Semi-dynamic path planning and positioning method based on multi-target particle swarm optimization

Abstract

The invention provides a semi-dynamic path planning and positioning method based on multi-target particle swarm optimization. The method comprises the following steps: step 1, determining a to-be-positioned area; step 2, performing node deployment on the to-be-positioned area according to a preset node deployment mode and a node number; 3, designing a moving track of an anchor node by adopting a static path planning method, and positioning an unknown node according to the position of the anchor node; and 4, aiming at unknown nodes which are not successfully positioned, optimizing the moving tracks of the anchor nodes by adopting a particle swarm algorithm, and positioning the unknown nodes according to the positions of the anchor nodes. The method can effectively improve the positioning precision and the positioning coverage rate of the unknown node.

Description

Technical Field

[0001] The invention relates to the technical field of wireless sensor network node positioning, and in particular to a semi-dynamic path planning positioning method based on multi-objective particle swarm optimization. Background Art

[0002] Location-Based Services (LBS) play a vital role in modern life. Whether in emergency rescue, target tracking, environmental monitoring and other fields, they play an important role and bring convenience to people's lives and work. Although the existing global positioning system (GPS) based on location services can achieve target positioning under certain conditions, in certain specific environments, such as deep forests, remote areas, indoor environments, etc., the satellite signal may be blocked or interfered, resulting in reduced positioning accuracy or inability to locate. The Global Navigation Satellite System (GNSS), as an extension and supplement to GPS, has improved global coverage and positioning accuracy to a certain extent, but it is also limited by signal propagation and reception conditions. The performance of GNSS may also be affected in extreme weather, electromagnetic interference or equipment failure. Therefore, it may not work properly in natural disasters or emergency rescue operations.

[0003] As an important part of modern information acquisition and processing, wireless sensor network node positioning technology is increasingly becoming a key link in achieving accurate perception and intelligent decision-making. Wireless sensor networks are widely used in natural disasters or emergency rescue operations due to their flexibility, adaptability, low power consumption, and low cost. Through wireless sensor network node positioning, the specific location of trapped people can be tracked and determined in real time, providing accurate guidance for rescuers and greatly improving rescue efficiency. At the same time, it also helps to timely discover potential safety hazards and risk areas, provide early warning information for rescuers, and ensure their safety.

[0004] The mainstream methods of existing wireless sensor network node positioning are mainly divided into positioning methods based on static path planning and positioning methods based on dynamic path planning. Although the static path planning positioning method has low computational complexity and high positioning efficiency, its path is fixed. When faced with emergencies or dynamic changes in network nodes, it may not be able to effectively locate; at the same time, when the nodes are sparsely distributed, there may be problems with redundant path overhead. Although the dynamic path planning positioning method has the advantages of high flexibility, strong adaptability, and real-time response, it requires the collection, processing, and analysis of real-time environmental information during the positioning process, which makes its computational complexity high and consumes more computing resources and energy. This will lead to a decrease in positioning performance or an increase in node energy consumption, thereby affecting the accuracy of the positioning results. Summary of the invention

[0005] In view of the problem that existing path planning methods are difficult to adapt to complex and changeable environments, resulting in low positioning accuracy and positioning coverage of unknown nodes, the present invention provides a semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization.

[0006] The present invention provides a semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization, comprising:

[0007] Step 1: Determine the area to be located;

[0008] Step 2: Deploy nodes on the area to be located according to a preset node deployment method and node quantity;

[0009] Step 3: Use static path planning method to design the moving trajectory of the anchor node and locate the unknown node according to the position of the anchor node;

[0010] Step 4: For unknown nodes that have not been successfully located, the particle swarm algorithm is used to optimize the movement trajectory of the anchor node and locate the unknown node according to the position of the anchor node.

[0011] Furthermore, in step 3, a static path planning method is used to design a moving trajectory of the anchor node, specifically including: setting two anchor nodes so that the two anchor nodes move along a preset triangular wave trajectory from two opposite ends of the area to be positioned.

