A three-dimensional obstacle avoidance path planning method based on spherical vector pigeon flock algorithm
By improving the pigeon flock algorithm by using spherical vectors and Tent chaos strategy, the problems of slow convergence and local optimality of traditional algorithms in three-dimensional path planning are solved, and efficient UAV path planning is achieved.
Patent Information
- Application Number
- CN202210425955.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-04-22
- Publication Date
- 2025-09-23
- Estimated Expiration
- 2042-04-22
AI Technical Summary
Traditional path planning algorithms have slow convergence speed in three-dimensional space, large computational complexity, and are prone to falling into local optimality, making it difficult to find the optimal path for the UAV.
An improved pigeon flock algorithm based on spherical vectors and Tent chaos strategy is adopted. By encoding the UAV path as a spherical vector and combining the amplitude, elevation angle and azimuth angle of the vector, the UAV path cost function is optimized, the global search capability is enhanced, and the Tent chaos strategy is used to improve the quality of the initial solution.
It improves the search efficiency and quality of UAV route planning, enhances the global search capability, and is able to find a better route in three-dimensional space, reduce the search space, and avoid local optimal traps.
Smart Images
Figure CN114777789B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of aircraft mission planning, in particular to a three-dimensional obstacle avoidance route planning method based on a spherical vector pigeon flock algorithm. Background Art
[0002] Drones (UAVs), with their advantages of high cost-effectiveness, flexibility, and stealth, have seen widespread adoption in both military and civilian fields in recent years, gaining global recognition. With the continuous advancement of aviation technology, electronic information technology, and related manufacturing processes, countries around the world have increased their attention and investment in the UAV sector. A comprehensive mission planning system is crucial for successful mission completion, and route planning is a core component and a key tool for improving UAV safety. UAV route planning involves finding an optimal or feasible flight path from a starting point to a destination within a specific mission context, while meeting the UAV's physical constraints and avoiding obstacles and threats. The algorithm used for route planning directly impacts the effectiveness of this route planning.
[0003] Traditional route planning methods, such as mathematical programming and artificial potential field methods, suffer from slow convergence when dealing with three-dimensional space, are computationally intensive, and sometimes fail to find the optimal route. With the increasing use of drones, their mission environments are becoming increasingly complex, posing significant challenges to the algorithm's computational complexity and timeliness. However, swarm intelligence optimization algorithms, which mimic the behavioral patterns of organisms in nature to explore the optimal solution within a certain range, can solve multidimensional search spaces and possess strong search capabilities and robustness. These algorithms offer significant advantages in the field of route planning and have been favored by many researchers.
[0004] The Pigeon Optimization (PIO) algorithm is a new type of swarm intelligence optimization algorithm. It mimics the homing behavior of pigeons to search for optimal solutions. Its simple structure and low number of parameters have made it widely used in various fields. However, like many other swarm intelligence optimization algorithms, the original Pigeon Optimization algorithm suffers from insufficient population diversity when searching near the global optimal solution, making it prone to falling into local optima. Summary of the Invention
[0005] The technical problem to be solved by the present invention is to provide a three-dimensional obstacle avoidance path planning method based on the spherical vector pigeon flock algorithm, which can improve the quality of the initial solution, enhance the global search capability of the pigeon flock algorithm, and realize the optimal path search of the UAV.
[0006] To solve the above technical problems, the present invention provides a three-dimensional obstacle avoidance path planning method based on a spherical vector pigeon flock algorithm, comprising the following steps:
[0007] Step 1: Establish a three-dimensional environment for UAV route planning;
[0008] Step 2: Determine the objective function of the UAV route planning task based on the various requirements and constraints of the UAV and its flight path;
[0009] Step 3: Use the improved pigeon flock algorithm based on spherical vectors and Tent chaos strategy to optimize the UAV route cost function in the route planning environment, and finally obtain the optimal route of the UAV.
