The present technology relates to an information processing device, an information processing method, and a program enabling follow-up by autonomous movement while appropriately capturing a follow-up target. It is driven for following a follow-up target, it recognizes the follow-up target, and a recognition algorithm for recognizing the follow-up target is changed on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target, and the drive is controlled on the basis of a position of the recognized follow-up target.

TECHNICAL FIELD

The present technology relates to an information processing device, an information processing method, and a program, and especially relates to an information processing device, an information processing method, and a program capable of following by autonomous movement while appropriately capturing a follow-up target.

BACKGROUND ART

Patent Documents 1 and 2 disclose an autonomous mobile robot that follows a follow-up target (mobile target) such as a moving person or object by autonomous movement.

CITATION LIST

Patent Document

SUMMARY OF THE INVENTION

Problems to be Solved by the Invention

If an obstacle is interposed between a follow-up target and an autonomous mobile robot in a case where the follow-up target turns a corner or the like, the follow-up target cannot be captured and the autonomous mobile robot cannot appropriately follow the following target in some cases.

The present technology has been made in view of such a situation, and enables follow-up by autonomous movement while appropriately capturing the follow-up target.

Solutions to Problems

An information processing device or a program according to the present technology is an information processing device including a drive unit that is driven for following a follow-up target, a recognition unit that recognizes the follow-up target, the recognition unit that changes a recognition algorithm for recognizing the follow-up target on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target, and a control unit that controls the drive unit on the basis of a position of the follow-up target recognized by the recognition unit, or a program that allows a computer to serve as such information processing device.

An information processing method according to the present technology is an information processing method of an information processing device including a drive unit, a recognition unit, and a control unit, the method including a drive step for the drive unit to be driven for following a follow-up target, a recognition step for the recognition unit to recognize the follow-up target, the recognition step of changing a recognition algorithm for recognizing the follow-up target on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target, and a control step for the control unit to control the drive at the drive step on the basis of a position of the follow-up target recognized at the recognition step.

In the information processing device, the information processing method, and the program according to the present technology, it is driven for following a follow-up target, it recognizes the follow-up target, and a recognition algorithm for recognizing the follow-up target is changed on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target, and the drive is controlled on the basis of a position of the recognized follow-up target.

MODE FOR CARRYING OUT THE INVENTION

Hereinafter, an embodiment of the present technology will be described with reference to the drawings.

<Embodiment of Autonomous Mobile Robot>

FIG.1is a block diagram illustrating a configuration example of an embodiment of an autonomous mobile robot to which the present technology is applied.

An autonomous mobile robot11of this embodiment inFIG.1autonomously moves, while capturing a moving object (mobile object) such as a person as a follow-up target, so as to follow the follow-up target.

The autonomous mobile robot11includes a sensor unit21, a follow-up target recognition unit22, a distance detection unit23, a follow-up target prediction unit24, a movement control unit25, and a drive unit26.

The sensor unit21includes a sensor that detects a position and the like of the follow-up target and a sensor that detects an obstacle and the like in a surrounding environment. The sensor unit21supplies sensor data output from various sensors to the follow-up target recognition unit22.

The follow-up target recognition unit22recognizes the follow-up target on the basis of the sensor data from the sensor unit21, and supplies information such as a position regarding the recognized follow-up target to the distance detection unit23and the follow-up target prediction unit24.

The follow-up target recognition unit22switches, on the basis of a distance from the autonomous mobile robot11to the follow-up target supplied from the distance detection unit23and the like, a recognition algorithm for recognizing the follow-up target to an appropriate recognition algorithm depending on the distance.

The distance detection unit23calculates the distance from the autonomous mobile robot11to the follow-up target (also referred to as a distance of the follow-up target or a follow-up distance) on the basis of the information regarding the follow-up target from the follow-up target recognition unit22and the like. The distance detection unit23supplies the calculated distance of the follow-up target to the follow-up target recognition unit22.

The follow-up target prediction unit24predicts a future trajectory of the follow-up target on the basis of the information regarding the follow-up target from the follow-up target recognition unit22. The follow-up target prediction unit24supplies the predicted trajectory of the follow-up target (predicted trajectory) to the movement control unit25.

On the basis of the predicted trajectory from the follow-up target prediction unit24, the movement control unit25determines a position (follow-up position) and a posture to which the autonomous mobile robot11is moved every time a new predicted trajectory is supplied. After determining the follow-up position and posture, the movement control unit25supplies a drive signal (operation signal) for moving the autonomous mobile robot11to the follow-up position and posture to the drive unit26.

The drive unit26includes one or a plurality of actuators that actuates a moving mechanism for the autonomous mobile robot11to move. For example, a moving direction and a moving speed of the autonomous mobile robot11are controlled by controlling a drive speed of each actuator. An operation signal for indicating the drive speed of the actuator is supplied from the movement control unit25to the drive unit26so that the autonomous mobile robot11moves along a trajectory for moving the autonomous mobile robot11from current position and posture to target follow-up position and posture.

FIG.2is a diagram illustrating a situation in which the autonomous mobile robot11(autonomous mobile robot to which the present technology is not applied) cannot capture the follow-up target. In A and B ofFIG.2, the autonomous mobile robot11is a robot that autonomously moves following the follow-up target. A person41is a follow-up target followed by the autonomous mobile robot11. Note that, a tracking target is not limited to a person. A trajectory42is a trajectory (route) through which the person41has moved or through which the person is scheduled to move. An obstacle51is any object that obstructs passage of the person41such as a shelf, a pillar, a wall and the like, for example. In the example inFIG.2, the obstacle51is a rectangular object.

In A ofFIG.2, the person41walks on a left side in the drawing of the obstacle51along the trajectory42toward an upper side in the drawing of the obstacle51. The autonomous mobile robot11follows the person41and moves behind the person41. Note that, hereinafter, an upper side, a lower side, a right side, and a left side in the drawing are simply referred to as an upper side, a lower side, a right side, and a left side.

B ofFIG.2illustrates a state after a lapse of a predetermined time from A ofFIG.2. In B ofFIG.2, the person41turns right at a corner on an upper left end of the obstacle51along the trajectory42, then moves on the upper side of the obstacle51toward a right side of the obstacle51. At that time, while the autonomous mobile robot11is moving on the left side of the obstacle51, a situation occurs in which the obstacle51is interposed between the autonomous mobile robot11and the person41, and the autonomous mobile robot11cannot capture the person41.

In this case, it is assumed that the autonomous mobile robot11grasps a map around a current position (arrangement of objects), and the number of trajectories42through which the person41can pass is limited to one. At that time, the autonomous mobile robot11predicts that the person41is moving on the upper side of the obstacle51along the trajectory42, and the autonomous mobile robot11can turn right at the corner on the upper left end of the obstacle51along the trajectory42to capture the person41again.

In contrast, in a case where the number of trajectories42is not limited to one, it is difficult for the autonomous mobile robot11to capture the person41again after the state in B ofFIG.2.

