CN201573208U - Device for realizing indoor service robot positioning and map creation simultaneously and rotor - Google Patents
Device for realizing indoor service robot positioning and map creation simultaneously and rotor Download PDFInfo
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- CN201573208U CN201573208U CN2009200471606U CN200920047160U CN201573208U CN 201573208 U CN201573208 U CN 201573208U CN 2009200471606 U CN2009200471606 U CN 2009200471606U CN 200920047160 U CN200920047160 U CN 200920047160U CN 201573208 U CN201573208 U CN 201573208U
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Abstract
The utility model discloses a device for realizing indoor service robot positioning and map creation simultaneously and an indoor service robot provided with the same. The device comprises an external sensor, an internal sensor and an information processing module, by aid of the robot moving in an external environment, measurement data of the external sensor and the internal sensor is recorded, characteristics of the environment is extracted, poses and characteristic maps of the robot can be obtained by aid of forecast in a recursive form and updating algorithm, and then the poses and the characteristic maps can be updated on condition of meeting characteristic matching. The mobile robot is placed in an unknown environment, positioning and map creation are integrated, the robot creates continuous maps of the unknown environments in an increment form, and simultaneously position of the robot in the maps is determined, thereby effectively increasing work efficiency at a time, increasing autonomy of the self-moving indoor service robot, and further highlighting intelligence of the robot.
Description
Technical field
The utility model relates to a kind of intelligent apparatus, especially relates to a kind of indoor service robot that realizes and locatees the device with map building simultaneously, and described indoor service robot is specifically related to sweeping robot, security robot or the like.
Background technology
Along with improving constantly of scientific and technological level and people's living standard, from arising at the historic moment of mobile indoor service robot, accepted by people gradually, robot is when oneself moves, carry out work by its built-in functional unit and provide service for people, time saving and energy saving work characteristics has been subjected to people's approval, thereby is used widely in the office domestic environment.
The overwhelming majority of Chu Xianing is from the artificial sweeping robot of mobile indoor service machine in the market, and it comprises: driver part, rechargeable supply unit and control module.Under the energy supply of the control of control module and supply unit, sweeping robot is done on pending working surface at random and is moved, and robot treats the work of treatment surface by its built-in cleaning unit and carries out cleaning surfaces and handle when moving at random.
Patent disclosure is WO02101477, and the artificial American I ROBOT of patent application company is described cleaning at random to have in more detail in this patent documentation.In the place ahead of sweeping robot, that is: the direction of motion is provided with impingement plate, and two sides of impingement plate are provided with crash sensor, and robot makes rectilinear motion fully when the execution stochastic model moves.Robot is when advancing, impingement plate and wall or other barrier bump, and make crash sensor be activated, and robot receives the activation signal of crash sensor, the control walking mechanism is rotated any angle, continues straightaway away from the barrier that is collided.
Except that disclosing sweeping robot, American I ROBOT company adopts the technical scheme of cleaning at random, Switzerland Electracux Co. is at the first generation of calendar year 2001 release and the second generation sweeping robot of follow-up release, name of product is: TRILOBITE (trilobita), its track route that adopts belongs to stochastic model too.TRILOBITE is in Normal and Qucik mode of operation, the sonac that sweeping robot is provided with by its place ahead and the cooperation of crash sensor, make robot in traveling process, in when, robot taking place being detected the wall in the place ahead or other barrier when, or the wall in robot and the place ahead or other barrier are when bumping, the signal output or the activation signal of crash sensor by sonac, make robot rotate an angle, continue straightaway away from corresponding barrier.
The above-mentioned sweeping robot of mentioning adopts random path to move, and the subregion duplicates the situation generation of cleaning though execution simply, has the subregion drain sweep inevitably, thereby coverage rate and a sweeping efficiency are once cleaned in influence.