[0012] Furthermore, step 4 specifically includes:

[0013] Step 4.1: Taking the unknown nodes that have not been successfully located as targets, generating at least 3 particles for each target, each particle representing a potential position of an anchor node, and setting the parameters of the particle swarm optimization algorithm, the parameters including the inertia weight and the learning factor;

[0014] Step 4.2: Calculate the Euclidean distance between each particle and the target, and use the Euclidean distance as the fitness value of the particle;

[0015] Step 4.3: Update the individual optimal solution pbest and the global optimal solution gbest according to the fitness values ​​of all particles;

[0016] Step 4.4: Update the speed and position of the particle according to the current individual optimal solution, global optimal solution and the parameters of the particle swarm optimization algorithm;

[0017] Step 4.5: Repeat steps 4.2 to 4.4 until the termination condition is met;

[0018] Step 4.6: Output the coordinate positions of all particles.

[0019] Furthermore, it also includes: when the anchor node encounters an obstacle during movement, a preset obstacle handling mechanism is used to avoid the obstacle;

[0020] The preset obstacle handling mechanism includes: a regular obstacle handling mechanism and an irregular obstacle handling mechanism; wherein the regular obstacle handling mechanism includes: according to the relative position between the original path of the anchor node and the obstacle, selecting a side of the obstacle that is closer to the original path, and making the anchor node detour along the side until it returns to the original path; the irregular obstacle handling mechanism includes: constructing a regular shape for the irregular obstacle, and then processing it according to the regular obstacle handling mechanism.

[0021] Furthermore, the positioning of the unknown node according to the position of the anchor node specifically includes: when the unknown node receives beacon information broadcast by at least three non-collinear anchor nodes, obtaining the signal strength value RSSI with the anchor node, and positioning the unknown node based on the RSSI ranging algorithm.

[0022] Furthermore, it also includes: using the least square method to correct the positioning error of the positioning result.

[0023] Furthermore, step 1 specifically includes:

[0024] Define the area boundary of the area to be located and analyze the area characteristics of the area to be located; wherein, analyzing the area characteristics of the area to be located includes analyzing whether the shape of the area to be located is regular and analyzing the size, shape and position of obstacles.

[0025] Furthermore, the node deployment method includes regular deployment and random deployment.

[0026] Beneficial effects of the present invention:

[0027] (1) The present invention combines static planning and particle swarm algorithm. The static path planning method is first used to design the moving trajectory of the anchor node to realize the positioning of the unknown node. Then, for the unknown node that has not been successfully positioned, the particle swarm algorithm is used to optimize the moving trajectory of the anchor node, which can ultimately improve the positioning performance of the unknown node.

[0028] (2) The present invention designs a mobile anchor node positioning algorithm based on a triangular wave trajectory. The algorithm uses two anchor nodes to move simultaneously to locate unknown nodes, which not only improves the positioning efficiency of unknown nodes, but also increases the fault tolerance of anchor nodes. At the same time, by analyzing the location distribution information of unknown nodes that have not been successfully located, an optimization algorithm is used to update the moving path of the anchor node to improve the positioning performance of the unknown node.

[0029] (3) A new obstacle handling mechanism is proposed. Different handling strategies are adopted for different types of obstacles. The optimized handling mechanism can effectively plan a path to avoid obstacles, reduce unnecessary detours and delays, and thus improve positioning efficiency.

[0030] (4) This invention analyzes the location distribution information of unknown nodes that have not been successfully located, uses the particle swarm optimization algorithm to update the movement trajectory of the anchor node, optimizes the processing mechanism of different obstacles in the positioning area, and finally uses the least squares method to determine the location of the unknown node. Experimental results show that this method has higher positioning accuracy and positioning coverage than the existing methods, and provides a new solution for high-precision wireless sensor network node positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic diagram of a process flow of a semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of an obstacle handling mechanism provided by an embodiment of the present invention;

[0033] Figure 3 The impact of the number of nodes on the positioning algorithm provided by the embodiment of the present invention;

[0034] Figure 4 This embodiment of the present invention provides an impact of the communication radius on the positioning algorithm. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, an embodiment of the present invention provides a semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization, comprising the following steps:

[0037] S101: Determine the area to be located;

[0038] Specifically, determining the area to be located mainly includes two aspects: one is to define the area boundary; the other is to analyze the regional characteristics. In terms of defining the area boundary, the boundary of the area to be located is determined according to the actual application requirements, and the length of the area to be located is set to L and the width is set to W. In terms of analyzing the regional characteristics, it is necessary to analyze whether the shape of the area is regular. The most discussed regional shapes are rectangular and C-shaped. At the same time, it is also necessary to analyze the size, shape and specific location of obstacles in order to better deploy nodes.

