JP2023122997A - Information processing device, information processing method and program - Google Patents

Abstract

To provide a technology which enables more accurate determination of a position at which an unmanned aircraft can safely land.SOLUTION: Provided is an information processing device which includes: a map generation part for detecting an object position based on the distance between an unmanned aircraft and the object, and the orientation of the object relative to the unmanned aircraft, which are detected on the basis of the sensor data measured by a sensor mounted on the unmanned aircraft, and generating an object map based on the object position; and a landing position determination part for determining a landing position of the unmanned aircraft based on the object map.SELECTED DRAWING: Figure 2

Description

The present invention relates to an information processing device, an information processing method, and a program.

Techniques for determining the landing position of an unmanned aerial vehicle have been known in recent years. For example, a candidate landing area is divided into multiple areas, and the area is captured by a camera. Based on the images obtained, deep learning is used to identify the presence or absence of objects in each area, and the areas where it is determined that there are no objects. is a safe landing position (see, for example, Patent Document 1).

JP 2019-196150 A

However, it is desirable to provide a technique that enables the unmanned aerial vehicle to more accurately determine a safe landing position.

Accordingly, the present invention has been made in view of the above problems, and an object of the present invention is to provide a technique for more accurately determining a position where an unmanned aerial vehicle can land safely.

In order to solve the above problems, according to one aspect of the present invention, the distance between the unmanned aerial vehicle and an object detected based on sensor data measured by a sensor mounted on the unmanned aerial vehicle, and the unmanned aerial vehicle a map generation unit that detects an object position based on the direction of the object with respect to the aircraft and generates an object map based on the object position; and determines a landing position of the unmanned aerial vehicle based on the object map. An information processing device is provided, comprising: a landing position determination unit that

The object map is divided into a plurality of areas, and the landing position determination unit determines whether each area is a detection area in which the object is detected based on the object map, A non-detection area that is not detected may be determined as the landing position.

When there are a plurality of non-detection areas, the landing position determining section may determine an area farthest from the detection area among the plurality of non-detection areas as the landing position.

The landing position determination unit determines whether or not the object is moving based on the speed of the object detected based on the sensor data, and determines whether the object is moving if the object is moving. A region farthest from the detected detection region may be determined as the landing position.

The landing position determination unit estimates an area through which the object passes as an estimated passage area based on the speed of the object detected based on the sensor data and the detection area, and determines the plurality of non-detection areas. Among them, the farthest area from the estimated passage area may be determined as the landing position.

Each of the plurality of areas is divided into a plurality of sub-areas, and the landing position determining unit selects a selected area from the plurality of areas when an object is detected in any of the plurality of areas. It may be determined whether an object is detected in each sub-region, and the sub-region in which the object is not detected may be determined as the landing position.

The landing position determiner may select the selected regions based on the number of objects per region, the size of objects, or the position of objects.

The map generator may calculate the latitude and longitude of the object as the object position based on the latitude and longitude of the unmanned aerial vehicle, the distance, and the direction detected by the position detection sensor.

The map generation unit generates a set range divided into a plurality of areas, maps detection of the object to a position corresponding to the object position in the set range, and converts the set range after mapping to the position corresponding to the object position. It may be generated as an object map.

The map generator may generate a combination of a latitude range and a longitude range based on the latitude and longitude of the unmanned aerial vehicle as the set range.

Based on the latitude and longitude of the unmanned aerial vehicle, the map generation unit selects from a plurality of candidate ranges each configured by a combination of a latitude range and a longitude range, based on the number of objects, the size of the objects, or the position of the objects. to select the setting range.

Further, according to an aspect of the present invention, the distance between the unmanned aerial vehicle and an object detected based on sensor data measured by a sensor mounted on the unmanned aerial vehicle, and the detecting an object position based on an object orientation; generating an object map based on the object position; and determining a landing position of the unmanned aerial vehicle based on the object map. A processing method is provided.

Further, according to an aspect of the present invention, a computer detects a distance between the unmanned aerial vehicle and an object based on sensor data measured by a sensor mounted on the unmanned aerial vehicle, and a map generation unit that detects an object position based on the direction of the object and generates an object map based on the object position; and a landing position that determines the landing position of the unmanned aerial vehicle based on the object map A program for functioning as an information processing apparatus including a determining unit is provided.

As described above, according to the present invention, it is possible to more accurately determine a safe landing position for an unmanned aerial vehicle.

