4), this implementation
In example, Δ h=0.48 < μd=13, it obtains the not unexpected sinking of ground of the topography of robot ambulation environment, downlink step, hang down
The obstacles situations such as straight abrupt slope；The maximum dropping distance that can be born when robot chassis height is h=5.7cm is calculated simultaneously are as follows:
Δhmax=μd- h=7.7cm；
5.4) by chassis height apart from knots modification Δ h and minimum constructive height coefficient of variation μuCompare: if-Δ h < μu, then e is set2
=1, it jumps ambient condition for indicating that T moment robot is in topography, if-Δ h > μu, then e is set2=0, when for indicating T
It carves robot and is in non-topography mutation ambient condition；In the present embodiment ,-Δ h=-0.48 > μu=-2, therefore e is set2=0, it uses
It is in non-topography mutation ambient condition in expression T moment robot, illustrates the not unexpected height in the address of robot ambulation environment
Platform, uplink step, compared with high obstacle object situations such as；The maximum climb that robot chassis of the invention simultaneously can be born are as follows:
Δhmin=2cm.
Step 6, the gradient changes ambient condition identification.
The ambient condition of robot ambulation, it is understood that there may be slope change situation, there are insecurity factors, for example, when encountering
The biggish environment of the gradient, the accuracy in robot ambulation direction and the size of the speed of travel can all cause robot security centainly
It influences, requires careful judgement, achieve the purpose that safe avoidance walking.
6.1) at the T moment, robot is obtained in the angulation change amount of three-dimensional space by gyro sensor:
{θx,θy,θz14.36 ° of }={, 5 °, -18.76 ° }
Wherein, θxRepresent the angulation change amount of horizontal axis in space, i.e. robot is swung left and right amplitude in the process of walking
Size；θyThe angulation change amount of the longitudinal axis in space is represented, i.e. the size of robot left and right turn amplitude in the process of walking, with ground
The face gradient is unrelated；θzRepresent the angulation change amount of vertical pivot in space, i.e. robot teeters the big of amplitude in walking environment
It is small；
6.2) analysis can obtain θxWith θzCan directly react walking environment surface relief situation, therefore pay close attention to this two
A angulation change amount, by comparing θxWith θz, robot current time is obtained because moving and the maximum angle knots modification of generation:
θmax=max | θx|,|θz|=18.76 °
6.3) manipulator shaft is set to rotation angle secure threshold θr=30 °, for indicating robot ambulation environment topography
The obvious boundary condition flat with topography of the gradient；
6.4) by robot current time maximum angle knots modification θmaxWith axial-rotation angle secure threshold θrCompare: if
θmax<θr, then e is set3=0, change ambient condition for indicating that robot is in the non-gradient；If θmax≥θr, then e is set3=
sin(θmax), the ambient condition changed for indicating the gradient.In the present embodiment, θmax=18.76 ° < θr=30 °, therefore e is set3
=0, indicate that robot is in the non-gradient and changes ambient condition.
Step 7, Fuzzy Calculation walking avoidance direction.