[0039] S102: Deploy nodes on the area to be located according to a preset node deployment mode and node quantity;

[0040] Specifically, the node deployment method mainly depends on the specific application scenario and monitoring requirements. Common deployment methods include regular deployment and random deployment. In regular deployment, nodes are deployed according to predetermined rules or patterns, such as a grid. This deployment method can ensure the uniform distribution of nodes, which is conducive to the connectivity and stability of the network. In random deployment, nodes are randomly scattered in the monitoring area. This deployment method is flexible and easy to implement. It is particularly suitable for situations where it is difficult to predetermine the node location or the monitoring area is complex and changeable. The number of nodes should be able to cover the entire monitoring area, while trying to ensure that each node can communicate effectively with its neighbors to maintain the connectivity of the network.

[0041] S103: Designing a moving trajectory of the anchor node using a static path planning method, and locating the unknown node according to the position of the anchor node;

[0042] Specifically, according to the characteristics of the area to be located and the node distribution, first select a suitable initial position as the starting point of the anchor node. This starting point should be able to cover the key area as quickly as possible; then design the moving trajectory of the anchor node. The trajectory should be able to cover the entire area to be located or the monitoring area, and try to ensure that each node can communicate effectively with the anchor node; finally, the obstacle handling mechanism must be considered to reduce unnecessary repeated coverage. Figure 2 As shown, an embodiment of the present invention designs a static path planning method for a triangular wave trajectory. In order to improve the positioning efficiency, the embodiment of the present invention sets two anchor nodes, so that the two anchor nodes move from two opposite ends of the area to be positioned according to a preset triangular wave trajectory.

[0043] S104: For unknown nodes that have not been successfully located, a particle swarm algorithm is used to optimize the movement trajectory of the anchor node, and the unknown node is located according to the position of the anchor node.

[0044] Specifically, in step S103, a static path planning method is used to design the mobile trajectory of the anchor node. The advantage of this method is that the path is pre-designed, which avoids real-time path calculation and frequent adjustment and optimization of the path, and helps to reduce computing overhead and maintenance costs. However, this method also has its inherent limitations. Since it does not take into account the real-time situation of the nodes in the network, once an obstacle is encountered, it may not be able to be flexibly adjusted, resulting in path failure. In addition, in a network environment with sparse node distribution, static path planning often generates unnecessary redundant paths, reducing the efficiency and practicality of the path. Therefore, in view of the limitations of static path planning, this step uses a particle swarm optimization algorithm to adjust the mobile trajectory of the anchor node.

[0045] In this embodiment, when locating an unknown node according to the position of an anchor node, the RSSI-based ranging algorithm is mainly used to locate the unknown node. Specifically, when the unknown node receives beacon information broadcast by at least three non-collinear anchor nodes, the signal strength value RSSI between the unknown node and the anchor node is obtained, and then the signal strength value is converted into the corresponding distance through the path loss model, as shown in formula (1).

[0046]

[0047] Among them, P r It is the signal strength received at a distance of dm from the anchor node, in dBm; P0 represents the signal strength received at a distance of d0m (d0 is usually 1m) from the anchor node; τ is the path loss exponent.

[0048] Furthermore, the RSSI-based ranging algorithm is affected by various environmental factors, so that the positioning result may have certain errors. Therefore, this embodiment also uses the least square method to correct the error.