FIG.3is a diagram illustrating a state in B ofFIG.2in a case where the obstacle52inFIG.2is a narrow object such as a wall. The obstacle52is the narrow object arranged in place of the obstacle52inFIG.2. That is,FIG.3illustrates a state in which the person41turns right at a corner on an upper left end of the obstacle52in a manner similar to the movement of the person41from A ofFIG.2to B ofFIG.2.

In this case, the obstacle52is interposed between the autonomous mobile robot11and the person41, and the autonomous mobile robot11cannot capture the person41. At that time, the autonomous mobile robot11cannot even predict whether the trajectory42after the person41turns right at a corner on an upper right end of the obstacle52is directed rightward, downward or the like. Therefore, it is difficult for the autonomous mobile robot11to capture the person41again after the person41turns right at the corner on the upper left end of the obstacle51.

FIGS.4and5are diagrams illustrating another situation in which the autonomous mobile robot11cannot capture the follow-up target. In A and B ofFIG.4and A and B ofFIG.5, the autonomous mobile robot11is a robot that autonomously moves following the follow-up target. A person41is a follow-up target followed by the autonomous mobile robot11. The trajectory42is the trajectory through which the person41has moved or through which the person is scheduled to move. A capturing range61represents a range in which the autonomous mobile robot11can capture the follow-up target.

In A ofFIG.4, the person41is walking toward an upper side along the trajectory42. The autonomous mobile robot11follows the person41while capturing the person41within the capturing range61and moves behind the person41.

B ofFIG.4illustrates a state after a lapse of a predetermined time from A ofFIG.4. In B ofFIG.4, the person41turns right along the trajectory42, and then moves toward a right side. At that time, when the person41deviates from the capturing range61, a situation occurs in which the autonomous mobile robot11cannot capture the person41.

In A ofFIG.5, the person41is walking toward the upper side along the trajectory42in a manner similar to that in A ofFIG.4. The autonomous mobile robot11follows the person41while capturing the person41within the capturing range61and moves behind the person41.

B ofFIG.5illustrates a state after a lapse of a predetermined time from A ofFIG.5. In B ofFIG.4, the person41rotates right along the trajectory42, and then moves toward a lower side. At that time, when the person41deviates from the capturing range61, a situation occurs in which the autonomous mobile robot11cannot capture the person41.

In cases inFIGS.4and5, there is a case where the trajectory42is not a specific trajectory limited by the obstacle or the like. In this case, it is difficult for the autonomous mobile robot11to predict the trajectory42. Therefore, it is difficult for the autonomous mobile robot11to capture the person41again after the state in B ofFIG.4or B ofFIG.5.

DESCRIPTION OF PRESENT TECHNOLOGY

The present technology controls the autonomous mobile robot11so that the states in B ofFIG.2,FIG.3, B ofFIG.4, and B ofFIG.5do not occur.

FIG.6is a diagram illustrating control of the autonomous mobile robot11for avoiding the state in B ofFIG.2, that is, a state in which the autonomous mobile robot11cannot capture the follow-up target due to the obstacle. A ofFIG.6is the same as A ofFIG.2, so that description thereof is omitted.

B ofFIG.6illustrates a state after a lapse of a predetermined time from A ofFIG.6. In B ofFIG.6, the person41turns right at the corner on the upper left end of the obstacle51along the trajectory42, then moves on the upper side of the obstacle51toward the right side of the obstacle51. The autonomous mobile robot11predicts in advance the trajectory42through which the person41is scheduled to move. As a result of the prediction, when the autonomous mobile robot11predicts that there is a possibility that the person41cannot be captured due to the obstacle51as in B ofFIG.2, this approaches the person41to a position closer than a position at a normal distance (position at a shorter distance). Therefore, in B ofFIG.6, even after the person41turns right at the corner on the upper left end of the obstacle51, the autonomous mobile robot11can continuously capture and follow the person41without being obstructed by the obstacle51.

In the case inFIG.3, similarly, the autonomous mobile robot11can avoid a state in which the person41cannot be captured due to the obstacle52by approaching the person41to a distance shorter than the normal distance.

FIG.7is a diagram illustrating control of the autonomous mobile robot11for avoiding the states in B ofFIG.4and B ofFIG.5, that is, states in which the autonomous mobile robot11cannot capture the follow-up target because the follow-up target goes out of the capturing range. A ofFIG.7is the same as A ofFIG.4, so that description thereof is omitted.

B ofFIG.7illustrates a state after a lapse of a predetermined time from A ofFIG.7. In B ofFIG.7, the person41turns right along the trajectory42, and then moves toward the right side. The autonomous mobile robot11predicts the trajectory42through which the person41is scheduled to move. On the basis of the result of the prediction, the autonomous mobile robot11moves while changing a direction of the capturing range61so that the person41does not deviate from the capturing range61as in B ofFIG.4or B ofFIG.5.

C ofFIG.7illustrates a state after a lapse of a predetermined time from B ofFIG.7. In C ofFIG.7, the person41moves toward the right side along the trajectory42. The autonomous mobile robot11moves while changing the direction of the capturing range61so that the person41does not deviate from the capturing range61as in the case in B ofFIG.7.

Here, the autonomous mobile robot11needs to move a certain distance in order to change the direction of the capturing range61in some cases. In this case, the autonomous mobile robot11is controlled to approach the person41to a distance shorter than the normal distance to change the direction of the capturing range61.

B′ ofFIG.7illustrates a state after a lapse of a predetermined time from A ofFIG.7in a case where the autonomous mobile robot11is controlled to approach the follow-up target to a distance shorter than the normal distance. The person41turns right along the trajectory42, and then moves toward the right side. The autonomous mobile robot11predicts the trajectory42through which the person41is scheduled to move. As a result of the prediction, in a case where there is a possibility that the person41deviates from the capturing range61with the normal movement of the autonomous mobile robot11and the change in direction of the capturing range61, the autonomous mobile robot11moves closer to the person41to a distance shorter than the normal distance, thereby changing the capturing range61to an appropriate direction.

Note that, in a case where the person41moves in the direction deviating from the capturing range61, the autonomous mobile robot11may control to approach the person41to a distance shorter than the normal distance as in B′ ofFIG.7.

Even in a case where the person41rotates as inFIG.5, the autonomous mobile robot11can move as in the case ofFIG.7, thereby avoiding the deviation of the person41from the capturing range61. The description of the control of the autonomous mobile robot11with respect to the movement of the person41inFIG.5is omitted.

As described above, in a case where the autonomous mobile robot11approaches the follow-up target to a distance shorter than the normal distance, there is a case where the capturing range61includes only a part of the follow-up target. At that time, if recognition processing (recognition algorithm) for recognizing the follow-up target is the same as that in a normal case, a situation in which the follow-up target cannot be appropriately captured might occur. The present technology enables the autonomous mobile robot11to appropriately recognize (capture) the follow-up target in a case of approaching the follow-up target to a distance shorter than the normal distance.