At above problem, the applicant has applied for " cleanable area of automatic cleaner and the recognition methods of barrier region " patent in calendar year 2001, and the patent No. is ZL01108048.5.This technical scheme has been pointed out a kind of recognition methods of cleanable area and barrier region of automatic cleaner.Be in the enclosed areas at one, carry out X-direction and Y direction scanning by dust catcher; By dust catcher X-direction scanning area and Y direction scanning area are carried out logic analysis again, the zone that X-direction scanning and Y direction scanning all fail to detect is the coordinates regional at barrier place, and the zone that X-direction scanning or Y direction scanning have detected is a cleanable area.The method theoretically, the notion by " logical AND " is to realize effectively identifying cleanable area and barrier region.But the locate mode that this pectination moves belongs to single locate mode, and it is fully by being similar to device such as encoder, by encoder being installed in the rotating speed of measuring walking mechanism on the walking mechanism, judging direction of rotation, thereby positions control.Yet walking mechanism is treating that working surface carries out can occurring skidding, losing phenomenons such as step inevitably when mobile.When this type of situation occurring, walking mechanism still is in the work running, encoder apparatus is then still at counting, but in fact, walking mechanism and sweeping robot are this moment with respect to treating that there is not the relation of moving in working surface, the counting of encoder has produced error signal thus, influences follow-up path scanning and path planning.
Based on all kinds of situations of above-mentioned proposition, therefore, be necessary to provide a kind of new mode to solve above-mentioned shortcoming.
The utility model content
Technical problem to be solved in the utility model is, provide a kind of the realization to locate simultaneously and the device of map building and mobile certainly indoor service robot, by can accurately judge position to the application of this device from mobile indoor service robot from mobile indoor service robot.
For solving the problems of the technologies described above, technical scheme provided by the utility model is:
The device with map building located simultaneously by a kind of indoor service robot that realizes, comprises external sensor, is used to survey the environmental information of described robot outside; Internal sensor is used to survey the positional information of described robot self; Message processing module, externally move in the environment by described robot, write down the measurement data of described external sensor and described internal sensor, environment is carried out feature extraction, utilize the prediction of recursive form and pose and the characteristics map that update algorithm draws described robot, satisfying under the condition of characteristic matching, realizing renewal corresponding described pose and characteristics map.
A kind of robot comprises robot body, control module, driver element, walking unit and functional unit, and the work of described control module control functional unit, and control driver element are by drive unit drives walking unit walking; The indoor service robot also comprises external sensor, is used to survey the environmental information of described robot outside; Internal sensor is used to survey the positional information of described robot self; Described control module externally moves in the environment by described robot, write down the measurement data of described external sensor and described internal sensor, environment is carried out feature extraction, utilize the prediction of recursive form and pose and the characteristics map that update algorithm draws described robot, satisfying under the condition of characteristic matching, realizing renewal corresponding described pose and characteristics map.
In the utility model, under circumstances not known, rely on sensor that the information of being obtained is carried out environmental modeling from mobile indoor service robot, utilize the environmental map of being created to estimate the pose of itself simultaneously, make robot in circumstances not known, create map, utilize map to realize real independent navigation simultaneously.The indoor service robot is placed in the circumstances not known, to locate with map building the two integrate, make robot create the continuous map of circumstances not known incrementally, determine its position in map simultaneously, this for indoor such as for the service robot that carries out work in family and the working environment, can very improve one action efficient effectively, improve the one action surface coverage, greatly improve the independence of mobile certainly indoor service robot, and further highlighted the level of intelligence of robot.
The bright book of accompanying drawing
Fig. 1 is the general structure schematic diagram of robot among the utility model one embodiment;
Fig. 2 is described in the utility model from mobile indoor service ROBOT CONTROL block diagram;
Fig. 3 has the basic controlling block diagram of the device of location and map building simultaneously for described in the utility model in mobile indoor service robot.
Fig. 4 has the basic functional principle flow chart of the device of location and map building simultaneously for described in the utility model in mobile indoor service robot.
Fig. 5 has the actual algorithm flow chart of location and map building simultaneously for described in the utility model in mobile indoor service robot.
The specific embodiment
It is a kind of from mobile sweeping robot that the utility model provides, and described self-movement robot has simultaneously location and map building and locatees current location and map feature from mobile sweeping robot.