It is a figure which shows the circuit structure of FMCW radar. 1 is a block diagram showing a functional configuration example of an information processing apparatus according to an embodiment of the present invention; FIG. FIG. 5 is a diagram showing an example of a setting range divided into multiple areas; FIG. 10 is a diagram showing an example of a situation in which an obstacle exists in an area; It is a figure which shows the example of the frequency spectrum obtained by distance FFT by a one-dimensional FFT processing part. FIG. 10 is a diagram for explaining an example in which a non-detection area where no obstacle is detected is determined as the landing position; FIG. 5 is a diagram for explaining an example of determining a landing position when a moving obstacle is detected; FIG. 10 is a diagram for explaining a case where an obstacle is detected in the entire area;

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description.

In addition, in this specification and drawings, a plurality of components having substantially the same functional configuration are distinguished by attaching different numerals after the same reference numerals. In addition, similar components in different embodiments are distinguished from each other by using the same reference numerals followed by different alphabets. However, when there is no particular need to distinguish between a plurality of constituent elements having substantially the same functional configuration, only the same reference numerals are used.

[Description of overview]
Next, an outline of an embodiment of the present invention will be described.

Techniques for determining the landing position of an unmanned aerial vehicle have been known in recent years. For example, a candidate landing area is divided into multiple areas, and the area is captured by a camera. Based on the images obtained, deep learning is used to identify the presence or absence of objects in each area, and the areas where it is determined that there are no objects. is disclosed as a safe landing position.

However, in identification by deep learning, depending on the quality of the training data, there may be cases where an object not included in the training data is included in the image to be identified. In such a case, it may not be possible to accurately determine a safe landing position. Therefore, in the embodiments of the present invention, a technique for more accurately determining a safe landing position for an unmanned aerial vehicle will be mainly described.

Furthermore, since identification by deep learning requires learning using a large amount of teacher data, the cost of collecting the teacher data is required. Therefore, in the embodiment of the present invention, a technique for determining a position where an unmanned aerial vehicle can land without incurring the cost of collecting a large amount of training data will also be described.

In addition, it is not possible to determine the landing position of the unmanned aerial vehicle simply by determining the area where no object is detected as the landing position when it is determined that the object exists in any of the plurality of areas. Therefore, in the embodiments of the present invention, a technique for reducing the possibility that the landing position of the unmanned aerial vehicle is not determined will also be described.

The outline of the embodiment of the present invention has been described above.

[Details of embodiment]
Next, details of embodiments of the present invention will be described.

(Description of configuration)
In the technology according to the embodiment of the present invention, a sensor is mounted on an unmanned aerial vehicle. Based on the sensor data measured by the sensor, the distance between the unmanned aerial vehicle and the object, the direction of the object relative to the unmanned aerial vehicle (hereinafter also referred to as "angle"), and the speed of the object are detected. These pieces of detected information are then used to determine the landing position of the unmanned aerial vehicle.

In the following description, the "object" is also referred to as an "obstacle" because the "object" can become an obstacle to the landing of the unmanned aerial vehicle. Also, in the following description, a case where an FMCW (Frequency Modulated Continuous Wave) radar is used as an example of a sensor will be described. However, as will be explained later in the modified example, the sensors mounted on the unmanned aerial vehicle are not limited to FMCW radars. Also, in the following description, "unmanned aerial vehicle" is also referred to as "drone".

(Circuit configuration of FMCW radar)
FIG. 1 is a diagram showing the circuit configuration of an FMCW radar. A circuit configuration example of the FMCW radar will be described with reference to FIG. As shown in FIG. 1, the FMCW radar comprises a synthesizer 11, a transmitting antenna 12, a receiving antenna 13 and a mixer 14. FIG. 1 also shows an obstacle B1.

A synthesizer 11 generates a signal. The synthesizer 11 is connected to the transmitting antenna 12 and outputs the generated signal to the transmitting antenna 12 . A transmitting antenna 12 transmits the signal generated by the synthesizer 11 . The receiving antenna 13 receives the signal transmitted by the transmitting antenna 12 and reflected by the obstacle B1.

