Information processing device, information processing method, and program

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

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] In recent years, a technique for determining the landing position of an unmanned aircraft has been known. For example, a technique is disclosed in which a range of landing candidates is divided into a plurality of regions, and the presence or absence of an object in each region is identified by deep learning from an image obtained by imaging the range with a camera, and a region determined to have no object is determined as a safe landing position (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, there is a demand for a technique that enables more accurately determining a position where an unmanned aircraft can land safely.

[0005] Therefore, 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 aircraft can land safely.

Means for Solving the Problems

[0006] In order to solve the above problems, according to an aspect of the present invention, based on the distance between the unmanned aircraft and an object detected based on sensor data measured by a sensor mounted on the unmanned aircraft, and the direction of the object with respect to the unmanned aircraft, an object position is detected, and based on the object position , divided into multiple regionsA map generation unit that generates an object map, and based on the object map For each region, it is determined whether or not the object is detected in that region, and the non-detection regions where the object is not detected are defined as follows: Landing position of the aforementioned unmanned aerial vehicle as Decide The system comprises a landing position determination unit, which, when there are multiple non-detection areas, determines whether the object is moving based on the velocity of the object detected based on the sensor data, and, if the object is moving, determines the area furthest from the detection area where the object was detected as the landing position. An information processing device is provided.

[0007] Furthermore, according to one aspect of the present invention, an information processing device is provided comprising: a map generation unit that detects the position of an object based on the distance between the unmanned aerial vehicle and an 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 generates an object map divided into a plurality of regions based on the object position; and a landing position determination unit that determines whether each region is a detection region where the object has been detected based on the object map, and determines the undetected region where the object has not been detected as the landing position of the unmanned aerial vehicle, wherein the landing position determination unit estimates the region through which the object will pass as an estimated passage region based on the velocity of the object detected based on the sensor data and the detection region, and determines the region furthest from the estimated passage region among the plurality of undetected regions as the landing position.

[0008] Furthermore, according to one aspect of the present invention, an information processing device is provided comprising: a map generation unit that detects the position of an object based on the distance between the unmanned aerial vehicle and an 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 generates an object map divided into a plurality of regions based on the object position; and a landing position determination unit that determines whether each region is a detection region in which the object has been detected based on the object map, and determines the non-detection region in which the object has not been detected as the landing position of the unmanned aerial vehicle, wherein each of the plurality of regions is divided into a plurality of sub-regions, and when an object is detected in any of the plurality of regions, the landing position determination unit determines for each sub-region whether an object has been detected in a selected region chosen from the plurality of regions, and determines the sub-region in which the object has not been detected as the landing position.

[0009] Furthermore, according to one aspect of the present invention, an information processing device is provided comprising: a map generation unit that detects the position of an object based on the distance between the unmanned aerial vehicle and an 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 generates an object map based on the object position; and a landing position determination unit that determines the landing position of the unmanned aerial vehicle based on the object map, wherein the map generation unit generates a set range as a combination of latitude ranges and longitude ranges based on the latitude and longitude of the unmanned aerial vehicle, which are divided into a plurality of regions, maps the detection of the object to the position corresponding to the object position in the set range, generates the set range after mapping as the object map, and the set range is selected from a plurality of candidate ranges, each composed of a combination of latitude ranges and longitude ranges based on the latitude and longitude of the unmanned aerial vehicle, based on the number of objects, the size of the objects, or the position of the objects.

[0012] The landing position determination unit may select the selected region based on the number of objects in each region, the size of the objects, or the position of the objects.

[0013] The map generation unit may calculate the object's latitude and longitude as the object's position based on the latitude and longitude of the unmanned aerial vehicle detected by the position detection sensor, the distance, and the direction.

[0017] Furthermore, according to one aspect of the present invention, the object's position is detected 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 sensors mounted on the unmanned aerial vehicle, and based on the object's position , divided into multiple regions To generate an object map, and based on the object map For each region, it is determined whether or not the object is detected in that region, and the non-detection regions where the object is not detected are defined as follows: Landing position of the aforementioned unmanned aerial vehicle as To make a decision, The system includes, in the case where there are multiple non-detection areas, it determines whether the object is moving based on the velocity of the object detected based on the sensor data, and if the object is moving, it determines the area furthest from the detection area where the object was detected as the landing position. Information processing methods are provided.