The utility model is artificially routine with sweeping machine, and the utility model is elaborated.As shown in Figure 1, be utility model one specific embodiment--the overall structure schematic diagram of-sweeping robot.As shown in Figure 1, be control structure composition frame chart of the present utility model.In conjunction with illustrated in figures 1 and 2, described sweeping robot comprises robot body 1, control module 2, driver element 3, walking unit 4 and functional unit 5, and control module 2 is positioned at this robot body 1 body.Control module 2 drives walking unit 4 by driver element 3 and rotates, and walking unit 4 drives robot body 1 and moves.Described functional unit 5 is a cleaning unit.
This sweeping robot has simultaneously the device of location and map building, is illustrated in figure 3 as to have the control block diagram of location and the device of map building simultaneously.This device is equipped with inside and outside sensor, and internal sensor is an odometer, is odometer acquisition module 30; External sensor is the single image sensor, is image capture module 10, is used to gather the image information for the treatment of working surface region.In the present embodiment, the image vision sensor is a cmos sensor, can certainly be other vision sensors such as CCD.In addition, device comprises that also message processing module 20, this message processing module 20 comprise feature extraction unit 210, characteristic matching comparing unit 220, pose and environmental characteristic updating block 230 and pose and environmental characteristic predicting unit 240.
Below in conjunction with Fig. 3 and Fig. 4 the basic functional principle flow chart is launched narration.Image capture module 10 is gathered the image information (step S100) for the treatment of working surface.The information via that image capture module 10 is collected is handled the geometric properties that forms external environment condition, represents that with parameters such as length, width, positions characteristics map can be expressed as: R=(fi|i=1,2 ..., M), wherein: fi is an environmental characteristic, and M is the characteristic in the map.Robot locate simultaneously with map building in, the IMAQ by imageing sensor obtains the contour feature and the positional information thereof of external environment condition.Carry out feature extraction for the image information that is obtained, the method for extracting feature adopts the hough transform method, comes to extract (step S110) to what can reflect environment on a large scale such as structured features such as straight line, line segment, angle, points by this method.Said hough transform method comprises weighting hough transform method, histogram method or the like.
The device 10 odometer acquisition modules 30 that are provided with are positioned on the chassis of sweeping robot, are connected with the walking unit of sweeping robot, are used to survey the duty of walking unit.The information of odometer acquisition module 30 is input to (step S150) among the system state equation, makes Minimum Mean Square Error by Kalman filtering and estimates, be i.e. system state variables X=[X
T r, X
T 1, X
T 2..., X
T N] T, Xr=[x wherein, y, θ] pose of expression robot, Xi=[x
iy
i] T represents the position coordinates of environmental characteristic, finishes prediction (step S140) to pose and map feature by equation.After the observation and extraction to environmental characteristic in step S110, be used for upgrading pose and characteristics map (step S130).State upgrades and comprises the increase of new feature, the deletion of disappearance feature, the renewal of repeated observation feature.Between prediction and upgrading, carry out characteristic matching (step S120).Have only matching characteristic just can be used for upgrading pose and characteristics map.
For step S120 characteristic matching, characteristic matching is whether difference derives from same feature to the observation of environment constantly.Characteristic matching is relevant with the theoretical method and the sensor model of employing.In common SLAM algorithm, observed quantity will be mated with system state variables, to determine more fresh target.Successful data are relevant to be related to correct observation and corresponding state variable coupling, detection and gets rid of falseness observation and the new track of initialization.Characteristic matching is the key technology of data fusion, and method is a lot.In indoor mobile service robot was realized simultaneously location and map building, most methods all is based on upgraded sequence and prediction covariance matrix.Upgrade sequence definition and be observation poor with based on the state variable predicted value of observation model,
Then gauged distance is decided to be d
2 k=v
T kS
-1 kv
k, S wherein
kFor upgrading covariance matrix.Meet Gaussian distribution if upgrade sequence, then v
T kv
kTo be X
2Distribute; When observation falls into X
2During fixing interval of certain that distributes, as can accepting observation, otherwise got rid of.Promptly according to X
2Between the zone of acceptability of distribute determining, draw the affirmation thresholding, and according to formula d
2 k=v
T kS
-1 kv
kThe gauged distance that draws relatively determines whether observation can be accepted.Filter out more new feature of nearest feature conduct according to the arest neighbors filtering method then.In addition,, choose the feature that convergence rate is had decisive role, and from map, delete other road sign feature for simplifying the amount of calculation of characteristic matching, very little to the influence of rate of convergence, but computation complexity will reduce greatly.
