阅读说明：本技术 无人机路径规划方法和装置 (unmanned aerial vehicle path planning method and device ) 是由 赵海涛 魏急波 李佳迅 熊俊 黄圣春 辜方林 周力 唐麒 于 2019-10-16 设计创作，主要内容包括：本发明提出一种无人机路径规划方法及装置,该方法以无人机和地面基站为节点构建拓扑网络,建立三维坐标系,并已知坐标系内所有节点的地面坐标和无人际飞行轨迹在地面上的坐标投影,包括：根据地面节点坐标或地面节点的邻居节点数量,确定地面节点访问顺序；通过凸优化方法在地面节点有效传输区域内寻找路径点；按照访问顺序连接给定起点、终点和寻找路径点步骤中寻找到的所有路径点,获得最优路径。该方案用于解决现有技术中时间最小化与路径变化相互影响等问题,优化无人机的路径,最大限度地减少总任务时间,以达到更高效的无人机和地面节点之间的数据采集和分发。(the invention provides a method and a device for planning a path of an unmanned aerial vehicle, wherein the method comprises the following steps of constructing a topological network by taking the unmanned aerial vehicle and a ground base station as nodes, establishing a three-dimensional coordinate system, and knowing ground coordinates of all the nodes in the coordinate system and coordinate projection of an unmanned aerial vehicle flight track on the ground, wherein the method comprises the following steps: determining a ground node access sequence according to the ground node coordinates or the number of neighbor nodes of the ground nodes; searching path points in the effective transmission area of the ground nodes by a convex optimization method; and connecting all the path points found in the steps of giving a starting point, giving an end point and finding the path points according to the access sequence to obtain the optimal path. The scheme is used for solving the problems that time minimization and path change influence each other and the like in the prior art, optimizing the path of the unmanned aerial vehicle, and reducing the total task time to the maximum extent so as to achieve more efficient data acquisition and distribution between the unmanned aerial vehicle and the ground node.)

1. A method for planning unmanned aerial vehicle paths is characterized in that a ground base station is used as a node to construct a topological network to interact with an unmanned aerial vehicle so as to finish data acquisition and data distribution, a three-dimensional coordinate system is established, and ground coordinates of all nodes in the coordinate system and coordinate projection of an unmanned aerial vehicle flight track on the ground are known, and the method comprises the following steps:

S01, determining the ground node access sequence according to the ground node coordinates or the number of the neighbor nodes of the ground node;

and S02, in the coordinate plane where the unmanned aerial vehicle is projected, according to the access sequence of the ground nodes, in the effective transmission area of the ground nodes between the starting point and the end point by a convex optimization method, and according to the constraint condition that the effective transmission area of the same ground node is only calculated once, finding the shortest path and outputting the shortest path.

2. the unmanned aerial vehicle path planning method of claim 1, further comprising, before S01:

s1, acquiring the number of ground nodes and the area covered by all ground nodes in the coordinate plane;

s2, obtaining a density value for describing the sparsity of the ground nodes according to the number of the ground nodes and the covered surface area value;

S3, when the density value of the ground node is less than or equal to a given threshold value, the unmanned aerial vehicle path planning is carried out by using a path planning method based on the segments;

the S01 includes:

and S101a, determining the ground node access sequence according to the ground node coordinates.

3. the method of claim 2, further comprising after S3:

s4, when the density value of the ground node is larger than the given threshold value, the group-based path planning method is used for path planning;

the S01 includes:

s101b, according to the ground node and the number of the neighbor nodes of the ground node, determining the access sequence of the ground node.

4. a method of path planning for unmanned aerial vehicles according to claim 2, wherein;

the S02 includes:

S201a, according to the access sequence of the ground nodes, finding the shortest path between the starting point and the end point in the effective transmission area of the ground nodes by a convex optimization method;

s202a, a path section is taken from the starting point or the end point of the shortest path, and a ground node of an effective transmission area intersected with the path section is obtained;

S203a, excluding all the obtained ground nodes and repeating S101a, S201a and S202 a; ending the circulation until all ground nodes are eliminated;

And S204a, connecting all the acquired path segments according to the acquisition order to form an optimal path.

5. the unmanned aerial vehicle path planning method of claim 4, wherein the segment-based path planning method comprises:

s101a, obtaining the access sequence of the ground nodes by a traveler problem algorithm according to the coordinate position of the ground nodes;

S102a, determining the maximum transmission radius of the ground node according to the wireless communication environment and the ground node transmitting power, and determining the effective transmission area according to the maximum transmission radius;

s201a, according to the access sequence and the effective transmission areas, calculating the path points in each effective transmission area which leads the shortest path by using a convex optimization algorithm and obtaining the initial shortest path;

s202a, the initial shortest path is composed of a plurality of line segments connected end to end, the ground nodes where the effective transmission area intersects with the first line segment in the connection sequence are deleted, the access sequence is determined again in the rest nodes, and the path is calculated by using a convex optimization algorithm;

s203a, repeating S101a, S201a and S202a until all ground nodes are deleted, and ending the path loop;

S204a, connecting all the acquired path segments according to the acquiring order, i.e. obtaining the final shortest path.