[0018] Furthermore, according to one aspect of the present invention, the computer detects 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 sensors mounted on the unmanned aerial vehicle, and based on the position of the object , divided into multiple regions A map generation unit that generates an object map, and based on the object map For each region, it is determined whether or not the object is detected in that region, and the non-detection regions where the object is not detected are defined as follows: Landing position of the aforementioned unmanned aerial vehicle asIt includes a landing position determination unit that makes a decision. The landing position determination unit, when there are multiple non-detection areas, determines whether the object is moving based on the velocity of the object detected based on the sensor data, and if the object is moving, determines the area furthest from the detection area where the object was detected as the landing position. A program that causes it to function as an information processing device is provided.

Advantages of the Invention

[0019] As described above, according to the present invention, it is possible to more accurately determine a position where the unmanned aircraft can land safely.

Brief Description of the Drawings

[0020] [Figure 1] It is a diagram showing the circuit configuration of an FMCW radar. [Figure 2] It is a block diagram showing a functional configuration example of an information processing device according to an embodiment of the present invention. [Figure 3] It is a diagram showing an example of a set range divided into a plurality of regions. [Figure 4] It is a diagram showing an example of a situation where there are obstacles in a region. [Figure 5] It is a diagram showing an example of a frequency spectrum obtained by distance FFT by a one-dimensional FFT processing unit. [Figure 6] It is a diagram for explaining an example in which a non-detection region where no obstacle is detected is determined as a landing position. [Figure 7] It is a diagram for explaining an example of determining a landing position when a moving obstacle is detected. [Figure 8] It is a diagram for explaining the case where obstacles are detected in all regions.

Embodiments for Carrying Out the Invention

[0021] Hereinafter, preferred embodiments of the present invention will be described in detail while referring to the accompanying drawings. In the present specification and drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant description is omitted.

[0022] Furthermore, in this specification and drawings, multiple components having substantially the same functional configuration are distinguished by denoting different numbers after the same reference numeral. Similar components of different embodiments are distinguished by denoting different letters after the same reference numeral. However, if there is no need to distinguish between multiple components having substantially the same functional configuration, only the same reference numeral is used.

[0023] [Summary] Next, an overview of embodiments of the present invention will be described.

[0024] In recent years, technologies for determining the landing position of unmanned aerial vehicles have become known. For example, one technology has been disclosed in which a potential landing area is divided into multiple regions, and from the images obtained by capturing these regions with a camera, the presence or absence of objects in each region is identified using deep learning, and the region where no objects are determined to be present is judged to be a safe landing position.

[0025] However, with deep learning-based identification, depending on the quality of the training data, objects not present in the training data may be included in the image to be identified. In such cases, it may not be possible to accurately determine a safe landing position. Therefore, embodiments of the present invention mainly describe a technique for more accurately determining a safe landing position for an unmanned aerial vehicle.

[0026] Furthermore, deep learning-based identification requires training using a large amount of training data, which incurs costs for collecting this data. Therefore, in this embodiment of the present invention, we will also describe a technique for determining a landing position for an unmanned aerial vehicle without incurring the costs of collecting a large amount of training data.

[0027] Furthermore, simply determining a landing position in an area where no objects are detected would make it impossible to determine the landing position of the unmanned aerial vehicle if objects are found to be present in any of the multiple areas. Therefore, in this embodiment of the present invention, a technique for reducing the possibility that the landing position of the unmanned aerial vehicle cannot be determined will also be described.

[0028] The embodiments of the present invention have been described above.

[0029] [Details of the embodiment] Next, we will describe the details of embodiments of the present invention.

[0030] (Structure description) In the technology according to the embodiment of the present invention, a sensor is mounted on the 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 velocity of the object are detected. This detected information is then used to determine the landing position of the unmanned aerial vehicle.

