技术领域technical field
本发明涉及一种无人机数据收集过程中的路径规划方法。The invention relates to a path planning method in the data collection process of an unmanned aerial vehicle.
背景技术Background technique
与目前传感网中主要的数据收集方法相比，基于无人机的数据收集方法具有很大的优势。首先，这种方法在形式上与基于移动sink节点的数据收集方法非常相似，因此其可以完全避免“能量空洞问题”。同时它又将数据采集和数据收集分离在不同的空间维度进行，因此也避免了“sink移动受限问题”以及“应用场景受限问题”。但是，现有的无人机大都由电池供电，航行时间非常短，如果遇到严峻的飞行环境，例如：飞行时障碍物较多，风阻过大等因素，其航行时间将会大大减少，而现有的传感网规模却越来越大。因此，如果利用单个无人机对传感网中所有数据感知节点进行数据收集工作，将会造成大量的数据缺失。Compared with the main data collection methods in current sensor networks, the UAV-based data collection method has great advantages. First, this method is very similar in form to the data collection method based on mobile sink nodes, so it can completely avoid the "energy hole problem". At the same time, it separates data collection and data collection in different spatial dimensions, so it also avoids the problem of "restricted sink movement" and "restricted application scenarios". However, most of the existing unmanned aerial vehicles are powered by batteries, and the flight time is very short. If they encounter severe flight environments, such as: many obstacles during flight, excessive wind resistance and other factors, the flight time will be greatly reduced. The scale of the existing sensor network is getting bigger and bigger. Therefore, if a single UAV is used to collect data from all data sensing nodes in the sensor network, a large amount of data will be missing.
虽然，基于多无人机相互协同进行数据收集的方法，可以解决由于无人机能量受限而引起的数据缺失问题，但是这类方法却有很大的缺点。首先增加多个无人机进行数据收集无疑会大大的增加网络成本，其次现有的多无人机相互协作进行数据收集的方法，大都假设周围环境对无人机的影响很少甚至没有，然而这点在实际环境中是不能办到的。因此，基于多无人机相互协同的数据收集方法很难在现实中应用。Although the method of data collection based on multi-UAV cooperation can solve the problem of data loss caused by the limited energy of UAVs, but this method has great shortcomings. First of all, adding multiple UAVs for data collection will undoubtedly greatly increase the cost of the network. Secondly, the existing methods for multi-UAVs to cooperate with each other for data collection mostly assume that the surrounding environment has little or no influence on UAVs. However, This cannot be done in the actual environment. Therefore, the data collection method based on multi-UAV cooperation is difficult to apply in reality.
发明内容Contents of the invention
针对上述现有技术中存在的问题或缺陷，本发明的目的在于，针对基于无人机的数据收集方法中由于无人机能量受限而引起的数据缺失问题，提供一种无人机数据收集过程中的路径规划方法，综合考虑感知节点随时可能采集到的异常数据和环境中随时可能出现的障碍物，动态地规划无人机的航行路线，最终使得无人机在能量受限的情况下，收集到的数据具有最大的数据价值。In view of the problems or defects in the above-mentioned prior art, the object of the present invention is to provide a UAV data collection method for the problem of data loss caused by the energy limitation of the UAV in the UAV-based data collection method. The path planning method in the process comprehensively considers the abnormal data that may be collected by the sensing node at any time and the obstacles that may appear in the environment at any time, and dynamically plans the flight route of the UAV, and finally makes the UAV under the condition of limited energy. , the collected data has the greatest data value.