<Details of Processing in Respective Components of Autonomous Mobile Robot11>

Processing of the follow-up target recognition unit22inFIG.1in a case where control is performed to bring the autonomous mobile robot11close to the follow-up target to a distance shorter than the normal distance in order to avoid the follow-up target from deviating from the capturing range61will be described.

The sensor unit21includes a sensor for detecting a direction and a distance of the follow-up target from the autonomous mobile robot11. The sensor unit21includes at least one of one or a plurality of cameras that images a color image (RGB image) or a monochrome image, or a ranging sensor capable of measuring a distance to a multipoint measurement point such as a light detection and ranging (LiDAR) or a ToF camera. The sensor included in the sensor unit21differs depending on processing performed by the follow-up target recognition unit22, and can be appropriately changed depending on a utilization case of the autonomous mobile robot11. Note that, the sensor of the sensor unit21is assumed to be fixed to the autonomous mobile robot11, but there is not limitation.

The follow-up target recognition unit22recognizes the follow-up target on the basis of the sensor data from the sensor unit21. The processing of recognizing the follow-up target (recognition algorithm) varies depending on a type of the sensor of the sensor unit21, and is changed depending on the distance of the follow-up target. The recognition algorithm for recognizing the follow-up target is roughly divided into a normal recognition algorithm (recognition algorithm for normal distance) in a case where the follow-up target is at the normal distance from the autonomous mobile robot11and a recognition algorithm for short distance in a case where the follow-up target approaches the autonomous mobile robot11. The recognition algorithm for short distance is used in a case where the autonomous mobile robot11(sensor) is too close to the follow-up target and it is difficult to recognize the follow-up target with the normal recognition algorithm.

As the normal recognition algorithm, for example, there are following first to fourth recognition algorithms, and any of them can be adopted.

The first recognition algorithm is an algorithm in a case where the sensor unit21includes the camera. In the first recognition algorithm, the follow-up target recognition unit22acquires an image (camera image) imaged by the camera as the sensor data. The follow-up target recognition unit22detects an image (image region) matching features such as an entire shape and color distribution of the follow-up target from within a region of the camera image acquired from the camera. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with an imaging range of the camera as the capturing range61.

The second recognition algorithm is an algorithm in a case where the sensor unit21includes the camera, and is different from the first recognition algorithm. In the second recognition algorithm, the follow-up target recognition unit22acquires an image (camera image) imaged by the camera as the sensor data. The follow-up target recognition unit22detects an image region of a moving object (mobile object) from within a region of the camera image on the basis of a change in time of the camera image acquired from the camera at predetermined time intervals. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with an imaging range of the camera as the capturing range61.

The third recognition algorithm is an algorithm in a case where the sensor unit21includes the ranging sensor. In the third recognition algorithm, the follow-up target recognition unit22acquires ranging data being a result of ranging by the ranging sensor as the sensor data. The ranging data is a set of data of distances to objects (measurement points) measured in a large number (a plurality) of different orientations (directions) from the ranging sensor. The follow-up target recognition unit22detects a region of a moving object (mobile object) from within a ranging range of the ranging sensor on the basis of a change in time of the ranging data acquired from the ranging sensor at predetermined time intervals. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with the ranging range of the ranging sensor as the capturing range61.

The fourth recognition algorithm is an algorithm in a case where the sensor unit21includes the ranging sensor, and is different from the third recognition algorithm. In the fourth recognition algorithm, the follow-up target recognition unit22acquires ranging data being a result of ranging by the ranging sensor as the sensor data. The follow-up target recognition unit22detects a region matching a shape of the follow-up target from within a measurement range of the ranging data acquired from the ranging sensor. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with the ranging range of the ranging sensor as the capturing range61.

(Recognition Algorithm for Short Distance)

As the recognition algorithm for short distance, there are following fifth to eighth recognition algorithms, and any of them can be adopted.

The fifth recognition algorithm is an algorithm in a case where the sensor unit21includes the camera, and is different from the first and second recognition algorithms being the normal recognition algorithms. In the fifth recognition algorithm, the follow-up target recognition unit22acquires an image (camera image) imaged by the camera as the sensor data. The follow-up target recognition unit22detects an image region (matching region) matching a representative color of the follow-up target from within a region of the camera image acquired from the camera. In a case where the matching region matching the representative color of the follow-up target is detected at a plurality of sites, as an example, the follow-up target recognition unit22compares the plurality of matching regions with an image region (matching region) of the follow-up target recognized from the camera image acquired one frame before from the camera of the sensor unit21(the camera image used in the recognition processing of the follow-up target one time before). As a result of the comparison, the follow-up target recognition unit22recognizes the matching region having the closest area as the image region to be follow-up target. Note that, the matching region may be detected by an HSV color space so that the color in the camera image is not affected by light. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with an imaging range of the camera as the capturing range61.

The sixth recognition algorithm is an algorithm in a case where the sensor unit21includes the camera, and is different from the first and second recognition algorithms being the normal recognition algorithms, and the fifth recognition algorithm being the recognition algorithm for short distance. In the sixth recognition algorithm, the follow-up target recognition unit22acquires an image (camera image) imaged by the camera as the sensor data. The follow-up target recognition unit22detects (extracts) an image region (matching region) matching a partial or local feature amount of the image of the follow-up target from within a region of the camera image acquired from the camera. Detection of the image region matching the feature amount of the follow-up target (matching) is performed in consideration of enlargement or reduction of the image of the follow-up target in the camera image. As a matching method, a well-known method of detecting (extracting) a local image feature amount such as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), histograms of oriented gradients (HOG), or Harr-like may be used. The follow-up target recognition unit22recognizes the detected matching region as the image region of the follow-up target. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with an imaging range of the camera as the capturing range61.

The seventh recognition algorithm is an algorithm in a case where the sensor unit21includes the camera, and is different from the first and second recognition algorithms being the normal recognition algorithms, and the fifth and sixth recognition algorithms being the recognition algorithms for short distance. In the sixth recognition algorithm, the follow-up target recognition unit22acquires an image (camera image) imaged by the camera as the sensor data. The follow-up target recognition unit22detects (extracts) an image region of the follow-up target or an image region (matching region) matching a partial or local feature amount of the follow-up target from within a region of the camera image acquired from the camera using an inference model in machine learning. The follow-up target recognition unit22recognizes the detected matching region as the image region of the follow-up target. The inference model has a structure of neural network such as convolutional neural network (CNN), for example. Learning of the inference model is performed, for example, by using a plurality of camera images (for five to ten frames) from several frames before the camera image in which the follow-up target recognition unit22recognizes the image region of the follow-up target the last as input data, and using the image region of the follow-up target recognized for the camera images as the input data as correct answer data. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with an imaging range of the camera as the capturing range61.