[0031] In the following explanation, since an "object" can be an obstacle to the landing of an unmanned aerial vehicle, the term "object" will also be referred to as an "obstacle." Furthermore, in the following explanation, an FMCW (Frequency Modulated Continuous Wave) radar will be used as an example of a sensor. However, as will be explained in the later modifications, the sensors mounted on an unmanned aerial vehicle are not limited to FMCW radar. Also, in the following explanation, the term "unmanned aerial vehicle" will also be referred to as a "drone."

[0032] (FMCW radar circuit configuration) Figure 1 shows the circuit configuration of an FMCW radar. An example of the FMCW radar circuit configuration will be explained with reference to Figure 1. As shown in Figure 1, the FMCW radar comprises a synthesizer 11, a transmitting antenna 12, a receiving antenna 13, and a mixer 14. An obstacle B1 is also shown in Figure 1.

[0033] The 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. The 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.

[0034] Mixer 14 is connected to both the transmitting antenna 12 and the receiving antenna 13, and receives signals transmitted by the transmitting antenna 12 and signals received by the receiving antenna 13 as inputs. Mixer 14 generates an IF (Intermediate Frequency) signal based on the signals transmitted by the transmitting antenna 12 and signals received by the receiving antenna 13. Mixer 14 outputs the generated IF signal to the information processing device 1 (Figure 2).

[0035] The above describes an example of the circuit configuration of an FMCW radar.

[0036] (Example of information processing device function configuration) Figure 2 is a block diagram showing an example of the functional configuration of the information processing device 1 according to an embodiment of the present invention. The example of the functional configuration of the information processing device 1 will be described with reference to Figure 2. The information processing device 1 processes the IF signal generated by the FMCW radar.

[0037] In the embodiments of the present invention, it is primarily assumed that the information processing device 1, like the FMCW radar, is mounted on a drone. 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 located at a location separate from the drone. In such a case, the FMCW radar and the information processing device 1 may be able to communicate with each other via wireless signals.

[0038] The information processing device 1 includes an arithmetic unit (processor) such as a CPU (Central Processing Unit), and its functions can be realized by the arithmetic unit loading a program stored in ROM (Read Only Memory) into RAM and executing it. In this case, a computer-readable recording medium on which the program is stored may also be provided. Alternatively, these blocks may be composed of dedicated hardware, or they may be composed of a combination of multiple hardware components.

[0039] The data necessary for calculations performed by the computing unit is stored in memory as appropriate. As shown in Figure 2, the information processing device 1 comprises a pre-processing unit 15, a one-dimensional FFT (Fast Fourier Transform) processing unit 16, a distance detection unit 17, a two-dimensional FFT processing unit 18, a velocity detection unit 19, a three-dimensional FFT processing unit 20, an angle detection unit 21, an obstacle map generation unit 22 (map generation unit), and a landing position determination unit 23.

[0040] The IF signal generated by the FMCW radar is input to the pre-processing unit 15. The pre-processing unit 15 is connected to the one-dimensional FFT processing unit 16. The one-dimensional FFT processing unit 16 is connected to the distance detection unit 17 and the two-dimensional FFT processing unit 18, respectively. The two-dimensional FFT processing unit 18 is connected to the velocity detection unit 19 and the three-dimensional FFT processing unit 20, respectively. The three-dimensional FFT processing unit 20 is connected to the angle detection unit 21.

[0041] The distance detection unit 17 is connected to the obstacle map generation unit 22. The angle detection unit 21 is connected to the obstacle map generation unit 22. The speed detection unit 19 is connected to the landing position determination unit 23. The obstacle map generation unit 22 is connected to the landing position determination unit 23. The signals output from the speed detection unit 19 and the obstacle map generation unit 22 are input to the landing position determination unit 23.

[0042] As an example, the landing position determination unit 23 determines the drone's landing position based on signals output from the speed detection unit 19 and the obstacle map generation unit 22, respectively. The drone controls its motors and other components to land 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.