为了实现上述目的，本发明采用如下技术方案：In order to achieve the above object, the present invention adopts the following technical solutions:
一种无人机数据收集过程中的路径规划方法，包括以下步骤：A path planning method in the data collection process of unmanned aerial vehicles, comprising the following steps:
步骤一，获取布设在传感网中的每个数据感知节点的物理位置；Step 1, obtaining the physical location of each data-aware node deployed in the sensor network;
步骤二，在无人机上搭载有数据收集节点，所述的数据收集节点为UAV节点，UAV节点中设置有数据收集任务队列DCTQ，数据收集任务队列DCTQ包含多条数据收集任务，每条数据收集任务表示收集传感网中一个数据感知节点的数据；Step 2, the UAV is equipped with a data collection node, the data collection node is a UAV node, and the UAV node is provided with a data collection task queue DCTQ, and the data collection task queue DCTQ includes multiple data collection tasks, each data collection task The task means collecting data of a data-aware node in the sensor network;
步骤三：计算数据收集任务队列DCTQ中每个任务所对应的收集效益，找出数据收集任务队列DCTQ中收集效益最大的任务，将该任务所对应的数据感知节点作为数据收集的目标节点；Step 3: Calculate the collection benefit corresponding to each task in the data collection task queue DCTQ, find out the task with the largest collection benefit in the data collection task queue DCTQ, and use the data sensing node corresponding to the task as the target node for data collection;
步骤四：无人机根据数据收集的目标节点的物理位置，朝着目标节点飞行，若飞行途中遇到障碍物则返回步骤三，若飞行途中未遇到障碍物，则无人机到达目标节点，采集到目标节点的数据，并将据收集任务队列DCTQ中已经完成的任务删除；Step 4: The UAV flies towards the target node according to the physical position of the target node collected by the data. If it encounters obstacles during the flight, it returns to Step 3. If no obstacles are encountered during the flight, the UAV arrives at the target node , collect the data of the target node, and delete the completed tasks in the data collection task queue DCTQ;
步骤五：如果无人机能量充足，且整体区域的数据采集任务还没有完成，则返回步骤三得到下一个数据收集的目标节点，直到无人机能量消耗完或整体区域的数据采集任务完成，无人机返回服务站。Step 5: If the energy of the UAV is sufficient, and the data collection task of the whole area has not been completed, return to Step 3 to get the target node of the next data collection, until the energy of the UAV is exhausted or the data collection task of the whole area is completed, The drone returns to the service station.
具体地，所述步骤三中的计算数据收集任务队列DCTQ中每个任务所对应的收集效益，具体方法如下：Specifically, the calculation of the collection benefit corresponding to each task in the data collection task queue DCTQ in the step 3 is as follows:
对于数据收集任务队列DCTQ中的第i条任务，其对应的收集效益计算公式如下：For the i-th task in the data collection task queue DCTQ, the corresponding collection benefit calculation formula is as follows:
其中，表示在数据收集过程中异常数据的出现概率，Ci表示第i条任务所对应的数据价值，PUAV表示UAV节点的实时位置，表示第i条任务中要对其数据进行采集的数据感知节点ni的位置，||PUAV-Pni||表示UAV节点和数据感知节点ni的直线距离，表示节点执行第i条任务的直接投入产出比的数学期望；函数表示，如果UAV节点执行完第i条任务的时候，UAV节点任务队列中所有任务投入产出比的最大值，因此则表示执行第i条任务所对应的间接最大投入产出比的数学期望。in, Indicates the occurrence probability of abnormal data during the data collection process, Ci indicates the data value corresponding to the i-th task, PUAV indicates the real-time position of the UAV node, Indicates the position of the data-aware node ni whose data is to be collected in the i-th task, ||PUAV -Pni || indicates the straight-line distance between the UAV node and the data-aware node ni , Indicates the mathematical expectation of the direct input-output ratio of the node performing the i-th task; the function Indicates that if the UAV node completes the i-th task, the maximum input-output ratio of all tasks in the UAV node task queue, so Then it represents the mathematical expectation of the indirect maximum input-output ratio corresponding to the execution of the i-th task.
具体地，所述步骤一中的获取传感网中每个数据感知节点的物理位置，具体的获取方法为在部署传感网中的数据感知节点时人工记录，或者利用节点定位技术获取。Specifically, the physical location of each data-aware node in the sensor network is acquired in the first step, and the specific acquisition method is to manually record when deploying the data-aware node in the sensor network, or to obtain it by using node positioning technology.
与现有技术相比，本发明具有以下技术效果：Compared with the prior art, the present invention has the following technical effects:
1、本发明为无人机节点挑选当前情况下投入产出比最高的节点，作为目的地来收集数据。因此，相比现有技术本发明则会更好的利用无人机的能量，收集更多的有“价值”的数据。1. The present invention selects the node with the highest input-output ratio in the current situation for the UAV node, and collects data as the destination. Therefore, compared with the prior art, the present invention can make better use of the energy of the drone and collect more "valuable" data.