The eighth recognition algorithm is an algorithm in a case where the sensor unit21includes the ranging sensor, and is different from the third and fourth recognition algorithms being the normal recognition algorithms. In the eighth recognition algorithm, the follow-up target recognition unit22acquires ranging data being a result of ranging by the ranging sensor as the sensor data. The follow-up target recognition unit22detects, from within the measurement range of the ranging data acquired from the ranging sensor, a region of a moving object (mobile object) the closest to the position where the follow-up target is recognized the last, as a region of the follow-up target. As a result, the follow-up target recognition unit22recognizes the follow-up target present in the capturing range61with the ranging range of the ranging sensor as the capturing range61. Note that, when detecting the region of the follow-up target, the follow-up target recognition unit22may improve recognition accuracy of the follow-up target by adding a size of the follow-up target and coincidence with an average moving speed of the follow-up target as a condition. For example, in a case where the follow-up target is a person, the condition regarding the moving speed (walking speed) is, for example, about 2 m/s or lower.

The distance detection unit23detects the distance of the follow-up target recognized by the follow-up target recognition unit22to the autonomous mobile robot11(the distance to the follow-up target).

In a case where the sensor unit21includes a plurality of cameras such as a stereo camera, the follow-up target recognition unit22detects a region (image region) of the follow-up target in the capturing range61(camera image of each camera) by any one of the first, second, and fifth to seventh recognition algorithms, and supplies information (follow-up target recognition information) specifying a position and a range of the region of the follow-up target in the capturing range61(camera image of each camera) to the distance detection unit23.

On the basis of the follow-up target recognition information from the follow-up target recognition unit22, the distance detection unit23detects a parallax amount (deviation amount) of the position of the image region of the follow-up target in the camera image of each camera, and detects (calculates) the distance of the follow-up target using the principle of triangulation. The detected distance of the follow-up target is supplied to the follow-up target recognition unit22.

In a case where the sensor unit21includes the ranging sensor, the follow-up target recognition unit22detects the region of the follow-up target in the capturing range61(ranging range of the ranging sensor) by the third, fourth, or eighth recognition algorithm, and supplies information (follow-up target recognition information) specifying a position and a range of the region of the follow-up target in the ranging range of the ranging sensor to the distance detection unit23.

On the basis of the follow-up target recognition information from the follow-up target recognition unit22and the ranging data from the sensor unit21or the follow-up target recognition unit22, the distance detection unit23extracts the distance of each measurement point in the region of the follow-up target in the ranging range of the ranging sensor, and detects (calculates), for example, an average value of the extracted distances of the measurement points as the distance of the follow-up target. The detected distance of the follow-up target is supplied to the follow-up target recognition unit22.

In a case where the sensor unit21includes both the camera and the ranging sensor, the follow-up target recognition unit22can recognize the region of the follow-up target in the capturing range61using any of the first to eighth recognition algorithm. In this case, the distance detection unit23can detect the distance of the follow-up target in a manner similar to that in a case where the sensor unit21described above includes a plurality of cameras or a case where the sensor unit21includes the ranging sensor.

In a case where the sensor unit21includes both the camera and the ranging sensor, and in a case where there is only one camera for recognizing the follow-up target, when the follow-up target recognition unit22recognizes the region of the follow-up target by any of the first, second, and fifth to seventh recognition algorithms, the distance detection unit23obtains the region of the follow-up target in the ranging range of the ranging sensor on the basis of the follow-up target recognition information from the follow-up target recognition unit22. On the basis of the obtained region of the follow-up target and the ranging data from the sensor unit21or the follow-up target recognition unit22, the distance detection unit23extracts the distance of each measurement point in the region of the follow-up target in the ranging range of the ranging sensor, and detects (calculates), for example, an average value of the extracted distances of the measurement points as the distance of the follow-up target. The detected distance of the follow-up target is supplied to the follow-up target recognition unit22. Note that, even in a case where the sensor unit21includes a plurality of cameras, when the follow-up target recognition unit22recognizes the region of the follow-up target by any of the first, second, and fifth to seventh recognition algorithms, the distance detection unit23can also detect the distance of the follow-up target in a manner similar to that in a case where there is only one camera.

(Recognition Algorithm Depending on Distance)

The follow-up target recognition unit22switches the recognition algorithm depending on the distance of the follow-up target from the distance detection unit23. For example, in a case where the distance of the follow-up target is a predetermined threshold (distance threshold) or longer, this is the normal distance suitable for the recognition of the follow-up target by the normal recognition algorithm. The normal distance is, for example, a distance at which the follow-up target recognition unit22can recognize an entire follow-up target. In a case where the distance of the follow-up target is shorter than the distance threshold, the follow-up target recognition unit22sets the short distance suitable for the recognition of the follow-up target by the algorithm for short distance.

Note that, the follow-up target recognition unit22is not limited to a case of directly switching the recognition algorithm of the follow-up target on the basis of the distance of the follow-up target. For example, when the follow-up target is recognized by the normal recognition algorithm, in a case where the recognition of the follow-up target becomes unstable or in a case where it is determined to be unstable (in a case where the recognition accuracy of the follow-up target is deteriorated), the follow-up target recognition unit22may regard that the distance of the follow-up target is a short distance (shorter than the distance threshold) and switch from the normal recognition algorithm to the recognition algorithm for short distance to recognize the follow-up target. A case where the recognition of the follow-up target becomes unstable includes a case where likelihood of the recognized follow-up target is smaller than a predetermined threshold, a case where the follow-up target cannot be recognized for a predetermined time or longer and the like. A case where it is determined that the recognition of the follow-up target becomes unstable includes a case where a part of the follow-up target is outside the capturing range61, a case where the distance of the follow-up target is shortened by a predetermined distance or longer from a state in which the distance is continuously maintained at a stable distance or the like.

The follow-up target prediction unit24acquires the follow-up target recognition information that specifies the position and range of the region of the follow-up target in the capturing range61from the follow-up target recognition unit22. The follow-up target recognition information includes the distance of the follow-up target.

The follow-up target prediction unit24detects a current position of the follow-up target (current position) on the basis of the follow-up target recognition information and self-position information for specifying the position and posture of the autonomous mobile robot11. The self-position information is obtained by information of a control amount of the drive unit26, information from a sensor such as an IMU that measures the self-position, self-position estimation using a technology of simultaneous localization and mapping (SLAM) or the like. A sensor that detects an external situation such as the LiDAR, camera, or ToF sensor used in the SLAM or the like may be a sensor shared by the sensor such as the camera, ranging sensor and the like of the sensor unit21that recognizes the follow-up target, or may be a sensor installed in the autonomous mobile robot11separately from the sensor that recognizes the follow-up target.

Note that, the follow-up target prediction unit24stores the position of the follow-up target detected up to the present time in a storage unit not illustrated as movement history information (time and position information) of the follow-up target.