In global layer, a series of local map is formed a connection layout, in the face of the matching problem between the map time, realizes that m indicates between complexity related with having n data between the map that indicates and the m and be exponential relationship, supposes that each sign i that observes has n
iIndividual possible coupling so need be at index space π for m sign
m i=1n
iThe correct coupling of middle search.The search volume of data association is relevant with the position error of the complexity of environment and robot, and the increase of the complexity of environment can make m increase, and the increase of error can make ni increase.
Based on to the description of location simultaneously with the device basic functional principle flow chart of map building, existing at present embodiment, the actual algorithm flow process of installing relevant location simultaneously and map building is done a simply description.As shown in Figure 5, device comprises internal sensor and external sensor, and described internal sensor is an odometer; Described external sensor is single cmos sensor, and this imageing sensor is positioned at the leading section of sweeping robot and is horizontal positioned, is used to gather the image information (step S200) for the treatment of working surface region.Adopt the hough transform method that the image information that is obtained is carried out extraction of straight line (step S210).Odometer is positioned on the chassis of sweeping robot, is connected with the driving wheel of sweeping robot, is used to survey the duty of driving wheel; The information of odometer is input among the system state equation, finishes prediction (step S270) pose and map feature by equation.For the environmental characteristic that obtains, itself and existing map datum need be carried out data related (step S220).When characteristic matching, find that to having or not new feature carries out difference (step S230).As finding to have new feature, then this feature is added state vector, simultaneously state is expanded dimension (step S240); As not finding to have new feature, then update mode, make up map (step S250).Then, obtain visual information (step S260) by what control strategy entered next round.Said control strategy specifically is meant: with the navigate mode of the straight line in the ambient image as feature, employing tracking straight line, the number of times that increases observation also reduces the accumulated error of odometer apace, the convergence rate of quickening Ka Leman filtering method.Here, the structure map of mentioning among the step S250, construction method commonly used have grating map method, characteristics map method and topology map method etc.In the present embodiment, the structure ground drawing method of employing is the characteristics map method.
Simultaneously in location and the process of map building, in the prediction and update algorithm for recursive form, the kalman filter method of in adopting present embodiment, describing, also can adopt particle filter method for device.Now particle filter method being done one simply describes.Localization for Mobile Robot is the processing to uncertain information, and this uncertain because of being to mainly contain: (1) robot is to the restriction of outside perception; (2) disturbance of external environment condition; (3) robot interior sensor errors.Any model of system noise and observation noise and hypothesis all have limitation, and the real-time of complicated probabilistic model influence decision-making.Particle filter approaches probability distribution with the method for sample set, can approach any type of probability distribution when sample number N → ∞.Therefore, particle filter can be expressed the posterior probability distribution based on observed quantity and controlled quentity controlled variable more accurately.Particle filter is the posterior probability estimation method that can see, can control the Markov chain at discrete time, part, location and map building when being mainly used under the outdoor overall situation at present.P (x
t| u
t, x
T-1) be the observation model of system.By observed quantity z
t=z
0..., z
tControlled quentity controlled variable u
t=u
0..., u
t, the posterior probability of ut recovery system distributes, and uses recursive form Bayes filtering algorithm usually: (x
t| Z
t, u
t)=constp (zt|xt) ∫ p (x
t| u
t, x
T-1) xp (x
T-1| z
T-1, u
T-1) dx
T-1Particle filter is easy to realize, do not need the linearizing non-linear model, but for the higher-dimension state space, sample number is very big, needs high arithmetic speed, to the requirement height of computer hardware.Therefore, particle filter is usually used in overall orientation problem.Particle filter the mobile robot locate simultaneously with map building in belong to higher-dimension and use, be primarily aimed at outdoor destructuring environment.Compare with Kalman filtering, particle filter has following three big superiority; 1, the amount of calculation of particle filter is 0 (N log K), and N is a sample number.Facts have proved that in certain uncertain region, N is a constant.2, particle filter can be handled the situation that posterior probability is non-Gauss, multi-model distribution, can utilize observation data more fully, handles NACK messages.And in this case, Kalman filtering will cause the relevant failure of data.3, use particle filter deal with data relevant issues and have stronger robustness.