2、随着无人机节点初始化总能量的增加，本发明中无人机节点收集到的数据可以更快的趋近于网络中的总数据价值。在无人机节点收集到的数据趋近于网络中的总数据价值之前，本发明中无人机节点在每次执行任务的时候都是以选择投入产出比最高的任务来执行，而现有技术则总是选择最省时的任务来执行，因此总体来看本发明中的无人机节点将会收集到更多有价值的数据。2. With the increase of the total energy of UAV node initialization, the data collected by UAV nodes in the present invention can approach the total data value in the network faster. Before the data collected by the UAV node approaches the total data value in the network, the UAV node in the present invention performs the task by selecting the task with the highest input-output ratio every time it performs a task, but now The technology always chooses the most time-saving task to execute, so generally speaking, the UAV node in the present invention will collect more valuable data.
下面结合附图和具体实施方式对本发明的方案做进一步详细的解释和说明。The solutions of the present invention will be further explained and described in detail below in conjunction with the accompanying drawings and specific embodiments.
附图说明Description of drawings
图1是本发明的流程图；Fig. 1 is a flow chart of the present invention;
图2是室外环境数据集数据字段示意图；Fig. 2 is a schematic diagram of the data field of the outdoor environment dataset;
图3是障碍物不同出现概率下TSP类算法数据总价值比较；Figure 3 is a comparison of the total value of TSP algorithm data under different occurrence probabilities of obstacles;
图4是无人机初始能量不同情况下TSP与DG类算法与DG算法数据总价值比较；Figure 4 is a comparison of the total data value of the TSP and DG algorithms and the DG algorithm under different initial energy conditions of the UAV;
具体实施方式detailed description
遵从上述技术方案，参见图1，本发明的无人机数据收集过程中的路径规划方法，包括以下步骤：Comply with above-mentioned technical scheme, referring to Fig. 1, the path planning method in the unmanned aerial vehicle data collection process of the present invention, comprises the following steps:
步骤一，获取布设在传感网中的每个数据感知节点的物理位置；具体的获取方法为在部署传感网中的数据感知节点时人工记录，或者利用节点定位技术获取。Step 1. Acquire the physical location of each data sensing node deployed in the sensor network; the specific acquisition method is to manually record when deploying the data sensing nodes in the sensor network, or to obtain using node positioning technology.
步骤二，在无人机上搭载数据收集节点，即UAV节点，UAV节点上设置有包含数据收集任务队列DCTQ，数据收集任务队列DCTQ包含多条数据收集任务，每条数据收集任务表示收集传感网中一个数据感知节点的数据。Step 2, the UAV is equipped with a data collection node, that is, a UAV node, and the UAV node is provided with a data collection task queue DCTQ, which contains multiple data collection tasks, and each data collection task represents the collection of sensor network The data of a data-aware node in .
步骤三：计算数据收集任务队列DCTQ中每个任务所对应的收集效益，找出此时数据收集任务队列DCTQ中收集效益最大的任务，该任务所对应的数据感知节点作为数据收集的目标节点。对于数据收集任务队列DCTQ中的第i条任务，其对应的收集效益可以按照如下公式计算得到：Step 3: Calculate the collection benefit corresponding to each task in the data collection task queue DCTQ, find out the task with the largest collection benefit in the data collection task queue DCTQ at this time, and the data sensing node corresponding to the task is the target node of data collection. For the i-th task in the data collection task queue DCTQ, its corresponding collection benefit can be calculated according to the following formula:
其中，表示在数据收集过程中异常数据的出现概率，Ci表示第i条任务所对应的数据价值，PUAV表示UAV节点的实时位置，表示第i条任务中要对其数据进行采集的数据感知节点ni的位置，||PUAV-Pni||表示UAV节点和数据感知节点ni的直线距离，因此，表示节点执行第i条任务的直接投入产出比的数学期望。函数表示，如果UAV节点执行完第i条任务的时候，UAV节点任务队列中所有任务投入产出比的最大值，因此则表示执行第i条任务所对应的间接最大投入产出比的数学期望。所以，公式(1)总体表示执行某个数据收集任务即收集某个节点的数据，所带来的直接投入产出比和最大间接投入产出比的数学期望之和。in, Indicates the occurrence probability of abnormal data during the data collection process, Ci indicates the data value corresponding to the i-th task, PUAV indicates the real-time position of the UAV node, Indicates the position of the data-aware node ni whose data is to be collected in the i-th task, ||PUAV -Pni || indicates the straight-line distance between the UAV node and the data-aware node ni , therefore, Indicates the mathematical expectation of the direct input-output ratio of the node performing the i-th task. function Indicates that if the UAV node completes the i-th task, the maximum input-output ratio of all tasks in the UAV node task queue, so Then it represents the mathematical expectation of the indirect maximum input-output ratio corresponding to the execution of the i-th task. Therefore, formula (1) generally represents the sum of the mathematical expectations of the direct input-output ratio and the maximum indirect input-output ratio brought about by performing a certain data collection task, that is, collecting the data of a certain node.
