Unmanned aerial vehicle obstacle avoidance method based on laser radar

Abstract

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on a laser radar in a closed space. The unmanned aerial vehicle obstacle avoidance method comprises two parts of obstacle detection and barrier-free path calculation. Wherein the obstacle detection part mainly depends on point cloud data to carry out judgment, and then the part further comprises the step of filtering some factors which possibly cause error detection of obstacles; according to the barrier-free path calculation part, a barrier-free path with the shortest time is calculated according to the relative position relation between the barrier and the unmanned aerial vehicle. The obstacle avoidance algorithm disclosed by the invention has very high universality and robustness, and can complete safe autonomous flight in a complex scene. The method has a great application value for the use of the unmanned aerial vehicle in a closed space.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and more specifically, to an obstacle avoidance method for unmanned aerial vehicles in a closed space. Background Art

[0002] With the development of economic globalization, mining enterprises urgently need to relieve the increasingly fierce market competition pressure through the construction of mine informatization and intelligentization. In recent years, there has been a problem of "labor shortage" in China, especially in the mining industry, which is a high-risk industry with frequent accidents, so this problem is more prominent. With the aggravation of population aging and the structural imbalance of the labor force, this situation will become more serious. Therefore, building an intelligent mine and changing the mine mining method have become major strategic needs for the survival and development of mining enterprises. The construction of an intelligent mine makes full use of new-generation information technologies such as 5G communication, industrial Internet, blockchain, cloud computing, artificial intelligence, etc., and highly integrates information technology with industrial technology and management technology, which is a process of transformation for these enterprises. At present, there is a strong demand for the application of unmanned aerial vehicles in the process of mine mining, such as underground mining reconnaissance and detection, goaf survey, etc. However, without the support of GPS, how to ensure the safe flight of unmanned aerial vehicles in a closed space has become a hot topic.

[0003] Most of the commonly used obstacle avoidance methods at present are to build maps of the surrounding environment, such as grid maps, octree maps, etc., and combine the A* algorithm to find the optimal path. The disadvantages of this method are also obvious. That is, an accurate and complete global map needs to be established before obstacle avoidance, which reduces the real-time performance and limits the use of this method to a certain extent. Secondly, this method has complex logic and large computational amount, resulting in problems such as high CPU load. Different from the commonly used methods, the present invention proposes an obstacle avoidance algorithm for unmanned aerial vehicles in a closed space, which does not need to establish a global map, and only needs to judge whether the path calculated in the local area where the unmanned aerial vehicle is located is optimal. This algorithm has high universality and robustness, and can complete safe autonomous flight in complex scenarios. It has great application value for the use of unmanned aerial vehicles in a closed space. Summary of the Invention

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] An obstacle avoidance method for an unmanned aerial vehicle based on lidar in a closed space, including obstacle detection and obstacle-free path calculation. Among them, the obstacle detection part mainly relies on point cloud data for judgment, and secondly, this part also includes filtering some factors that may cause misdetection of obstacles; the obstacle-free path calculation part calculates an obstacle-free path with the shortest time through the relative position relationship between the obstacle and the unmanned aerial vehicle.

[0006] Preferably, the obstacle detection is characterized in that dust and various impurities in the environment are filtered out by judging the density of the point cloud distribution.

[0007] Preferably, the obstacle detection is characterized in that points with similar point cloud coordinates are combined, and the maximum circumscribed circle of the point cloud with similar coordinates is calculated as the size of the obstacle.

[0008] Preferably, the obstacle-free path is characterized in that there is a safety detection area in the flight direction of the drone, and this area is a cylinder with a length of L and a width of 2Rb, where Rb is the radius of the safety area.

[0009] Preferably, the obstacle-free path is characterized in that the optimal adjustment distance of the drone is calculated by calculating the overlapping size of the safety area and the obstacle.

[0010] Preferably, the obstacle-free path is characterized in that the adjustment direction of the drone path is the opposite direction of the orientation of the obstacle relative to the drone.