Mixer 14 is connected to each of transmitting antenna 12 and receiving antenna 13 , and signals transmitted by transmitting antenna 12 and signals received by receiving antenna 13 are input to mixer 14 . Mixer 14 generates an IF (Intermediate Frequency) signal based on the signal transmitted by transmitting antenna 12 and the signal received by receiving antenna 13 . Mixer 14 outputs the generated IF signal to information processing apparatus 1 (FIG. 2).

The circuit configuration example of the FMCW radar has been described above.

(Example of functional configuration of information processing device)
FIG. 2 is a block diagram showing a functional configuration example of the information processing device 1 according to the embodiment of the present invention. A functional configuration example of the information processing apparatus 1 will be described with reference to FIG. The information processing device 1 processes an IF signal generated by the FMCW radar.

It should be noted that, in the embodiment of the present invention, it is mainly assumed that the information processing device 1 is also mounted on a drone like the FMCW radar. However, the information processing device 1 does not necessarily have to be mounted on a drone. For example, the information processing device 1 may be provided at a location away from the drone. In such a case, the FMCW radar and the information processing device 1 may be able to communicate with each other through radio signals.

The information processing apparatus 1 includes an arithmetic unit (processor) such as a CPU (Central Processing Unit). Functionality can be realized. At this time, a computer-readable recording medium recording the program may also be provided. Alternatively, these blocks may be composed of dedicated hardware, or may be composed of a combination of multiple pieces of hardware.

Data required for calculation by the calculation device is appropriately stored in the memory. As shown in FIG. 2, the information processing apparatus 1 includes a preprocessing unit 15, a one-dimensional FFT (Fast Fourier Transform) processing unit 16, a distance detection unit 17, a two-dimensional FFT processing unit 18, and a speed detection unit 19. , a three-dimensional FFT processor 20 , an angle detector 21 , an obstacle map generator 22 (map generator), and a landing position determiner 23 .

An IF signal generated by the FMCW radar is input to the preprocessing section 15 . The preprocessing section 15 is connected to the one-dimensional FFT processing section 16 . The one-dimensional FFT processing section 16 is connected to the distance detection section 17 and the two-dimensional FFT processing section 18 respectively. The two-dimensional FFT processing section 18 is connected to each of the velocity detection section 19 and the three-dimensional FFT processing section 20 . The three-dimensional FFT processing section 20 is connected to the angle detection section 21 .

The distance detector 17 is connected to the obstacle map generator 22 . The angle detection section 21 is connected to the obstacle map generation section 22 . The speed detection section 19 is connected to the landing position determination section 23 . The obstacle map generation section 22 is connected to the landing position determination section 23 . Signals output from the speed detection unit 19 and the obstacle map generation unit 22 are input to the landing position determination unit 23 .

As an example, the landing position determination unit 23 determines the landing position of the drone based on signals output from the speed detection unit 19 and the obstacle map generation unit 22, respectively. The drone controls its motors and the like so that it lands at the landing position determined by the landing position determination unit 23 . As will be explained later, for example, the landing position can be represented by a combination of latitude and longitude.

The circuit configuration example of the FMCW radar has been described above.

(Description of operation)
Next, an operation example of the drone according to the embodiment of the present invention will be described. First, the drone generates a set range divided into multiple regions. More specifically, the drone generates a combination of latitude range and longitude range as the set range. Here, it is mainly assumed that the set range is a combination of a predetermined latitude range and longitude range. However, as will also be explained in later variations, the set range may be generated based on the latitude and longitude of the drone.

FIG. 3 is a diagram showing an example of a setting range divided into a plurality of areas. Referring to FIG. 3, drone D1 is shown. Also, referring to FIG. 3, a setting range H1 divided into a plurality of regions (ie, regions R1 to R9) is shown. Each of the regions R1 to R9 corresponds to a candidate determined as the landing position of the drone D1. Typically, the landing position of the drone D1 is the ground E1, so the set range H1 is generated at a position corresponding to the ground E1 (that is, the height of the set range H1 is zero).

Also, the setting range H1 is generated by combining the latitude range and the longitude range. In the example shown in FIG. 3, the setting range H1 is divided into a total of 9 areas consisting of 3 vertical areas and 3 horizontal areas. The direction in which the setting range H1 is divided, the number of divisions, etc.) are not limited. Also, the sizes and shapes of the regions R1 to R9 may not be the same.

Assume that the drone D1 lands in one of the regions R1 to R9 in the state where the set range H1 is generated in this way. In such a case, the drone D1 determines at which position in the set range H1 the obstacle is detected, and depending on the determination result, determines at which position in the set range H1 it is safe to land. .