6. The unmanned aerial vehicle path planning method of claim 3,

The S01 includes:

s101b, the ground node and the neighbor nodes of the ground node form a node cluster, and the access sequence of the node cluster is determined according to the number of nodes contained in each node cluster.

7. the unmanned aerial vehicle path planning method of claim 6, wherein the S02 includes:

s201b, acquiring the intersection of the effective transmission areas of the ground nodes contained in each node cluster;

s202b, searching a path point which enables the path to be shortest in an intersection through a convex optimization method between the starting point and the end point;

and S203b, connecting all path points according to the access sequence of the node cluster to form an optimal path.

8. the unmanned aerial vehicle path planning method of claim 7, wherein S101b comprises:

s401, when the effective transmission areas of other ground nodes are intersected with the effective transmission area of the current ground node, taking the other ground nodes as neighbor nodes of the current ground node, and acquiring the number of all neighbor nodes of the current ground node; s402, sequentially placing the ground nodes with the largest number of neighbor nodes as virtual group nodes of the node cluster where the ground nodes are placed into a virtual group node set;

and S403, determining the access sequence of all the virtual group nodes according to the traveler problem algorithm.

9. The method of claim 8, wherein the S401 comprises:

establishing a neighbor node relation indication matrix corresponding to the ground node in an effective transmission area corresponding to the current ground node, wherein the number of rows and columns is equal to the number of all nodes, and the distance between two nodes corresponding to the rows and the columns is in the effective transmission area of any one node, so that the corresponding element in the neighbor node relation matrix is assigned to be 1;

the S402 includes:

and according to the neighbor node relation indication matrix obtained in the step S401, marking the ground nodes with the most neighbor nodes in the effective transmission area as virtual group nodes, placing the virtual group nodes and the neighbor nodes in a virtual group node set, deleting the virtual group nodes and the neighbor nodes, and repeating the process until all the ground nodes with the neighbor nodes are deleted to obtain the virtual node set.

10. An unmanned aerial vehicle path planning device, comprising a memory and a processor, wherein the memory stores an unmanned aerial vehicle path planning program, and the processor executes the steps of the unmanned aerial vehicle path planning method according to any one of claims 1 to 9 when running the unmanned aerial vehicle path planning program.

Technical Field

The invention belongs to the technical field of wireless communication networks, and relates to a method and a device for planning a path of an unmanned aerial vehicle.

Background

With the advent of the internet of things, data and information collection became the basis for the realization of the functions of the internet of things. Similar to a wireless sensor network, in the process of implementing data collection in the internet of things, the prior art provides many communication protocols and routing algorithms but still cannot ensure that network connection is smooth and free of blockage. Because of the mobility of the ground nodes, especially in an emergency situation, the situation that the communication protocol and the routing algorithm work inefficiently is obvious. In response to this situation, the solution is to strip the mobility of the drone and forward the data to the last receiver after the data is collected by the drone in flight.

Compared with the traditional method, the unmanned aerial vehicle auxiliary data acquisition method has the following advantages: the unmanned aerial vehicle has mobility and flexibility in a three-dimensional space, and due to a high-probability LoS (line of sight) link mode, the unmanned aerial vehicle can complete data acquisition in a more reliable mode; due to LoS link, the transmission range from the ground node to the unmanned aerial vehicle can be expanded, and the motion path of the unmanned aerial vehicle is shortened; the data acquisition process is almost independent of the ground node network, and the complexity and cost of the ground node network deployment and management are reduced. Because the data acquisition scheduling is closely coupled with the operation track of the unmanned aerial vehicle, the efficient data acquisition is closely related to the optimized operation track of the unmanned aerial vehicle. Due to the fact that connectivity of traditional routing type wireless data acquisition and distribution cannot be guaranteed due to non-accurate or emergency sensor deployment, flexible data acquisition and distribution by using an unmanned machine is a new mode. In an emergency scene, the timeliness of data is particularly important. The drone is required to acquire and distribute valid data in a minimum amount of time.