[0010] Preferably, in step 1, establishing a three-dimensional environment for UAV route planning is specifically as follows: determining the mission map boundary, threat area and UAV flight mission information, including the starting point coordinates S (x s ,y s ,z s ) and the target point coordinates E(x e ,y e ,z e ), the number of waypoints n;
[0011] In the planning space, the flight path of the UAV is represented by many waypoints. The spherical vector-based pigeon flock algorithm SPIO encodes each path as a set of vectors. Each vector describes the movement of the UAV from one waypoint to another. These vectors are represented in the spherical coordinate system. The spherical coordinate system includes three components: amplitude ρ∈(0,path_length), elevation angle ψ∈(-π / 2,π / 2) and azimuth angle φ∈(-π,π). A flight path Ω with N nodes i It is represented by a 3N-dimensional hypersphere vector;
[0012] Ω i =(ρ i1 ,ψ i1 ,φ i1 ,ρ i2 ,ψ i2 ,φ i2 ,…,ρ iN ,ψ iN ,φ iN ),N=n-2
[0013] By describing the position of the pigeon as Ω i , the velocity associated with the pigeon can be described by an increment vector:
[0014] ΔΩ i =(Δρ i1 ,Δψ i1 ,Δφ i1 ,Δρ i2 ,Δψ i2 ,Δφ i2 ,…,Δρ iN,Δψ iN ,Δφ iN )
[0015] The ball vector (ρ ij ,ψ ij ,φ ij ) is represented by u ij , speed (Δρ ij ,Δψ ij ,Δφ ij ) is expressed as Δu ij .
[0016] Preferably, in step 2, according to the various requirements and constraints of the UAV and its flight path, the objective function of the UAV route planning task is determined as follows: considering the flight path X i Related optimality, safety and feasibility constraints, the total cost function F(X i ) is defined as follows:
[0017]
[0018] where b k is the weight coefficient, F1(X i ) refers to the route length cost, F2(X i ) refers to the cost of the threat, F3(X i ) refers to the flight altitude cost, F4(X i ) refers to the smoothness cost, and the decision variable is the flight path X i , represented by n waypoints that the UAV needs to fly through. Each waypoint corresponds to a path node in the search map, and its coordinates are P ij =(x ij ,y ij ,z ij );
[0019] The cost of the route length F1(X i ) is defined as follows:
[0020]
[0021] Assume that K is the set of all threats, each threat is defined in a cylinder, and the center coordinate of its projection is C k , with a radius of R k , for a given route segment The threat cost associated with it to C k The distance d k By considering the diameter D of the drone and the danger distance S, the cost of the threat cost F2(X i ) is defined as follows:
[0022]
[0023] During the flight, the minimum and maximum altitudes are h min and h max , height cost F3(X i ) is calculated by the following formula:
[0024]
[0025]
[0026] Among them, h ij is the flight height relative to the ground, H ij is the waypoint P ij The calculation formula of the relevant height cost is H for all waypoints ij The sum can be obtained by the height cost F3(X i );
[0027] Smoothness cost F4(X i ) Evaluate turning and climbing angles;
[0028] Turning angle Two consecutive route segments and The angle between the projections on the horizontal plane Oxy, the turning angle is calculated by the following formula:
[0029]
[0030] Climb angle ψ ij It is a route segment Its projection on the horizontal plane The included angle between them, the climbing angle is calculated by the following formula:
[0031]
[0032] Smoothness cost F4(X i ) can be calculated by the following formula:
[0033]
[0034] Among them, a1 and a2 are the penalty coefficients of turning angle and climbing angle respectively.
[0035] Preferably, in step 3, the improved pigeon flock algorithm based on spherical vectors and Tent chaos strategy is used to optimize the UAV route cost function in the route planning environment, and finally the optimal route of the UAV is obtained, which specifically includes the following steps:
[0036] Step 3.1: Initialize the population size, the number of iterations in the map and compass operator operation phase, the number of iterations in the landmark operator operation phase, the map compass factor, and the cost weights in the algorithm. Determine the upper and lower limits of the search based on the planning space and perform population initialization.
[0037] Step 3.2: Start the map and compass operator iteration phase and calculate the fitness value of each pigeon in the current pigeon flock according to the total cost function formula;
[0038] Step 3.3: Update the local optimal solution and the global optimal solution, and determine whether the number of map and compass operator iterations has been reached. If so, proceed to the next step; otherwise, jump to the previous step for the next iteration.
[0039] Step 3.4: Start the landmark operator iteration phase. All pigeons are sorted according to the current pigeon group fitness value. Some pigeons with low fitness values will follow the pigeons with high fitness values. Then, the center of all pigeons is found. All pigeons will adjust their flight direction to follow the center. Finally, the optimal solution parameters and optimal cost value are stored.
[0040] Step 3.5: Determine whether the landmark operator iteration number has been reached. If so, stop the landmark operator operation and output the result. Otherwise, execute the previous step.
[0041] Preferably, in step 3.1, when the pigeon population is initialized, the population is initialized using the Tent chaotic map, and the formula of the Tent chaotic map is:
[0042]
[0043] Right now
[0044] Among them, N T is the number of particles in the chaotic sequence; rand(0,1) is a random number within [0,1].