[0043] The above describes an example of the circuit configuration of an FMCW radar.

[0044] (Operation description) Next, an example of the operation of a drone according to an 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 set range as a combination of a latitude range and a longitude range. Here, we mainly assume that the set range is a predetermined combination of a latitude range and a longitude range. However, as will be explained in the later modifications, the set range may also be generated based on the latitude and longitude of the drone.

[0045] Figure 3 shows an example of a setting range divided into multiple regions. Referring to Figure 3, drone D1 is shown. Also referring to Figure 3, a setting range H1 is shown divided into multiple regions (i.e., regions R1 to R9). Each of regions R1 to R9 corresponds to a candidate to be determined as the landing position of drone D1. Typically, the landing position of drone D1 is the ground E1, so the setting range H1 is generated at the position corresponding to the ground E1 (i.e., the height of the setting range H1 is zero).

[0046] Furthermore, the setting range H1 is generated by a combination of latitude and longitude ranges. In the example shown in Figure 3, the setting range H1 is divided into a total of 9 regions consisting of 3 vertical and 3 horizontal regions, but how the setting range H1 is divided into multiple regions (for example, the direction in which the setting range H1 is divided, the number of divisions, etc.) is not limited. Also, the size and shape of regions R1 to R9 do not have to be the same.

[0047] In this state where the defined range H1 has been generated, let's consider the case where drone D1 lands in one of the areas R1 to R9. In this case, drone D1 determines where an obstacle is detected within the defined range H1, and based on the determination result, it decides which location within the defined range H1 is safe to land at.

[0048] Figure 4 shows an example of a situation where obstacles are present in region R5. Referring to Figure 4, obstacles R11 and R12 are present in region R5. In this case, drone D1 can use the FMCW radar (Figure 1) to detect the presence of obstacles B11 and B12 in region R5.

[0049] First, let's explain the operation of the FMCW radar (Figure 1). The synthesizer 11 generates a time-varying signal called a chirp signal. The transmitting antenna 12 then emits the chirp signal, which is reflected by an object. The receiving antenna 13 receives the reflected wave from the object. The transmitted signal from the transmitting antenna 12 and the received signal from the receiving antenna 13 are input to the mixer 14.

[0050] Mixer 14 multiplies the transmitted signal and the received signal to generate an IF signal with a frequency corresponding to the difference between the frequencies of the transmitted signal and the received signal. Mixer 14 outputs the generated IF signal to the information processing device 1.

[0051] Next, the operation of the information processing device 1 (Figure 2) will be explained. The pre-processing unit 15 acquires the IF signal generated by the mixer 14. The pre-processing unit 15 performs various processing on the IF signal. Examples of processing performed by the pre-processing unit 15 include removal of the DC (Direct Current) component and interference reduction. The IF signal after processing by the pre-processing unit 15 is output to the one-dimensional FFT processing unit 16.

[0052] The one-dimensional FFT processing unit 16 performs a distance FFT on the IF signal after processing by the pre-processing unit 15. Through this, the one-dimensional FFT processing unit 16 obtains a frequency spectrum.

[0053] Figure 5 shows an example of a frequency spectrum obtained by distance FFT performed by the one-dimensional FFT processing unit 16. The frequency spectrum shown in Figure 5 has peaks at frequencies f1 and f2. These peaks indicate the presence of an obstacle.

[0054] 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 detection unit 17 extracts peaks from the frequency spectrum obtained by the one-dimensional FFT processing unit 16. Then, the distance detection unit 17 detects the distance between the drone D1 and the obstacle by converting the frequencies f1 and f2, which are the frequencies at which the peaks are obtained, into distances. This allows it to determine whether or not an obstacle is present at each distance.

[0055] To detect the speed of an obstacle, a time-continuous collection of chirp signals called a chirp frame is used. The transmitting antenna 12 transmits the chirp frame, and the mixer 14 obtains an IF signal for each chirp. The one-dimensional FFT processing unit 16 performs a distance FFT on each IF signal to obtain the frequency spectrum for each IF signal. A peak appears in the frequency spectrum indicating the presence of an obstacle.