FIG. 4 is a diagram showing an example of a situation in which an obstacle exists in region R5. Referring to FIG. 4, an obstacle R11 and an obstacle R12 are present in the region R5. In such a case, the drone D1 can detect the presence of the obstacle B11 and the obstacle B12 in the region R5 using the FMCW radar (FIG. 1).

First, the operation of the FMCW radar (FIG. 1) will be described. The synthesizer 11 generates a signal whose frequency changes with time, called a chirp signal. When the transmitting antenna 12 emits a chirp signal, the chirp signal is reflected by the object. A receiving antenna 13 receives a reflected wave from an object. A signal transmitted by the transmitting antenna 12 and a signal received by the receiving antenna 13 are each input to the mixer 14 .

The mixer 14 multiplies the transmission signal and the reception signal to generate an IF signal having a frequency corresponding to the frequency difference between the transmission signal and the reception signal. Mixer 14 outputs the generated IF signal to information processing apparatus 1 .

Next, the operation of the information processing device 1 (FIG. 2) will be described. A preprocessing unit 15 acquires the IF signal generated by the mixer 14 . The preprocessing unit 15 performs various types of processing on the IF signal. Examples of processing executed by the preprocessing unit 15 include removal of DC (Direct Current) components, reduction of interference, and the like. The IF signal processed by the preprocessing unit 15 is output to the one-dimensional FFT processing unit 16 .

The one-dimensional FFT processing unit 16 performs distance FFT on the IF signal processed by the preprocessing unit 15 . Thereby, the one-dimensional FFT processor 16 obtains a frequency spectrum.

FIG. 5 is a diagram showing an example of a frequency spectrum obtained by distance FFT by the one-dimensional FFT processor 16. In FIG. The frequency spectrum shown in FIG. 5 has peaks at frequencies f1 and f2. This peak indicates the presence of an obstacle.

The distance detection unit 17 detects the distance between the drone D1 and the obstacle based on the frequency spectrum obtained by the one-dimensional FFT processing unit 16. More specifically, the distance detector 17 extracts peaks from the frequency spectrum obtained by the one-dimensional FFT processor 16 . Then, the distance detection unit 17 detects the distance between the drone D1 and the obstacle by converting the peak frequencies f1 and f2 into a distance. Thereby, it can be determined whether or not an obstacle exists for each distance.

To detect the speed of an obstacle, a group of time-continuous chirp signals called a chirp frame is used. Chirp frames are transmitted by a transmit antenna 12 and an IF signal for each chirp is obtained by a mixer 14 . A one-dimensional FFT processor 16 performs a distance FFT on each IF signal to obtain a frequency spectrum for each IF signal. A peak appears in the frequency spectrum indicating the presence of an obstacle.

The two-dimensional FFT processor 18 further applies FFT (velocity FFT) to the entire data of peaks existing at similar positions in the chirp frame. Thereby, the two-dimensional FFT processor 18 obtains an angular frequency spectrum.

A velocity detector 19 detects the velocity of the obstacle based on the angular frequency spectrum obtained by the two-dimensional FFT processor 18 . More specifically, the velocity detector 19 extracts peaks from the angular frequency spectrum obtained by the two-dimensional FFT processor 18 . Then, the speed detection unit 19 detects the speed of the obstacle by converting the peak angular frequency into speed.

A plurality of receiving antennas 13 are used to detect the direction of the obstacle with reference to the position of the drone D1.

The three-dimensional FFT processing unit 20 further applies FFT (angle FFT) to the velocity FFT results corresponding to each of the plurality of receiving antennas 13 . Thereby, the three-dimensional FFT processor 20 obtains a spectrum.

The angle detection section 21 extracts peaks from the spectrum obtained by the three-dimensional FFT processing section 20 . This peak corresponds to the phase difference between the receiving antennas 13 . Therefore, the angle detection unit 21 detects the direction in which the obstacle exists with the position of the drone D1 as a reference by converting the phase difference into an angle.

The obstacle map generation unit 22 determines the distance between the drone D1 and the obstacle detected by the distance detection unit 17 and the direction of the obstacle based on the position of the drone D1 detected by the angle detection unit 21. Based on this, the position of the obstacle is detected. Then, the obstacle map generator 22 generates an obstacle map based on the positions of the detected obstacles. More specifically, the obstacle map generation unit 22 maps the detection of an obstacle to a position in the set range H1 corresponding to the position of the detected obstacle, thereby mapping the set range H1 after mapping to the position of the obstacle. Can be generated as a map.