本发明引入“收集效益”CG(Collection Gain:CG)这一概念。CG在物理意义上表示，UAV节点在当前状况下，收集一个节点的数据所带来的直接投入产出比和最大间接投入产出比的数学期望之和。它直接反映UAV节点的能量使用效率。因此，如果一条数据收集任务所对应的CG值越高，则UAV节点认为执行该任务的优先级越高。The present invention introduces the concept of "collection gain" CG (Collection Gain: CG). In a physical sense, CG represents the sum of the mathematical expectations of the direct input-output ratio and the maximum indirect input-output ratio brought about by the UAV node collecting the data of a node under the current situation. It directly reflects the energy usage efficiency of UAV nodes. Therefore, if the CG value corresponding to a data collection task is higher, the UAV node considers that the priority of executing the task is higher.
上述步骤可用如下代码表示：The above steps can be represented by the following code:
步骤四：无人机根据数据收集的目标节点的物理位置，朝着数据收集的目标节点飞行，若飞行途中遇到障碍物则返回步骤三重新找出数据收集任务队列DCTQ中收集效益最大的任务，若飞行途中未遇到障碍物，则无人机到达数据收集的目标节点，采集到目标节点的数据，并将据收集任务队列DCTQ中已经完成的任务删除，即将数据收集任务队列DCTQ中收集效益最大的任务删除。Step 4: According to the physical position of the target node of data collection, the UAV flies towards the target node of data collection. If obstacles are encountered during the flight, return to step 3 to find out the task with the greatest collection benefit in the data collection task queue DCTQ , if no obstacles are encountered during the flight, the UAV will reach the target node of data collection, collect the data of the target node, and delete the completed tasks in the data collection task queue DCTQ, that is, collect the data in the data collection task queue DCTQ The task with the greatest benefit is deleted.
步骤五：如果无人机能量充足，且整体区域的数据采集任务还没有完成，则返回步骤三得到下一个数据收集的目标节点，该下一个数据收集的目标节点为将步骤四中的数据收集任务队列DCTQ中收集效益最大的任务删除后，重新计算得到的数据收集任务队列DCTQ中收集效益最大的任务，直到无人机能量消耗完或整体区域的数据采集任务完成，无人机返回服务站。Step 5: If the energy of the UAV is sufficient and the data collection task of the whole area has not been completed, return to step 3 to get the target node of the next data collection, which is the data collection in step 4 After the task with the greatest collection benefit in the task queue DCTQ is deleted, the task with the greatest collection benefit in the data collection task queue DCTQ is recalculated until the energy of the UAV is exhausted or the data collection task of the entire area is completed, and the UAV returns to the service station .
对节点数据收集的路径规划问题，其物理意义在于：当UAV节点处于数据收集工作最开始的时候，UAV节点遇到障碍物改变已有航行路线的时候，UAV节点接收到异常感知数据收集请求的时候，UAV节点执行完某个数据收集任务的时候，UAV节点根据其数据收集任务队列DCTQ，如何选择数据收集目的节点的问题。UAV节点数据收集的目标节点，应该满足以下特点：距离UAV节点当前位置较近；此节点具有较大的数据价值；收集完此节点的数据之后，下一个目标节点距离此节点也较近。The physical meaning of the path planning problem for node data collection is: when the UAV node is at the beginning of the data collection work, when the UAV node encounters an obstacle and changes the existing navigation route, the UAV node receives an abnormal perception data collection request. When the UAV node completes a certain data collection task, the UAV node selects the data collection destination node according to its data collection task queue DCTQ. The target node for UAV node data collection should meet the following characteristics: it is closer to the current location of the UAV node; this node has greater data value; after collecting the data of this node, the next target node is also closer to this node.