[0049] In static path planning, the moving trajectory of the anchor node is pre-designed; and during the positioning process, the anchor node will move strictly according to the pre-designed trajectory. However, this method lacks flexibility and is difficult to adapt to changes in different environments or task requirements. In order to overcome this limitation, the present invention introduces a particle swarm optimization algorithm to dynamically optimize the moving trajectory of the anchor node. The particle swarm optimization algorithm is an evolutionary algorithm specifically used to solve continuous optimization problems. Its basic principle is to find the optimal solution to the problem by simulating the social behavior of bird flocks and using the collaboration and information sharing between individuals in the group. In wireless sensor networks, the particle swarm optimization algorithm can dynamically adjust the moving path of the anchor node to better adapt to different environments and task requirements. The semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization provided in an embodiment of the present invention analyzes the position distribution information of unknown nodes that have not been successfully positioned (i.e., nodes that cannot be successfully positioned when moving using the existing method or the trajectory designed in the present invention according to the static path planning method), and uses the particle swarm optimization algorithm to update the moving trajectory of the anchor node, which can improve the positioning performance of the unknown node.

[0050] On the basis of the above embodiments, the embodiment of the present invention provides a mechanism for handling obstacles. The embodiment of the present invention divides the shapes of major obstacles into two categories: one is regular obstacles; the other is irregular obstacles. Different processing mechanisms are adopted for obstacles of different shapes to ensure the robustness and positioning performance of the network. Regular obstacles generally refer to physical obstacles with clear geometric shapes, sizes, and relatively standardized structures. These obstacles have a certain impact on the propagation of wireless signals in the network and the communication between nodes, resulting in a reduction in the network coverage area, thereby weakening the network's monitoring capabilities and data collection capabilities. Therefore, the processing mechanism adopted by this embodiment for regular obstacles is, Figure 2 As shown in (a), according to the relative position of the original path of the anchor node and the obstacle, a side of the obstacle that is closer to the original path is selected, and the anchor node is made to detour along this side until it returns to the original path.

[0051] Irregular obstacles usually refer to obstacles that do not conform to conventional shapes, sizes, or structures. Due to their irregularity, identification and obstacle avoidance in areas such as node positioning and path planning become more challenging. In order to effectively handle these irregular obstacles, the processing mechanism adopted in this embodiment is as follows: Figure 2 As shown in (b): First, a regular shape (usually a rectangle by default) is constructed around the irregular obstacle; then it is processed according to the regular obstacle processing mechanism. Figure 2 (b) clearly shows an example of the application of this mechanism, where the dark gray area represents irregular obstacles. The figure also draws a rectangular boundary around the obstacle and details the movement trajectory of the anchor node when encountering such an obstacle.

[0052] In one embodiment, a particle swarm algorithm is used to optimize the movement trajectory of the anchor node. The specific process is as follows:

[0053] S401, initializing the particle swarm: taking the unknown nodes that have not been successfully located as targets, randomly generating at least 3-5 particles for each target, each particle represents a potential solution (i.e., the potential position of the anchor node) and has two attributes: position and velocity; setting the parameters of the particle swarm optimization algorithm, including the inertia weight and the learning factor.

[0054] S402, calculate fitness: for each particle, calculate the Euclidean distance between its position and the target as the fitness value of the particle, as shown in formula (2), and set the fitness threshold to T.

[0055]

[0056] where p i Represents the position of the particle, with coordinates (x i ,y i ), t j Indicates the location of the target, with coordinates (x j ,y j ).

[0057] S403, update individual optimal solution and global optimal solution: each particle searches for the optimal solution in the search space individually and records it as the individual extreme value (i.e., the Euclidean distance is small), denoted by pbest; then the individual extreme value is shared with other particles in the entire particle swarm, and the optimal individual extreme value is found as the current global optimal solution of the entire particle swarm, denoted by gbest.