[0056] The two-dimensional FFT processing unit 18 applies another FFT (velocity FFT) to the entire dataset of peaks located at similar positions within the chirp frame. This allows the two-dimensional FFT processing unit 18 to obtain the angular frequency spectrum.

[0057] The velocity detection unit 19 detects the velocity of the obstacle based on the angular frequency spectrum obtained by the two-dimensional FFT processing unit 18. More specifically, the velocity detection unit 19 extracts peaks from the angular frequency spectrum obtained by the two-dimensional FFT processing unit 18. Then, the velocity detection unit 19 detects the velocity of the obstacle by converting the angular frequencies at which the peaks are obtained into velocity.

[0058] Multiple receiving antennas 13 are used to detect the direction of obstacles relative to the position of drone D1.

[0059] The three-dimensional FFT processing unit 20 applies another FFT (angle FFT) to the results of the velocity FFT corresponding to each of the multiple receiving antennas 13. Through this process, the three-dimensional FFT processing unit 20 obtains a spectrum.

[0060] The angle detection unit 21 extracts a peak from the spectrum obtained by the three-dimensional FFT processing unit 20. This peak corresponds to the phase difference between the receiving antennas 13. The angle detection unit 21 then converts this phase difference into an angle to detect the direction in which an obstacle exists, relative to the position of the drone D1.

[0061] The obstacle map generation unit 22 detects the location of an obstacle based on the distance between the drone D1 and the obstacle detected by the distance detection unit 17 and the direction in which the obstacle exists relative to the position of the drone D1 detected by the angle detection unit 21. Then, the obstacle map generation unit 22 generates an obstacle map based on the detected location of the obstacle. More specifically, the obstacle map generation unit 22 can generate the mapped set range H1 as an obstacle map by mapping the detection of an obstacle to the corresponding location within the set range H1.

[0062] For example, the obstacle map generation unit 22 calculates the latitude and longitude of an obstacle as the location of the obstacle based on the distance between the drone D1 and the obstacle, and the direction in which the obstacle is located relative to the position of the drone D1. The set range H1 has a latitude range and a longitude range. The obstacle map generation unit 22 can then generate an obstacle map by mapping the detection of an obstacle to the latitude and longitude of the detected obstacle in the set range H1 that correspond to the latitude and longitude of the detected obstacle.

[0063] Typically, a GPS (Global Positioning System) sensor may be used as the position detection sensor. However, the position detection sensor is not limited to a GPS sensor.

[0064] The landing position determination unit 23 determines the landing position of the drone D1 based on the obstacle map. This allows for a more accurate determination of a safe landing position for the drone D1.

[0065] More specifically, the obstacle map is divided into multiple regions. Based on the obstacle map, the obstacle map generation unit 22 determines whether a region is a detected region where obstacles are found or a non-detected region where no obstacles are found. The obstacle map generation unit 22 can then determine the non-detected region where no obstacles are found as the landing position for the drone D1. This allows for the determination of a safe area as the landing position for the drone D1.

[0066] Figure 6 illustrates an example where an undetected area, where no obstacles are detected, is determined as the landing position. Referring to Figure 6, the obstacle map M1 is shown. Since obstacle B11 is detected in area R4, area R4 is a detected area. Similarly, since obstacle B12 is detected in area R5, area R5 is a detected area. On the other hand, areas R1-R3, R5-R7, and R9 are undetected areas where no obstacles are detected.

[0067] Therefore, the landing position determination unit 23 may determine one of the undetected regions R1-R3, R5-R7, or R9 as the landing position. For example, if there are multiple undetected regions, as shown in Figure 6, the landing position determination unit 23 may determine R3, the undetected region furthest from the detected regions R4 and R8, as the landing position for the drone D1. This allows for the determination of a safer area as the landing position for the drone D1.

[0068] Alternatively, the obstacle may not be stationary and may be moving. Therefore, the landing position determination unit 23 may determine whether or not the obstacle is moving based on the speed of the obstacle detected by the speed detection unit 19. If the obstacle is moving, the landing position determination unit 23 may determine the area furthest from the detection area where the moving obstacle was detected as the landing position.