Referring to Fig. 3, this step is implemented as follows:
7.1) sensor measurement distance is divided in proportion, control domain U needed for obtaining fuzzy obstacle avoidance algorithmSWith control
Domain threshold value set TS:
Common division methods have etc. than dividing, it is symmetrical divide and not grade ratio division, the present invention is according to robot ambulation item
Part do not wait than dividing to measurement distance, is first divided into the higher distance range of ultrasonic sensor measurement sensitivity
Closely, in, distance set [0,40] is classified as " close " in this example, distance set by the distance set of remote three classifications
Be classified as " in ", distance setIt is classified as " remote ", obtains control domain USWith domain threshold value set TS:
US=(0 ,+∞)
7.2) subordinating degree function R (d) is set:
Common subordinating degree function has minimum operation method function, product operation method function, minimax operation method function, this hair
The bright minimum operation method function of middle selection, and the domain threshold value set T obtained according to step (7.1)SRange of variables is adjusted, is set
Determine subordinating degree function R (d),
In the present embodiment, multi-source range information D0=133.77,140.71,13.71,4.81,5,5,114.39,
16.30 } with three kinds of environmental state information e1=1, e2=0, e3=0 substitutes into fuzzy obstacle avoidance algorithm, and membership vector is calculated
R0:
R0={ 1,1,0.7715 }
7.3) to membership vector R0Ambiguity solution obtains the output valve F (t) of fuzzy obstacle avoidance algorithm
Common ambiguity solution method has gravity model appoach, height method, area-method etc., this example selects gravity model appoach to membership vector
R0Ambiguity solution solves and obtains output valve F (t)=2.74 of fuzzy obstacle avoidance algorithm；
7.4) robot ambulation mode M is determined:
To output valve F (t) round of fuzzy obstacle avoidance algorithm, robot ambulation avoidance mode M is obtained；
In this example, the corresponding relationship of walking mode and direction of travel is preset are as follows:
| Walking mode M | 1 | 2 | 3 |
| Direction of travel | It walks to the left | It is honest to walk forward | It walks to the right |
To output valve F (t) floor operation of fuzzy obstacle avoidance algorithm, obtain M=3, i.e., robot ambulation next step direction to
It is right.
Corresponding different multi-source range information D0With local environment type, the fuzzy of different values can be obtained with above-mentioned steps
Obstacle avoidance algorithm exports F (t), and then obtains different M values, such as M=1 or M=2, so that walking executes in next step for robot
Different modes.
The corresponding relationship of the classification of above-mentioned walking mode, the classification of direction of travel and walking mode and direction of travel is
One of experiment situation, the method divided according to robotically-driven mode, fuzzy domain is different, and there are also other strokes for walking mode
The mode of dividing, direction of travel also have more careful dividing condition, and there are many modes for the corresponding relationship of the two, are not limited to above situation.
Step 8, mapping walking variable.
Although the output of fuzzy obstacle avoidance algorithm is consistent with the scale of fuzzy domain, cannot be controlled directly as robot
The input of system, not only variable unit is different for the two, and variable meaning is not also identical；It needs to calculate by change of scale, makes to obscure
The output of obstacle avoidance algorithm is converted to the control variable for adapting to robot dynamical system.
8.1) time ratio r is calculatedt:
According to the output valve F (t) of fuzzy obstacle avoidance algorithm, the time ratio r of calculating robot's walkingt:
In this example, output valve F (t)=2.74 of obstacle avoidance algorithm are obscured, time ratio r is calculatedt=0.26；
8.2) according to time ratio rtWith default unit driving time Δ t0=2S, calculating robot in next step walking when
Between Tr:
Tr=rtΔt0=0.52s
Time ratio rtIt indicates in the judging result of this avoidance, when the time of robot ambulation accounts for the driving of default unit
Between ratio magnitude；As it can be seen that the travel time that avoidance obtains each time is different from, therefore the distance that robot walks every time
It is not identical；Next step direction of travel is calculated according to environmental form and range information by avoidance traveling method in robot,
Compared with the method for traditional fixed step size time, avoidance traveling method of the invention is more flexible.
Step 9, robot ambulation is driven.
By travel time Tr=0.52s, walking avoidance direction M=3, speed of travel v=10cm/s input robot power
System drives one step of robot ambulation.
Return step 2, by measuring multi-source range information, environment-identification type and fuzzy avoidance walking direction,
Circulation executes driving robot ambulation.
Robot obstacle-avoiding traveling method and the effect of conventional conditions barrier-avoiding method of the invention are compared, as a result as attached
Fig. 5.Wherein, attached drawing 5 (a) illustrates the robot ambulation track of conventional conditions barrier-avoiding method, and attached drawing 5 (b) illustrates the present invention
In the robot ambulation track based on information fusion and the robot ambulation barrier-avoiding method of environment sensing.It can be obtained by attached drawing 5
Out, avoidance traveling method of the invention, each step direction of travel and step-length are flexible and changeable, while improving the safety of walking.