In addition, the environmental information from different sensors can provide redundancy for feature extraction, the accuracy of raising environmental characteristic location and the reliability of feature identification.The uniformity of syncretizing mechanism is described and guaranteed in the system that the processing and the application need of redundancy carried out uncertain geological information.In the present embodiment, external sensor only is provided with single cmos image sensor, for making the identification of positioning accuracy and feature more reliable, accurate, external sensor can also comprise laser sensor, mate to come environment-identification feature effectively by vision sensor from data and laser data that gray level image extracts, reject the fuzzy characteristics in the environmental map.Or external sensor comprises infrared sensor or sonar sensor, by with effective cooperation of imageing sensor improve precision.And more accurate for ease of measuring accuracy, more clearly obtain the depth information of environmental characteristic, extract three-dimensional feature, external sensor can be realized by two or a plurality of cmos image sensor are set.Certainly, more clearly obtain the depth information of environmental characteristic, extract three-dimensional feature, external sensor also can be provided with two or a plurality of ccd image sensor.When adopting single or multiple ccd image sensor, also can be by actual needs, be used in combination with laser, ultrasonic or infrared sensor.
Certainly, except that the external sensor described in the present embodiment comprises the cmos image sensor, also can be according to the real work needs, external sensor only comprises laser sensor, sonar sensor, infrared sensor or wherein a plurality of any combinations.Except that the internal sensor described in the present embodiment is the odometer, also can be according to the real work needs, internal sensor adopts checkout gears such as gyroscope.
It should be noted that at last: above embodiment is only unrestricted in order to the technical scheme of explanation utility model.Although the utility model is had been described in detail with reference to the foregoing description, those of ordinary skill in the art is to be understood that, still can make amendment and be equal to replacement the technical solution of the utility model, and not breaking away from the spirit and scope of the technical program, it all should be encompassed in the middle of the claim scope of the present utility model.
Claims (11)
1. realize that the indoor service robot locatees the device with map building simultaneously for one kind, it is characterized in that, comprising:
External sensor is used to survey the environmental information of described positioner outside;
Internal sensor is used to survey the positional information of described positioner self;
Message processing module, externally move in the environment by described positioner, write down the measurement data of described external sensor and described internal sensor, environment is carried out feature extraction, utilize the prediction of recursive form and pose and the characteristics map that update algorithm draws described positioner, satisfying under the condition of characteristic matching, realizing renewal corresponding described pose and characteristics map.
2. device according to claim 1 is characterized in that described external sensor comprises single or multiple cmos image sensors.
3. device according to claim 1 is characterized in that described external sensor comprises single or multiple ccd image sensors.
4. according to claim 2 or 3 described devices, it is characterized in that described external sensor also comprises laser, sonar or infrared sensor.
5. device according to claim 1 is characterized in that, described internal sensor is an odometer.
6. robot, comprise robot body, control module, driver element, walking unit and functional unit, described control module set is in the inside of service robot, its control functional unit work, and control driver element are by drive unit drives walking unit walking, it is characterized in that, described indoor service robot also comprises positioner, and this positioner comprises external sensor, is used to survey the environmental information of described robot outside; Internal sensor is used to survey the positional information of described robot self; Described control module externally moves in the environment by described robot, write down the measurement data of described external sensor and described internal sensor, environment is carried out feature extraction, utilize the prediction of recursive form and pose and the characteristics map that update algorithm draws described robot, satisfying under the condition of characteristic matching, realizing renewal corresponding described pose and characteristics map.
7. robot according to claim 6 is characterized in that described control module comprises message processing module, is used to accept the information of described external sensor and internal sensor transmission, realizes the renewal to described robot pose and characteristics map.
8. robot according to claim 6 is characterized in that described external sensor comprises single or multiple cmos image sensors.
9. robot according to claim 6 is characterized in that described external sensor comprises single or multiple ccd image sensors.
10. robot according to claim 6 is characterized in that, described internal sensor is an odometer.
11. robot according to claim 6 is characterized in that, described functional unit is a cleaning unit.
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