[0069] Figure 7 illustrates an example of determining the landing position when a moving obstacle is detected. Referring to Figure 7, the obstacle map M2 is shown. Since obstacle B13 is detected in region R7, region R7 is a detected region. On the other hand, regions R1-R6, R8, and R9 are undetected regions where no obstacles are detected. Furthermore, obstacle B13 is moving from region R7 towards region R8.

[0070] Therefore, the landing position determination unit 23 determines that the obstacle B13 is moving based on the speed of the obstacle B13 detected by the speed detection unit 19. At this time, the landing position determination unit 23 may determine the area R3, which is the furthest from area R7, the detection area where the moving obstacle B13 was detected, as the landing position. This allows for the determination of a safer area for the drone D1 to land, taking into account the movement of obstacles.

[0071] Alternatively, the landing position determination unit 23 may estimate the area through which the obstacle B13 will pass as the estimated passing area, based on the speed of the obstacle B13 detected by the speed detection unit 19 and the detection area, region R7. Here, we assume that the obstacle B13 moves in a straight line. In this case, the landing position determination unit 23 determines that the obstacle B13 is moving from region R7 to region R8, and determines that the areas R7 to R9 located on the straight line from region R7 to region R8 are the estimated passing areas.

[0072] In this case, the landing position determination unit 23 may determine the landing position to be one of the regions R1 to R3, which is the furthest from the predicted passing regions R7 to R9, among the multiple undetected regions R1 to R6, R8, and R9. This allows for the determination of a safer area for the drone D1 to land, taking into account the predicted passing regions of obstacles. Note that, although it is assumed here that obstacle B13 moves in a straight line, the direction of movement of obstacle B13 does not have to be in a straight line.

[0073] Each of the regions R1 to R9 may be divided into multiple sub-regions. The landing position determination unit 23 may, if an obstacle is detected in any of the regions R1 to R9, determine for each sub-region whether an obstacle has been detected in a region selected from regions R1 to R9 (hereinafter also referred to as the "selected region"). The landing position determination unit 23 may then determine a sub-region in which no obstacles are detected as the landing position. This can reduce the possibility that the landing position of the drone D1 cannot be determined.

[0074] The selected region may be chosen according to any criteria. For example, the selected region may be chosen based on the number of obstacles, the size of the obstacles, or the location of the obstacles in each of the regions R1 to R9. For example, the region with the fewest obstacles may be selected. Alternatively, the region with the smallest size of the obstacles may be selected. Alternatively, the region with the obstacles closest to the edge may be selected. Here, we assume that region R5 is selected.

[0075] Figure 8 illustrates the case where obstacles are detected throughout the entire region. Referring to Figure 8, region R5 is divided into multiple sub-regions (sub-regions r1 to r4). In the example shown in Figure 8, region R5 is divided into a total of four sub-regions consisting of two vertical sub-regions and two horizontal sub-regions, but how region R5 is divided into multiple sub-regions (for example, the direction in which region R5 is divided, the number of divisions, etc.) is not limited. Also, the size and shape of sub-regions r1 to r4 do not have to be the same.

[0076] Furthermore, the sub-regions R1 to R9 may be the same or different.

[0077] Referring to the obstacle map M3, obstacle B12 is detected in sub-region r2, and obstacle B11 is detected in sub-region r3. That is, the landing position determination unit 23 determines sub-regions r2 and r3 as detected regions. On the other hand, the landing position determination unit 23 determines sub-regions r1 and r4 as non-detected regions. The landing position determination unit 23 may determine either sub-region r1 or sub-region r4 as the landing position.

[0078] The embodiments of the present invention have been described in detail above.

[0079] [Explanation of effects] As described above, according to the embodiments of the present invention, by detecting obstacles through direct measurement using radar, areas where safe landing is possible can be easily and accurately identified. Furthermore, according to the embodiments of the present invention, by selecting an area away from moving obstacles as the landing location, the possibility of the drone coming into contact with the obstacle during landing can be reduced.

