【无人机路径规划】基于遗传算法的路径规划优化算法,以在具有多个障碍的环境中找到从起点到终点的最优或次优路径附Matlab代码

最新推荐文章于 2025-12-06 14:50:32 发布

原创最新推荐文章于 2025-12-06 14:50:32 发布 · 477 阅读

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#无人机 #算法 #matlab

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🔥 内容介绍

一、技术背景与核心需求

在无人机单机作业场景（如电力巡检单点排查、灾后定点救援、城市快递配送）中，环境常包含大量静态障碍（如建筑、树木、输电塔）与少量动态障碍（如临时施工区域），核心需求是：

障碍安全规避：路径需与所有障碍保持安全距离（如多旋翼无人机安全距离≥5m），避免碰撞风险；

路径最优性：在无碰撞前提下，最小化路径长度（降低能耗）、路径平滑度（减少姿态调整次数）、飞行时间（提升作业效率）；

多约束适配：满足无人机物理运动约束（最大转弯半径
Rmin
、最大爬升率
vzmax
，若为三维场景）；

鲁棒性：面对不规则障碍（如不规则建筑群、密集树林），算法需稳定生成可行路径，避免陷入局部最优（如绕障后无法返回目标方向）。

传统路径规划算法（如 A*、RRT）在多障碍环境中存在局限：A * 依赖栅格地图，障碍密集时栅格数量激增导致计算量暴增；RRT 随机采样效率低，易生成冗余绕障路径。遗传算法（Genetic Algorithm, GA）作为全局优化算法，通过 “种群迭代进化” 模拟生物自然选择，无需栅格建模，可直接在连续空间中搜索最优路径，且能通过多目标适应度函数平衡路径 “安全性 - 最优性 - 约束满足”，成为多障碍环境下无人机路径规划的优选方案。

⛳️ 运行结果

📣 部分代码

#####

##plot through working window

>> x1=out.x; x0=x1.Data; y1=out.y; y0=y1.Data;

>> plot(x0,y0);

>> hold on

>> a0 = 7.5087;

a1=-2.1673;

b1 = 4.7392;

a2 = -0.2270 ;

b2 = -0.5158 ;

a3 = 0.1598 ;

b3 = -0.3852 ;

a4 = 0.1270 ;

b4 = 0.2219 ;

a5 = -0.2227 ;

b5 = 0.0365 ;

w = 0.3127 ;

xg=linspace(0,20,100);

yg= a0 + a1*cos(xg*w) + b1*sin(xg*w) + a2*cos(2*xg*w) + b2*sin(2*xg*w) + a3*cos(3*xg*w) + b3*sin(3*xg*w) + a4*cos(4*xg*w) + b4*sin(4*xg*w) + a5*cos(5*xg*w) + b5*sin(5*xg*w);

>> plot(xg,yg);

>> hold on

Data.Obs(1).S = [1,4; 2,4; 2,1; 1,1;1,4]; Data.Obs(2).S = [3,6; 4,6; 4,3; 3,3;3,6]; Data.Obs(3).S = [6,4; 7,4; 7,1; 6,1;6,4]; Data.Obs(4).S = [8,10; 9,10; 9,5; 8,5;8,10]; Data.Obs(5).S = [10,14; 14,14; 14,12; 10,12;10,14]; Data.Obs(6).S = [14,8; 18,8; 18,6; 14,6;14,8]; % 创建一个新的图形窗口

hold on;

for i = 1:length(Data.Obs)

xy = Data.Obs(i).S;

plot(xy(:,1), xy(:,2), 'LineWidth', 2, 'Color', rand(1,3));

end

##### GA

This readme document is intended to outline and explain the main features and implementation details of the MATLAB code. The code implements a genetic algorithm (GA) based path planning optimization algorithm to find the optimal or sub-optimal path from the starting point to the end point in an environment with multiple obstacles.

1. Code overview

The code first sets the necessary parameters and data structures through a series of initialization steps, including the location of obstacles, starting and ending coordinates, population size, chromosome length, number of iterations, mutation probability, and crossover probability. The code then continuously evolves the population through genetic algorithms to find a path that does not intersect with obstacles and is as optimized as possible.

2. Main steps

2.1 Initialization

• Use clc; clear; close all; warning off; Clear the command window, workspace, and graphics window, and turn off MATLAB warnings.

• Use addpath(genpath(pwd)); Add the current directory and its subdirectories to the path of MATLAB so that custom functions can be loaded and invoked.

• Set the Data structure Data, including boundary B, starting point and ending point S_E, population size, chromosome length, etc.

• Define obstacle Obs, with each obstacle defined by a series of vertices arranged in clockwise order.

2.2 Initial population generation

• Call intpop function to generate initial population. This function generates an initial solution set based on the data provided, such as population size and chromosome length.

2.3 Checking and Repairing Paths

• Use the check_crossing function on each individual (that is, each path) to check for an intersection with an obstacle.

If an intersection is found, a new waypoint is generated using the newpop function until the path no longer intersects with any obstacles.

2.4 Evolutionary process

• Through multiple iterations, using the ideas of genetic algorithms (selection, crossover, variation) to optimize the population.

• Calculate the fitness of an individual using the cal_Fitness function.

• Use the environmental_sele function to select the environment and keep good individuals in the external archive set Qop.

In non-final iterations, a new population is generated by tournament selection binary_tournament_selection, crosscross_mutation, and smoothing operation delete_point.

2.5 Result output and visualization

• After the last iteration, the non-dominant individual (i.e. the optimal solution set) is selected for output.

Use the plotting function to draw the optimal path and obstacles into the graph window so you can visually view the results.

3. Precautions

• The Goals function in the code is used to calculate the value of the objective function, that is, to evaluate the pros and cons of the path.

4. Conclusion

The MATLAB code implements a path planning optimization algorithm based on genetic algorithm, which can find the optimal path from the starting point to the end point in the environment with complex obstacles. By iterating and evolving the population, the algorithm can gradually optimize the path until it finds an optimal or suboptimal solution that meets the requirements