Patent ID: 12223698

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a method for searching a path by using a three-dimensional reconstructed map, which is applied to a computer system. The method is loaded and performed by a computer to compute the flight path of an unmanned aerial vehicle, thereby controlling the unmanned aerial vehicle. Thus, the unmanned aerial vehicle can drive automatically.

Before computing the flight path, environment information is retrieved by devices to build a three-dimensional map model in an environment. Referring toFIG.1, the devices for retrieving the environment information include a point-cloud map-retrieving device10and a photographic device20. The point-cloud map-retrieving device10is configured to generate three-dimensional point-cloud map information.

In the embodiment, the point-cloud map-retrieving device10is a lidar and the three-dimensional point-cloud map information is three-dimensional lidar point-cloud map information. The point-cloud map-retrieving device10emits lidar signals to the environment, detects distance data points according to the lidar signals, and combines the distance data points into the three-dimensional point-cloud map information according to the actual coordinates of the distance data points.

The photographic device20is configured to generate three-dimensional material map information. In the embodiment, the photographic device20is a camera and the three-dimensional material map information is three-dimensional image information. The photographic device20provides image for capturing the environment and combines the images into the three-dimensional material map information.

After collecting the three-dimensional point-cloud map information and the three-dimensional material map information, the point-cloud map-retrieving device10and the photographic device20provide them for a processing device30. The processing device30computes the flight path of the unmanned aerial vehicle.

The processing device30is installed on the unmanned aerial vehicle (not illustrated). In the embodiment, the processing device30includes a processor32, a database34, a wind sensor36, and a signal transmitter38. The processor32is a computation device, such as a central processing unit (CPU). The processor can load the method for searching a path by using a three-dimensional reconstructed map and compute the flight path of the unmanned aerial vehicle, thereby generating three-dimensional path information. The database34is coupled to the processor32. The database34may be a storage device, such as a memory or a hard disk. The wind sensor36is coupled to the processor32and configured to sense the level of wind to generate wind information. The processor32receives the wind information. The signal transmitter38is coupled to the processor32. The signal transmitter38may be a network transceiver or a data transmission interface such as a universal serial bus (USB). The signal transmitter38is configured to receive external information, such as the three-dimensional point-cloud map information, the three-dimensional material map information, etc.

After generating the three-dimensional point-cloud map information and the three-dimensional material map information, the point-cloud map-retrieving device10and the photographic device20transmit them to the processing device30. The point-cloud map-retrieving device10and the photographic device20transmit the three-dimensional point-cloud map information and the three-dimensional material map information to the processor32through the signal transmitter38, such that the processor32computes the three-dimensional path information with the method of the present invention. After receiving the three-dimensional point-cloud map information and the three-dimensional material map information, the processor32stores them into the database34. When the waypoint is changed later and the flight path is recalculated by the processor32, the three-dimensional point-cloud map information and the three-dimensional material map information in the database34can be used.

After describing how to obtain the three-dimensional point-cloud map information and the three-dimensional material map information and the system using the method of the present invention, the method for searching a path by using a three-dimensional reconstructed map is described in detail. Referring toFIG.1andFIG.2, in Step S10, the processor30receives the three-dimensional point-cloud map information generated by the point-cloud map-retrieving device10and the three-dimensional material map information generated by the photographic device20.

In Step S12, the processing device30clusters the three-dimensional point-cloud map information with a clustering algorithm to obtain clustering information, thereby presenting and identifying obstacle areas in the future. In the embodiment, the clustering algorithm is density-based spatial clustering of applications with noise (DBSCAN). Simultaneously, the processing device30identifies material attributes of objects in the three-dimensional point-cloud map information with a material neural network model to obtain material attribute information. The material neural network model is pre-trained using historical data of material attributes of different objects. For example, image data of obstacles such as trees and stones are inputted to the material neural network model in advance. The image data are used to train the material neural network model to effectively identify material attributes of objects in the three-dimensional material map information, thereby identifying the obstacle areas in the future. The material is identified based on the color of the image. The material attributes can also be regarded as color attributes.

In Step S14, the processing device30retrieves the coordinate information of the three-dimensional point-cloud map information and the three-dimensional material map information, and fuses the three-dimensional point-cloud map information and the three-dimensional material map information based on the coordinate information, thereby outputting fused map information. The fused map information apparently presents the clustering information and the material attributes of all objects, thereby identifying the obstacle areas.