[0080] Furthermore, according to embodiments of the present invention, in cases where obstacles exist in any of the areas, it becomes possible to determine a sub-area within the area as a safe landing location based on the obstacle map. This reduces the possibility that the drone may be unable to land in cases where obstacles exist in any of the areas.

[0081] [Explanation of variations] Although preferred embodiments of the present invention have been described in detail above with reference to the attached drawings, the present invention is not limited to these examples. It is clear to any person with ordinary skill in the art to which the present invention belongs that various modifications or alterations can be conceived within the scope of the technical idea described in the claims, and these are also understood to fall within the technical scope of the present invention.

[0082] In the above explanation, FMCW radar was used as an example of an obstacle detection sensor mounted on drone D1. However, the obstacle detection sensor mounted on drone D1 is not limited to this example. For example, the obstacle detection sensor mounted on drone D1 may be a radar of another type, LiDAR (Light Detection and Ranging), or any other sensor.

[0083] The above primarily assumes that the setting range H1 is a predetermined combination of latitude and longitude ranges. However, the setting range H1 may also be generated based on the latitude and longitude of the drone D1. That is, the obstacle map generation unit 22 may generate the setting range H1 as a combination of latitude and longitude ranges based on the latitude and longitude of the drone D1 detected by the position detection sensor mounted on the drone D1.

[0084] For example, it is conceivable that the relative latitude and longitude offset amount of the setting range H1 based on the latitude and longitude of drone D1, and the latitude and longitude width of the setting range H1 are predetermined. In such a case, the obstacle map generation unit 22 may generate the setting range H1 based on the latitude and longitude of drone D1, the offset amount, and the latitude and longitude width of the setting range H1.

[0085] Alternatively, the obstacle map generation unit 22 may generate multiple candidate ranges, each composed of a combination of latitude and longitude ranges, based on the latitude and longitude of the drone D1. The obstacle map generation unit 22 may then select a set range H1 from among the multiple candidate ranges.

[0086] The setting range H1 may be selected according to any conditions. For example, the setting range H1 may be selected based on the number of obstacles, the size of the obstacles, or the location of the obstacles in each of several candidate ranges. For example, the candidate range with the fewest obstacles may be selected as the setting range H1. Alternatively, the candidate range with the smallest size of the obstacles may be selected as the setting range H1. Alternatively, the candidate range with the obstacles closest to the edge may be selected as the setting range H1.

[0087] Furthermore, the above mainly assumed cases where the detection area where an obstacle is detected is not determined as the landing position for drone D1. However, even if an obstacle is detected in the detection area, it may be determined as the landing position for drone D1 depending on the shape of the obstacle. For example, if the shape of the obstacle has a flat top surface, the landing position determination unit 23 may determine that top surface as the landing position. The shape of the obstacle can be determined, for example, by the shape of the peaks in the frequency spectrum obtained by the one-dimensional FFT processing unit 16. [Explanation of Symbols]

[0088] 1. Information Processing Device 11 Synthesizers 12 Transmitting Antennas 13 Receiving antenna 14 Mixer 15 Pre-processing 16 One-dimensional FFT processing section 17 Distance detection unit 18 Two-dimensional FFT processing section 19 Speed ​​detection unit 20 3D FFT processing section 21 Angle detection unit 22 Obstacle Map Generation Unit 23. Landing position determination unit

Claims

1. A map generation unit detects the object's position based on the distance between the unmanned aerial vehicle and the object, and the orientation of the object relative to the unmanned aerial vehicle, as detected by sensor data measured by sensors mounted on the unmanned aerial vehicle, and generates an object map divided into multiple regions based on the object's position. A landing position determination unit determines whether each region is a detection region where the object has been detected based on the object map, and determines the non-detection regions where the object has not been detected as the landing position of the unmanned aerial vehicle. Equipped with, The landing position determination unit, when there are multiple non-detection areas, determines whether the object is moving based on the velocity of the object detected based on the sensor data, and if the object is moving, determines the area furthest from the detection area where the object was detected as the landing position. Information processing device.