仿真实验Simulation
1、无人机数据收集方法仿真参数介绍1. Introduction of simulation parameters of UAV data collection method
首先，本发明使用的数据集是部署在榆林镇北台长城遗址内的41个数据感知节点在2015年12月1日到2015年12月20日之间所采集的数据。这41个节点底层采用CC2530硬件结构，负责采集长城遗址土壤内部15cm深度的温度值。数据采集的范围是-20℃-40℃之间。具体的数据格式如图2所示：First of all, the data set used in the present invention is the data collected by 41 data sensing nodes deployed in the Beitai Great Wall site in Yulin Town between December 1, 2015 and December 20, 2015. The bottom layer of these 41 nodes adopts the CC2530 hardware structure, which is responsible for collecting the temperature value at a depth of 15cm inside the soil of the Great Wall ruins. The range of data collection is between -20°C and 40°C. The specific data format is shown in Figure 2:
第一个字段Nodeid，表示采集这条数据的节点所对应的节点编号第二个字段SenseTime，表示节点采集这条数据的时间，第三个字段StoreTime，表示数据库存储这条数据的时间，第四个字段Data，表示节点采集的15cm深度的土壤内部温度值。The first field, Nodeid, indicates the node number corresponding to the node that collected this data. The second field, SenseTime, indicates the time when the node collected this data. The third field, StoreTime, indicates the time when the database stored this data. The fourth A field Data, which represents the internal temperature value of the soil at a depth of 15cm collected by the node.
采用应用误差为0.01时，网络中的数据关键性节点和其相应的覆盖范围。并且在节点数据收集之前，它已经获取了数据关键性节点的所有信息，以及网络中所有数据感知节点的地理位置。根据现实环境中的情况，在仿真中本文假设节点每航行一个单位长度耗费0.02个单位能量，在航行的过程中总是以单位时间内航行1.6个单位长度的速度匀速运行。本文假设，节点在数据收集的过程中每躲避一次障碍物所耗费的能量在0.02-0.04个单位能量之间，偏离航线的范围在2个单位长度之内。本文假设所要涉及的监测环境总体上处于平稳的状态。因此，数据感知节点不会频繁的采集到异常感知数据，即使采集到的异常感知数据其异常度也不会很高，因此在仿真中本文将异常感知数据的数据价值取值范围设为5-10之间，异常感知数据出现的概率为10％。When the application error is 0.01, the data-critical nodes in the network and their corresponding coverage areas. And before the node data is collected, it has obtained all the information of the data-critical nodes, as well as the geographic location of all data-aware nodes in the network. According to the situation in the real environment, in the simulation, it is assumed that the node consumes 0.02 unit energy per unit length of voyage, and always runs at a constant speed of 1.6 unit length per unit time during the voyage process. In this paper, it is assumed that in the process of data collection, the energy consumed by the node to avoid obstacles every time is between 0.02-0.04 unit energy, and the range of deviation from the route is within 2 unit lengths. This paper assumes that the monitoring environment to be involved is generally in a stable state. Therefore, data sensing nodes will not frequently collect abnormal sensing data, even if the abnormality of the collected abnormal sensing data is not very high, so in the simulation, this paper sets the data value range of abnormal sensing data to 5- Between 10, the probability of abnormal perception data appearing is 10%.
2、算法仿真结果分析2. Analysis of algorithm simulation results
当UAV节点在数据收集过程中，利用本文提出的DG算法与TSP类算法分别进行路径规划时，比较UAV节点最终收集到的数据所具有总数据价值。由于仿真过程中，引入了随机函数来刻画异常数据和障碍物出现的概率，所以针对每种情况下的结果，本文都是将仿真程序运行了20次，并取所有运行结果的平均值作为最终的结果来展示。When the UAV node uses the DG algorithm and the TSP algorithm proposed in this paper to plan the path in the process of data collection, compare the total data value of the data finally collected by the UAV node. In the simulation process, a random function is introduced to describe the probability of abnormal data and obstacles, so for the results in each case, this paper runs the simulation program 20 times, and takes the average of all the running results as the final results to display.