In Step S16, the processing device30identifies obstacle areas and non-obstacle areas in the fused map information based on an obstacle neural network model, the clustering information, and the material attribute information. The obstacle neural network model is pre-trained using historical data of material attribute information and clustering information of different obstacle areas. For example, the point-cloud map-retrieving device10and the photographic device20obtained the three-dimensional point-cloud map information and the three-dimensional material map information of obstacles such as trees and stones in the past. The point-cloud map-retrieving device10and the photographic device20calculate the historical parameters of the obstacles, including those of clustering information and material attribute information. The historical parameters of the obstacles are inputted to the obstacle neural network model for training. Hence, the obstacle neural network can identify the obstacle areas. The remaining unidentifiable non-attribute areas can be regarded as non-obstacle areas.

In Step S18, the processing device30obtains the obstacle areas and the non-obstacle areas in the fused map information. The processing device can set a destined waypoint and generate three-dimensional path information according to the non-obstacle areas. The three-dimensional path information, including values in the X axis, the Y axis, and the Z axis of a three-dimensional coordinate system in actual space, is used to control the movement of the unmanned aerial vehicle in three-dimensional actual space. The three-dimensional path information comprises path points connected with each other. The path points keep distance where an obstacle-avoidance circle is formed from the obstacle areas. As a result, there is a space barrier between the unmanned aerial vehicle and the obstacle in the embodiment. In other words, the unmanned aerial vehicle may have an obstacle-avoidance circle with a radius of 2 m. The unmanned aerial vehicle is 2 m from the obstacle. The foregoing condition needs to be considered when the three-dimensional path information is planned. The path will not be generated in the obstacle-avoidance circle if there is an obstacle within the obstacle-avoidance circle with a radius of 2 m.

In addition to directly generating the three-dimensional path information of the unmanned aerial vehicle, the embodiment further generates parameters for adjusting the three-dimensional path information. Specifically, the processor32is coupled to a wind sensor36for generating wind information. The wind information is used as parameters for adjusting the three-dimensional path information of the unmanned aerial vehicle. The wind information is used to adjust the radius of the obstacle-avoidance circle and the flight height. Referring toFIG.1andFIG.3, in Step S20, the wind sensor36inputs the wind information to the processor32. In Step S22, the processor32determines whether the wind information is greater than a wind-level threshold or determines whether a headwind occurs based on the wind information. When the processor32determines whether the headwind occurs, the wind sensor36detects and transmits the direction of wind to the processor32. If the driving direction of the unmanned aerial vehicle is opposite to the direction of wind, the headwind occurs. When the processor32determines that the wind information is greater than the wind-level threshold or that the headwind occurs, the procedure proceeds to Step S24. In Step S24, the distance of the obstacle-avoidance circle is decreased. That is to say, the radius of the obstacle-avoidance circle is decreased, such that the unmanned aerial vehicle is closer to the obstacle when the unmanned aerial vehicle flies. Simultaneously, positions in the z axis of the three-dimensional path information are decreased to generate first three-dimensional path information. In the embodiment, the distance of the obstacle-avoidance circle is decreased by at most 0.7 m. The positions in the z axis of the three-dimensional path information are decreased by 0.5˜2 m. When the processor32determines that the wind information is not greater than the wind-level threshold or that the headwind does not occur, the procedure proceeds to Step S26. In Step S26, the distance of the obstacle-avoidance circle is increased to generate second three-dimensional path information. That is to say, the radius of the obstacle-avoidance circle is increased, such that the unmanned aerial vehicle is farther away from the obstacle when the unmanned aerial vehicle flies. The distance of the obstacle-avoidance circle is increased by at most 0.5 m.

In conclusion, the present invention effectively determines obstacle areas and flight spaces based on three-dimensional point-cloud map information and three-dimensional material map information to generate an accurate flight path. Thus, an unmanned aerial vehicle flies in a smaller space to improve the precision of flight and the success rate of obstacle avoidance, and the size of the unmanned aerial vehicle can be made smaller. The present invention adjusts the three-dimensional path information according to wind-level parameters to provide an optimized flight path. The present invention reduces the number of parameters required for generating a flight path with sensors and the size and weight of the unmanned aerial vehicle, so as to improve flight performance and decrease the production cost of the unmanned aerial vehicle.

The embodiments described above are only to exemplify the present invention but not to limit the scope of the present invention. Therefore, any equivalent modification or variation according to the shapes, structures, features, or spirit disclosed by the present invention is to be also included within the scope of the present invention.