Claims (1)
1. being included the following steps: based on the robot ambulation barrier-avoiding method of information fusion and environment sensing
(1) by being deployed in the sensor of robot fuselage, the multi-source range information in robot ambulation environment is obtained:
D0={ d0,d1,...,dk,...,dn, wherein dkIndicate the surveyed range information of k-th of sensor, the value of k is from 0 to n, and n is
Number of probes；
(2) according to multi-source range information D0, the global weighted average distance information D of calculating robot；
(3) hazardous environment state recognition:
The global safety state threshold β of (3a) calculating robotDWith fault condition threshold βS；
βD=r, βS=Δ tv+r
Wherein, r indicates that robot geometric radius, Δ t indicate the robot single step iteration time, and v indicates robot ambulation speed；
(3b) is by robot global weighted average distance information D and global safety state threshold βDIt is compared, environment-identification is dangerous
State: if D < βD, then it is judged as precarious position, robot stops walking, if D >=βD, then it is judged as safe condition, executes step
(3c)；
(3c) by robot direction of travel measurement distance S and fault condition threshold βSIt is compared, identifies walking failure shape
State: if S < βS, then it is judged as malfunction, robot stops walking, if S >=βS, then it is judged as non-faulting state, executes step
(4)；
(4) general environment state recognition:
(4a) utilizes multi-source range information D0, obtain along wall environment status information e1；
(4a1) was removed robot and leaned to one side orientation measurement range information l respectively at the continuous j moment1,l2,...,li,...,lj
In minimum range lminWith maximum distance lmax, calculate separately export-oriented averagely fluctuation Weighted distance LNWith introversive average fluctuation weighting
Distance LM:
Wherein, liIndicate that i-th of moment leans to one side direction range information, the value of i is from 1 to j, and j is time of measuring number；ρnFor
Export-oriented coefficient of variation, ρmTo be interior to coefficient of variation；
(4a2) is by minimum range lminWeighted distance L is averagely fluctuated with export-orientedNCompare: if lmin< LN, then e is set1=0, it is used for
Indicate that robot is in non-and walks ambient condition along wall, if lmin≥LN, execute step (4a3)；
(4a3) is by maximum distance lmaxWeighted distance L is averagely fluctuated with introversiveMCompare: if lmax> LM, then e is set1=0, if lmax
≤LM, then e is set1=1, ambient condition is walked along wall for indicating that robot is in；
(4b) utilizes multi-source range information D0, obtain topography mutation environmental state information e2:
(4b1) the T moment and its before p moment, by downward sensor on front side of robot, the robot chassis measured
Height distance information h(T),h(T-1),...,h(T-i),...,h(T-p), calculating robot the T moment chassis height apart from knots modification
Δ h:
Wherein, h(T-i)Indicate i-th of moment robot chassis height range information before the T moment, the value of i be from 1 to
P, p indicate the time of measuring number before the T moment；
Maximum height coefficient of variation μ is arranged in (4b2)d, minimum constructive height coefficient of variation μu；
(4b3) is by chassis height apart from knots modification Δ h and maximum height coefficient of variation μdCompare: if Δ h >=μd, then e is set2=-
1, for indicating that T moment robot is in topography bust ambient condition, if Δ h < μd, execute step (4b4)；
(4b4) is by chassis height apart from knots modification Δ h and minimum constructive height coefficient of variation μuCompare: if-Δ h < μu, then e is set2=
1, it jumps ambient condition for indicating that T moment robot is in topography, if-Δ h > μu, then e is set2=0, for indicating the T moment
Robot is in non-topography mutation ambient condition；
(4c) obtains the gradient and changes environmental state information e using gyro sensor metrical information θ at the top of robot3；
(4c1) is obtained by gyro sensor metrical information θ at the top of robot for indicating robot at the angle of three-dimensional space