图3是DG算法与TSP类算法在数据收集过程中遇到障碍物概率不同的情况下，UAV节点收集的数据所对应的数据总价值的比较图。图中的紫红色曲线和蓝色曲线分别是UAV节点使用DG算法和TSP类算法最终的仿真结果。从图的整体来看，两种算法在障碍物出现概率增加的时候，UAV节点收集到的数据，其具有的总价值都会降低。当障碍物出现的概率增加到一定值的时，UAV节点收集到的数据所具有的总价值趋近于0，也就是说这个时候UAV节点将几乎收集不到数据。造成这种情况的原因有两点：第一当障碍物出现的概率增加的时候，UAV节点会消耗过多的能量用来躲避障碍物。单从收集数据的角度来看，这些消耗的能量则是被白白浪费掉了，真正用于数据收集的有效能量则急剧减少，因此无论基于那种方法最终的总数据价值都会减少。第二当障碍物出现的概率增加时，UAV节点会由于躲避障碍物而频繁的偏离当前的数据收集的航线，因此对于UAV节点来说它会走很多的“冤枉路”，白白的消耗很多能量，最终也是因为用于数据收集的有效能量减少，从而收集到的数据总价值变少。Figure 3 is a comparison diagram of the total data value corresponding to the data collected by the UAV node when the DG algorithm and the TSP algorithm have different probability of encountering obstacles during the data collection process. The fuchsia curve and the blue curve in the figure are the final simulation results of the UAV node using the DG algorithm and the TSP algorithm respectively. From the overall point of view of the figure, when the probability of the occurrence of obstacles increases for the two algorithms, the total value of the data collected by the UAV node will decrease. When the probability of the occurrence of obstacles increases to a certain value, the total value of the data collected by the UAV node tends to 0, that is to say, the UAV node will hardly collect data at this time. There are two reasons for this situation: first, when the probability of an obstacle increases, the UAV node will consume too much energy to avoid the obstacle. From the perspective of data collection alone, the energy consumed is wasted, and the effective energy actually used for data collection is drastically reduced, so no matter which method is used, the final total data value will decrease. Second, when the probability of obstacles increases, the UAV node will frequently deviate from the current data collection route due to avoiding obstacles, so for the UAV node, it will take a lot of "wrong roads" and consume a lot of energy in vain , and ultimately because the available energy for data collection is reduced, the total value of the collected data is reduced.
从图3中也可以清楚的看到，在大部分的情况下DG算法总是优于TSP类算法。这是因为TSP类算法的核心思想是让节点的数据尽可能早的被收集完。因此在对UAV节点的路径规划中，总是挑选与当前位置相近的节点作为目的地来收集数据，在数据收集的过程中完全没有数据价值的概念。而DG算法的核心的思想是为UAV节点挑选当前情况下投入产出比最高的节点，作为目的地来收集数据。因此，相比TSP类算法DG算法则会更好的利用自己的能量，收集更多的有“价值”的数据。但是当障碍物出现的概率增加到一定量的时候，TSP算法则优于DG算法。这是因为，当障碍物出现概率非常大的时候，UAV节点会频繁的偏离航线，进而有可能改变数据收集的目的地，这个时候UAV节点的运动状态趋近于随机状态。因此，UAV节点只会“碰巧”地出现在某个节点的附近。这个时候TSP类的算法则会收集该节点的数据。而对于DG算法，如果“碰巧”这个节点正好是未被收集的数据关键性节点，那么该节点的数据就会被收集。而当这个节点是数据平凡节点的时候，对于DG算法来说除非该数据平凡节点采集的是异常数据，它才会收集，如果是正常数据，它将不会理睬。综上所述，当障碍物出现概率增加的时候DG算法“碰巧”遇到可以收集数据的节点的概率将会非常小，此时利用DG算法UAV节点将几乎收集不到数据，所以在障碍物出现概率增加到一定量的时候TSP类的算法将优于DG算法。It can also be clearly seen from Figure 3 that the DG algorithm is always better than the TSP algorithm in most cases. This is because the core idea of the TSP algorithm is to allow the data of the nodes to be collected as early as possible. Therefore, in the path planning of UAV nodes, the node close to the current position is always selected as the destination to collect data, and there is no concept of data value in the process of data collection. The core idea of the DG algorithm is to select the node with the highest input-output ratio in the current situation for the UAV node, as the destination to collect data. Therefore, compared with the TSP algorithm, the DG algorithm will make better use of its own energy and collect more "valuable" data. But when the probability of obstacles increases to a certain amount, the TSP algorithm is better than the DG algorithm. This is because, when the probability of the occurrence of obstacles is very high, the UAV node will frequently deviate from the route, which may change the destination of data collection. At this time, the motion state of the UAV node tends to be random. Therefore, UAV nodes will only appear in the vicinity of a certain node by "coincidentally". At this time, the algorithm of the TSP class will collect the data of the node. For the DG algorithm, if the node "coincidentally" happens to be a critical data node that has not been collected, then the data of the node will be collected. And when this node is a data ordinary node, for the DG algorithm, unless the data ordinary node collects abnormal data, it will collect it. If it is normal data, it will ignore it. To sum up, when the probability of obstacles increases, the probability that the DG algorithm "coincidentally" encounters a node that can collect data will be very small. At this time, the UAV node using the DG algorithm will hardly collect data. When the occurrence probability increases to a certain amount, the TSP algorithm will be better than the DG algorithm.