[0058] S404, update speed and position: all particles in the particle swarm update the speed and position of the particles according to the current individual extreme value pbest found and the current global optimal solution gbest shared by the entire particle swarm, as well as parameters such as inertia weight and learning factor. The specific formula is as follows:

[0059] v i =w×v i +c1×rand()×(pbest i -p i )+c2×rand()×(gbest i -p i ) (3)

[0060] p i =p i +v i (4)

[0061] Among them, v i is the velocity of the particle, p iis the current position of the particle, c1 and c2 are learning factors, and w is the inertia weight, which is non-negative and is used to adjust the impact of the particle's current speed on the next move. When the value of w is large, the particle tends to maintain its current speed, which helps the algorithm to conduct extensive exploration in the search space, thereby enhancing the global optimization ability; when the value of w is small, it helps the algorithm to conduct a more detailed search in the area close to the optimal solution, enhancing the local optimization ability.

[0062] S405, iterative optimization: repeat steps S402-S404 until the termination condition is met, such as reaching the maximum number of iterations, the fitness value is less than a threshold, etc.

[0063] S406. Output the coordinate positions of all particles.

[0064] In order to verify the feasibility and effectiveness of the present invention, a large number of experiments were carried out in a simulation environment, and compared with typical algorithms SQUARE_SPIRAL, M-curves, LH, Z-curve, SSD and PP-MMAN.

[0065] In this experiment, the design of the anchor node's moving trajectory is mainly discussed from the perspective of communication range. The communication range of the anchor node directly determines the maximum distance that the anchor node can effectively transmit data. The moving step length, that is, the distance covered by each step of the anchor node during the movement, is also a key factor. The reasonable choice of step length will directly affect the size of the area that the anchor node can traverse within a specific time. In wireless sensor networks, the communication range of the anchor node has a significant impact on the positioning performance of unknown nodes. In the experiment, the positioning error and positioning coverage are mainly discussed in depth or the positioning performance of the algorithm is evaluated. Among them, the positioning error is a key indicator for evaluating the accuracy of the positioning algorithm. It indicates the deviation between the estimated position and the actual position; the positioning coverage reflects the coverage ability of the positioning algorithm for the entire network. It indicates the ratio of the number of unknown nodes that can be successfully located in the network to the total number of unknown nodes.

[0066] 1. Experimental setup

[0067] The experiment was simulated with the help of MATLAB platform. The simulation experiment was carried out in an area of ​​100m×100m. Among them, 50-300 unknown nodes were randomly deployed, the communication radius of the anchor node ranged from 5m to 40m, and the moving step of the anchor node was set to 6.25m, 12.5m, and 25m. The specific parameters are shown in Table 1.

[0068] Table 1 Experimental parameter settings

[0069]

[0070] (II) Experiment on the impact of the number of nodes on the positioning algorithm

[0071] The number of nodes refers to the sum of the number of anchor nodes and unknown nodes. When the number of anchor nodes is fixed, in order to better study the impact of the number of nodes on the positioning algorithm, we conducted a series of experiments, setting the communication radius of the anchor node to 6.25m and gradually increasing the number of unknown nodes from 50 to 300. The experimental results are evaluated by two key indicators: the positioning error and positioning coverage of the algorithm, so as to more accurately understand the impact of the number of nodes on the positioning algorithm. The positioning error and positioning coverage of the algorithm are as follows: Figure 3 shown.

[0072] from Figure 3 It is observed that with the increase in the number of unknown nodes, the positioning error shows an overall downward trend. It is worth noting that the method proposed in the present invention performs better than other comparative algorithms in terms of positioning error. Among them, the positioning errors of the SQUARE_SPIRAL algorithm and the SSD algorithm increase slightly with the increase in the number of unknown nodes. There may be two main reasons for this phenomenon: first, with the increase in the number of unknown nodes, the complexity of the network also increases, resulting in more frequent communication conflicts and interferences between nodes, which in turn increases the uncertainty in the positioning process and increases the positioning error; second, the insufficient number of anchor nodes makes it difficult for unknown nodes to obtain sufficient beacon information for accurate positioning, thereby increasing the positioning error.