The follow-up target prediction unit24predicts the trajectory (future trajectory) of the follow-up target on the basis of the detected current position of the follow-up target, movement history information and the like. The trajectory is information indicating time at predetermined time intervals and a position (predicted position) at each time.

Any method such as a method using a particle filter or a method using a moving model is used, for example, to predict the trajectory of the follow-up target.

For example, in the method using the particle filter, the follow-up target prediction unit24estimates a trajectory through which the current follow-up target is predicted to move in the future on the basis of the movement history information (movement trajectory information) when the follow-up target (not limited to the current follow-up target) has moved in the past in the same section and the movement history information up to the present time of the current follow-up target. The movement history information when the follow-up target has moved in the past is stored in advance in a storage unit not illustrated. When predicting the trajectory of the follow-up target, the follow-up target prediction unit24supposes a large number of states (trajectories) and calculates likelihood of each supposed trajectory. The follow-up target prediction unit24sets, as the trajectory of the follow-up target, the trajectory having the highest likelihood among the large number of trajectories.

In the method using the moving model, for example, the autonomous mobile robot11predicts the trajectory of the follow-up target on the assumption that the follow-up target moves at a current moving speed and along a route having the same curvature radius as the current one.

In another method (social force model) using the moving model, the trajectory of the follow-up target is predicted on the assumption that the follow-up target moves so as to avoid another mobile object.

Note that, the follow-up target prediction unit24may store a map of a section where the follow-up target moves in a storage unit not illustrated in advance. In this case, the follow-up target prediction unit24may predict the trajectory of the follow-up target only on a passage through which the follow-up target can move on the basis of the stored map.

The follow-up target prediction unit24may predict the trajectory of the follow-up target on the condition that the follow-up target moves while avoiding the obstacle in consideration of the presence of the obstacle. The follow-up target prediction unit24may detect the position of the obstacle on the basis of the map stored in advance or on the basis of the sensor data obtained by the ranging sensor such as the LiDAR. The ranging sensor in this case may be the sensor shared by the sensor that recognizes the follow-up target, or may be the sensor installed in the autonomous mobile robot11separately from the sensor that recognizes the follow-up target.

(Movement Control of Autonomous Mobile Robot11)

The movement control unit25determines a position (follow-up position) and a posture (orientation) which the autonomous mobile robot11should reach at a point in time (predicted time) after a lapse of a predetermined time from the present time on the basis of the trajectory of the follow-up target predicted by the follow-up target prediction unit24.

When determining the follow-up position and posture, the movement control unit25satisfies following first and second conditions in principle (except for a case of exception).

The first condition is that the follow-up position of the autonomous mobile robot11is separated from the position of the follow-up target (predicted position) by the normal distance at the predicted time in a case where the follow-up target moves along the trajectory predicted by the follow-up target prediction unit24. As described above, the normal distance is a distance suitable for the follow-up target recognition unit22to recognize the follow-up target by the normal recognition algorithm, and refers to a distance within a range equal to or longer than a predetermined distance threshold. Note that, the normal distance may be a distance equal to or longer than a predetermined distance threshold and equal to or shorter than a predetermined upper limit value. In principle, the movement control unit25determines the follow-up position so that a distance between the predicted position of the follow-up target and the follow-up position of the autonomous mobile robot11becomes a substantially constant distance within a range of the normal distance.

The second condition is that the capturing range61in the follow-up position and posture of the autonomous mobile robot11includes (captures) the follow-up target in the predicted position at the predicted time in a case where the follow-up target moves along the trajectory predicted by the follow-up target prediction unit24.

The movement control unit25determines the follow-up position and posture of the autonomous mobile robot11after a lapse of a predetermined time so as to satisfy the first and second conditions except for a case of exception. The movement control unit25further determines the trajectory of the autonomous mobile robot11so that the position and the posture of the autonomous mobile robot11after a lapse of a predetermined time become the determined follow-up position and posture.

The movement control unit25considers the presence of the obstacle when determining the follow-up position and posture of the autonomous mobile robot11. The position of the obstacle is information that can also be considered by the follow-up target prediction unit24, and is detected by the movement control unit25similarly to a case of being detected by the follow-up target prediction unit24.

When determining the follow-up position and posture of the autonomous mobile robot11in accordance with the principle, the movement control unit25determines the follow-up position and posture of the autonomous mobile robot11after a lapse of a predetermined time except for a case where there is a possibility that the obstacle is interposed between the autonomous mobile robot11and the follow-up target or a case where there is a possibility that the follow-up target deviates from the capturing range61. In a case of exception, the movement control unit25disables limitation by the first condition and determines the follow-up position and posture of the autonomous mobile robot11so as to satisfy the second condition. Note that, the second condition is satisfied also in a case where the capturing range61of the autonomous mobile robot11includes only a part of the follow-up target.

At that time, the movement control unit25may determine the follow-up position and posture of the autonomous mobile robot11so that the distance between the predicted position of the follow-up target and the follow-up position of the autonomous mobile robot11becomes shorter than the normal distance (the above-described distance threshold), thereby avoiding a situation in which the obstacle is interposed between the autonomous mobile robot11and the follow-up target or a situation in which the follow-up target deviates from the capturing range61. Note that, in a case where these situations can be avoided when the distance between the predicted position of the follow-up target and the follow-up position of the autonomous mobile robot11is the normal distance, the movement control unit25does not necessarily determine the follow-up position and posture of the autonomous mobile robot11so that the distance becomes shorter than the normal distance (distance threshold described above).

As a case of exception, when determining the follow-up position and posture of the autonomous mobile robot11after a lapse of a predetermined time, the movement control unit25determines the trajectory of the autonomous mobile robot11so that the position and posture of the autonomous mobile robot11after a lapse of a predetermined time become the determined follow-up position and posture.

Here, when determining the trajectory of the autonomous mobile robot11in a case of the principle or in a case of exception, a trajectory plan setting method such as Hybrid A* that prevents collision with an obstacle in consideration of the posture of the autonomous mobile robot11may be used. Moreover, for the determined trajectory, a method such as Dynamic WaveArc (DWA) for changing the trajectory in consideration of dynamic obstacles and dynamics of the autonomous mobile robot11may be used. A learning method such as reinforcement learning may be used so that the autonomous mobile robot11can move toward the follow-up position and posture without colliding with an obstacle.

In a case of the principle, or in a case of exception, when the trajectory of the autonomous mobile robot11is determined, a drive signal (operation signal) for moving the autonomous mobile robot11is supplied to the drive unit26according to the trajectory. As a result, the autonomous mobile robot11moves along the trajectory determined by the movement control unit25.

Note that, in a case where an object or an obstacle of the same type as the follow-up target is interposed between the follow-up target and the autonomous mobile robot11, in a case where the autonomous mobile robot11is too close to the follow-up target or the like, a situation in which the distance detection unit23cannot appropriately detect the distance of the follow-up target might occur. The movement control unit25may forcibly stop the movement of the autonomous mobile robot11in a case where the distance detection unit23notifies the same that such a situation occurs. The movement control unit25resumes the movement of the autonomous mobile robot11in a case where the distance detection unit23notifies the same that the distance of the follow-up target can be appropriately detected while the autonomous mobile robot11stops moving.