[0045] Preferably, in step 3.2, in the map and compass operator stage, each iteration updates the pigeon's speed and position according to the following formula:
[0046]
[0047]
[0048] Among them, R is the map compass operator, rand is a random number, q g is the global optimal position of the current population.
[0049] Preferably, in step 3.3, the local optimal solution and the global optimal solution are updated, and it is determined whether the number of map and compass operator iterations has been reached. If the number of iterations has been reached, the next step is performed; otherwise, the next step is skipped to the previous step for the next iteration.
[0050] Q i =(q i1 ,q i2 ,…,q i,N ) and Q g =(q g1 ,q g2 ,…,q g,N ) are vector sets representing the local optimal solution and the global optimal solution of pigeon i, respectively. In order to determine Q i and Q g , the flight path Ω based on the vector i Mapping to direct path X i , in order to evaluate the relevant costs, the vector u ij =(ρ ij ,ψ ij ,φ ij )∈Ω i To waypoint P ij =(x ij ,y ij ,z ij )∈X i The mapping can be done as follows:
[0051] x ij =x i,j-1 +ρ ij sinψ ij cosφ ij
[0052] y ij =y i,j-1 +ρ ij sinψ ij sinφ ij
[0053] z ij =z i,j-1 +ρ ij cosψ ij
[0054] Denoting the mapping as ξ:Ω→X, the local and global optimal solutions can be calculated as follows:
[0055]
[0056] Preferably, in step 3.4, in the landmark operator stage, the position of the pigeon is updated according to the following formula:
[0057]
[0058]
[0059] Among them, fitness is the fitness function.
[0060] The beneficial effects of the present invention are as follows: for the UAV route planning problem in three-dimensional space, a new pigeon flock optimization algorithm based on spherical vectors is proposed. The safety of UAV flight can be enhanced by the relationship between the amplitude, elevation and azimuth components of the vector and the speed, turning angle and climb angle of the UAV. The algorithm searches for solutions in the configuration space, which can increase the probability of searching for high-quality solutions; more importantly, in the route planning process, the constraints related to the turning angle and climb angle can be directly implemented through the elevation and azimuth of the spherical vector, which can greatly reduce the search space; in some cases, such as when the UAV flies at a constant speed, the amplitude of the spherical vector can be fixed to further reduce the search space and expand the search ability of the algorithm in the configuration space; in the population initialization stage of the pigeon flock algorithm, the tent chaos strategy is introduced to obtain a chaotic sequence, which improves the quality of the initial solution, enhances the global search ability of the pigeon flock algorithm, and realizes the optimal route search of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS
[0061] Figure 1 Schematic diagram of threat cost of the present invention.
[0062] Figure 2 Schematic diagram of the height cost of the present invention.
[0063] Figure 3 Schematic diagram of the method of the present invention.
[0064] FIG4( a ) is a three-dimensional view of the UAV route obtained based on the spherical vector pigeon flock algorithm in an embodiment of the present invention.
[0065] FIG4( b ) is a three-dimensional view of the UAV route obtained based on the original pigeon flock algorithm in an embodiment of the present invention.
[0066] FIG5( a ) is a top view of the UAV route obtained based on the spherical vector pigeon flock algorithm in an embodiment of the present invention.
[0067] FIG5( b ) is a top view of the UAV route obtained based on the original pigeon flock algorithm in an embodiment of the present invention.
[0068] FIG6( a ) is a convergence curve diagram of the spherical vector pigeon flock algorithm according to an embodiment of the present invention.
[0069] FIG6( b ) is a convergence curve diagram based on the original pigeon flock algorithm in an embodiment of the present invention. DETAILED DESCRIPTION
[0070] like Figure 3 As shown, a three-dimensional obstacle avoidance path planning method based on a spherical vector pigeon flock algorithm includes the following steps:
[0071] Step 1: Establish a 3D environment for UAV route planning;
[0072] Determine the mission map boundary, threat area and UAV flight mission information, including the starting point coordinates S(x s ,y s ,z s ) and the target point coordinates E(x e ,y e ,z e ), the number of waypoints n.
[0073] In the planning space, the flight path of a UAV can be represented by a number of waypoints. The Spherical Vector Pigeon Swarming Algorithm (SPIO) encodes each path as a set of vectors, each describing the movement of the UAV from one waypoint to another. These vectors are expressed in spherical coordinates, which consist of three components: amplitude ρ∈(0,path_length), elevation angle ψ∈(-π / 2,π / 2), and azimuth angle φ∈(-π,π). A flight path Ω with N nodes i It is represented by a 3N-dimensional hypersphere vector.