For example, the obstacle map generation unit 22 calculates the latitude and longitude of the obstacle based on the distance between the drone D1 and the obstacle and the direction in which the obstacle exists with respect to the position of the drone D1. is calculated as the position of The setting range H1 has a latitude range and a longitude range. Therefore, the obstacle map generation unit 22 can generate an obstacle map by mapping the detected obstacle to the latitude and longitude in the set range H1 corresponding to the latitude and longitude of the detected obstacle.

Note that typically, a GPS (Global Positioning System) sensor can be used as the position detection sensor. However, the position detection sensor is not limited to the GPS sensor.

The landing position determination unit 23 determines the landing position of the drone D1 based on the obstacle map. Thereby, the position where the drone D1 can land safely can be determined with higher accuracy.

More specifically, the obstacle map is divided into multiple regions. Therefore, based on the obstacle map, the obstacle map generation unit 22 determines whether it is a detection area where an obstacle is detected or a non-detection area where no obstacle is detected. Then, the obstacle map generator 22 can determine a non-detection area where no obstacle is detected as the landing position of the drone D1. Thereby, a safe area can be determined as the landing position of the drone D1.

FIG. 6 is a diagram for explaining an example in which a non-detection area where no obstacle is detected is determined as the landing position. Referring to FIG. 6, an obstacle map M1 is shown. Since the obstacle B11 is detected in the area R4, the area R4 is a detection area. Similarly, the area R5 is a detection area because the obstacle B12 is detected in the area R5. On the other hand, areas R1 to R3, R5 to R7, and R9 are non-detection areas where obstacles are not detected.

Therefore, the landing position determination unit 23 may determine any one of the non-detection regions R1 to R3, R5 to R7, and R9 as the landing position. For example, when there are a plurality of non-detection areas, as shown in FIG. 6, the landing position determining unit 23 selects the non-detection area R3, which is the farthest non-detection area from the detection areas R4 and R8, of the drone D1. It may be determined as a landing position. Thereby, a safer area can be determined as the landing position of the drone D1.

Alternatively, the obstacle is not always stationary, and it can be assumed that the obstacle is moving. Therefore, the landing position determination section 23 may determine whether or not the obstacle is moving based on the speed of the obstacle detected by the speed detection section 19 . Then, when the obstacle is moving, the landing position determination unit 23 may determine the area farthest from the detection area where the moving obstacle is detected as the landing position.

FIG. 7 is a diagram for explaining an example of landing position determination when a moving obstacle is detected. Referring to FIG. 7, an obstacle map M2 is shown. Since the obstacle B13 is detected in the area R7, the area R7 is a detection area. On the other hand, areas R1 to R6, R8, and R9 are non-detection areas where obstacles are not detected. Also, the obstacle B13 is moving from the region R7 toward the region R8.

Therefore, the landing position determining section 23 determines that the obstacle B13 is moving based on the speed of the obstacle B13 detected by the speed detecting section 19 . At this time, the landing position determination unit 23 may determine the farthest region R3 from the region R7, which is the detection region where the moving obstacle B13 is detected, as the landing position. As a result, a safer area can be determined as the landing position of the drone D1 in consideration of movement of obstacles.

Alternatively, the landing position determination unit 23 may estimate the area through which the obstacle B13 passes as the estimated passage area based on the speed of the obstacle B13 detected by the speed detection unit 19 and the detection area R7. good. Here, it is assumed that the obstacle B13 moves along a straight line. In such a case, the landing position determination unit 23 determines that the obstacle B13 is moving from the region R7 to the region R8, and presumably passes through the regions R7 to R9 located on a straight line from the region R7 to the region R8. Determined as an area.

At this time, the landing position determination unit 23 selects one of the regions R1 to R3, which is the furthest from the estimated passage regions R7 to R9, among the plurality of non-detection regions R1 to R6, R8, and R9. It may be determined as a landing position. As a result, a safer area can be determined as the landing position of the drone D1, taking into account the estimated passage area of the obstacle. Although it is assumed here that the obstacle B13 moves along a straight line, the moving direction of the obstacle B13 does not have to be along a straight line.

Each of the regions R1 to R9 may be divided into a plurality of sub-regions. Then, when an obstacle is detected in any of the regions R1 to R9, the landing position determination unit 23 detects an obstacle in a region selected from the regions R1 to R9 (hereinafter also referred to as “selected region”). It may be determined for each sub-region whether an object is detected. Then, the landing position determination unit 23 may determine a sub-region in which no obstacle is detected as the landing position. This may reduce the possibility that the landing position of drone D1 is not determined.

The selection area may be selected according to any condition. As an example, the selected regions may be selected based on the number of obstacles, the size of the obstacles, or the position of the obstacles in each of the regions R1-R9. For example, the area with the fewest number of obstacles may be selected as the selection area. Alternatively, the area with the smallest obstacle size may be selected as the selection area. Alternatively, an area where the position of the obstacle is closest to the edge may be selected as the selection area. Here, it is assumed that the region R5 is selected as the selected region.

FIG. 8 is a diagram for explaining a case where obstacles are detected in the entire area. Referring to FIG. 8, region R5 is divided into a plurality of sub-regions (sub-regions r1 to r4). In the example shown in FIG. 8, the region R5 is divided into a total of four sub-regions consisting of two vertical sub-regions and two horizontal sub-regions. Whether it is divided (for example, the direction in which the region R5 is divided, the number of divisions, etc.) is not limited. Also, the sizes and shapes of the sub-regions r1-r4 may not be the same.

Also, the division into sub-regions of the regions R1 to R9 may be the same or different.

Referring to the obstacle map M3, an obstacle B12 is detected in the sub-region r2, and an obstacle B11 is detected in the sub-region r3. That is, the landing position determination unit 23 determines each of the sub-region r2 and the sub-region r3 as detection regions. On the other hand, the landing position determination unit 23 determines each of the sub-region r1 and the sub-region r4 as non-detection regions. The landing position determination unit 23 may determine either sub-region r1 or sub-region r4 as the landing position.

The details of the embodiments of the present invention have been described above.

[Explanation of effect]
As described above, according to the embodiments of the present invention, a safe landing area can be easily and accurately identified by detecting obstacles by direct measurement using radar. Embodiments of the present invention also reduce the likelihood of collision between the obstacle and the drone during landing by selecting a landing location away from moving obstacles.

Furthermore, according to the embodiment of the present invention, it is possible to determine a sub-area within the area as a safe landing position based on the obstacle map, such as when an obstacle exists in any area. . This may reduce the likelihood that the drone will not be able to land, such as when there are obstacles in either area.

[Explanation of modification]
Although the preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention belongs can conceive of various modifications or modifications within the scope of the technical idea described in the claims. It is understood that these also naturally belong to the technical scope of the present invention.

In the above, the FMCW radar has been described as an example of the obstacle detection sensor mounted on the drone D1. However, the obstacle detection sensor mounted on the drone D1 is not limited to this example. For example, the obstacle detection sensor mounted on the drone D1 may be a radar by another method, may be LiDAR (Light Detection and Ranging), or may be another sensor. .

In the above, it is mainly assumed that the setting range H1 is a combination of a predetermined latitude range and longitude range. However, the setting range H1 may be generated based on the latitude and longitude of the drone D1. That is, the obstacle map generation unit 22 generates a combination of the latitude range and the longitude range as the setting range H1 based on the latitude and longitude of the drone D1 detected by the position detection sensor mounted on the drone D1. good.

For example, it is assumed that the relative latitude and longitude offset amount of the setting range H1 with respect to the latitude and longitude of the drone D1 and the latitude width and longitude width of the setting range H1 are predetermined. In such a case, the obstacle map generator 22 may generate the setting range H1 based on the latitude and longitude of the drone D1, the offset amount, and the latitude width and longitude width of the setting range H1.

Alternatively, the obstacle map generation unit 22 may generate a plurality of candidate ranges each configured by a combination of a latitude range and a longitude range, based on the latitude and longitude of the drone D1. Then, the obstacle map generator 22 may select the set range H1 from a plurality of candidate ranges.

The setting range H1 may be selected according to any conditions. As an example, the setting range H1 may be selected based on the number of obstacles, the size of the obstacles, or the position of the obstacles in each of the multiple candidate ranges. For example, a candidate range with the smallest number of obstacles may be selected as the set range H1. Alternatively, a candidate range with the smallest obstacle size may be selected as the set range H1. Alternatively, a candidate range in which the position of the obstacle is closest to the edge may be selected as the set range H1.