图4是当节点初始化能量不同的情况下，分别利用DG算法与TSP类的算法来规划路径，最终收集到的数据所具有的总价值的比较图。图中紫红色曲线是DG算法的仿真结果，图中蓝色的曲线是TSP类算法的仿真结果。从图中可以明显的看出，随着UAV节点初始化总能量的增加，利用两种算法收集到的数据其总数据价值都会增加。当UAV节点总能量增加到一定值μ的时候，利用两种算法收集到的数据都趋近于网络中的总数据价值Ω，此时当UAV节点初始化能量继续增加的时候，两者的结果都基本保持在Ω左右不变。(在本仿真环境下，网络中理论的数据价值总和Ω＝42)。这是因为当UAV节点的总能量很小的时候无论是DG算法还是TSP类算法都不能保证UAV节点能完成自己任务队列中的所有数据收集任务。当UAV节点的总能量变大的时候UAV节点执行任务的“能力”则得到加强，因此其最终收集到的数据总价值会变高。但是当UAV节点的能量小于μ之前，相同的初始化总能量下，DG算法明显优于TSP类算法。这是因为在能量小于μ之前，虽然两者都没有能力完全执行完任务队列中的任务，但是DG算法在每次执行任务的时候都是以选择投入产出比最高的任务来执行，而TSP类的算法则总是选择最省时的任务来执行，因此总体来看DG算法将会收集到更多有价值的数据。Figure 4 is a comparison diagram of the total value of the collected data when the node initialization energy is different, using the DG algorithm and the TSP algorithm to plan the path respectively. The purple-red curve in the figure is the simulation result of the DG algorithm, and the blue curve in the figure is the simulation result of the TSP algorithm. It can be clearly seen from the figure that with the increase of the total energy of UAV node initialization, the total data value of the data collected by the two algorithms will increase. When the total energy of the UAV node increases to a certain value μ, the data collected by the two algorithms are close to the total data value Ω in the network. At this time, when the initialization energy of the UAV node continues to increase, the results of both methods are equal to Basically remain unchanged at around Ω. (In this simulation environment, the theoretical data value sum in the network Ω=42). This is because neither the DG algorithm nor the TSP algorithm can guarantee that the UAV node can complete all the data collection tasks in its task queue when the total energy of the UAV node is small. When the total energy of the UAV node becomes larger, the "ability" of the UAV node to perform tasks is enhanced, so the total value of the data it finally collects will become higher. But when the energy of the UAV node is less than μ, under the same initial total energy, the DG algorithm is obviously better than the TSP algorithm. This is because before the energy is less than μ, although neither of them has the ability to fully execute the tasks in the task queue, the DG algorithm selects the task with the highest input-output ratio to execute each time the task is executed, while TSP Algorithms of the class always choose the most time-saving tasks to execute, so overall, the DG algorithm will collect more valuable data.
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| CN201610537734.2ACN106203697A (en) | 2016-07-08 | 2016-07-08 | A kind of paths planning method during Unmanned Aerial Vehicle Data collection |
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