2. A map generation unit detects the object's position based on the distance between the unmanned aerial vehicle and the object, and the orientation of the object relative to the unmanned aerial vehicle, as detected by sensor data measured by sensors mounted on the unmanned aerial vehicle, and generates an object map divided into multiple regions based on the object's position. A landing position determination unit determines whether each region is a detection region where the object has been detected based on the object map, and determines the non-detection regions where the object has not been detected as the landing position of the unmanned aerial vehicle. Equipped with, The landing position determination unit estimates the area through which the object will pass based on the velocity of the object detected based on the sensor data and the detection area, and determines the area furthest from the estimated passage area among the multiple non-detection areas as the landing position. Information processing device.

3. A map generation unit detects the object's position based on the distance between the unmanned aerial vehicle and the object, and the orientation of the object relative to the unmanned aerial vehicle, as detected by sensor data measured by sensors mounted on the unmanned aerial vehicle, and generates an object map divided into multiple regions based on the object's position. A landing position determination unit determines whether each region is a detection region where the object has been detected based on the object map, and determines the non-detection regions where the object has not been detected as the landing position of the unmanned aerial vehicle. Equipped with, Each of the aforementioned multiple regions is divided into multiple sub-regions, The landing position determination unit, when an object is detected in any of the multiple regions, determines for each sub-region whether an object was detected in the selected region chosen from the multiple regions, and determines the sub-region in which no object was detected as the landing position. Information processing device.

4. A map generation unit detects the object's position based on the distance between the unmanned aerial vehicle and the object, and the orientation of the object relative to the unmanned aerial vehicle, as detected by sensor data measured by sensors mounted on the unmanned aerial vehicle, and generates an object map based on the object's position. A landing position determination unit that determines the landing position of the unmanned aerial vehicle based on the object map, Equipped with, The map generation unit generates a set range consisting of a combination of latitude and longitude ranges based on the latitude and longitude of the unmanned aerial vehicle, divided into multiple regions; maps the detection of the object to the position corresponding to the object's location within the set range; and generates the mapped set range as the object map. The setting range is selected from a plurality of candidate ranges, each composed of a combination of latitude and longitude ranges, based on the latitude and longitude of the unmanned aerial vehicle, based on the number of objects, the size of the objects, or the location of the objects. Information processing device.

5. The landing position determination unit selects the selected region based on the number of objects in each region, the size of the objects, or the position of the objects. The information processing apparatus according to claim 3.

6. The map generation unit calculates the latitude and longitude of the object as the object's position based on the latitude and longitude of the unmanned aerial vehicle detected by the position detection sensor, the distance, and the direction. The information processing apparatus according to any one of claims 1 to 3.

7. Based on sensor data measured by sensors mounted on the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the object, and the orientation of the object relative to the unmanned aerial vehicle, the object's position is detected, and based on the object's position, an object map divided into multiple regions is generated. Based on the object map, it is determined whether each region is a detection region where the object has been detected, and the non-detection regions where the object is not detected are determined as the landing position of the unmanned aerial vehicle. Equipped with, If there are multiple non-detection areas, it is determined whether the object is moving based on the velocity of the object detected based on the sensor data, and if the object is moving, the area furthest from the detection area where the object was detected is determined as the landing position. Information processing methods.

8. Computers, A map generation unit detects the object's position based on the distance between the unmanned aerial vehicle and the object, and the orientation of the object relative to the unmanned aerial vehicle, as detected by sensor data measured by sensors mounted on the unmanned aerial vehicle, and generates an object map divided into multiple regions based on the object's position. A landing position determination unit determines whether each region is a detection region where the object has been detected based on the object map, and determines the non-detection regions where the object has not been detected as the landing position of the unmanned aerial vehicle. Equipped with, The landing position determination unit, when there are multiple non-detection areas, determines whether the object is moving based on the velocity of the object detected based on the sensor data, and if the object is moving, determines the area furthest from the detection area where the object was detected as the landing position. A program that makes an information processing device function.

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