Moreover, in the above description, it is mainly assumed that the detection area where the obstacle is detected is not determined as the landing position of the drone D1. However, even a detection area where an obstacle is detected may be determined as the landing position of the drone D1 depending on the shape of the obstacle. For example, if the obstacle has a flat top surface, the landing position determining unit 23 may determine the top surface as the landing position. The shape of the obstacle can be grasped by the shape of the peak of the frequency spectrum obtained by the one-dimensional FFT processing section 16, for example.

1 information processing device 11 synthesizer 12 transmitting antenna 13 receiving antenna 14 mixer 15 preprocessing unit 16 one-dimensional FFT processing unit 17 distance detection unit 18 two-dimensional FFT processing unit 19 speed detection unit 20 three-dimensional FFT processing unit 21 angle detection unit 22 failure Object map generation unit 23 Landing position determination unit

Claims (13)

Detecting the position of an object based on the distance between the unmanned aerial vehicle and the object and the direction of the object relative to the unmanned aerial vehicle, which are detected based on sensor data measured by a sensor mounted on the unmanned aerial vehicle. and a map generator that generates an object map based on the object position;
a landing position determination unit that determines a landing position of the unmanned aerial vehicle based on the object map;
An information processing device. the object map is divided into a plurality of regions,
The landing position determination unit determines whether each area is a detection area in which the object is detected based on the object map, and determines a non-detection area in which the object is not detected as the landing position.
The information processing device according to claim 1 . When there are a plurality of non-detection areas, the landing position determination unit determines an area farthest from the detection area among the plurality of non-detection areas as the landing position.
The information processing apparatus according to claim 2. The landing position determination unit determines whether or not the object is moving based on the speed of the object detected based on the sensor data, and determines whether the object is moving if the object is moving. determining the farthest area from the detected detection area as the landing position;
The information processing apparatus according to claim 3. The landing position determination unit estimates an area through which the object passes as an estimated passage area based on the speed of the object detected based on the sensor data and the detection area, and determines the plurality of non-detection areas. Among them, the area farthest from the estimated passing area is determined as the landing position;
The information processing apparatus according to claim 2. each of the plurality of regions is divided into a plurality of sub-regions,
The landing position determination unit determines, for each sub-region, whether an object is detected in a selected region selected from the plurality of regions when an object is detected in any of the plurality of regions, and determining an undetected sub-region as the landing position;
The information processing apparatus according to claim 2. The landing position determiner selects the selected regions based on the number of objects per region, the size of objects, or the position of objects.
The information processing device according to claim 6 . The map generation unit calculates the latitude and longitude of the object as the object position based on the latitude and longitude of the unmanned aerial vehicle, the distance, and the direction detected by a position detection sensor.
The information processing apparatus according to any one of claims 1 to 7. The map generation unit generates a set range divided into a plurality of areas, maps detection of the object to a position corresponding to the object position in the set range, and converts the set range after mapping to the position corresponding to the object position. generated as an object map,
The information processing apparatus according to any one of claims 1 to 8. The map generation unit generates a combination of a latitude range and a longitude range based on the latitude and longitude of the unmanned aerial vehicle as the setting range.
The information processing apparatus according to claim 9 . Based on the latitude and longitude of the unmanned aerial vehicle, the map generation unit selects from a plurality of candidate ranges each configured by a combination of a latitude range and a longitude range, based on the number of objects, the size of the objects, or the position of the objects. to select the setting range;
The information processing apparatus according to claim 10. Detecting the position of an object based on the distance between the unmanned aerial vehicle and the object and the direction of the object relative to the unmanned aerial vehicle, which are detected based on sensor data measured by a sensor mounted on the unmanned aerial vehicle. and generating an object map based on the object positions;
determining a landing position of the unmanned aerial vehicle based on the object map;
A method of processing information, comprising: the computer,
Detecting the position of an object based on the distance between the unmanned aerial vehicle and the object and the direction of the object relative to the unmanned aerial vehicle, which are detected based on sensor data measured by a sensor mounted on the unmanned aerial vehicle. and a map generator that generates an object map based on the object position;
a landing position determination unit that determines a landing position of the unmanned aerial vehicle based on the object map;
A program that functions as an information processing device comprising

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