Spend knots modification { θx,θy,θz}；Wherein, θxRepresent the angulation change amount of horizontal axis in space, θyRepresent the angulation change of the longitudinal axis in space
Amount, θzRepresent the angulation change amount of vertical pivot in space；
(4c2) obtains robot maximum angle knots modification: θmax=max | θx|,|θz|}；
Manipulator shaft is arranged to rotation angle secure threshold θ in (4c3)r；
(4c4) is by robot current time maximum angle knots modification θmaxWith axial-rotation angle secure threshold θrCompare, if θmax<
θr, then e is set3=0, change ambient condition for indicating that robot is in the non-gradient；If θmax≥θr, then e is set3=sin
(θmax), the ambient condition changed for indicating the gradient；
(5) walking avoidance direction is calculated:
(5a) divides the measurement distance of sensor in proportion, i.e., by the higher distance range of ultrasonic sensor measurement sensitivity
It is divided into closely, in, the distance set of remote three classifications, control domain U needed for obtaining fuzzy obstacle avoidance algorithmSWith control domain threshold
Value set TS；
US=(0 ,+∞)
Wherein, [0,40] indicates closely to gather,For middle distance set,To gather at a distance；
(5b) chooses minimum operation method function as subordinating degree function R (d), calculates multi-source range information D0It is obtained with step (4)
Three kinds of environmental state information e1,e2,e3, obtain membership vector R0；
(5c) is with gravity model appoach to membership vector R0Ambiguity solution obtains the output valve F (t) of fuzzy obstacle avoidance algorithm；
(5d) obtains robot ambulation avoidance direction M to output valve F (t) round of fuzzy obstacle avoidance algorithm；
(6) mapping walking variable:
(6a) is according to the output valve F (t) for obscuring obstacle avoidance algorithm, the time ratio r of calculating robot's walkingt:
(6b) is according to time ratio rtWith unit driving time Δ t0, the time T of calculating robot's next step walkingr:
Tr=rtΔt0
(7) robot ambulation is driven:
Robot ambulation speed v is arranged in (7a)；
(7b) is by travel time Tr, walking avoidance direction M, speed of travel v input robot dynamical system, drive robot ambulation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610898505.3A CN106406311B (en) | 2016-10-14 | 2016-10-14 | Robot ambulation barrier-avoiding method based on information fusion and environment sensing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610898505.3A CN106406311B (en) | 2016-10-14 | 2016-10-14 | Robot ambulation barrier-avoiding method based on information fusion and environment sensing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106406311A CN106406311A (en) | 2017-02-15 |
| CN106406311B true CN106406311B (en) | 2019-03-26 |
Family
ID=59229672
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610898505.3A Active CN106406311B (en) | 2016-10-14 | 2016-10-14 | Robot ambulation barrier-avoiding method based on information fusion and environment sensing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106406311B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018163614A (en) * | 2017-03-27 | 2018-10-18 | カシオ計算機株式会社 | Autonomous mobile device, autonomous mobile method and program |
| CN107703935A (en) * | 2017-09-12 | 2018-02-16 | 安徽胜佳和电子科技有限公司 | Multiple data weighting fusions carry out method, storage device and the mobile terminal of avoidance |
| CN108303989B (en) * | 2018-01-17 | 2018-09-14 | 上海思岚科技有限公司 | A kind of method and apparatus moved along wall for mobile robot |
| CN209224071U (en) * | 2018-11-19 | 2019-08-09 | 炬星科技（深圳）有限公司 | The sensor placement system of robot |
| CN109814600A (en) * | 2019-03-12 | 2019-05-28 | 重庆大学 | A kind of unmanned plane cable tunnel inspection flying method |
| CN110244739A (en) * | 2019-06-27 | 2019-09-17 | 湖北艾博智能装备有限公司 | A kind of aerial automatic carriage delivery method, device and computer storage medium |
| CN110456791A (en) * | 2019-07-30 | 2019-11-15 | 中国地质大学（武汉） | A kind of leg type mobile robot object ranging and identifying system based on monocular vision |
| CN111273658A (en) * | 2020-01-13 | 2020-06-12 | 北京理工大学 | Mobile robot and obstacle detection method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6377878B1 (en) * | 1999-06-24 | 2002-04-23 | Sandia Corporation | Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic |
| JP2004249748A (en) * | 2003-02-18 | 2004-09-09 | Univ Nihon | Automobile collision prevention controlling system |
| CN101758827A (en) * | 2010-01-15 | 2010-06-30 | 南京航空航天大学 | Automatic obstacle avoiding method of intelligent detection vehicle based on behavior fusion in unknown environment |
| CN103645733A (en) * | 2013-12-02 | 2014-03-19 | 江苏建威电子科技有限公司 | A robot automatically finding a charging station and a system and method for automatically finding a charging station thereof |
-
2016
- 2016-10-14 CN CN201610898505.3A patent/CN106406311B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6377878B1 (en) * | 1999-06-24 | 2002-04-23 | Sandia Corporation | Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic |
| JP2004249748A (en) * | 2003-02-18 | 2004-09-09 | Univ Nihon | Automobile collision prevention controlling system |
| CN101758827A (en) * | 2010-01-15 | 2010-06-30 | 南京航空航天大学 | Automatic obstacle avoiding method of intelligent detection vehicle based on behavior fusion in unknown environment |
| CN103645733A (en) * | 2013-12-02 | 2014-03-19 | 江苏建威电子科技有限公司 | A robot automatically finding a charging station and a system and method for automatically finding a charging station thereof |
Non-Patent Citations (3)
| Title |
|---|
| 一种移动机器人自主避障与导航方法;储星;《机械科学与技术》;20160630;第35卷(第6期);第939-945页 |
| 动态环境下基于实际隶属函数的移动机器人路径规划;熊开封等;《科技通报》;20150930;第31卷(第9期);第168-173页 |
| 基于改进模糊算法的移动机器人避障;彭玉青等;《计算机应用》;20150831;第35卷(第8期);第2256-2260页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106406311A (en) | 2017-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106406311B (en) | Robot ambulation barrier-avoiding method based on information fusion and environment sensing | |
| WO2021254367A1 (en) | Robot system and positioning navigation method | |
| CN106325275B (en) | A kind of system of robot navigation, method and device | |
| Jung et al. | Development of a laser-range-finder-based human tracking and control algorithm for a marathoner service robot | |
| US11740625B2 (en) | Plurality of autonomous mobile robots and controlling method for the same | |
| KR102015498B1 (en) | A plurality of autonomous cleaner and a controlling method for the same | |
| CN109318890A (en) | A kind of unmanned vehicle dynamic obstacle avoidance method based on dynamic window and barrier potential energy field | |
| CN109325476B (en) | Human body abnormal posture detection system and method based on three-dimensional vision | |
| US11435759B2 (en) | Plurality of autonomous mobile robots and controlling method for the same | |
| CN103995984A (en) | Robot path planning method and device based on elliptic constrains | |
| US20210323618A1 (en) | Identifying Stairs from Footfalls | |
| KR102615962B1 (en) | Moving robot and Controlling method for the same | |
| US20210338031A1 (en) | Robot cleaner and method for controlling same | |
| CN109828580A (en) | A kind of Mobile Robot Formation's tracking and controlling method based on separate type ultrasonic wave | |
| US20200016751A1 (en) | Mobile robot for avoiding non-driving area and method for avoiding non-driving area of mobile robot | |
| Zhao et al. | Terrain classification and adaptive locomotion for a hexapod robot Qingzhui | |
| CN107289929A (en) | The detection method and system and chip of barrier are encountered by a kind of robot | |
| Batavia et al. | Autonomous coverage operations in semi-structured outdoor environments | |
| Haq et al. | Room mapping using ultrasonic range sensor on the atracbot (autonomous trash can robot): A simulation approach | |
| Talebpour et al. | On-board human-aware navigation for indoor resource-constrained robots: A case-study with the ranger | |
| Jordan et al. | Real-time model based path planning for wheeled vehicles | |
| Rejab et al. | Design and implementation of intelligent mobile robot based on microcontroller by using three ultrasonic sensors | |
| Andersen | Mobile robot navigation | |
| Visser et al. | Amsterdam Oxford Joint Rescue Forces-Team Description Paper-Virtual Robot competition-Rescue Simulation League-RoboCup 2008 | |
| Thorpe | Mobile robots |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |