JP2009223757A - Autonomous mobile body, control system, and self-position estimation method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an autonomous mobile body, capable of accurately estimating its self-position with improved accuracy even if an optically special material such as glass is present in a moving area, and to provide a control system of the autonomous mobile body, and a self-position estimation method. <P>SOLUTION: The autonomous mobile body 10 includes a laser range finder 17 which acquires environmental information indicating the position and shape of an object present in the moving area P, and can move while recognizing the self-position based on the environmental information acquired by the laser range finder 17 by use of a movement map 20 related to the moving are P. In the mobile body 10, the movement map 20 includes information indicating the optical reflectivity attribute of the object. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to an autonomous mobile body that moves within a specific moving area, a control system for the autonomous mobile body, and a self-position estimation method used for controlling the autonomous mobile body.

  In recent years, mobile bodies such as autonomous mobile robots that can move autonomously within a limited area inside a building or outdoors based on surrounding environmental information are being developed. Such a moving body enables autonomous movement by creating a movement route from the current position to a specific target point on a movement map stored in advance with respect to the movement area. For this reason, generally, it has a function of recognizing its own position in the moving area.

In order to control such an autonomous mobile body, techniques described in Patent Documents 1 and 2, for example, are known as techniques for causing an autonomous mobile body to recognize its own position. The automatic guided vehicle system disclosed in Patent Document 1 estimates the self-position of the automatic guided vehicle by measuring a reflector installed in the environment with a laser range scanner. Since the position where the reflecting plate is installed is known, the self position can be calculated from the measured position of the reflecting plate by the principle of triangulation.
Japanese Patent No. 3316841 JP 2006-79325 A

  However, the conventional technique described in Patent Document 1 has a problem that a complicated operation is required to install the reflector, and a large-scale facility work is required for self-position estimation. For this reason, there is a need for an infrastructureless self-position estimation method that does not require extensive construction work.

  As a method of estimating the self-location without infrastructure, a method of estimating the self-location by comparing the map information of the moving area and the acquired environment information has been proposed. For example, the autonomous mobile device described in Patent Document 2 recognizes its own position by comparing environmental information acquired by a laser radar with map information. As such a self-position estimation method, a method using a grid map is often used. The grid map is a map showing a moving region in a simulated manner, and presence information such as walls and obstacles is registered on the map (for example, whether or not a predetermined area on the map is a wall is 0). Or it is registered with one digital information).

  In the self-location estimation method using the grid map, the actual data of the autonomous mobile body is compared by comparing the data measured by the autonomous mobile body in the actual environment with the data measured by the autonomous mobile body on the grid map. Estimate the self-position. If the autonomous mobile body is equipped with a laser range finder, the actual self-position of the autonomous mobile body can be determined by comparing the data acquired by the laser range finder with the data virtually measured on the grid map. It is possible to calculate with high accuracy.

  However, the self-position estimation method using the laser range finder has a problem that self-position estimation cannot be performed accurately when an optically special material such as glass exists in the moving region. Since the laser range finder is a sensor that measures the distance by reflecting the laser beam, the laser totally reflects when it is incident on the glass at a shallow incident angle, but when it is incident at a deep angle such as the front, it passes through the glass and measures the glass itself. It has the property of doing. Therefore, when an optically special material such as glass is present in the moving region, self-position estimation cannot be performed accurately using a grid map that does not consider laser reflection.

  This will be specifically described with reference to FIG. FIG. 11A is a diagram illustrating a state in which the autonomous mobile body 10 acquires environment information with a laser in the actual environment P, and FIG. 11B corresponds to the situation illustrated in FIG. FIG. 8 is a diagram showing a state where a laser is virtually measured on a grid map 20. As shown in FIG. 11 (a), in the actual environment P, the laser reflects the glass G1 and measures the object B1 existing in front of the autonomous mobile body 10, whereas as shown in FIG. 11 (b). On the grid map 20, since the grid map 20 does not have information regarding the glass G1, the laser cannot go straight and measure the object B1. That is, since the reflection of the laser is not taken into consideration on the grid map 20, the distance to the object B1 cannot be measured on the grid map 20. For this reason, in the actual environment P shown in FIG. 11A, the laser measures the distance (d1 + d2) in the direction of the angle θ. However, on the grid map 20 shown in FIG. (D3) is measured. Therefore, when the measured distances are compared with each other, the measured distance data hardly match and an accurate self-position estimation result cannot be obtained.

  As described above, in the self-position estimation method using the laser range finder, when an optically special material such as glass exists in the moving region, the data measured by the autonomous moving body in the actual environment and the grid It was impossible to accurately compare the data measured by the autonomous mobile body virtually on the map, and the self-position estimation of the autonomous mobile body could not be performed accurately.

  The present invention has been made to solve such a problem, and an object thereof is to provide an autonomous mobile body, an autonomous mobile body control system, and a self-position estimation method capable of improving the accuracy of self-position estimation. And

  The autonomous mobile body according to the present invention includes a laser range finder that acquires environmental information indicating the position and shape of an object existing in a moving area, and uses the movement map related to the moving area to acquire the environment acquired by the laser range finder. An autonomous mobile body that can move while recognizing its own position based on information, wherein the movement map includes information indicating a light reflectance attribute of the object.

  As a result, even when there is an optically special material such as glass in the moving area, the self-position can be estimated in consideration of the information indicating the light reflectance attribute of the object. The accuracy of self-position estimation can be improved.

  In addition, an acquisition unit that irradiates laser light from the laser range finder, acquires environmental information based on reflection of the irradiated laser light, a self-position candidate calculation unit that calculates a self-position candidate of the autonomous mobile body, A self-position estimating unit that recognizes an estimated self-position based on the acquired environment information and the calculated self-position candidate, and the self-position estimating unit sets a light reflectance attribute of the object on the movement map. Self-position candidate calculated by the self-position candidate calculation unit based on the reflected laser light and the environment information acquired by the acquisition unit based on the reflected information And the corrected self-position candidate may be recognized on the movement map as the estimated self-position. Thus, by reflecting the laser beam based on the information indicating the light reflectance attribute of the object on the movement map, the data acquired by the autonomous mobile body in the actual environment and the virtual measurement on the movement map were performed. Based on the data, the self-position estimation of the autonomous mobile body can be executed with higher accuracy.

  Furthermore, the self-position estimation unit virtually depicts the laser beam with respect to the object from the estimated self-position calculated by the self-position candidate calculation unit on the movement map, and When the incident angle is equal to or greater than a predetermined threshold, the laser beam is reflected, and the self-imaging is performed based on the laser beam measurement distance virtually depicted and the laser beam measurement distance acquired by the acquisition unit. The self-position candidate calculated by the position candidate calculation unit may be corrected, and the corrected self-position candidate may be recognized on the movement map as an estimated self-position. In this way, by reflecting the laser beam when the incident angle is greater than or equal to a predetermined threshold on the movement map, data can be measured more accurately on the movement map, and the self-position estimation of the autonomous mobile body can be performed more accurately. It can be executed with high accuracy.

  The predetermined threshold value may be variable according to the material of the object. By changing the threshold value according to the material of the object, it is possible to measure the data more accurately on the movement map.

  Furthermore, the self-position estimation unit calculates the self-position calculated by the self-position candidate calculation unit based on the virtually measured laser light measurement distance and the laser light measurement distance acquired by the acquisition unit. The weight of the candidate may be calculated according to the correlation value of the measured distance, and the estimated self position may be determined from the self position candidate based on the calculated weight and recognized on the movement map. As described above, the self-position estimation of the autonomous mobile object is performed by determining the estimated self-position according to the correlation value between the virtually measured laser light measurement distance and the actually obtained laser light measurement distance. It can be executed easily and accurately.

  The self-position candidate calculation unit may calculate a self-position candidate based on the direction and distance of movement of the autonomous mobile body. The self-position candidate calculation unit may calculate a self-position candidate based on the moving direction and distance of the autonomous mobile body such as a so-called odometry method. According to such a self-position calculation method that does not use a signal from the outside, the situation in which a signal cannot be obtained due to an obstacle or the like is reduced, so that continuous autonomous movement can be performed well.

  Furthermore, the movement map may be a grid map created by virtually depicting grid lines connecting lattice points arranged at substantially constant intervals in the movement area. When such a grid map is used as map information for determining the movement path of an autonomous mobile body, the position of the autonomous mobile body can be grasped accurately and easily, and a movement path on the map can be easily created. it can.

  An autonomous mobile body control system according to the present invention includes a laser range finder that acquires environmental information indicating the position and shape of an object existing in a moving region, irradiates a laser beam from the laser range finder, and the irradiated laser. Estimation based on acquisition means for acquiring environment information based on reflection of light, self-position candidate calculation means for calculating a self-position candidate of the autonomous mobile body, the acquired environment information and the calculated self-position candidate A self-position estimating means for recognizing the self-position, and using a movement map related to the movement area, an autonomous mobile body control system capable of moving while recognizing the self-position based on the environmental information acquired by the acquisition means And the self-position estimation means virtually transmits the laser beam based on information indicating a light reflectance attribute of the object on the movement map. Reflecting and correcting the self-position candidate calculated by the self-position candidate calculation means based on the environment information acquired by the acquisition means and recognizing the corrected self-position candidate as the estimated self-position on the movement map It is.

  The self-position estimation method according to the present invention is an autonomous mobile body including a laser range finder that acquires environmental information indicating the position and shape of an object existing in a moving area, and uses the movement map related to the moving area to A self-position estimation method for recognizing the self-position of the autonomous mobile body based on environmental information acquired by a range finder, wherein the movement map includes information indicating a light reflectance attribute of the object.

  According to the present invention, even when an optically special material such as glass is present in the moving region, the autonomous moving body capable of improving the self-position estimation accuracy of the autonomous moving body, It is an object to provide a control system and a self-position estimation method.

Embodiment 1 of the Invention
The autonomous mobile body according to the first embodiment includes a laser range finder that acquires environmental information indicating the position and shape of an object existing in the moving area. It is an autonomous mobile body that can move while recognizing its own position based on the acquired environmental information. Here, the autonomous moving body according to the first embodiment is characterized in that the movement map includes information indicating the light reflectance attribute of the object.

  The autonomous mobile body according to Embodiment 1 of the present invention will be described below with reference to the drawings. In this Embodiment 1, an autonomous mobile body shall show the example which is a wheel drive type which moves on a plane autonomously by driving a pair of wheels.

  FIG. 1 schematically shows an embodiment in which a vehicle-type autonomous mobile body 10 moves in a limited movement region P (region surrounded by a broken line) on the floor 1. In FIG. 1, it is assumed that an object is not particularly placed in the movement area P on the floor 1, and the autonomous mobile body 10 can arbitrarily move in the movement area P. .

  As shown in FIG. 2, the autonomous mobile body 10 is an opposing two-wheeled mobile body including a box-shaped mobile body 10 a, a pair of opposing wheels 11 and 11, and casters 12. 11, 11 and the caster 12 support the movable body main body 10a horizontally. Furthermore, inside the mobile body 10a, there are drive units (motors) 13 and 13 for driving the wheels 11 and 11, respectively, a counter 14 for detecting the rotation speed of the wheels, and a control signal for driving the wheels. And a calculation unit 15 for transmitting the control signal to the drive units 13 and 13 and a battery 16 for supplying power to these components. And in the storage area 15a such as a memory as a storage unit provided inside the calculation unit 15, along with a program for controlling the moving speed, moving direction, moving distance, etc. of the autonomous mobile body 10 based on the control signal, The shape and size of the map information P are stored.

  Further, a laser range finder 17 for recognizing environmental information in a moving direction (front) and an ultrasonic sensor 18 for recognizing environmental information are provided on the front surface of the moving body 10a. Although the laser range finder 17 and the ultrasonic sensor 18 are configured so that their attachment positions are substantially the same, they are depicted as being attached at different positions for convenience in the drawings.

  Although the detailed structure of the laser range finder 17 is omitted, a light source for irradiating laser light at a predetermined spread angle with respect to the front of the moving body 10a and reflected light of the laser light emitted from the light source. A light receiving unit for receiving light. Then, object detection (sensing) based on the principle of so-called TOF (Time of Flight) is performed, which detects the position of the object reflected by the laser beam based on the angle irradiated with the laser beam and the time required for reflection. Is called. Details of the procedure for obtaining environmental information (position and shape of the object to be sensed) by the laser range finder 17 will be described later.

  The ultrasonic sensor 18 includes an ultrasonic irradiation unit that irradiates ultrasonic waves simultaneously at a predetermined spread angle with respect to the front of the moving body 10a, and a reception unit that receives a reflection of the irradiated ultrasonic waves. And based on the intensity | strength of the received ultrasonic wave, the rough position and shape of the object which exist in the area | region which irradiated the ultrasonic wave are sensed.

  Moreover, the autonomous mobile body 10 includes a self-position acquisition unit for acquiring its own position. This self-position acquisition unit includes the counter 14 and the calculation unit 15 described above. That is, by calculating the rotational speed of the wheels 11, 11 detected by the counter 14 in the calculation unit 15, information such as the moving speed and distance of the autonomous mobile body 10 is obtained. The self position (odometry position) of the autonomous mobile body 10 is calculated.

  And the positional information as a self-position candidate obtained from these self-position acquisition parts is suitably corrected based on environment information obtained by a laser range funder 17 described later. Details of the method for correcting the position information will be described later.

  The autonomous mobile body 10 configured in this way independently controls the drive amount of the pair of wheels 11 and 11 so that the vehicle travels straight, moves in a curve (turns), moves backward, and rotates on the spot (the midpoint of both wheels). Movement such as turning around the center. Then, the autonomous mobile body 10 autonomously creates a movement route to the designated destination in the movement area P in accordance with a command from an external server (not shown) that designates the movement location. Then, the vehicle reaches the destination by moving so as to follow the movement route.

  Next, a grid map as a movement map created based on the shape of the movement area P stored in the storage area 15a and an object (an obstacle such as a wall) included in the movement area P will be described. The storage area 15a provided inside the calculation unit 15 has a grid that connects the lattice points arranged at substantially constant intervals m (for example, 10 cm) inside the moving area P on the floor 1 as an outer frame. A grid map obtained by virtually depicting a line is stored.

  FIG. 3 illustrates an example of the grid map described above. The grid map 20 depicts grid lines 22 that connect lattice points arranged at substantially constant intervals m inside the outer frame 21 simulating the shape of the map information P. Then, using the grid unit 23 surrounded by the grid line 22, the location corresponding to the self-position of the autonomous mobile body 10, the movement end point that is the destination, and the movement direction of the autonomous mobile body 10 at the movement end point Is identified. The interval m can be appropriately changed according to conditions such as the curvature of the autonomous mobile body 10 that can be moved and the accuracy of recognizing the absolute position, and can also be used as a threshold value when it is determined that the vehicle has slipped. . In addition, when it is known that fixed objects such as walls and obstacles exist in the moving area P, the storage area is stored in a form in which the positions of these known objects are registered in advance on the grid map 20. It is assumed that it is stored in 15a.

  The state of the position corresponding to each grid unit on the grid map 20 is expressed using an evaluation value indicating a wall (that is, an obstacle) and information indicating a light reflectance attribute of the object. The evaluation value wall_LRF indicating that the grid unit is a wall (that is, an obstacle) takes a value of 1 if it is a wall and 0 if it is not a wall. The information indicating the light reflectance attribute of the object in the grid unit includes an evaluation value indicating that the object is an optically special material such as glass, and an angle indicating the direction in which the object is arranged. When the object is glass, the evaluation value glass_LRF indicating that the object in the grid unit is glass takes a value of 1 when the object is glass and 0 when it is not glass. In addition, when the object is glass, an angle [deg] indicating the direction in which the glass is arranged in the grid unit takes a value of α [deg] indicating the direction of the glass surface. The evaluation value in units of grids may be stored in the grid map as a result of sensing by the autonomous mobile body 10 with the laser range finder 17 or may be registered in advance by the user.

  In addition, the calculation unit 15 can create a movement route from the movement start point to the movement end point that is the destination, with the self-position specified on the grid map 20 as a movement start point. Furthermore, the autonomous mobile body 10 moves along the created movement route while obtaining its own position in real time as described above (for example, acquiring its own position information on the map information every 10 [msec]). In detail, as shown in FIG. 4, the autonomous mobile body 10 recognizes the movement start point Q0, and moves the movement path from the movement start point to the target movement end point Qn at intermediate points Q1, Q2,. .. Is defined on the grid map 20, and a moving path is created on the grid map 20 by connecting these intermediate points. The details of the method for identifying this movement route will be omitted, but taking into account the position of obstacles added on the map information, etc., creating a new movement route from the current position to the destination point, etc. A technique is preferably used.

  Next, a method for acquiring environmental information on the front surface of the autonomous mobile body 10 using the laser range finder 17 described above will be described in detail.

  First, the autonomous mobile body 10 irradiates the front surface with the laser light L, and the position SP (sensing) of the object that reflects the laser light L that exists in the sensing region S1 located within a predetermined distance from the autonomous mobile body 10. Point). Specifically, the laser range finder 17 according to the first embodiment emits at a predetermined spread angle and sets a region at a predetermined distance L from the autonomous mobile body 10 as a measurable range. That is, when the reflected light of the laser beam L irradiated from the laser range finder 17 is received, the self-position of the autonomous mobile body 10 when the laser beam L is irradiated and the laser beam irradiated from the laser range finder 17. The point where the irradiated laser light is reflected is specified from the irradiation direction and the time from the irradiation of the laser light to the reception of the reflected light.

  Data measured by the laser range finder 17 is output as a polar coordinate system defined by the distance d from the laser center and the angle φ in the laser irradiation direction. When the resolution when measuring 180 degrees forward of the traveling direction of the autonomous mobile body 10 is 1.0 degree, 181 data are measured by one scan. The sensing result obtained by the laser range finder 17 is stored in the storage unit 15a.

  FIG. 5 shows a state where a wall W <b> 1 is provided in front of the autonomous mobile body 10 in the movement area P. As shown in FIG. 5, the autonomous mobile body 10 irradiates the front surface with laser light from a laser range finder 17. At this time, the wall W <b> 1 is included in the sensing region of the laser range finder 17. In such a case, the distance to the wall W1 sensed by the laser range finder 17 is stored.

  Next, with reference to FIGS. 6 to 10, a method for estimating the current self-position from among self-position candidates calculated by the odometry method using the environment information acquired by the laser range finder will be described. In the first embodiment, a method called MCL (Monte Carlo Localization) is adopted as a self-position estimation method. This is a technique for expressing the probability expression of the position of the robot using position candidate points called particles. FIG. 6 is a flowchart showing an outline of a self-position estimation method using MCL.

  First, the autonomous mobile body 10 that has started moving within the moving area recognizes its own position on the grid map 20 at the point where movement starts, and stores it on the grid map 20 stored in advance (step S101).

  Thereafter, the self-position candidate based on the odometry information is calculated while continuing to move by driving the wheel provided on the moving body 10a (step S102). Specifically, first, the amount of movement is calculated from the rotation angle of the wheels of the autonomous mobile body 10. Here, the movement amount dX = (dx, dy, dθ) of the autonomous mobile body 10 can be easily calculated by obtaining the rotation angle of the wheel with an encoder and solving the forward kinematics. Then, a plurality of particle positions as position information are extracted from the odometry information. Then, each particle position is moved in consideration of noise related to the movement amount of the autonomous mobile body 10. That is, the calculated movement amount of the robot is moved by giving a particle with noise added thereto. Here, the particle position is represented as a candidate point for the self position of the autonomous mobile body 10. The plurality of particles EP moved in this way are represented by coordinate points as a set of discrete variables distributed around the autonomous mobile body 10 and having a certain degree of variation, for example, as shown in FIG.

  And while the autonomous mobile body 10 continues a movement, environmental information is acquired by the laser range finder 17 (step S103). The distance d from the laser center and the angle φ of the laser irradiation direction at that time are acquired for the object existing in the moving region, and these d and φ are associated and stored as environment information.

  Then, based on the environment information acquired by the laser range finder 17, the calculation unit 15 as a control computer determines the weight of each particle (step S104). That is, the probability that can be a self-position at that position is obtained. Hereinafter, a method for determining the weights of particles will be described in detail with reference to FIGS. FIG. 8 is a flowchart showing a particle weight determination process.

  First, the control computer 15 extends a straight line from each particle position along the measurement direction of the laser range finder 17 on the grid map 20 (step S201). That is, the laser beam of the laser range finder 17 is virtually depicted from each particle position on the grid map 20. The straight line is extended by a predetermined distance unit along the measurement direction of the laser range finder 17. For example, as shown in FIG. 9A, on the grid map 20, a straight line is extended from the particle position part_1 along the measurement direction L_D1 of the laser range finder 17 every predetermined distance unit d0.

  Then, the control computer 15 extends the straight line along the measurement direction of the laser range finder 17 and, for the grid unit at a point away from the particle by a predetermined distance on the straight line, whether the grid unit is glass or not. Is determined (step S202). As a result of the determination, if it is not glass, the process proceeds to step S207.

  On the other hand, as a result of the determination, if the grid unit is glass (that is, if the virtually drawn laser beam encounters a grid unit recorded as glass), the control computer 15 determines the grid unit. The direction α of the glass surface recorded in the unit is read (step S203).

  Then, the control computer 15 calculates the incident angle of the laser beam on the glass surface by comparing the glass surface direction α with the laser beam angle β, where β is the angle of the laser beam emitted from the autonomous mobile body 10. (Step S204). The angles α and β are angles from a predetermined reference coordinate. For example, in FIG. 9B, the direction and the grid unit G of the glass with respect to the direction from the particle position part_1 to the grid unit G of the glass Is defined as α, and the angle formed between the direction and the laser beam is defined as β.

  Then, the control computer 15 determines whether or not the angle formed by the glass surface direction α and the laser beam angle β (that is, the incident angle) is equal to or greater than a predetermined threshold (step S205). When the incident angle is smaller than a predetermined threshold value, it is determined that the laser beam is an angle that does not totally reflect, and the glass grid unit is treated as a measurement of the wall grid unit.

  On the other hand, if the incident angle is equal to or greater than the predetermined threshold, it is determined that the incident angle with respect to the glass surface is shallow, and the laser beam direction is changed to reflect (step S206). For example, as shown in FIG. 9B, the straight line extended from the particle position part_1 is reflected by the glass grid unit G, the direction L_D1 is changed to the reflected direction L_D1_R, and extended again. Note that the predetermined threshold regarding the incident angle may be appropriately adjusted according to the material of the object. That is, by setting a threshold corresponding to the material for each grid unit, reflection can be reproduced more accurately on the grid map.

  Thus, in the process of extending a straight line along the measurement direction of the laser range finder 17 from the particle position, when encountering a grid unit indicating glass, based on the light reflectance information and the angle information of the straight line, Reflect the straight line. Then, the control computer 15 determines whether or not the extended straight line has reached the wall (step S207). If the result of determination is that the wall has not been reached, the process returns to step S201 to further extend the straight line.

  As a result of extending the straight line by the predetermined distance unit, when the straight line reaches the wall, return to the grid unit immediately before reaching the wall and perform the same investigation in a finer distance unit. Also good. In other words, it may be determined whether or not a grid unit that is separated by a finer distance unit from the immediately preceding grid unit is a wall. In this way, it is examined in more detailed distance units whether or not the point gradually moving away from the particle position has reached the wall. Thereby, the distance to the wall can be measured with higher accuracy. For example, as shown in FIG. 9A, when a straight line is extended by a predetermined distance unit d0 from the particle position part_1 along the measurement direction L_D1 of the laser range finder 17, when the wall W is reached, The straight line is extended again by the fine distance unit d1.

On the other hand, when the straight line reaches the wall, the control computer 15 sets the distance of the straight line as the measurement distance d of the particle position (step S208). Then, the control computer 15 calculates a correlation value between the distance data of the particles at a certain irradiation angle by using the measurement model shown in the following equation 1 (step S209). Here, P (dk) represents a correlation value between the measurement distance dk on the grid map 20 and the distance zk actually measured by the laser range finder 17 at the irradiation angle φ of the k-th laser beam. The measurement model shown in Equation 1 is also called a sensor model, and Equation 1 means that the distance data dk at a certain irradiation angle of the laser light follows a normal distribution with the distance data zk as an average.

  Further, the control computer 15 calculates the correlation value between the particle distance data for all the laser beam irradiation angles from the particle position (ie, until the determination condition in step S210 is satisfied, steps S201 to S209). Repeat the process in step 2).

The control computer 15 calculates the correlation value between the distance data for all the irradiation angles (that is, after calculating the correlation value between the distance data for all the 181 distance data) and then weights all the particles. Is calculated (step S211). First, the control computer 15 multiplies all correlation values P (dk) between the distance data of each irradiation angle for each particle using the following formula 2. Here, wi ′ indicates a temporary weight of the i-th particle.

Further, the control computer 15 calculates the temporary weight wi ′ for all the particles, and then calculates the weight wi of each particle using the following equation 3. Here, the value of the temporary weight wi ′ of the i-th particle is normalized by the sum of the temporary weights wi ′ of all the particles. N represents the number of particles.

  In this way, the control computer 15 determines the weight of each particle based on the distance data measured for each particle on the grid map 20 and the distance data actually measured by the autonomous mobile body 10 using the laser range finder 17. To decide.

  Returning to FIG. Next, the control computer 15 recognizes the particle position having the largest weight as the estimated self-position of the autonomous mobile body 10 (step S105). Then, the coordinates of the particle position recognized as the estimated self position are stored on the grid map 20 as the self position (step S106).

  Further, the control computer 15 executes resampling of the particle position based on the determined weight of the particle position (step S107). Specifically, resampling can be performed based on the cumulative sum of the weights of the particle positions as shown in FIG. First, the control computer 15 calculates a random value between 0 and 1. Then, the particle position corresponding to the calculated random value is taken over as a new particle position. For example, if the random value is 0.15, the fifth particle position part_5 corresponds to FIG. 10, and the position (x, y, θ) of the particle is taken over. This resampling process is repeated for the number of particle positions to create a new particle group. According to such a method, since a particle position having a larger weight is more easily selected, resampling can be easily performed according to the weight of the particle position. In FIG. 10, for example, the weight of the first particle position part_1 is 0.04, the weight of the second particle position part_2 is 0.03, the weight of the third particle position part_3 is 0.01, It is assumed that the weight of the particle position part_4 is 0.05, the weight of the fifth particle position part_1 is 0.07,..., And the sum of the weights of these particle positions is 1.

  Then, the control computer 15 proceeds to step S108 and determines whether or not to end the movement. If the movement is to be continued, the control computer 15 returns to step S102 to obtain the particle position based on the odometry method again. If it is determined in step S108 that the movement is to be ended, after the movement stop process is performed, the process waits until the next command is received.

  As described above, when performing the particle filter processing on the candidate point of the self-position calculated by the odometry method, the reflection of the laser beam is virtually controlled using the light reflectance attribute information of the optically special material such as glass. The self-position estimation is performed while depicting When the laser beam is extended from the particle position on the grid map 20, the grid unit in the middle is recorded as glass, and the angle formed between the incident angle of the laser beam and the glass surface is equal to or greater than the threshold value. (When the incident angle is shallow), the self-position estimation accuracy can be improved by reflecting the laser light and virtually reproducing the principle of the actual laser.

Other embodiments.
In the above-described embodiment, the autonomous mobile body is a wheel-driven mobile body that autonomously moves on a plane, but the present invention can also be applied to a leg-type mobile body.

  It should be noted that the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the gist of the present invention already described.

It is the schematic which shows a mode that the autonomous mobile body which concerns on 1st Embodiment is located in the moving area which moves. It is the schematic showing the internal structure of the autonomous mobile body shown in FIG. 1 simply. It is a figure showing an example of the grid map memorize | stored in the mobile body main body shown in FIG. It is a figure which shows a mode that the movement path | route for an autonomous mobile body to move on the grid map shown in FIG. 4 is produced. It is the schematic which shows a mode that an autonomous mobile body performs sensing using a laser range finder within a movement area | region. It is a flowchart which shows the procedure for estimating the self-position of an autonomous mobile body in the autonomous mobile body which concerns on 1st Embodiment. It is the schematic which shows the example of the particle position calculated when the autonomous mobile body continued a movement. It is a flowchart which shows the procedure for calculating the weight of a particle position. It is the schematic which shows a mode that the measurement distance of a particle position is calculated. It is the schematic which shows a mode that the resampling process of a particle position is performed. It is the schematic which shows a mode that the conventional autonomous mobile body performs sensing using a laser range finder within a movement area | region.

Explanation of symbols

1 floor, 10 autonomous mobile body, 10a mobile body,
11 wheels, 12 casters, 13 drive units, 14 counters,
15 control computer (arithmetic unit), 15a storage area (storage unit), 16 battery,
17 Laser range finder, 18 Ultrasonic sensor,
20 grid map, 21 outer frame, 22 grid lines,
P movement area, S1 sensing area, SP sensing point,
W wall, G glass, B object, EP particle position

Claims (16)

A laser range finder that acquires environmental information indicating the position and shape of an object present in the moving area is provided, and the self-position is recognized based on the environmental information acquired by the laser range finder using a moving map related to the moving area. An autonomous mobile body that can move while
The autonomous moving body, wherein the movement map includes information indicating a light reflectance attribute of the object. An acquisition unit that irradiates laser light from the laser range finder and acquires environmental information based on reflection of the irradiated laser light, a self-position candidate calculation unit that calculates a self-position candidate of the autonomous mobile body, and the acquisition A self-position estimation unit that recognizes an estimated self-position based on the environmental information and the calculated self-position candidate,
The self-position estimation unit virtually reflects the laser beam based on information indicating the light reflectance attribute of the object on the movement map, and is acquired by the virtually reflected laser beam and the acquisition unit. The self-position candidate calculated by the self-position candidate calculation unit is corrected based on the environmental information, and the corrected self-position candidate is recognized on the movement map as an estimated self-position. The autonomous mobile body described. The self-position estimation unit virtually depicts the laser light with respect to the object from the estimated self-position calculated by the self-position candidate calculation unit on the movement map, and the incident angle of the depicted laser light is The self-position candidate calculation based on the measured distance of the laser beam virtually reflected and the measured distance of the laser beam acquired by the acquisition unit when the laser beam is reflected when the predetermined threshold value is exceeded The autonomous mobile body according to claim 2, wherein the self-position candidate calculated by the unit is corrected, and the corrected self-position candidate is recognized on the movement map as an estimated self-position. The autonomous mobile body according to claim 3, wherein the predetermined threshold is variable according to a material of the object. The self-position estimation unit calculates the weight of the self-position candidate calculated by the self-position candidate calculation unit based on the virtually measured laser light measurement distance and the laser light measurement distance acquired by the acquisition unit. The autonomous system according to claim 2, wherein a self-position is calculated from the self-position candidate based on the calculated weight and recognized on the movement map. Moving body. The autonomous mobile body according to claim 2, wherein the self-position candidate calculation unit calculates a self-position candidate based on a direction and a distance traveled by the autonomous mobile body. 7. The grid map created by virtually depicting grid lines connecting lattice points arranged at substantially constant intervals in the movement region. 7. The autonomous mobile body according to item 1. A laser range finder that acquires environmental information indicating the position and shape of an object existing in a moving region is provided, and laser light is emitted from the laser range finder, and environmental information is acquired based on reflection of the irradiated laser light. Means, self-position candidate calculation means for calculating a self-position candidate for the autonomous mobile body, and self-position estimation means for recognizing an estimated self-position based on the acquired environment information and the calculated self-position candidate. A control system for an autonomous mobile body that can move while recognizing its own position based on environmental information acquired by the acquisition means using a movement map relating to the movement area,
The self-position estimation unit virtually reflects the laser beam based on information indicating the light reflectance attribute of the object on the movement map, and is acquired by the virtually reflected laser beam and the acquisition unit. An autonomous mobile body that corrects the self-position candidate calculated by the self-position candidate calculating means based on the environmental information and recognizes the corrected self-position candidate as an estimated self-position on the movement map. Control system. The self-position estimation means virtually depicts the laser beam with respect to the object from the estimated self-position calculated by the self-position candidate calculation means on the movement map, and the incident angle of the depicted laser light is The self-position candidate calculation based on the measured distance of the laser beam virtually reflected and the measured distance of the laser beam acquired by the acquisition unit when the laser beam is reflected when the predetermined threshold value is exceeded The autonomous mobile control system according to claim 8, wherein the self-position candidate calculated by the means is corrected, and the corrected self-position candidate is recognized on the movement map as an estimated self-position. In an autonomous mobile body equipped with a laser range finder that acquires environmental information indicating the position and shape of an object existing in the moving area, based on the environmental information acquired by the laser range finder using a movement map related to the moving area. A self-position estimation method for recognizing the self-position of the autonomous mobile body,
The self-position estimation method, wherein the movement map includes information indicating a light reflectance attribute of the object. Irradiating a laser beam from the laser range finder, acquiring environmental information based on reflection of the irradiated laser beam, calculating a self-position candidate of the autonomous mobile body, the acquired environmental information, and the Recognizing an estimated self-position based on the calculated self-position candidate,
In the step of recognizing the self-position, the laser beam is virtually reflected on the movement map based on information indicating the light reflectance attribute of the object, and the virtually reflected laser beam and the acquisition unit The self-location according to claim 10, wherein the calculated self-location candidate is corrected based on the environment information acquired in step S, and the corrected self-position candidate is recognized as an estimated self-location on the movement map. Estimation method. In the step of recognizing the self-position, the laser beam is virtually depicted with respect to the object from the calculated estimated self-position on the movement map, and the incident angle of the depicted laser light is equal to or greater than a predetermined threshold value If the laser beam is reflected, the calculated self-position candidate is corrected based on the virtually measured laser beam measurement distance and the laser beam measurement distance acquired by the acquisition unit. The self-position estimation method according to claim 11, wherein the corrected self-position candidate is recognized on the movement map as an estimated self-position. The self-position estimation method according to claim 12, wherein the predetermined threshold value is variable according to a material of the object. In the step of recognizing the self-position, the weight of the calculated self-position candidate is calculated based on the virtually measured laser light measurement distance and the laser light measurement distance acquired by the acquisition unit. The self-position estimation method according to claim 11, wherein the self-position is calculated according to the correlation value, and an estimated self-position is determined from the self-position candidates based on the calculated weight and recognized on the movement map. The self-position estimation method according to claim 11, wherein in the step of recognizing the self-position, a self-position candidate is calculated based on a direction and a distance in which the autonomous mobile body has moved. 16. The grid map created by virtually depicting grid lines connecting lattice points arranged at substantially constant intervals in the movement region. The self-position estimation method according to claim 1.

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