Some prior art proposes unmanned aerial vehicle path design with the aim of optimizing the performance of data acquisition. In the existing method, only a sensing area is generally considered, and a specific ground node is not considered. The perceptual area is divided into several sub-areas related to the priority of the data samples. The drone movement path is designed by determining the order of visiting the sub-areas, with the goal that the drone collects as many higher priority data samples as possible with limited energy. The method cannot provide an accurate and efficient unmanned aerial vehicle operation path. In most existing methods for acquiring data from a large wireless sensor network, a multi-hop routing algorithm and a clustering forming algorithm based on density are designed emphatically. The drone path is simply to determine the order of travel of the group, and the problem is modeled as a classical traveler problem. Furthermore, the cluster radius should be carefully chosen to balance the multi-hop routing and the number of clusters, but no specific solution is given. The method also relates to a minimum value data compression acquisition method based on a spanning tree of a routing mechanism and a K-means method based on a cluster forming algorithm with almost equal size; and a nearest neighbor algorithm is provided simply aiming at the problem of designing a track based on the problem of the traveling salesman. A disadvantage is that this method presupposes that any two sensors in the wireless sensor network are within radio range, but in practice this cannot be achieved.

The main problems of the existing unmanned aerial vehicle path planning technology mainly include:

(1) ground nodes that use routing schemes and cluster heads to relay consume more energy in data acquisition than ground nodes that use cluster formation algorithms, which may shorten the life cycle of the network;

(2) most approaches consider energy efficiency or energy minimization, but in many emergency situations, task time minimization should be most considered.

(3) although the drone path has been designed, the need to optimize speed and altitude is the same as for transmission scheduling, which may affect the performance of the drone data acquisition.

Disclosure of Invention

the invention provides a method and a device for planning a path of an unmanned aerial vehicle, which are used for overcoming the defects of time minimization, mutual influence of path change and the like in the prior art. The invention equates the time minimization problem to the shortest path problem, and decomposes the shortest path problem into the track optimization problem, thereby realizing the shortest path planning.

In order to achieve the purpose, the invention provides an unmanned aerial vehicle shortest path planning method, which comprises the following steps of constructing a topological network by taking a ground base station as a node, interacting with an unmanned aerial vehicle to finish data acquisition and data distribution, establishing a three-dimensional coordinate system, and knowing ground coordinates of all nodes in the coordinate system and coordinate projection of an unmanned aerial vehicle flight track on the ground, wherein the method comprises the following steps:

S01, determining the access sequence of the ground nodes according to the coordinates of the ground nodes or the number of the neighbor nodes of the ground nodes;

and S02, in the coordinate plane where the unmanned aerial vehicle is projected, according to the access sequence of the ground nodes, in the effective transmission area of the ground nodes between the starting point and the end point by a convex optimization method, and according to the constraint condition that the effective transmission area of the same ground node is only calculated once, searching the shortest path and outputting the shortest path.

in order to achieve the above object, the present invention further provides an unmanned aerial vehicle path planning apparatus, which includes a memory and a processor, wherein the memory stores an unmanned aerial vehicle path planning program, and the processor executes the steps of the unmanned aerial vehicle path planning method when operating the unmanned aerial vehicle path planning program.

The invention provides a method and a device for planning a path of an unmanned aerial vehicle, which are used for optimizing the path of the unmanned aerial vehicle and reducing the total task time to the maximum extent. So as to achieve more efficient data acquisition and distribution between the drone and the ground node. The invention provides a segment-based path planning method for determining a ground node access sequence according to the position of a ground node, which avoids the problem of repeated travel through constraint conditions in the planning process of the path node. Compared with the existing algorithm, the track optimization algorithm based on the segment and the track optimization algorithm based on the group are shorter in track, and the track optimization algorithm based on the group is lower in calculation complexity; furthermore, it is suggested that the time minimized design is an effective solution by comparison with the reference.

drawings

fig. 1 shows an overall environment of a method for planning a path of an unmanned aerial vehicle according to an embodiment;

fig. 2 shows the path length of the unmanned aerial vehicle obtained by using the existing path planning method AO in the case where the number of ground nodes is 20 in the urban scene in the first embodiment;

Fig. 3 shows the path length of the drone obtained by using the segment-based path planning method STOA in the case where the number of ground nodes is 20 in the urban scenario according to the first embodiment;

Fig. 4 shows the path length of the drone obtained by using the group-based path planning method GTOA in the case where the number of ground nodes is 20 in the urban scenario according to the first embodiment;

fig. 5 shows the path length of the unmanned aerial vehicle obtained by using the existing path planning method AO in the case where the number of ground nodes is 40 in the urban scene in the first embodiment;

Fig. 6 shows the path length of the drone obtained by using the segment-based path planning method STOA in the case where the number of ground nodes is 40 in the urban scenario according to the first embodiment;

fig. 7 shows the path length of the drone obtained by using the group-based path planning method GTOA in the case where the number of ground nodes is 40 in the urban scenario according to the first embodiment;

Fig. 8 shows three methods for estimating path length variation under different numbers of ground nodes with a fixed transmission power of 50dBm at the ground node according to the first embodiment.

Fig. 9 shows three methods for estimating path length variation under different numbers of ground nodes with a fixed transmission power of 55dBm at the ground node according to the first embodiment.

Detailed Description