[0074] Ω i =(ρ i1 ,ψ i1 ,φ i1 ,ρ i2 ,ψ i2 ,φ i2 ,…,ρ iN ,ψ iN ,φ iN ),N=n-2
[0075] By describing the position of the pigeon as Ω i , the velocity associated with the pigeon can be described by an increment vector:
[0076] ΔΩ i =(Δρ i1 ,Δψ i1 ,Δφ i1 ,Δρ i2 ,Δψ i2 ,Δφ i2 ,…,Δρ iN ,Δψ iN ,Δφ iN )
[0077] The ball vector (ρ ij ,ψ ij ,φ ij) is represented by u ij , speed (Δρ ij ,Δψ ij ,Δφ ij ) is expressed as Δu ij .
[0078] Step 2: Determine the objective function of the UAV route planning task based on the various requirements and constraints of the UAV and its flight path;
[0079] Consider the flight path X i Related optimality, safety and feasibility constraints, the total cost function F(X i ) is defined as follows:
[0080]
[0081] where b k is the weight coefficient, F1(X i ) refers to the route length cost, F2(X i ) refers to the cost of the threat, F3(X i ) refers to the flight altitude cost, F4(X i ) refers to the smoothness cost. The decision variable is the flight path X i , represented by n waypoints that the UAV needs to fly through. Each waypoint corresponds to a path node in the search map, and its coordinates are P ij =(x ij ,y ij ,z ij ).
[0082] The cost of the route length F1(X i ) is defined as follows:
[0083]
[0084] Assume that K is the set of all threats, each threat is defined in a cylinder, and the center coordinate of its projection is C k , with a radius of R k ,like Figure 1 For a given route segment The threat cost associated with it to C k The distance d k By considering the diameter D of the drone and the danger distance S, the cost of the threat cost F2(X i ) is defined as follows:
[0085]
[0086] During the flight, the minimum and maximum altitudes are h min and h max , height cost F3(X i ) can be calculated by the following formula:
[0087]
[0088]
[0089] Among them, h ij is the flight altitude relative to the ground, such as Figure 2 As shown, H ij is the waypoint P ij The calculation formula of the relevant height cost is H for all waypoints ij The sum can be obtained by the height cost F3(X i ).
[0090] Smoothness cost F4(X i ) to evaluate the turning angle and climb angle.
[0091] Turning angle Two consecutive route segments and The angle between the projections on the horizontal plane Oxy. The turning angle can be calculated as follows:
[0092]
[0093] Climb angle ψ ij It is a route segment Its projection on the horizontal plane The included angle between them, the climbing angle can be calculated by the following formula:
[0094]
[0095] Smoothness cost F4(X i ) can be calculated by the following formula:
[0096]
[0097] Among them, a1 and a2 are the penalty coefficients of turning angle and climbing angle respectively.
[0098] Step 3: Optimize the UAV route cost function in the route planning environment using the improved pigeon flock algorithm based on spherical vectors and Tent chaos strategy, and finally obtain the optimal route of the UAV, which includes:
[0099] Step 3.1: Initialize the population size, the number of iterations in the map and compass operator operation phase, the number of iterations in the landmark operator operation phase, the map compass factor, and the cost weights in the algorithm. Determine the upper and lower limits of the search based on the planning space and perform population initialization.
[0100] The chaos map adopts Tent chaos map, and its analytical formula is:
[0101]
[0102] Right now
[0103] Among them, N T is the number of particles in the chaotic sequence; rand(0,1) is a random number within [0,1].
[0104] Step 3.2: Start the map and compass operator iteration phase and calculate the fitness value of each pigeon in the current pigeon flock according to the total cost function formula;
[0105] In the map and compass operator phase, each iteration updates the pigeon's speed and position according to the following formula:
[0106]
[0107]
[0108] Among them, R is the map compass operator, rand is a random number, q g is the global optimal position of the current population.
[0109] Step 3.3: Update the local optimal solution and the global optimal solution, and determine whether the number of map and compass operator iterations has been reached. If so, proceed to the next step; otherwise, jump to the previous step for the next iteration.