[0073] The positioning coverage of the LH algorithm in the figure is relatively low, which is mainly because the distance between adjacent anchor nodes in its trajectory design is large, resulting in the beacon information received by the unknown node being insufficient to support its positioning. In contrast, the positioning coverage of the SSD algorithm and the PP-MMAN algorithm is also low, which is attributed to the fact that their moving trajectories fail to cover the boundaries of the positioning area and do not consider the situation where the unknown nodes located in the boundary area receive insufficient beacon information. The SQUARE_SPIRAL algorithm, the M-curves algorithm, and the Z-curve algorithm perform relatively well in terms of positioning coverage, thanks to their ability to cover more potential locations of anchor nodes in the moving trajectory design. However, these algorithms do not fully consider the situation where the unknown nodes at the boundary receive insufficient beacon information, so their positioning coverage fails to reach 100%. The method proposed in the present invention dynamically adjusts the moving trajectory of the anchor node according to the distribution of the unknown nodes at the boundary by introducing the particle swarm optimization algorithm, thereby achieving a 100% positioning coverage. In short, when the number of anchor nodes is constant, the number of nodes will affect the positioning coverage of the algorithm.

[0074] (III) Experiment on the impact of communication radius on positioning algorithm

[0075] (1) Positioning error

[0076] In theory, when the communication range of the anchor node increases, the number of anchor node signals that the unknown node can receive increases, which seems to improve positioning accuracy. However, this is not the case in reality. As the communication range of the anchor node increases, the signal strength will gradually attenuate, which may increase the uncertainty of the distance estimation. Especially in the positioning method based on RSSI ranging, since the relationship between signal attenuation and distance is nonlinear, this attenuation will lead to an increase in positioning error. At the same time, the increased communication range makes the signal more susceptible to interference from environmental factors, such as signal attenuation caused by obstructions, multipath effects, etc. These interference factors will further reduce the accuracy and reliability of positioning. For the specific experimental verification process, please refer to Figure 4 .

[0077] (2) Positioning coverage

[0078] The communication range of anchor nodes has a direct impact on the positioning coverage of the network. Properly increasing the communication range of anchor nodes can effectively expand the network coverage, allowing more distant unknown nodes to establish communication links with them, thereby reducing the number of anchor node deployments or shortening their moving paths. At the same time, it can also ensure that unknown nodes receive enough beacon information to achieve accurate positioning. For the specific experimental process, please refer to Figure 4 However, if the communication range is too large, it may lead to increased communication conflicts and interference between unknown nodes, thereby reducing the communication efficiency and positioning performance of the entire network; on the contrary, although a small communication range can reduce communication conflicts and interference, in order to ensure full coverage of the network, it is necessary to increase the number of anchor nodes deployed, which will undoubtedly increase costs.

[0079] The communication radius of the anchor node is a key factor in determining the maximum distance it can communicate with unknown nodes, which in turn affects the accuracy and coverage of the positioning algorithm. In order to further study the impact of the anchor node communication radius on the performance of the positioning algorithm, in the experiment, we set the moving step of the anchor node to 6.25m and randomly deployed 100 unknown nodes in the positioning area. By gradually increasing the communication radius of the anchor node from 5m to 40m, we analyzed the performance changes of the positioning algorithm under different communication radius. The experimental results are shown in Figure 2. Figure 4 shown.

[0080] exist Figure 4In the results, the positioning error of the algorithm decreases with the increase of the communication radius. This is because as the communication radius of the anchor node increases, the unknown node can receive more beacon information to increase the positioning accuracy of the algorithm. When the communication radius increases from 10m to 30m, the positioning error decreases significantly; when the communication radius further increases to 40m, due to the influence of factors such as signal attenuation and multipath effect, the positioning error decreases slowly, and some algorithms even show a slight increase. Nevertheless, compared with other comparison algorithms, the positioning error of the proposed algorithm remains at a low level within the range of communication radius.

[0081] At the same time, as the communication radius increases, the positioning coverage of the algorithm also increases significantly until it reaches full coverage. Specifically, when the communication radius is 5m, since the communication radius of the anchor node is smaller than the moving step, the number of unknown nodes that can receive sufficient beacon information is greatly reduced, resulting in a low positioning coverage of the algorithm; when the communication radius is greater than or equal to 20m, the positioning coverage of all algorithms reaches 100%, indicating that all unknown nodes can receive sufficient beacon information for positioning.

[0082] It is worth noting that the positioning coverage of the proposed algorithm reaches 100% at the beginning. This is because the present invention adopts a special method in the algorithm design: unknown nodes that have not been successfully located are taken as targets, and at least 3-5 particles are randomly initialized for each target. At the same time, Euclidean distance is used as the fitness function, and corresponding thresholds are set to evaluate the performance of particles, and finally the potential position of the anchor node is determined, thereby ensuring that all unknown nodes can be successfully located and achieving 100% positioning coverage.

[0083] Table 2 Experimental results of the influence of communication radius on positioning algorithm

[0084]

[0085] From the data in Table 2, it can be seen that although the communication radius has a certain impact on the positioning performance of all algorithms, it has the smallest overall impact on the method proposed in the present invention compared to the LH algorithm, Z-curve algorithm and SSD algorithm. In terms of average algorithm coverage, the method proposed in the present invention is significantly better than other comparison algorithms. In contrast, the average coverage of the SQUARE_SPIRAL algorithm and the LH algorithm is relatively low.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization, characterized in that: include: Step 1: Determine the area to be located; Step 2: Deploy nodes on the area to be located according to a preset node deployment method and node quantity; Step 3: Use static path planning method to design the moving trajectory of the anchor node and locate the unknown node according to the position of the anchor node; Step 4: For unknown nodes that have not been successfully located, the particle swarm algorithm is used to optimize the movement trajectory of the anchor node and locate the unknown node according to the position of the anchor node.

2. A semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to claim 1, characterized in that: In step 3, a static path planning method is used to design the moving trajectory of the anchor node, specifically including: setting two anchor nodes so that the two anchor nodes move along a preset triangular wave trajectory from two opposite ends of the area to be positioned.

3. The semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to claim 1 is characterized in that: Step 4 specifically includes: Step 4.1: Taking the unknown nodes that have not been successfully located as targets, generating at least 3 particles for each target, each particle representing a potential position of an anchor node, and setting the parameters of the particle swarm optimization algorithm, the parameters including the inertia weight and the learning factor; Step 4.2: Calculate the Euclidean distance between each particle and the target, and use the Euclidean distance as the fitness value of the particle; Step 4.3: Update the individual optimal solution pbest and the global optimal solution gbest according to the fitness values ​​of all particles; Step 4.4: Update the speed and position of the particle according to the current individual optimal solution, global optimal solution and the parameters of the particle swarm optimization algorithm; Step 4.5: Repeat steps 4.2 to 4.4 until the termination condition is met; Step 4.6: Output the coordinate positions of all particles.

4. A semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to any one of claims 1 to 3, characterized in that: Also includes: When the anchor node encounters an obstacle during movement, it uses the preset obstacle handling mechanism to avoid the obstacle; The preset obstacle handling mechanism includes: a regular obstacle handling mechanism and an irregular obstacle handling mechanism; wherein the regular obstacle handling mechanism includes: according to the relative position between the original path of the anchor node and the obstacle, selecting a side of the obstacle that is closer to the original path, and making the anchor node detour along the side until it returns to the original path; the irregular obstacle handling mechanism includes: constructing a regular shape for the irregular obstacle, and then processing it according to the regular obstacle handling mechanism.

5. A semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to any one of claims 1 to 3, characterized in that: The positioning of the unknown node according to the position of the anchor node specifically includes: when the unknown node receives beacon information broadcast by at least three non-collinear anchor nodes, obtaining the signal strength value RSSI with the anchor node, and positioning the unknown node based on the RSSI ranging algorithm.

6. A semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to claim 5, characterized in that: Also includes: The least square method is used to correct the positioning error of the positioning result.

7. A semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to any one of claim 4, characterized in that: Step 1 specifically includes: Define the area boundary of the area to be located and analyze the area characteristics of the area to be located; wherein, analyzing the area characteristics of the area to be located includes analyzing whether the shape of the area to be located is regular and analyzing the size, shape and position of obstacles.

8. The semi-dynamic path planning and positioning method based on multi-objective particle swarm optimization according to claim 7 is characterized in that: The node deployment methods include regular deployment and random deployment.

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