<Entire Processing of Autonomous Mobile Robot11>

FIG.8is a flowchart illustrating an entire flow of follow-up processing executed by the autonomous mobile robot11to follow the follow-up target to move. Note that,FIG.8illustrates a case where the follow-up target is the person41as illustrated inFIGS.2to7.

InFIG.8, the follow-up target recognition unit22recognizes the person41as the follow-up target on the basis of the sensor data from the sensor unit21at step S11. That is, the follow-up target recognition unit22recognizes the position of the person41as the follow-up target on the basis of the region of the person41in the capturing range61and the distance of the person41detected by the distance detection unit23. The processing proceeds from step S11to step S13.

At step S12, in parallel with the processing at step S11, the movement control unit25recognizes the obstacle present around the autonomous mobile robot11on the basis of the sensor data from the sensor unit21. The processing proceeds from step S12to step S13.

At step S13, the follow-up target prediction unit24predicts the trajectory (position) of the person41in the future on the basis of the position of the person41recognized at step S11. The processing proceeds from step S13to step S14.

At step S14, the movement control unit25determines the follow-up position and posture after a lapse of a predetermined time of the autonomous mobile robot11on the basis of the position of the person41predicted at step S13and the presence (position) of the obstacle recognized at step S12. The processing proceeds from step S14to step S15.

At step S15, the movement control unit25determines the trajectory (action plan) through which the autonomous mobile robot11should move in accordance with the follow-up position and posture determined at step S14, and drives the autonomous mobile robot11according to the determined trajectory. The processing returns from step S15to steps S11and S12, and the processing at steps S11to S15is repeatedly executed.

<Processing of Determining Follow-up Position and Posture>

FIG.9is a flowchart illustrating processing related to the determination of the follow-up position and posture of the autonomous mobile robot11at step S14inFIG.8.

At step S31, the follow-up target recognition unit22determines whether or not the person41as the follow-up target can be recognized by the normal recognition algorithm, that is, whether or not the distance of the person41detected by the distance detection unit23is the normal distance that can be recognized by the normal recognition algorithm.

In a case where it is determined at step S31that the person41as the follow-up target can be recognized by the normal recognition algorithm, that is, in a case where it is determined that the distance of the person41as the follow-up target is the normal distance, the processing proceeds to step S32.

At step S32, the follow-up target recognition unit22recognizes the person41as the follow-up target by the normal recognition algorithm. Note that, since the follow-up target to be recognized is the person41, the recognition of the person by the normal recognition algorithm is described as normal person recognition at step S32inFIG.9. The processing proceeds from step S32to step S34.

In a case where it is determined at step S31that the person41as the follow-up target cannot be recognized by the normal recognition algorithm, that is, in a case where it is determined that the distance of the person41as the follow-up target is a distance shorter than the normal distance, the processing proceeds to step S33.

At step S33, the follow-up target recognition unit22recognizes the person41as the follow-up target by the recognition algorithm for short distance. Note that, since the follow-up target to be recognized is the person41, the recognition of the person by the recognition algorithm for short distance is described as person recognition for short distance at step S33inFIG.9. The processing proceeds from step S33to step S34.

At step S34, the follow-up target prediction unit24predicts the future trajectory of the person41as the follow-up target on the basis of the position of the person41as the follow-up target recognized at step S32or S33. The processing proceeds from step S34to step S36.

Note that, step S35is processing in which the movement control unit25acquires the position of the obstacle by obstacle recognition at step S12inFIG.8.

At step S36, the movement control unit25determines whether or not the person41as the follow-up target might be hidden by the obstacle as seen from the sensor unit21of the autonomous mobile robot11on the basis of the trajectory of the person41as the follow-up target predicted at step S34and the position of the obstacle acquired at step S35.

In a case where it is determined at step S36that the person41as the follow-up target might be hidden by the obstacle, the processing proceeds to step S37, and the movement control unit25determines the follow-up position of the autonomous mobile robot11to be a position (position at a short distance) closer to the person41as the follow-up target at a shorter distance than the normal distance in a case of the principle.

In a case where it is determined at step S36that the person41as the follow-up target might not be hidden by the obstacle, the processing proceeds to step S38, and the movement control unit25determines the follow-up position of the autonomous mobile robot11to be a position at the normal distance from the person41as the follow-up target according to the principle.

When the processing at step S37or S38is executed, the processing in this flowchart ends.

As described above, according to the present technology, the recognition algorithm for recognizing the follow-up target is switched depending on the distance between the autonomous mobile robot11(sensor unit21) and the follow-up target. Therefore, even in a case where the autonomous mobile robot11is brought close to the follow-up target in order to avoid a situation in which the follow-up target cannot be recognized (captured) due to the obstacle and the like, the follow-up target can be appropriately captured and the autonomous mobile robot11can be allowed to follow the follow-up target.

Hereinafter, specific examples of use of the autonomous mobile robot11will be described.

Specific Example 1

As Specific Example 1 in a case where the autonomous mobile robot11is utilized, there is a case where the autonomous mobile robot11carries a load while following a person in a factory.

A utilization environment of the autonomous mobile robot11in Specific Example 1 is as follows.As a dynamic obstacle, there are a person, a carriage, an automatic door and the like.As a static obstacle, there is a corner of a passage. Since a width of the passage is secured to some extent, there are few obstacles on the passage itself.A user of the autonomous mobile robot11and people around the same are accustomed to the robot.A person might temporarily stop due to other work.The person as the follow-up target often moves at a low speed to some extent.As a situation in which the autonomous mobile robot11fails to follow, there mainly is a case where the person as the follow-up target turns at a corner.

In the utilization environment in Specific Example 1, processing and control regarding the autonomous mobile robot11may be optimized in the following form.

The movement control unit25that determines the follow-up position and posture of the autonomous mobile robot11, determines the trajectory thereof, drives the same and the like controls the autonomous mobile robot11as follows.

When determining that the person as the follow-up target is shielded from the autonomous mobile robot11at the corner, the movement control unit25allows the autonomous mobile robot11to move so as to approach the person as the follow-up target to such an extent without danger in consideration of the load. Note that, the movement control unit25prevents the autonomous mobile robot11from approaching the follow-up target to a distance shorter than a braking distance when the autonomous mobile robot11is braked in a case where the person as the follow-up target suddenly stops. In a case where the distance at which the autonomous mobile robot11and the person as the follow-up target can be closest to each other is limited on the basis of the braking distance, the movement control unit25considers the maximum brake amount of the autonomous mobile robot11and considers that the maximum brake amount is limited depending on a weight and fragility of the load. Since the braking distance can be shortened by reducing the moving speed, the movement control unit25reduces the moving speed of the autonomous mobile robot11as the autonomous mobile robot11approaches the person as the follow-up target when bringing the autonomous mobile robot11close to the person as the follow-up target.

Since workers often wear the same clothes in a factory, the follow-up target recognition unit22that recognizes the follow-up target adopts the eighth recognition algorithm using the ranging sensor as the recognition algorithm for short distance.

In a case where the follow-up target prediction unit24that predicts the trajectory of the follow-up target has a map (a map for using a robot) indicating a passable passage in the factory, this predicts the trajectory of the person as the follow-up target using the map. In a case where the follow-up target prediction unit24has prior knowledge about a conveyance place, this predicts the trajectory of the person as the follow-up target on the basis of the information.

Specific Example 2

As Specific Example 2 in a case where the autonomous mobile robot11is utilized, there is a case where the autonomous mobile robot11creates a map for robot while following a person in a factory.

A utilization environment of the autonomous mobile robot11in Specific Example 2 is similar to that in Specific Example 1, so that this is omitted.

In the utilization environment in Specific Example 2, processing and control regarding the autonomous mobile robot11may be optimized in the following form.

The movement control unit25that determines the follow-up position and posture of the autonomous mobile robot11, determines the trajectory thereof, drives the same and the like controls the autonomous mobile robot11as follows.

When determining that the person as the follow-up target is shielded from the autonomous mobile robot11at the corner, the movement control unit25allows the autonomous mobile robot11to move so as to approach the person as the follow-up target to such an extent without danger. Note that, the movement control unit25prevents the autonomous mobile robot11from approaching the follow-up target to a distance shorter than a braking distance when the autonomous mobile robot11is braked in a case where the person as the follow-up target suddenly stops. In a case where the distance at which the autonomous mobile robot11and the person as the follow-up target can be closest to each other is limited on the basis of the braking distance, the movement control unit25considers the maximum brake amount of the autonomous mobile robot11. Since the braking distance can be shortened by reducing the moving speed, the movement control unit25reduces the moving speed of the autonomous mobile robot11as the autonomous mobile robot11approaches the person as the follow-up target when bringing the autonomous mobile robot11close to the person as the follow-up target.

Specific Example 2 is a case where the autonomous mobile robot11is utilized to create the map for robot in the factory, so that the follow-up target prediction unit24that predicts the trajectory of the follow-up target does not have the map for robot. The follow-up target prediction unit24predicts the trajectory of the person as the follow-up target by a method using a particle filter or a method using a moving model, for example.

Specific Example 3

As Specific Example 3 in a case where the autonomous mobile robot11is utilized, there is a case where the autonomous mobile robot11carries a load while following a person in a factory.

A utilization environment of the autonomous mobile robot11in Specific Example 3 is as follows.As a dynamic obstacle, there are a person, a carriage, an automatic door and the like.As a static obstacle, there is a corner of a passage. There are many corners. A width of the passage is narrow, and there is an obstacle on the passage.A user of the autonomous mobile robot11and people around the same are accustomed to the robot.

A person might temporarily stop due to other work.As a situation in which the autonomous mobile robot11fails to follow, there is a case where the person as the follow-up target turns at a corner.As a situation in which the autonomous mobile robot11fails to follow, there is a case that the autonomous mobile robot11fails to capture the person as the follow-up target while performing avoidance operation to avoid the person and the like.

In the utilization environment in Specific Example 3, processing and control regarding the autonomous mobile robot11may be optimized in the following form.

The movement control unit25that determines the follow-up position and posture of the autonomous mobile robot11, determines the trajectory thereof, drives the same and the like controls the autonomous mobile robot11as follows.

When determining that the person as the follow-up target is shielded from the autonomous mobile robot11when reaching the corner and the like, the movement control unit25allows the autonomous mobile robot11to move so as to approach the person as the follow-up target. Note that, the movement control unit25prevents the autonomous mobile robot11from approaching the follow-up target to a distance shorter than a braking distance when the autonomous mobile robot11is braked in a case where the person as the follow-up target suddenly stops. Moreover, the movement control unit25does not allow the autonomous mobile robot11to approach the person to a distance in consideration of a personal space that the person feels uncomfortable or shorter. The movement control unit25predicts a trajectory of a pedestrian walking in the vicinity other than the follow-up target. In a case where the movement control unit25determines that the pedestrian tries to cut in between the person as the follow-up target and the autonomous mobile robot11on the basis of the predicted trajectory of the pedestrian, this brings the autonomous mobile robot11close to the person as the follow-up target to such an extent that the pedestrian cannot cut in. As a result, it is possible to prevent the capture of the person as the follow-up target from being obstructed by other pedestrians.

In a case where a plurality of persons exists in a narrow road width in the recognition algorithm for short distance, the follow-up target recognition unit22that recognizes the follow-up target often erroneously recognizes another person as the follow-up target in the recognition algorithm that recognizes the mobile object as the person as the follow-up target, so that this adopts the sixth recognition algorithm using feature amount extraction as the recognition algorithm for short distance.

The follow-up target prediction unit24that predicts the trajectory of the follow-up target deduces a passable passage by using a map in an office, and predicts the trajectory of the person as the follow-up target by using a method using a particle filter or the like.

Specific Example 4

As Specific Example 4 in a case where the autonomous mobile robot11is utilized, there is a case where the autonomous mobile robot11carries a load while following a person in an airport.

A utilization environment of the autonomous mobile robot11in Specific Example 4 is as follows.Examples of a dynamic obstacle include a person, a carry case and a cart carried by a person and the like.As a static obstacle, there is a pillar and the like. This is often an open space and there are few static obstacles.A user of the autonomous mobile robot11and people around the same do not move while being conscious of the autonomous mobile robot11.The person as the follow-up target often moves at a normal walking speed.As a situation in which the autonomous mobile robot11fails to follow, there is a case where the person as the follow-up target turns at a corner.As a situation in which the autonomous mobile robot11fails to follow, there is a case that the autonomous mobile robot11fails to capture the person as the follow-up target while performing avoidance operation to avoid the person and the like.As a situation in which the autonomous mobile robot11fails to follow, there is a case where the dynamic obstacle cuts in between the autonomous mobile robot11and the person as the follow-up target.As a situation in which the autonomous mobile robot11fails to follow, there is a case where the autonomous mobile robot11does not move to all directions but is of a two-wheel differential type and the like, for example, a case where the person as the follow-up target greatly changes the moving direction to a lateral direction and the like and deviates from the capturing range61.

In the utilization environment in Specific Example 4, processing and control regarding the autonomous mobile robot11may be optimized in the following form.

The movement control unit25that determines the follow-up position and posture of the autonomous mobile robot11, determines the trajectory thereof, drives the same and the like controls the autonomous mobile robot11as follows.

The movement control unit25predicts a trajectory of a pedestrian walking in the vicinity other than the follow-up target as in the case of Specific Example 3. In a case where the movement control unit25determines that the pedestrian tries to cut in between the person as the follow-up target and the autonomous mobile robot11on the basis of the predicted trajectory of the pedestrian, this brings the autonomous mobile robot11close to the person as the follow-up target to such an extent that the pedestrian cannot cut in. Even in a case where the autonomous mobile robot11is brought close to the person as the follow-up target so that the pedestrian cannot cut in, in a case where the moving direction of the pedestrian does not change, there is a possibility of collision, so that the movement control unit25stops bringing the autonomous mobile robot11close to the person as the follow-up target and gives priority to collision avoidance with the pedestrian. The movement control unit25directs the autonomous mobile robot11in a direction in which presence likelihood of the person as the follow-up target is high, and attempts to recognize the person as the follow-up target by the follow-up target recognition unit22. In a case where the person cannot be recognized, the movement control unit25(or the follow-up target prediction unit24) estimates the trajectory of the person as the follow-up target.

When the follow-up target recognition unit22that recognizes the follow-up target uses a method based on a distance when recognizing the person when the person as the follow-up target approaches the autonomous mobile robot11, erroneous recognition with the people around occurs, so that this adopts the sixth recognition algorithm using feature amount extraction as the recognition algorithm for short distance.

The above-described series of processing in the autonomous mobile robot11can be executed by hardware or can be executed by software. In a case where a series of processing is executed by the software, a program forming the software is installed on a computer. Here, the computer includes a computer incorporated in dedicated hardware, a general-purpose personal computer capable of executing various functions by installing various programs and the like, for example.

FIG.10is a block diagram illustrating a configuration example of the hardware of the computer in a case where the computer executes each processing executed by the autonomous mobile robot11with a program.

In the computer, a central processing unit (CPU)201, a read only memory (ROM)202, and a random access memory (RAN)203are connected to each other by a bus204.

An input/output interface205is further connected to the bus204. The input/output interface205is connected to an input unit206, an output unit207, a storage unit208, a communication unit209, and a drive210.

The input unit206includes a keyboard, a mouse, a microphone and the like. The output unit207includes a display, a speaker and the like. The storage unit208includes a hard disk, a non-volatile memory and the like. The communication unit209includes a network interface and the like. The drive210drives a removable medium211such as a magnetic disk, an optical disk, a magnetooptical disk, or a semiconductor memory.

In the computer configured as described above, for example, the CPU201loads the program stored in the storage unit208into the RAM203via the input/output interface205and the bus204and executes the program, thereby performing the above-described series of processing.

The program executed by the computer (CPU201) can be provided by being recorded on the removable medium211as a package medium or the like, for example. Furthermore, the program can be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital broadcasting.

In the computer, the program can be installed in the storage unit208via the input/output interface205by loading the removable medium211in the drive210. Furthermore, the program can be received by the communication unit209via a wired or wireless transmission medium and installed on the storage unit208. Additionally, the program may be installed in advance on the ROM202and the storage unit208.

Note that, the program executed by the computer may be a program for processing in time series in the order described in this specification, or a program for processing in parallel or at a necessary timing such as when a call is made.

The present technology can also have the following configurations.

An information processing device including:a drive unit that is driven for following a follow-up target;a recognition unit that recognizes the follow-up target, the recognition unit that changes a recognition algorithm for recognizing the follow-up target on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target; anda control unit that controls the drive unit on the basis of a position of the follow-up target recognized by the recognition unit.

The information processing device according to (1) described above, in whichthe recognition unitsets, in a case where the distance to the follow-up target is a normal distance equal to or longer than a predetermined distance threshold, the recognition algorithm to a recognition algorithm for normal distance, andsets, in a case where the distance to the follow-up target is shorter than the distance threshold, the recognition algorithm to a recognition algorithm for short distance.

The information processing device according to (2) described above, in whichthe recognition unitdetects a region matching an entire feature of the follow-up target from an image imaged by a camera as the recognition algorithm for normal distance.

The information processing device according to (2) described above, in whichthe recognition unitdetects a region of a mobile body as a region of the follow-up target from an image imaged by a camera as the recognition algorithm for normal distance.

The information processing device according to (2) described above, in whichthe recognition unitdetects a region of a mobile body as a region of the follow-up target from a ranging range ranged by a ranging sensor as the recognition algorithm for normal distance.

The information processing device according to (2) described above, in whichthe recognition unitdetects a region matching a shape of the follow-up target from a ranging range ranged by a ranging sensor as the recognition algorithm for normal distance.

The information processing device according to any one of (2) to (6) described above, in whichthe recognition unitdetects a region matching a representative color of the follow-up target from an image imaged by a camera as the recognition algorithm for short distance.

The information processing device according to any one of (2) to (6), in whichthe recognition unitdetects a region matching a feature amount corresponding to the follow-up target from an image imaged by a camera as the recognition algorithm for short distance.

The information processing device according to any one of (2) to (6) described above, in whichthe recognition unitdetects a region of a mobile body as a region of the follow-up target from a ranging range ranged by a ranging sensor as the recognition algorithm for short distance.

The information processing device according to any one of (2) to (9) described above, further including:a prediction unit that predicts a trajectory of the follow-up target, in whichthe control unit controls, in a case where it is predicted that recognition of the recognition unit with respect to the follow-up target that moves along the trajectory predicted by the prediction unit is obstructed by an obstacle, the drive unit so that a distance to the follow-up target becomes shorter than the distance threshold.

The information processing device according to any one of (2) to (10) described above, further including:a prediction unit that predicts a trajectory of the follow-up target, in whichthe control unit controls, in a case where it is predicted that the follow-up target that moves along the trajectory predicted by the prediction unit deviates from a capturing range in which the recognition unit is able to recognize the follow-up target, the drive unit so that a distance to the follow-up target becomes shorter than the distance threshold.

The information processing device according to any one of (2) to (11) described above, in whichthe normal distance is a distance at which the recognition unit is able to recognize an entire follow-up target.

The information processing device according to any one of (1) to (12) described above, in which the follow-up target is a person.

An information processing method of an information processing device including a drive unit, a recognition unit, and a control unit, the method including:a drive step for the drive unit to be driven for following a follow-up target;a recognition step for the recognition unit to recognize the follow-up target, the recognition step of changing a recognition algorithm for recognizing the follow-up target on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target; anda control step for the control unit to control the drive at the drive step on the basis of a position of the follow-up target recognized at the recognition step.

A program that allows a computer to serve as:a drive unit that is driven for following a follow-up target;a recognition unit that recognizes the follow-up target, the recognition unit that changes a recognition algorithm for recognizing the follow-up target on the basis of a distance to the follow-up target or recognition accuracy of the follow-up target based on the distance to the follow-up target; anda control unit that controls the drive unit on the basis of a position of the follow-up target recognized by the recognition unit.

REFERENCE SIGNS LIST