[0011] An obstacle avoidance algorithm for drones based on lidar provided by the present invention can handle the obstacle avoidance of drones in complex scenarios and greatly protect the safety of drone flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0013] Figure 1 It is a schematic diagram of obstacle detection for the drone obstacle avoidance algorithm;

[0014] Figure 2 It is a schematic diagram of the safety area for the drone obstacle avoidance algorithm;

[0015] Figure 3 It is a schematic diagram of the overlap degree between the safety area and the obstacle for the drone obstacle avoidance algorithm;

[0016] Figure 4 It is a schematic diagram of the obstacle avoidance flight of the drone for the drone obstacle avoidance algorithm. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] See Figure 1 The obstacle detection algorithm of the drone determines the size, quantity, and relative position relationship of obstacles by numerically analyzing the point cloud distribution. This process is carried out in two steps. The first step is point cloud screening, and the second step is obstacle determination. Figure 1 It is a schematic diagram of the obstacle detection algorithm. The black spots in the figure represent the radar point cloud distribution. In the first step of the algorithm, the point cloud data with high density is screened out by judging the point cloud distribution density for the next calculation. The point cloud with sparse or scattered distribution is considered dust or environmental impurities, and this part of the point cloud data is filtered out and does not participate in the subsequent calculation. In the second step of the algorithm, the points with similar coordinates in the point cloud are combined, and the combined obstacles are sorted, such as 1, 2, 3, 4 in the figure, and the maximum circumscribed circle of the point cloud with similar coordinates is calculated as the size of the obstacle.

[0019] The obstacle calculation formula is as follows:

[0020]

[0021] In the formula, yh is the maximum value of the point cloud in the Y direction of a certain obstacle, yd is the minimum value in the Y direction, xr is the maximum value in the X direction, and xl is the minimum value in the X direction. The radius of this obstacle is the maximum value of Rh and Rw.

[0022] See Figure 2 , the obstacle-free path calculation of the drone is based on the initial path. By detecting the orientation of the obstacle relative to the drone and the overlap degree between the obstacle and the safety area in the safety detection area 1, the minimum adjustment distance for the drone to avoid obstacles is calculated. See Figure 3 , the center coordinate of the safety area of the drone is O1, the radius of the safety area is Rb, the circular coordinate of the obstacle is O2, the radius of the obstacle is Rz, and the overlapping dimension between the safety area and the obstacle is l. The azimuth angle of the obstacle relative to the drone is θ. Therefore, the adjustment azimuth of the drone is the opposite direction of the obstacle, that is, θ + 180°. The minimum adjustment distance is l, and the center coordinate of the adjusted safety area is O3. The calculation formula for the adjustment distance l is as follows:

[0023] l = R b + R z - O1O2

[0024] In the formula, O1O2 is the distance between the centers of the safety area and the obstacle. Finally, the schematic diagram of the drone bypassing the obstacle is asFigure 4 As shown. In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0025] The method for a drone to avoid obstacles in a closed space provided by the present invention has been introduced in detail above. Specific examples are used herein to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for an unmanned aerial vehicle to avoid obstacles based on lidar in a closed space, characterized in that Including: The UAV obstacle avoidance method consists of two parts, including obstacle detection and obstacle-free path. Among them, the obstacle detection part mainly relies on point cloud data for judgment. Secondly, this part also includes filtering out some factors that may cause false detection of obstacles; the obstacle-free path calculation part calculates an obstacle-free path with the shortest time through the relative position relationship between the obstacle and the UAV.

2. The obstacle detection according to claim 1, wherein Dust and various impurities in the environment are filtered out by judging the density of the point cloud distribution.

3. The obstacle detection according to claim 1, wherein Points with similar point cloud coordinates are combined, and the maximum circumscribed circle of the point cloud with similar coordinates is calculated as the size of the obstacle.

4. The barrier-free path according to claim 1, wherein There is a safety detection area in the flight direction of the UAV. This area is a cylinder with a length of L and a width of 2Rb, where Rb is the radius of the safety area.

5. The barrier-free path according to claim 1, wherein The optimal adjustment distance of the UAV is calculated by calculating the overlapping size of the safety area and the obstacle.

6. The barrier-free path according to claim 1, wherein The azimuth of the UAV path adjustment is the opposite direction of the azimuth of the obstacle relative to the UAV.

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