[0110] Q i =(q i1 ,q i2 ,…,q i,N ) and Q g =(q g1 ,q g2 ,…,q g,N ) are vector sets representing the local optimal solution and the global optimal solution of pigeon i. In order to determine Q i and Q g , the flight path Ω based on the vector i Mapping to direct path X i , in order to evaluate the relevant costs. The vector u ij =(ρ ij ,ψ ij ,φij )∈Ω i To waypoint P ij =(x ij ,y ij ,z ij )∈X i The mapping can be done as follows:
[0111] x ij =x i,j-1 +ρ ij sinψ ij cosφ ij
[0112] y ij =y i,j-1 +ρ ij sinψ ij sinφ ij
[0113] z ij =z i,j-1 +ρ ij cosψ ij
[0114] Denoting the mapping as ξ:Ω→X, the local and global optimal solutions can be calculated as follows:
[0115]
[0116]
[0117] Step 3.4: Start the landmark operator iteration phase. All pigeons are sorted according to the current pigeon group fitness value. Some pigeons with low fitness values will follow the pigeons with high fitness values. Then, the center of all pigeons is found. All pigeons will adjust their flight direction to follow the center. Finally, the optimal solution parameters and optimal cost value are stored.
[0118] In the landmark operator stage, the pigeon's position is updated according to the following formula:
[0119]
[0120]
[0121] Among them, fitness is the fitness function.
[0122] Step 3.5: Determine whether the landmark operator iteration number has been reached. If so, stop the landmark operator operation and output the result. Otherwise, execute the previous step.
[0123] In order to verify the feasibility and effectiveness of the method, further explanation is given below in combination with simulation experiments.
[0124] The planning space is shown in Figure 4(a) and Figure 4(b), which contains 6 threat zones. The threat zone data is shown in Table 1. The starting coordinates of the UAV are (200, 100, 150) and the ending coordinates are (800, 800, 150).
[0125] Table 1 Threat zone data
[0126]
[0127]
[0128] The relevant parameters of the pigeon flock algorithm were set to 50, and the number of iterations was set to 200, including 150 for the map compass operator and 50 for the landmark operator. Planning simulations were performed using the spherical vector-based pigeon flock algorithm and the original pigeon flock algorithm under the same threat environment. The simulation results calculated based on the set parameters, namely the route planning route map of the spherical vector-based pigeon flock algorithm, are shown in Figures 5(a) and 5(b), and the convergence curves are shown in Figures 6(a) and 6(b).
Claims
1. A three-dimensional obstacle avoidance path planning method based on spherical vector pigeon flock algorithm, characterized in that: The steps include: Step 1: Establish a three-dimensional environment for UAV route planning; Step 2: Determine the objective function of the UAV route planning task based on the various requirements and constraints of the UAV and its flight path; Step 3: Optimize the UAV route cost function in the route planning environment using the improved pigeon flock algorithm based on spherical vectors and Tent chaos strategy, and finally obtain the optimal route of the UAV; specifically, the following steps are included: Step 3.1: Initialize the population size, the number of iterations in the map and compass operator operation phase, the number of iterations in the landmark operator operation phase, the map compass factor, and the cost weights in the algorithm. Determine the upper and lower limits of the search based on the planning space and perform population initialization. Step 3.2: Start the map and compass operator iteration phase and calculate the fitness value of each pigeon in the current pigeon flock according to the total cost function formula; Step 3.3: Update the local optimal solution and the global optimal solution, and determine whether the number of map and compass operator iterations has been reached. If so, proceed to the next step; otherwise, jump to the previous step for the next iteration. Step 3.4: Start the landmark operator iteration phase. All pigeons are sorted according to the current pigeon group fitness value. Some pigeons with low fitness values will follow the pigeons with high fitness values. Then, the center of all pigeons is found. All pigeons will adjust their flight direction to follow the center. Finally, the optimal solution parameters and optimal cost value are stored. Step 3.5: Determine whether the landmark operator iteration number has been reached. If so, stop the landmark operator operation and output the result. Otherwise, execute the previous step.
2. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon flock algorithm according to claim 1, characterized in that: In step 1, the three-dimensional environment for UAV route planning is established by determining the mission map boundary, threat area and UAV flight mission information, including the starting point coordinates S (x s ,y s ,z s ) and the target point coordinates E(x e ,y e ,z e ), the number of waypoints n; In the planning space, the flight path of the UAV is represented by many waypoints. The spherical vector-based pigeon flock algorithm SPIO encodes each path as a set of vectors. Each vector describes the movement of the UAV from one waypoint to another. These vectors are represented in the spherical coordinate system. The spherical coordinate system includes three components: amplitude ρ∈(0,path_length), elevation angle ψ∈(-π / 2,π / 2) and azimuth angle φ∈(-π,π). A flight path Ω with N nodes i It is represented by a 3N-dimensional hypersphere vector; Oh i =(ρ i1 ,ψ i1 ,f i1 ,r i2 ,ψ i2 ,f i2 ,...,r iN ,ψ iN ,f iN ),N=n-2 By describing the position of the pigeon as Ω i , the velocity associated with the pigeon is described by an increment vector: DO i =(Dr. i1 ,Dp i1 ,Df i1 ,Dr. i2 ,Dp i2 ,Df i2 ,…,Dr. iN ,Dp iN ,Df iN ) The ball vector (ρ ij ,ψ ij ,φ ij ) is represented by u ij , speed (Δρ ij ,Δψ ij ,Δφ ij ) is expressed as Δu ij .
3. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon flock algorithm according to claim 1, characterized in that: In step 2, according to the various requirements and constraints of the UAV and its flight path, the objective function of the UAV route planning task is determined as follows: considering the flight path X i Related optimality, safety and feasibility constraints, the total cost function F(X i ) is defined as follows: where b k is the weight coefficient, F1(X i ) refers to the route length cost, F2(X i ) refers to the cost of the threat, F3(X i ) refers to the flight altitude cost, F4(X i ) refers to the smoothness cost, and the decision variable is the flight path X i , represented by n waypoints that the UAV needs to fly through. Each waypoint corresponds to a path node in the search map, and its coordinates are P ij =(x ij ,y ij ,z ij ); The cost of the route length F1(X i ) is defined as follows: Assume that K is the set of all threats, each threat is defined in a cylinder, and the center coordinate of its projection is C k , with a radius of R k , for a given route segment The threat cost associated with it to C k The distance d k By considering the diameter D of the drone and the danger distance S, the cost of the threat cost F2(X i ) is defined as follows: During the flight, the minimum and maximum altitudes are h min and h max , height cost F3(X i ) is calculated by the following formula: Among them, h ij is the flight height relative to the ground, H ij is the waypoint P ij The calculation formula of the relevant height cost is H for all waypoints ij The sum is the height cost F3(X i ); Smoothness cost F4(X i ) Evaluate turning and climbing angles; Turning angle Two consecutive route segments and The angle between the projections on the horizontal plane Oxy, the turning angle is calculated by the following formula: Climb angle ψ ij It is a route segment Its projection on the horizontal plane The included angle between them, the climbing angle is calculated by the following formula: Smoothness cost F4(X i ) is calculated by the following formula: Among them, a1 and a2 are the penalty coefficients of turning angle and climbing angle respectively.
4. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon flock algorithm according to claim 1, characterized in that: In step 3.1, when initializing the pigeon population, the Tent chaotic map is used to initialize the population. The formula of the Tent chaotic map is: Right now Among them, N T is the number of particles in the chaotic sequence; rand(0,1) is a random number within [0,1].
5. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon flock algorithm according to claim 1, characterized in that: In step 3.2, during the map and compass operator phase, each iteration updates the pigeon's speed and position according to the following formula: Among them, R is the map compass operator, rand is a random number, q g is the global optimal position of the current population.
6. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon flock algorithm according to claim 1, characterized in that: In step 3.3, update the local optimal solution and the global optimal solution, and determine whether the number of map and compass operator iterations has been reached. If so, proceed to the next step; otherwise, jump to the previous step for the next iteration. Q i =(q i1 ,q i2 ,…,q i,N ) and Q g =(q g1 ,q g2 ,…,q g,N ) are vector sets representing the local optimal solution and the global optimal solution of pigeon i, respectively. In order to determine Q i and Q g , the flight path Ω based on the vector i Mapping to direct path X i , in order to evaluate the relevant costs, the vector u ij =(ρ ij ,ψ ij ,φ ij )∈Ω i To waypoint P ij =(x ij ,y ij ,z ij )∈X i The mapping is performed as follows: x ij =x i,j-1 +r ij sinψ ij cosφ ij and ij =and i,j-1 +ρ ij sinψ ij sinφ ij With ij =z i,j-1 +ρ ij cosψ ij Denoting the mapping as ξ:Ω→X, the local and global optimal solutions can be calculated as follows:
7. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon flock algorithm according to claim 1, characterized in that: In step 3.4, during the landmark operator phase, the pigeon's position is updated as follows: Among them, fitness is the fitness function.